From 1bbd5b7cf6fafe66b48d1e18bff542b8aa4636d5 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 14:42:12 -0400
Subject: [PATCH 01/47] ignore roo

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 972088b..d869900 100755
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,7 @@
 # -----------------------------
 capabilities/
 plans/
+.rooignore
 
 # -----------------------------
 # Python bytecode / caches

From 1d66b0313148eaa5267755d406dcfac5d5c3b0ca Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 14:51:34 -0400
Subject: [PATCH 02/47] WU-1: scaffold v2.0.0 Phase 1 packages and bump version

Creates the additive Phase 1 package structure off of version/2.0.0:
manifest/, providers/, session/, planning/, cli/. Each new package ships
with a docstring describing its Phase 1 role and its hooks for later
phases (telemetry, AI assistants, Kubernetes/cloud providers, ML
advisor).

scalable/manifest/schema.py defines the frozen v1 schema dataclasses
(ManifestModel, ProjectConfig, TargetConfig, ComponentConfig, TaskConfig)
and SCHEMA_VERSION = 1. The schema is intentionally implemented with
stdlib dataclasses so manifest validation works without the optional
[ai] extra (resolves Phase 1 plan section 9 open question #1).

scalable/manifest/errors.py declares the ManifestError hierarchy used by
the parser, validator, and Phase 4 AI migration assistant.

scalable/cli/main.py is a Phase 1 stub for the [project.scripts] entry
point; the real validate / plan --dry-run wiring lands in WU-10.

pyproject.toml: version bumped to 2.0.0a1, pyyaml pinned explicitly,
empty placeholder extras for ai/cloud/kubernetes registered so
pip install scalable[ai] resolves cleanly from day one, scalable
console script registered, packages.find used so the new sub-packages
are picked up by setuptools.

Verified: existing 73 unit tests pass unchanged; ruff clean on all new
modules. No public API removed or renamed.

Refs plans/v2.0.0_phase1_plan.md WU-1.
---
 pyproject.toml                 |  14 ++-
 scalable/cli/__init__.py       |  16 +++
 scalable/cli/main.py           |  50 ++++++++
 scalable/manifest/__init__.py  |  23 ++++
 scalable/manifest/errors.py    |  37 ++++++
 scalable/manifest/schema.py    | 204 +++++++++++++++++++++++++++++++++
 scalable/planning/__init__.py  |  16 +++
 scalable/providers/__init__.py |  22 ++++
 scalable/session/__init__.py   |  13 +++
 9 files changed, 392 insertions(+), 3 deletions(-)
 create mode 100644 scalable/cli/__init__.py
 create mode 100644 scalable/cli/main.py
 create mode 100644 scalable/manifest/__init__.py
 create mode 100644 scalable/manifest/errors.py
 create mode 100644 scalable/manifest/schema.py
 create mode 100644 scalable/planning/__init__.py
 create mode 100644 scalable/providers/__init__.py
 create mode 100644 scalable/session/__init__.py

diff --git a/pyproject.toml b/pyproject.toml
index 15e88d3..0611cb8 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "scalable"
-version = "1.1.0"
+version = "2.0.0a1"
 description = "Assist with running models on job queing systems like Slurm"
 authors = [
     { name = "Shashank Lamba" },
@@ -29,6 +29,7 @@ dependencies = [
     "xxhash >= 3.4.1",
     "numpy >= 1.26.4",
     "pandas >= 2.2.3",
+    "pyyaml >= 6.0",
 ]
 classifiers = [
     "Intended Audience :: Science/Research",
@@ -54,15 +55,22 @@ dev = [
     "mypy >= 1.8",
     "pytest-cov >= 4.0",
 ]
+# v2.0.0 phase placeholders. Empty until later phases populate them so that
+# `pip install scalable[ai|cloud|kubernetes]` resolves cleanly from day one
+# and downstream pinning of the extras name is stable.
+ai = []
+cloud = []
+kubernetes = []
 
 [project.urls]
 "Github" = "https://github.com/JGCRI/scalable/tree/master/scalable"
 
 [project.scripts]
+scalable = "scalable.cli.main:main"
 scalable_bootstrap = "scalable.utilities:run_bootstrap"
 
-[tool.setuptools]
-packages = ["scalable"]
+[tool.setuptools.packages.find]
+include = ["scalable", "scalable.*"]
 
 [tool.setuptools.package-data]
 scalable = ["scalable_bootstrap.sh", "Dockerfile"]
diff --git a/scalable/cli/__init__.py b/scalable/cli/__init__.py
new file mode 100644
index 0000000..8141e49
--- /dev/null
+++ b/scalable/cli/__init__.py
@@ -0,0 +1,16 @@
+"""``scalable`` console-script CLI (v2.0.0 Phase 1).
+
+Phase 1 implements two subcommands -- ``scalable validate`` and
+``scalable plan --dry-run`` -- both of which operate purely on a manifest
+plus provider abstractions and never instantiate a scheduler.
+
+The remaining subcommand namespace (``run``, ``diagnose``, ``explain``,
+``init-component``, ``compose``, ``report``) is registered as Phase 1 stubs
+that print a phase-pointer message on invocation. This locks the UX
+namespace early so third-party CLIs don't collide with future Scalable
+verbs and so Phases 2-5 only fill behaviour rather than surface.
+"""
+
+from __future__ import annotations
+
+__all__: list[str] = []
diff --git a/scalable/cli/main.py b/scalable/cli/main.py
new file mode 100644
index 0000000..f4df63a
--- /dev/null
+++ b/scalable/cli/main.py
@@ -0,0 +1,50 @@
+"""``scalable`` console entry-point dispatcher.
+
+Phase 1 stub. The real subcommand wiring lands in WU-10
+(``scalable validate`` and ``scalable plan --dry-run``); the remaining
+subcommands (``run``, ``diagnose``, ``explain``, ``init-component``,
+``compose``, ``report``) print a phase-pointer message until later phases
+implement them.
+
+This module exists in WU-1 only so the ``scalable = "scalable.cli.main:main"``
+console script registered in ``pyproject.toml`` resolves at install time.
+"""
+
+from __future__ import annotations
+
+import sys
+
+_PHASE1_NOT_IMPLEMENTED_MESSAGE = (
+    "scalable CLI: Phase 1 scaffolding only. "
+    "Subcommands `validate` and `plan --dry-run` arrive in work-unit 10. "
+    "See plans/v2.0.0_phase1_plan.md."
+)
+
+
+def main(argv: list[str] | None = None) -> int:
+    """Entry-point referenced by ``[project.scripts] scalable = ...``.
+
+    Phase 1 placeholder: prints a clear "not yet wired up" message and
+    exits with status code 2 (matches argparse's convention for usage
+    errors) so downstream automation that introspects the exit code knows
+    to wait for WU-10.
+
+    Parameters
+    ----------
+    argv : list of str, optional
+        Argument vector excluding the program name. Defaults to
+        ``sys.argv[1:]``. Accepted for testability and to match the final
+        signature that WU-10 will deliver.
+
+    Returns
+    -------
+    int
+        Process exit code. Always ``2`` until WU-10 lands.
+    """
+    del argv  # unused in the WU-1 stub
+    print(_PHASE1_NOT_IMPLEMENTED_MESSAGE, file=sys.stderr)
+    return 2
+
+
+if __name__ == "__main__":  # pragma: no cover - exercised via console script
+    raise SystemExit(main(sys.argv[1:]))
diff --git a/scalable/manifest/__init__.py b/scalable/manifest/__init__.py
new file mode 100644
index 0000000..02aa26c
--- /dev/null
+++ b/scalable/manifest/__init__.py
@@ -0,0 +1,23 @@
+"""Declarative ``scalable.yaml`` manifest layer (v2.0.0 Phase 1).
+
+This package is the durable, provider-neutral source of truth for a Scalable
+project. It supersedes the legacy ``Dockerfile``-as-config discovery driven
+by :class:`scalable.utilities.ModelConfig`, which is preserved with a
+deprecation warning during the v2.0.0 transition.
+
+Public Phase 1 surface (populated by subsequent work units):
+
+* :mod:`scalable.manifest.schema` -- frozen schema dataclasses + ``SCHEMA_VERSION``.
+* :mod:`scalable.manifest.parser` -- YAML loader with ``${VAR}`` expansion.
+* :mod:`scalable.manifest.validate` -- cross-field validation + report types.
+* :mod:`scalable.manifest.adapter` -- pure ``ManifestModel`` -> legacy
+  :class:`scalable.core.JobQueueCluster` translation reused by every provider.
+* :mod:`scalable.manifest.errors` -- exception hierarchy.
+
+The schema is versioned (``version: 1``) so Phase 3 cloud overlays and Phase 4
+AI migration assistants can evolve without breaking existing manifests.
+"""
+
+from __future__ import annotations
+
+__all__: list[str] = []
diff --git a/scalable/manifest/errors.py b/scalable/manifest/errors.py
new file mode 100644
index 0000000..5ad4ac1
--- /dev/null
+++ b/scalable/manifest/errors.py
@@ -0,0 +1,37 @@
+"""Exception hierarchy for the manifest layer.
+
+Each error type carries enough context (path, key, value) to drive the
+``scalable validate`` CLI's structured report and the Phase 4 AI migration
+assistant's diff output. They subclass :class:`ValueError` so legacy
+callers' ``except ValueError`` clauses keep working.
+"""
+
+from __future__ import annotations
+
+
+class ManifestError(ValueError):
+    """Base class for all manifest-layer errors."""
+
+
+class ManifestParseError(ManifestError):
+    """Raised when YAML parsing or env-var expansion fails."""
+
+
+class ManifestSchemaError(ManifestError):
+    """Raised when the document violates the v1 schema (missing required
+    fields, wrong types, unknown top-level keys, version mismatch).
+    """
+
+
+class ManifestValidationError(ManifestError):
+    """Raised when cross-field validation fails (unknown component
+    reference, unresolvable provider, malformed memory string, ...).
+    """
+
+
+__all__ = [
+    "ManifestError",
+    "ManifestParseError",
+    "ManifestSchemaError",
+    "ManifestValidationError",
+]
diff --git a/scalable/manifest/schema.py b/scalable/manifest/schema.py
new file mode 100644
index 0000000..751bc39
--- /dev/null
+++ b/scalable/manifest/schema.py
@@ -0,0 +1,204 @@
+"""Frozen ``scalable.yaml`` v1 schema dataclasses.
+
+The schema is intentionally implemented with stdlib :mod:`dataclasses` rather
+than :mod:`pydantic` so manifest validation works without the optional
+``scalable[ai]`` extra installed (see Phase 1 plan, open question #1).
+
+The shape mirrors the canonical example in
+``plans/v2.0.0_development_phases.md`` and is **frozen for v2.0.0**: any
+schema change requires bumping :data:`SCHEMA_VERSION`. Phase 3 overlays and
+Phase 4 AI migration assistants are expected to layer on top of this v1
+without breaking existing manifests.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any
+
+#: Current manifest schema version. The parser refuses higher versions with
+#: an actionable error so users on older Scalable releases see a clear
+#: incompatibility message rather than a silent partial parse.
+SCHEMA_VERSION: int = 1
+
+
+@dataclass(frozen=True)
+class ProjectConfig:
+    """Top-level project metadata block.
+
+    Attributes
+    ----------
+    name : str
+        Human-readable project name. Used in run identifiers (Phase 2) and
+        plan/manifest fingerprints.
+    default_storage : str | None
+        Default artifact/output storage URI (e.g. ``s3://bucket/runs/``,
+        ``/shared/scalable/runs``). Phase 1 records the value but does not
+        write to it; Phase 3 wires up the artifact backends.
+    local_cache : str | None
+        Per-project local cache path; complements the process-wide
+        :class:`scalable.common.Settings` cache directory.
+    """
+
+    name: str
+    default_storage: str | None = None
+    local_cache: str | None = None
+
+
+@dataclass(frozen=True)
+class TargetConfig:
+    """A named execution target that selects a deployment provider.
+
+    Attributes
+    ----------
+    name : str
+        The key under ``targets:`` in the manifest (e.g. ``"local"``,
+        ``"hpc"``, ``"gke"``).
+    provider : str
+        The :class:`~scalable.providers.base.DeploymentProvider` ``name``
+        attribute that handles this target. Phase 1 supports ``"local"``
+        and ``"slurm"``; later phases register additional providers.
+    options : Mapping[str, Any]
+        Provider-specific options (queue, account, walltime, namespace, ...).
+        Unknown keys are passed through to the provider and surfaced as
+        warnings rather than errors so Phase 1 manifests can carry
+        forward-compatible fields for Phase 3 cloud/k8s providers.
+    """
+
+    name: str
+    provider: str
+    options: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass(frozen=True)
+class ComponentConfig:
+    """A reusable container/runtime profile referenced by tasks and providers.
+
+    Maps directly onto the legacy ``add_container(tag=..., dirs=..., path=...,
+    cpus=..., memory=..., preload_script=...)`` call in
+    :class:`scalable.core.JobQueueCluster`. The manifest-to-legacy adapter
+    in :mod:`scalable.manifest.adapter` performs that translation in one
+    pure function so every provider shares it.
+
+    Attributes
+    ----------
+    name : str
+        The component key under ``components:`` in the manifest. This is
+        also the Dask resource ``tag`` used by
+        :meth:`scalable.client.ScalableClient.submit`.
+    image : str | None
+        Container image reference (e.g. ``ghcr.io/jgcri/scalable-gcam:7.0``).
+        Optional in Phase 1 ``local`` mode (``containers: none``).
+    runtime : str | None
+        Container runtime (``"apptainer"`` or ``"docker"``). When omitted,
+        the provider's default applies (``apptainer`` on Slurm, ``none``
+        on local).
+    cpus : int
+        CPU cores reserved per worker. Defaults to ``1``.
+    memory : str | None
+        Memory string parseable by :func:`dask.utils.parse_bytes`
+        (``"8G"``, ``"500MB"``, ``"20G"``).
+    mounts : Mapping[str, str]
+        Bind-mount mapping. Schema convention is ``{host_path:
+        container_path}`` (matches the example in the master plan); the
+        adapter normalises this to the legacy ``dirs`` argument expected by
+        :meth:`scalable.core.JobQueueCluster.add_container`.
+    env : Mapping[str, str]
+        Environment variables forwarded into the container at worker launch.
+    tags : list[str]
+        Free-form labels (``"iam"``, ``"climate"``, ``"compiled"``). Reserved
+        for Phase 4 AI assistants and Phase 2 telemetry filtering; not used
+        for routing in Phase 1.
+    preload_script : str | None
+        Optional Dask worker preload script path; passes through to
+        ``add_container(preload_script=...)``.
+    """
+
+    name: str
+    image: str | None = None
+    runtime: str | None = None
+    cpus: int = 1
+    memory: str | None = None
+    mounts: dict[str, str] = field(default_factory=dict)
+    env: dict[str, str] = field(default_factory=dict)
+    tags: list[str] = field(default_factory=list)
+    preload_script: str | None = None
+
+
+@dataclass(frozen=True)
+class TaskConfig:
+    """A logical task definition pinned to a component.
+
+    Phase 1 records task definitions but does not yet execute on them; the
+    dry-run planner uses them to size worker groups. Phase 4 AI planners
+    use this map to infer DAG structure.
+
+    Attributes
+    ----------
+    name : str
+        Task key under ``tasks:`` in the manifest.
+    component : str
+        Name of the :class:`ComponentConfig` this task runs in. Validated
+        for existence by :mod:`scalable.manifest.validate`.
+    cache : bool
+        Whether the task's results should be cached. Phase 1 honours this
+        flag only at the manifest level; Phase 2 wires it into the
+        :func:`scalable.caching.cacheable` decorator metadata, and Phase 3
+        extends it to remote artifact stores.
+    outputs : Mapping[str, str]
+        Declared outputs (``{"database": "dir"}``). Reserved for Phase 3
+        artifact tracking.
+    """
+
+    name: str
+    component: str
+    cache: bool = False
+    outputs: dict[str, str] = field(default_factory=dict)
+
+
+@dataclass(frozen=True)
+class ManifestModel:
+    """Parsed, validated representation of a ``scalable.yaml`` document.
+
+    Instances are immutable so the canonicalised JSON used for
+    ``manifest_lock`` (Phase 1 §3.3) is stable across the lifetime of a
+    :class:`scalable.session.ScalableSession`.
+
+    Attributes
+    ----------
+    version : int
+        Schema version (always equal to :data:`SCHEMA_VERSION` once parsed).
+    project : ProjectConfig
+        Project metadata block.
+    targets : Mapping[str, TargetConfig]
+        Named execution targets; the key matches ``TargetConfig.name``.
+    components : Mapping[str, ComponentConfig]
+        Component definitions; the key matches ``ComponentConfig.name``.
+    tasks : Mapping[str, TaskConfig]
+        Task definitions; the key matches ``TaskConfig.name``.
+    raw : Mapping[str, Any]
+        The raw, post-env-expansion document. Carried so providers can
+        introspect forward-compatible keys without losing fidelity, and so
+        Phase 2 telemetry can record the exact manifest a run was launched
+        from.
+    source_path : str | None
+        Filesystem path the manifest was loaded from, if any.
+    """
+
+    version: int
+    project: ProjectConfig
+    targets: dict[str, TargetConfig]
+    components: dict[str, ComponentConfig]
+    tasks: dict[str, TaskConfig]
+    raw: dict[str, Any]
+    source_path: str | None = None
+
+
+__all__ = [
+    "ComponentConfig",
+    "ManifestModel",
+    "ProjectConfig",
+    "SCHEMA_VERSION",
+    "TargetConfig",
+    "TaskConfig",
+]
diff --git a/scalable/planning/__init__.py b/scalable/planning/__init__.py
new file mode 100644
index 0000000..03b21f2
--- /dev/null
+++ b/scalable/planning/__init__.py
@@ -0,0 +1,16 @@
+"""Plan and dry-run primitives (v2.0.0 Phase 1).
+
+Phase 1 ships a deterministic dry-run planner that converts a
+:class:`~scalable.manifest.schema.ManifestModel` plus a target into a
+provider-neutral :class:`~scalable.providers.base.ScalePlan` plus a
+``manifest_lock`` SHA-256 fingerprint. No workers are launched.
+
+Phase 4 plugs the AI workflow planner in here (objective/policy-driven
+plan synthesis); Phase 5 layers an ML-trained resource advisor on top.
+The Phase 1 ``manifest_lock`` canonicalisation rules are documented and
+test-pinned so Phase 2 telemetry can durably reference manifests.
+"""
+
+from __future__ import annotations
+
+__all__: list[str] = []
diff --git a/scalable/providers/__init__.py b/scalable/providers/__init__.py
new file mode 100644
index 0000000..318ad20
--- /dev/null
+++ b/scalable/providers/__init__.py
@@ -0,0 +1,22 @@
+"""Deployment provider layer (v2.0.0 Phase 1).
+
+A :class:`~scalable.providers.base.DeploymentProvider` is the seam between
+the declarative :class:`~scalable.manifest.schema.ManifestModel` and a
+concrete Dask cluster backend. Phase 1 ships:
+
+* :class:`~scalable.providers.local.LocalProvider` -- Dask ``LocalCluster``
+  for laptops and CI.
+* :class:`~scalable.providers.slurm.SlurmProvider` -- adapter around the
+  existing :class:`scalable.slurm.SlurmCluster` HPC path.
+
+Phase 3 will register :class:`KubernetesProvider`, :class:`CloudProvider`,
+and :class:`StaticProvider` against the same protocol via the registry's
+``entry_points("scalable.providers")`` hook (see
+:mod:`scalable.providers.registry`). The protocol is intentionally free of
+Slurm-specific fields so AI planners (Phase 4) and ML resource advisors
+(Phase 5) can operate on a provider-neutral plan.
+"""
+
+from __future__ import annotations
+
+__all__: list[str] = []
diff --git a/scalable/session/__init__.py b/scalable/session/__init__.py
new file mode 100644
index 0000000..8aaa585
--- /dev/null
+++ b/scalable/session/__init__.py
@@ -0,0 +1,13 @@
+"""Top-level ``ScalableSession`` user entry point (v2.0.0 Phase 1).
+
+:class:`~scalable.session.session.ScalableSession` is the public face of
+the v2.0.0 API surface advertised in the master plan
+(``ScalableSession.from_yaml(...)``, ``session.plan(...)``,
+``session.start(...)``). Phase 1 ships a minimal deterministic
+implementation; Phases 2-5 layer telemetry, AI planning, and ML resource
+advice onto the same surface without breaking the constructor signatures.
+"""
+
+from __future__ import annotations
+
+__all__: list[str] = []

From b0c3ba4337d52887e03d001633bff48717035ab0 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 15:17:57 -0400
Subject: [PATCH 03/47] resize logo

---
 README.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 6e0d599..ae01c36 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,11 @@
-![Scalable logo](docs/images/scalable_logo_nobkg.png)
+<p align="center">
+    <img src="docs/images/scalable_logo_nobkg.png" alt="Scalable logo" width="320" />
+</p>
 
 # Scalable
 
 [![PyPI](https://img.shields.io/pypi/v/scalable.svg)](https://pypi.org/project/scalable/)
-[![Python](https://img.shields.io/pypi/pyversions/scalable.svg)](https://pypi.org/project/scalable/)
+[![Python](https://img.shields.io/badge/python-3.11%20%7C%203.12%20%7C%203.13-blue.svg)](https://pypi.org/project/scalable/)
 [![Docs](https://readthedocs.org/projects/scalable/badge/?version=latest)](https://jgcri.github.io/scalable/)
 
 Scalable is a Python framework for orchestrating containerized, distributed workflows on HPC systems. It integrates container lifecycle management, scheduler-aware resource provisioning, and a Dask-based execution model so multi-stage scientific workflows can run consistently at scale.

From 0ed8671d9dce8ba395be09246332b4e2441eae96 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 15:19:25 -0400
Subject: [PATCH 04/47] resize logo

---
 README.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 6e0d599..ae01c36 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,11 @@
-![Scalable logo](docs/images/scalable_logo_nobkg.png)
+<p align="center">
+    <img src="docs/images/scalable_logo_nobkg.png" alt="Scalable logo" width="320" />
+</p>
 
 # Scalable
 
 [![PyPI](https://img.shields.io/pypi/v/scalable.svg)](https://pypi.org/project/scalable/)
-[![Python](https://img.shields.io/pypi/pyversions/scalable.svg)](https://pypi.org/project/scalable/)
+[![Python](https://img.shields.io/badge/python-3.11%20%7C%203.12%20%7C%203.13-blue.svg)](https://pypi.org/project/scalable/)
 [![Docs](https://readthedocs.org/projects/scalable/badge/?version=latest)](https://jgcri.github.io/scalable/)
 
 Scalable is a Python framework for orchestrating containerized, distributed workflows on HPC systems. It integrates container lifecycle management, scheduler-aware resource provisioning, and a Dask-based execution model so multi-stage scientific workflows can run consistently at scale.

From 65d539e104bc7e7e5b3a3240510527d53ff3dc00 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 15:20:15 -0400
Subject: [PATCH 05/47] resize logo

---
 README.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 6e0d599..ae01c36 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,11 @@
-![Scalable logo](docs/images/scalable_logo_nobkg.png)
+<p align="center">
+    <img src="docs/images/scalable_logo_nobkg.png" alt="Scalable logo" width="320" />
+</p>
 
 # Scalable
 
 [![PyPI](https://img.shields.io/pypi/v/scalable.svg)](https://pypi.org/project/scalable/)
-[![Python](https://img.shields.io/pypi/pyversions/scalable.svg)](https://pypi.org/project/scalable/)
+[![Python](https://img.shields.io/badge/python-3.11%20%7C%203.12%20%7C%203.13-blue.svg)](https://pypi.org/project/scalable/)
 [![Docs](https://readthedocs.org/projects/scalable/badge/?version=latest)](https://jgcri.github.io/scalable/)
 
 Scalable is a Python framework for orchestrating containerized, distributed workflows on HPC systems. It integrates container lifecycle management, scheduler-aware resource provisioning, and a Dask-based execution model so multi-stage scientific workflows can run consistently at scale.

From 2f6c7fda3c702f9096feb0c12ea40290e90de45d Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 15:56:40 -0400
Subject: [PATCH 06/47] WU-2: add scalable.yaml parser with env expansion +
 schema checks

---
 scalable/manifest/parser.py        | 426 +++++++++++++++++++++++++++++
 tests/unit/test_manifest_parser.py | 321 ++++++++++++++++++++++
 2 files changed, 747 insertions(+)
 create mode 100644 scalable/manifest/parser.py
 create mode 100644 tests/unit/test_manifest_parser.py

diff --git a/scalable/manifest/parser.py b/scalable/manifest/parser.py
new file mode 100644
index 0000000..f8a4922
--- /dev/null
+++ b/scalable/manifest/parser.py
@@ -0,0 +1,426 @@
+"""YAML loader for ``scalable.yaml`` v1 manifests.
+
+Phase 1 responsibilities:
+
+* Load YAML from a file path, string, or already-parsed mapping.
+* Expand ``${VAR}`` and ``${VAR:-default}`` references against
+  :data:`os.environ` so manifests stay portable across machines without
+  templating tools.
+* Reject unknown top-level keys (defense-in-depth against typos like
+  ``component:``); unknown keys *inside* ``targets[*]`` are passed through
+  to the provider — see Phase 1 plan §3.3 (forward compatibility for
+  Phase 3 cloud / Kubernetes overlays).
+* Refuse documents whose ``version:`` differs from
+  :data:`scalable.manifest.schema.SCHEMA_VERSION` with a clear message.
+* Build immutable :class:`~scalable.manifest.schema.ManifestModel` and
+  child dataclasses; cross-field semantic checks are deferred to
+  :mod:`scalable.manifest.validate`.
+
+The parser is deterministic — given the same input bytes and environment
+the resulting :class:`ManifestModel.raw` is byte-identical, which is a
+prerequisite for the Phase 1 ``manifest_lock`` fingerprint computed by
+:mod:`scalable.planning.dryrun`.
+"""
+
+from __future__ import annotations
+
+import os
+import re
+from collections.abc import Mapping
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+from .errors import ManifestParseError, ManifestSchemaError
+from .schema import (
+    SCHEMA_VERSION,
+    ComponentConfig,
+    ManifestModel,
+    ProjectConfig,
+    TargetConfig,
+    TaskConfig,
+)
+
+__all__ = [
+    "expand_env_vars",
+    "load_manifest",
+    "parse_manifest",
+]
+
+# Recognised top-level keys for v1. The order is preserved for diagnostic
+# messages; semantically the set is what matters.
+_TOP_LEVEL_KEYS: frozenset[str] = frozenset(
+    {"version", "project", "targets", "components", "tasks"}
+)
+_REQUIRED_TOP_LEVEL_KEYS: frozenset[str] = frozenset({"version", "project"})
+
+# Recognised per-component keys. Unknown keys here are a hard error —
+# component definitions are part of the schema, not provider passthrough.
+_COMPONENT_KEYS: frozenset[str] = frozenset(
+    {"image", "runtime", "cpus", "memory", "mounts", "env", "tags", "preload_script"}
+)
+_TASK_KEYS: frozenset[str] = frozenset({"component", "cache", "outputs"})
+_PROJECT_KEYS: frozenset[str] = frozenset({"name", "default_storage", "local_cache"})
+
+# ${VAR} and ${VAR:-default} expansion. Anchored to require curly braces so
+# bare ``$HOME`` style sequences (which YAML users frequently want as
+# literals in mounts/paths) are left untouched.
+_ENV_VAR_PATTERN: re.Pattern[str] = re.compile(
+    r"""
+    \$\{                       # opening ${
+        (?P<name>[A-Za-z_][A-Za-z0-9_]*)
+        (?:                    # optional :- default form
+            :-(?P<default>[^}]*)
+        )?
+    \}                         # closing }
+    """,
+    re.VERBOSE,
+)
+
+
+def expand_env_vars(value: Any, env: Mapping[str, str] | None = None) -> Any:
+    """Recursively expand ``${VAR}`` references inside a parsed YAML tree.
+
+    Parameters
+    ----------
+    value : Any
+        A parsed YAML value (``str``, ``int``, ``bool``, ``None``,
+        ``list``, ``dict``).
+    env : Mapping[str, str] | None
+        Environment to resolve against. Defaults to :data:`os.environ`.
+        An explicit, restricted mapping is supported so unit tests stay
+        deterministic.
+
+    Returns
+    -------
+    Any
+        A value of the same shape, with ``${VAR}`` references replaced.
+
+    Raises
+    ------
+    ManifestParseError
+        If a ``${VAR}`` reference has no value and no ``${VAR:-default}``
+        clause was provided.
+    """
+    environment = os.environ if env is None else env
+
+    def _expand_str(s: str) -> str:
+        def _sub(match: re.Match[str]) -> str:
+            name = match.group("name")
+            default = match.group("default")
+            if name in environment:
+                return environment[name]
+            if default is not None:
+                return default
+            raise ManifestParseError(
+                f"environment variable {name!r} referenced in manifest "
+                f"is not set and no default (${{{name}:-...}}) was provided"
+            )
+
+        return _ENV_VAR_PATTERN.sub(_sub, s)
+
+    if isinstance(value, str):
+        return _expand_str(value)
+    if isinstance(value, list):
+        return [expand_env_vars(item, env) for item in value]
+    if isinstance(value, dict):
+        return {k: expand_env_vars(v, env) for k, v in value.items()}
+    return value
+
+
+def load_manifest(
+    source: str | os.PathLike[str],
+    *,
+    env: Mapping[str, str] | None = None,
+) -> ManifestModel:
+    """Load and parse a manifest from a filesystem path.
+
+    Parameters
+    ----------
+    source : str or path-like
+        Path to a ``scalable.yaml`` document.
+    env : Mapping[str, str] | None
+        Optional environment override for ``${VAR}`` expansion. Defaults
+        to :data:`os.environ`.
+
+    Returns
+    -------
+    ManifestModel
+        A frozen, immutable model.
+
+    Raises
+    ------
+    ManifestParseError
+        If the file cannot be read or the YAML is malformed.
+    ManifestSchemaError
+        If the document violates the v1 schema.
+    """
+    path = Path(source)
+    try:
+        text = path.read_text(encoding="utf-8")
+    except OSError as exc:  # pragma: no cover - exercised via integration
+        raise ManifestParseError(
+            f"could not read manifest at {path!s}: {exc}"
+        ) from exc
+    return parse_manifest(text, env=env, source_path=str(path))
+
+
+def parse_manifest(
+    source: str | Mapping[str, Any],
+    *,
+    env: Mapping[str, str] | None = None,
+    source_path: str | None = None,
+) -> ManifestModel:
+    """Parse a manifest from a YAML string or already-parsed mapping.
+
+    Parameters
+    ----------
+    source : str or Mapping
+        Either a YAML document as a string or an already-loaded mapping.
+        Tests usually pass a mapping directly so they don't depend on
+        round-tripping YAML.
+    env : Mapping[str, str] | None
+        Environment override for ``${VAR}`` expansion.
+    source_path : str | None
+        Optional originating file path (carried into ``ManifestModel``).
+
+    Returns
+    -------
+    ManifestModel
+    """
+    if isinstance(source, str):
+        try:
+            raw_doc = yaml.safe_load(source)
+        except yaml.YAMLError as exc:
+            raise ManifestParseError(f"malformed YAML: {exc}") from exc
+    else:
+        raw_doc = dict(source)
+
+    if raw_doc is None:
+        raise ManifestSchemaError("manifest document is empty")
+    if not isinstance(raw_doc, dict):
+        raise ManifestSchemaError(
+            f"manifest must be a mapping at the top level, got {type(raw_doc).__name__}"
+        )
+
+    expanded = expand_env_vars(raw_doc, env=env)
+    if not isinstance(expanded, dict):  # pragma: no cover - defensive
+        raise ManifestSchemaError("manifest top-level must remain a mapping after expansion")
+
+    _check_top_level_keys(expanded)
+    _check_version(expanded)
+
+    project = _build_project(expanded.get("project") or {})
+    targets = _build_targets(expanded.get("targets") or {})
+    components = _build_components(expanded.get("components") or {})
+    tasks = _build_tasks(expanded.get("tasks") or {})
+
+    return ManifestModel(
+        version=int(expanded["version"]),
+        project=project,
+        targets=targets,
+        components=components,
+        tasks=tasks,
+        raw=expanded,
+        source_path=source_path,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Internal builders
+# ---------------------------------------------------------------------------
+
+
+def _check_top_level_keys(doc: Mapping[str, Any]) -> None:
+    unknown = set(doc) - _TOP_LEVEL_KEYS
+    if unknown:
+        raise ManifestSchemaError(
+            "unknown top-level manifest key(s): "
+            + ", ".join(sorted(unknown))
+            + f" (allowed: {sorted(_TOP_LEVEL_KEYS)})"
+        )
+    missing = _REQUIRED_TOP_LEVEL_KEYS - set(doc)
+    if missing:
+        raise ManifestSchemaError(
+            "manifest missing required top-level key(s): " + ", ".join(sorted(missing))
+        )
+
+
+def _check_version(doc: Mapping[str, Any]) -> None:
+    version = doc.get("version")
+    if not isinstance(version, int) or isinstance(version, bool):
+        raise ManifestSchemaError(
+            f"manifest 'version' must be an integer, got {type(version).__name__}"
+        )
+    if version != SCHEMA_VERSION:
+        raise ManifestSchemaError(
+            f"manifest schema version {version!r} is not supported by this "
+            f"Scalable build (expected {SCHEMA_VERSION}). Upgrade Scalable or "
+            "downgrade the manifest."
+        )
+
+
+def _require_mapping(value: Any, *, where: str) -> dict[str, Any]:
+    if value is None:
+        return {}
+    if not isinstance(value, Mapping):
+        raise ManifestSchemaError(
+            f"{where} must be a mapping, got {type(value).__name__}"
+        )
+    return dict(value)
+
+
+def _build_project(value: Any) -> ProjectConfig:
+    block = _require_mapping(value, where="'project'")
+    unknown = set(block) - _PROJECT_KEYS
+    if unknown:
+        raise ManifestSchemaError(
+            f"unknown 'project' key(s): {', '.join(sorted(unknown))} "
+            f"(allowed: {sorted(_PROJECT_KEYS)})"
+        )
+    name = block.get("name")
+    if not isinstance(name, str) or not name.strip():
+        raise ManifestSchemaError("'project.name' is required and must be a non-empty string")
+    default_storage = block.get("default_storage")
+    if default_storage is not None and not isinstance(default_storage, str):
+        raise ManifestSchemaError("'project.default_storage' must be a string when set")
+    local_cache = block.get("local_cache")
+    if local_cache is not None and not isinstance(local_cache, str):
+        raise ManifestSchemaError("'project.local_cache' must be a string when set")
+    return ProjectConfig(
+        name=name.strip(),
+        default_storage=default_storage,
+        local_cache=local_cache,
+    )
+
+
+def _build_targets(value: Any) -> dict[str, TargetConfig]:
+    block = _require_mapping(value, where="'targets'")
+    out: dict[str, TargetConfig] = {}
+    for tname, tspec in block.items():
+        if not isinstance(tname, str) or not tname:
+            raise ManifestSchemaError(f"target name must be a non-empty string, got {tname!r}")
+        spec_map = _require_mapping(tspec, where=f"'targets.{tname}'")
+        provider = spec_map.pop("provider", None)
+        if not isinstance(provider, str) or not provider:
+            raise ManifestSchemaError(
+                f"'targets.{tname}.provider' is required and must be a string"
+            )
+        # Everything else is provider-specific options. We deliberately do
+        # not validate keys here so Phase 3 cloud overlays can carry
+        # forward-compatible fields without a parser change. The validator
+        # surfaces unknown keys as warnings.
+        out[tname] = TargetConfig(name=tname, provider=provider, options=dict(spec_map))
+    return out
+
+
+def _build_components(value: Any) -> dict[str, ComponentConfig]:
+    block = _require_mapping(value, where="'components'")
+    out: dict[str, ComponentConfig] = {}
+    for cname, cspec in block.items():
+        if not isinstance(cname, str) or not cname:
+            raise ManifestSchemaError(
+                f"component name must be a non-empty string, got {cname!r}"
+            )
+        spec_map = _require_mapping(cspec, where=f"'components.{cname}'")
+        unknown = set(spec_map) - _COMPONENT_KEYS
+        if unknown:
+            raise ManifestSchemaError(
+                f"unknown 'components.{cname}' key(s): {', '.join(sorted(unknown))} "
+                f"(allowed: {sorted(_COMPONENT_KEYS)})"
+            )
+        cpus_value = spec_map.get("cpus", 1)
+        if not isinstance(cpus_value, int) or isinstance(cpus_value, bool) or cpus_value < 1:
+            raise ManifestSchemaError(
+                f"'components.{cname}.cpus' must be a positive integer (got {cpus_value!r})"
+            )
+        image_value = spec_map.get("image")
+        if image_value is not None and not isinstance(image_value, str):
+            raise ManifestSchemaError(
+                f"'components.{cname}.image' must be a string when set"
+            )
+        runtime_value = spec_map.get("runtime")
+        if runtime_value is not None and not isinstance(runtime_value, str):
+            raise ManifestSchemaError(
+                f"'components.{cname}.runtime' must be a string when set"
+            )
+        memory_value = spec_map.get("memory")
+        if memory_value is not None and not isinstance(memory_value, str):
+            raise ManifestSchemaError(
+                f"'components.{cname}.memory' must be a string when set "
+                f"(e.g. '8G', '500MB'); got {type(memory_value).__name__}"
+            )
+        mounts_value = spec_map.get("mounts") or {}
+        if not isinstance(mounts_value, Mapping):
+            raise ManifestSchemaError(
+                f"'components.{cname}.mounts' must be a mapping of host:container paths"
+            )
+        env_value = spec_map.get("env") or {}
+        if not isinstance(env_value, Mapping):
+            raise ManifestSchemaError(
+                f"'components.{cname}.env' must be a mapping of NAME:VALUE pairs"
+            )
+        # Coerce env values to str so later providers don't have to.
+        env_map = {str(k): str(v) for k, v in env_value.items()}
+        tags_value = spec_map.get("tags") or []
+        if not isinstance(tags_value, list) or not all(isinstance(t, str) for t in tags_value):
+            raise ManifestSchemaError(
+                f"'components.{cname}.tags' must be a list of strings"
+            )
+        preload_value = spec_map.get("preload_script")
+        if preload_value is not None and not isinstance(preload_value, str):
+            raise ManifestSchemaError(
+                f"'components.{cname}.preload_script' must be a string when set"
+            )
+        out[cname] = ComponentConfig(
+            name=cname,
+            image=image_value,
+            runtime=runtime_value,
+            cpus=cpus_value,
+            memory=memory_value,
+            mounts=dict(mounts_value),
+            env=env_map,
+            tags=list(tags_value),
+            preload_script=preload_value,
+        )
+    return out
+
+
+def _build_tasks(value: Any) -> dict[str, TaskConfig]:
+    block = _require_mapping(value, where="'tasks'")
+    out: dict[str, TaskConfig] = {}
+    for tname, tspec in block.items():
+        if not isinstance(tname, str) or not tname:
+            raise ManifestSchemaError(
+                f"task name must be a non-empty string, got {tname!r}"
+            )
+        spec_map = _require_mapping(tspec, where=f"'tasks.{tname}'")
+        unknown = set(spec_map) - _TASK_KEYS
+        if unknown:
+            raise ManifestSchemaError(
+                f"unknown 'tasks.{tname}' key(s): {', '.join(sorted(unknown))} "
+                f"(allowed: {sorted(_TASK_KEYS)})"
+            )
+        component = spec_map.get("component")
+        if not isinstance(component, str) or not component:
+            raise ManifestSchemaError(
+                f"'tasks.{tname}.component' is required and must be a string"
+            )
+        cache = spec_map.get("cache", False)
+        if not isinstance(cache, bool):
+            raise ManifestSchemaError(
+                f"'tasks.{tname}.cache' must be a boolean when set"
+            )
+        outputs = spec_map.get("outputs") or {}
+        if not isinstance(outputs, Mapping):
+            raise ManifestSchemaError(
+                f"'tasks.{tname}.outputs' must be a mapping when set"
+            )
+        out[tname] = TaskConfig(
+            name=tname,
+            component=component,
+            cache=cache,
+            outputs={str(k): str(v) for k, v in outputs.items()},
+        )
+    return out
diff --git a/tests/unit/test_manifest_parser.py b/tests/unit/test_manifest_parser.py
new file mode 100644
index 0000000..9f663db
--- /dev/null
+++ b/tests/unit/test_manifest_parser.py
@@ -0,0 +1,321 @@
+"""Unit tests for :mod:`scalable.manifest.parser` (Phase 1 WU-2).
+
+This suite focuses on syntax/schema parsing and environment expansion only.
+Cross-field semantic checks (unknown component references, provider registry
+lookups, memory parseability, mount path policy) are covered in
+``test_manifest_validate.py`` under WU-3.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+
+from scalable.manifest.errors import ManifestParseError, ManifestSchemaError
+from scalable.manifest.parser import expand_env_vars, load_manifest, parse_manifest
+from scalable.manifest.schema import SCHEMA_VERSION
+
+
+def test_expand_env_vars_expands_recursive_tree() -> None:
+    env = {
+        "ROOT": "/data",
+        "IMAGE_TAG": "7.0",
+        "OMP": "6",
+    }
+    tree = {
+        "a": "${ROOT}/inputs",
+        "b": ["x", "${ROOT}/outputs", {"c": "ghcr.io/demo:${IMAGE_TAG}"}],
+        "d": {"OMP_NUM_THREADS": "${OMP}"},
+    }
+
+    out = expand_env_vars(tree, env=env)
+
+    assert out == {
+        "a": "/data/inputs",
+        "b": ["x", "/data/outputs", {"c": "ghcr.io/demo:7.0"}],
+        "d": {"OMP_NUM_THREADS": "6"},
+    }
+
+
+def test_expand_env_vars_supports_default_clause() -> None:
+    tree = {
+        "path": "${UNSET_VAR:-/tmp/default}",
+        "nested": ["${OTHER_UNSET:-fallback}"]
+    }
+    out = expand_env_vars(tree, env={})
+    assert out == {"path": "/tmp/default", "nested": ["fallback"]}
+
+
+def test_expand_env_vars_raises_when_unset_without_default() -> None:
+    with pytest.raises(ManifestParseError, match="not set"):
+        expand_env_vars({"path": "${MISSING}"}, env={})
+
+
+def test_parse_manifest_from_mapping_success() -> None:
+    manifest = {
+        "version": SCHEMA_VERSION,
+        "project": {"name": "ia-workflow", "local_cache": "./.scalable/cache"},
+        "targets": {
+            "local": {
+                "provider": "local",
+                "max_workers": 4,
+                "containers": "none",
+            }
+        },
+        "components": {
+            "gcam": {
+                "image": "ghcr.io/jgcri/scalable-gcam:7.0",
+                "runtime": "apptainer",
+                "cpus": 6,
+                "memory": "20G",
+                "mounts": {"/host/data": "/data"},
+                "env": {"OMP_NUM_THREADS": "6"},
+                "tags": ["iam", "climate"],
+            }
+        },
+        "tasks": {
+            "run_gcam": {
+                "component": "gcam",
+                "cache": True,
+                "outputs": {"database": "dir"},
+            }
+        },
+    }
+
+    model = parse_manifest(manifest)
+
+    assert model.version == SCHEMA_VERSION
+    assert model.project.name == "ia-workflow"
+    assert "local" in model.targets
+    assert model.targets["local"].provider == "local"
+    # target extras are provider passthrough and preserved
+    assert model.targets["local"].options["max_workers"] == 4
+    assert model.targets["local"].options["containers"] == "none"
+    assert model.components["gcam"].cpus == 6
+    assert model.tasks["run_gcam"].cache is True
+
+
+def test_parse_manifest_from_yaml_string_with_env_expansion() -> None:
+    yaml_text = """
+version: 1
+project:
+  name: integrated-assessment-workflow
+  default_storage: ${STORAGE_URI}
+targets:
+  hpc:
+    provider: slurm
+    queue: short
+    account: GCIMS
+components:
+  gcam:
+    image: ghcr.io/jgcri/scalable-gcam:${GCAM_TAG}
+    runtime: apptainer
+    cpus: 6
+    memory: 20G
+tasks:
+  run_gcam:
+    component: gcam
+    cache: true
+"""
+    env = {
+        "STORAGE_URI": "s3://my-bucket/scalable-runs/",
+        "GCAM_TAG": "7.0",
+    }
+
+    model = parse_manifest(yaml_text, env=env)
+
+    assert model.project.default_storage == "s3://my-bucket/scalable-runs/"
+    assert model.components["gcam"].image == "ghcr.io/jgcri/scalable-gcam:7.0"
+
+
+def test_load_manifest_from_file_sets_source_path(tmp_path: Path) -> None:
+    manifest_path = tmp_path / "scalable.yaml"
+    manifest_path.write_text(
+        """
+version: 1
+project:
+  name: demo
+""".lstrip(),
+        encoding="utf-8",
+    )
+
+    model = load_manifest(manifest_path)
+
+    assert model.project.name == "demo"
+    assert model.source_path == str(manifest_path)
+
+
+def test_parse_manifest_rejects_empty_document() -> None:
+    with pytest.raises(ManifestSchemaError, match="empty"):
+        parse_manifest("")
+
+
+def test_parse_manifest_rejects_non_mapping_top_level() -> None:
+    with pytest.raises(ManifestSchemaError, match="top level"):
+        parse_manifest("- not\n- a\n- mapping\n")
+
+
+def test_parse_manifest_rejects_malformed_yaml() -> None:
+    with pytest.raises(ManifestParseError, match="malformed YAML"):
+        parse_manifest("version: [1\n")
+
+
+def test_parse_manifest_rejects_unknown_top_level_keys() -> None:
+    with pytest.raises(ManifestSchemaError, match="unknown top-level"):
+        parse_manifest(
+            {
+                "version": 1,
+                "project": {"name": "demo"},
+                "componentz": {},
+            }
+        )
+
+
+def test_parse_manifest_rejects_missing_required_top_level_keys() -> None:
+    with pytest.raises(ManifestSchemaError, match="missing required"):
+        parse_manifest({"version": 1})
+
+
+def test_parse_manifest_rejects_non_integer_version() -> None:
+    with pytest.raises(ManifestSchemaError, match="must be an integer"):
+        parse_manifest({"version": "1", "project": {"name": "demo"}})
+
+
+def test_parse_manifest_rejects_unsupported_version() -> None:
+    with pytest.raises(ManifestSchemaError, match="not supported"):
+        parse_manifest({"version": 999, "project": {"name": "demo"}})
+
+
+def test_parse_manifest_rejects_invalid_project_name() -> None:
+    with pytest.raises(ManifestSchemaError, match=r"project\.name"):
+        parse_manifest({"version": 1, "project": {"name": ""}})
+
+
+def test_parse_manifest_rejects_unknown_project_key() -> None:
+    with pytest.raises(ManifestSchemaError, match="unknown 'project'"):
+        parse_manifest(
+            {
+                "version": 1,
+                "project": {"name": "demo", "unknown": "x"},
+            }
+        )
+
+
+def test_parse_manifest_rejects_target_without_provider() -> None:
+    with pytest.raises(ManifestSchemaError, match=r"targets\.local\.provider"):
+        parse_manifest(
+            {
+                "version": 1,
+                "project": {"name": "demo"},
+                "targets": {"local": {}},
+            }
+        )
+
+
+def test_parse_manifest_rejects_unknown_component_key() -> None:
+    with pytest.raises(ManifestSchemaError, match=r"unknown 'components\.gcam'"):
+        parse_manifest(
+            {
+                "version": 1,
+                "project": {"name": "demo"},
+                "components": {
+                    "gcam": {
+                        "cpus": 1,
+                        "weird": True,
+                    }
+                },
+            }
+        )
+
+
+def test_parse_manifest_rejects_component_invalid_types() -> None:
+    with pytest.raises(ManifestSchemaError, match="cpus"):
+        parse_manifest(
+            {
+                "version": 1,
+                "project": {"name": "demo"},
+                "components": {"gcam": {"cpus": 0}},
+            }
+        )
+
+    with pytest.raises(ManifestSchemaError, match="memory"):
+        parse_manifest(
+            {
+                "version": 1,
+                "project": {"name": "demo"},
+                "components": {"gcam": {"memory": 32}},
+            }
+        )
+
+    with pytest.raises(ManifestSchemaError, match="mounts"):
+        parse_manifest(
+            {
+                "version": 1,
+                "project": {"name": "demo"},
+                "components": {"gcam": {"mounts": ["/a:/b"]}},
+            }
+        )
+
+    with pytest.raises(ManifestSchemaError, match="env"):
+        parse_manifest(
+            {
+                "version": 1,
+                "project": {"name": "demo"},
+                "components": {"gcam": {"env": ["OMP=6"]}},
+            }
+        )
+
+    with pytest.raises(ManifestSchemaError, match="tags"):
+        parse_manifest(
+            {
+                "version": 1,
+                "project": {"name": "demo"},
+                "components": {"gcam": {"tags": "climate"}},
+            }
+        )
+
+
+def test_parse_manifest_rejects_unknown_task_key() -> None:
+    with pytest.raises(ManifestSchemaError, match=r"unknown 'tasks\.run_gcam'"):
+        parse_manifest(
+            {
+                "version": 1,
+                "project": {"name": "demo"},
+                "tasks": {
+                    "run_gcam": {
+                        "component": "gcam",
+                        "unexpected": "x",
+                    }
+                },
+            }
+        )
+
+
+def test_parse_manifest_rejects_task_invalid_types() -> None:
+    with pytest.raises(ManifestSchemaError, match="component"):
+        parse_manifest(
+            {
+                "version": 1,
+                "project": {"name": "demo"},
+                "tasks": {"run_gcam": {"component": 5}},
+            }
+        )
+
+    with pytest.raises(ManifestSchemaError, match="cache"):
+        parse_manifest(
+            {
+                "version": 1,
+                "project": {"name": "demo"},
+                "tasks": {"run_gcam": {"component": "gcam", "cache": "yes"}},
+            }
+        )
+
+    with pytest.raises(ManifestSchemaError, match="outputs"):
+        parse_manifest(
+            {
+                "version": 1,
+                "project": {"name": "demo"},
+                "tasks": {"run_gcam": {"component": "gcam", "outputs": ["dir"]}},
+            }
+        )

From 5b6b7b9c625bc3b12738b89229d5089cca2f2375 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 15:57:51 -0400
Subject: [PATCH 07/47] WU-3: add manifest semantic validator + validation
 report tests

---
 scalable/manifest/validate.py        | 199 +++++++++++++++++++++++++++
 tests/unit/test_manifest_validate.py | 189 +++++++++++++++++++++++++
 2 files changed, 388 insertions(+)
 create mode 100644 scalable/manifest/validate.py
 create mode 100644 tests/unit/test_manifest_validate.py

diff --git a/scalable/manifest/validate.py b/scalable/manifest/validate.py
new file mode 100644
index 0000000..9a6e7f3
--- /dev/null
+++ b/scalable/manifest/validate.py
@@ -0,0 +1,199 @@
+"""Semantic validation for parsed ``scalable.yaml`` manifests.
+
+The parser in :mod:`scalable.manifest.parser` enforces structural schema
+shape (required keys, value types, known top-level fields). This module
+adds cross-field checks that require seeing the whole manifest at once,
+without coupling to provider implementations.
+
+Phase 1 checks:
+
+* every ``tasks[*].component`` exists in ``components``;
+* each target references a known provider name;
+* component mount paths are absolute on both host and container sides;
+* component memory strings are parseable by :func:`dask.utils.parse_bytes`.
+
+The return type is a structured report instead of exceptions so
+``scalable validate`` can print multiple issues in one run.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import PurePosixPath
+
+from dask.utils import parse_bytes
+
+from .schema import ManifestModel
+
+__all__ = [
+    "ValidationIssue",
+    "ValidationReport",
+    "validate_manifest",
+]
+
+
+@dataclass(frozen=True)
+class ValidationIssue:
+    """Single validation finding.
+
+    Attributes
+    ----------
+    path : str
+        Manifest path-ish location (e.g. ``targets.local.provider``).
+    message : str
+        Human-readable message.
+    code : str | None
+        Stable machine-readable code for tooling (optional in Phase 1).
+    """
+
+    path: str
+    message: str
+    code: str | None = None
+
+
+@dataclass
+class ValidationReport:
+    """Structured validation output consumed by CLI and session APIs."""
+
+    errors: list[ValidationIssue] = field(default_factory=list)
+    warnings: list[ValidationIssue] = field(default_factory=list)
+
+    @property
+    def ok(self) -> bool:
+        """Whether the manifest passed validation with no errors."""
+        return not self.errors
+
+
+def validate_manifest(
+    manifest: ManifestModel,
+    *,
+    known_providers: set[str] | None = None,
+) -> ValidationReport:
+    """Validate a parsed manifest and return a structured report.
+
+    Parameters
+    ----------
+    manifest : ManifestModel
+        Parsed model from :func:`scalable.manifest.parser.parse_manifest`.
+    known_providers : set[str] | None
+        Provider names accepted by this runtime. Defaults to
+        ``{"local", "slurm"}`` in Phase 1 and can be replaced by the
+        provider registry in WU-4.
+    """
+    report = ValidationReport()
+    providers = known_providers or {"local", "slurm"}
+
+    # ------------------------------------------------------------------
+    # Target/provider checks
+    # ------------------------------------------------------------------
+    if not manifest.targets:
+        report.warnings.append(
+            ValidationIssue(
+                path="targets",
+                message="no targets declared; session startup requires a target",
+                code="W_NO_TARGETS",
+            )
+        )
+    for target_name, target in manifest.targets.items():
+        if target.provider not in providers:
+            report.errors.append(
+                ValidationIssue(
+                    path=f"targets.{target_name}.provider",
+                    message=(
+                        f"unknown provider {target.provider!r}; "
+                        f"known providers: {sorted(providers)}"
+                    ),
+                    code="E_UNKNOWN_PROVIDER",
+                )
+            )
+
+    # ------------------------------------------------------------------
+    # Component checks
+    # ------------------------------------------------------------------
+    for component_name, component in manifest.components.items():
+        # Memory parseability (shape already type-checked by parser)
+        if component.memory is not None:
+            try:
+                parsed = parse_bytes(component.memory)
+            except Exception:
+                report.errors.append(
+                    ValidationIssue(
+                        path=f"components.{component_name}.memory",
+                        message=(
+                            f"memory value {component.memory!r} is not parseable; "
+                            "use values like '8G', '500MB', or '1024MiB'"
+                        ),
+                        code="E_BAD_MEMORY",
+                    )
+                )
+            else:
+                if parsed <= 0:
+                    report.errors.append(
+                        ValidationIssue(
+                            path=f"components.{component_name}.memory",
+                            message="memory must be greater than zero",
+                            code="E_NONPOSITIVE_MEMORY",
+                        )
+                    )
+
+        # Mount path absoluteness
+        for host_path, container_path in component.mounts.items():
+            if not _is_absolute_posix_like(host_path):
+                report.errors.append(
+                    ValidationIssue(
+                        path=f"components.{component_name}.mounts[{host_path!r}]",
+                        message="host mount path must be absolute",
+                        code="E_RELATIVE_HOST_MOUNT",
+                    )
+                )
+            if not _is_absolute_posix_like(container_path):
+                report.errors.append(
+                    ValidationIssue(
+                        path=(
+                            f"components.{component_name}.mounts"
+                            f"[{host_path!r}]"
+                        ),
+                        message="container mount path must be absolute",
+                        code="E_RELATIVE_CONTAINER_MOUNT",
+                    )
+                )
+
+    # ------------------------------------------------------------------
+    # Task/component cross references
+    # ------------------------------------------------------------------
+    known_components = set(manifest.components)
+    for task_name, task in manifest.tasks.items():
+        if task.component not in known_components:
+            report.errors.append(
+                ValidationIssue(
+                    path=f"tasks.{task_name}.component",
+                    message=(
+                        f"unknown component {task.component!r}; "
+                        f"known components: {sorted(known_components)}"
+                    ),
+                    code="E_UNKNOWN_COMPONENT",
+                )
+            )
+
+    if not manifest.tasks:
+        report.warnings.append(
+            ValidationIssue(
+                path="tasks",
+                message="no tasks declared; planning will produce an empty task set",
+                code="W_NO_TASKS",
+            )
+        )
+
+    return report
+
+
+def _is_absolute_posix_like(path: str) -> bool:
+    """Return whether a path is absolute in POSIX terms.
+
+    Manifest paths are provider/runtime-oriented (containers, Linux HPC,
+    object-store mount shims), so we normalize using POSIX semantics.
+    """
+    if not isinstance(path, str) or not path:
+        return False
+    return PurePosixPath(path).is_absolute()
+
diff --git a/tests/unit/test_manifest_validate.py b/tests/unit/test_manifest_validate.py
new file mode 100644
index 0000000..db4ac15
--- /dev/null
+++ b/tests/unit/test_manifest_validate.py
@@ -0,0 +1,189 @@
+"""Unit tests for :mod:`scalable.manifest.validate` (Phase 1 WU-3)."""
+
+from __future__ import annotations
+
+from scalable.manifest.parser import parse_manifest
+from scalable.manifest.validate import validate_manifest
+
+
+def _base_manifest() -> dict:
+    return {
+        "version": 1,
+        "project": {"name": "demo"},
+        "targets": {
+            "local": {
+                "provider": "local",
+                "max_workers": 4,
+            }
+        },
+        "components": {
+            "gcam": {
+                "cpus": 2,
+                "memory": "8G",
+                "mounts": {
+                    "/host/data": "/data",
+                },
+            }
+        },
+        "tasks": {
+            "run_gcam": {
+                "component": "gcam",
+                "cache": True,
+            }
+        },
+    }
+
+
+def test_validate_manifest_ok_for_valid_manifest() -> None:
+    model = parse_manifest(_base_manifest())
+
+    report = validate_manifest(model)
+
+    assert report.ok is True
+    assert report.errors == []
+    assert report.warnings == []
+
+
+def test_validate_manifest_warns_when_no_targets_or_tasks() -> None:
+    manifest = {
+        "version": 1,
+        "project": {"name": "demo"},
+        "targets": {},
+        "components": {},
+        "tasks": {},
+    }
+    model = parse_manifest(manifest)
+
+    report = validate_manifest(model)
+
+    assert report.ok is True
+    assert len(report.warnings) == 2
+    warning_paths = {w.path for w in report.warnings}
+    assert "targets" in warning_paths
+    assert "tasks" in warning_paths
+
+
+def test_validate_manifest_errors_for_unknown_provider() -> None:
+    manifest = _base_manifest()
+    manifest["targets"]["local"]["provider"] = "kubernetes"
+    model = parse_manifest(manifest)
+
+    report = validate_manifest(model, known_providers={"local", "slurm"})
+
+    assert report.ok is False
+    assert len(report.errors) == 1
+    issue = report.errors[0]
+    assert issue.path == "targets.local.provider"
+    assert issue.code == "E_UNKNOWN_PROVIDER"
+    assert "unknown provider" in issue.message
+
+
+def test_validate_manifest_accepts_custom_known_provider_set() -> None:
+    manifest = _base_manifest()
+    manifest["targets"]["local"]["provider"] = "kubernetes"
+    model = parse_manifest(manifest)
+
+    report = validate_manifest(model, known_providers={"local", "slurm", "kubernetes"})
+
+    assert report.ok is True
+    assert report.errors == []
+
+
+def test_validate_manifest_errors_for_unknown_task_component() -> None:
+    manifest = _base_manifest()
+    manifest["tasks"]["run_gcam"]["component"] = "missing_component"
+    model = parse_manifest(manifest)
+
+    report = validate_manifest(model)
+
+    assert report.ok is False
+    assert len(report.errors) == 1
+    issue = report.errors[0]
+    assert issue.path == "tasks.run_gcam.component"
+    assert issue.code == "E_UNKNOWN_COMPONENT"
+    assert "unknown component" in issue.message
+
+
+def test_validate_manifest_errors_for_unparseable_memory() -> None:
+    manifest = _base_manifest()
+    manifest["components"]["gcam"]["memory"] = "not-a-memory"
+    model = parse_manifest(manifest)
+
+    report = validate_manifest(model)
+
+    assert report.ok is False
+    assert len(report.errors) == 1
+    issue = report.errors[0]
+    assert issue.path == "components.gcam.memory"
+    assert issue.code == "E_BAD_MEMORY"
+    assert "not parseable" in issue.message
+
+
+def test_validate_manifest_errors_for_nonpositive_memory() -> None:
+    manifest = _base_manifest()
+    manifest["components"]["gcam"]["memory"] = "0B"
+    model = parse_manifest(manifest)
+
+    report = validate_manifest(model)
+
+    assert report.ok is False
+    assert len(report.errors) == 1
+    issue = report.errors[0]
+    assert issue.path == "components.gcam.memory"
+    assert issue.code == "E_NONPOSITIVE_MEMORY"
+    assert "greater than zero" in issue.message
+
+
+def test_validate_manifest_errors_for_relative_host_mount_path() -> None:
+    manifest = _base_manifest()
+    manifest["components"]["gcam"]["mounts"] = {
+        "relative/host/path": "/data",
+    }
+    model = parse_manifest(manifest)
+
+    report = validate_manifest(model)
+
+    assert report.ok is False
+    assert len(report.errors) == 1
+    issue = report.errors[0]
+    assert issue.path == "components.gcam.mounts['relative/host/path']"
+    assert issue.code == "E_RELATIVE_HOST_MOUNT"
+
+
+def test_validate_manifest_errors_for_relative_container_mount_path() -> None:
+    manifest = _base_manifest()
+    manifest["components"]["gcam"]["mounts"] = {
+        "/host/data": "relative/container/path",
+    }
+    model = parse_manifest(manifest)
+
+    report = validate_manifest(model)
+
+    assert report.ok is False
+    assert len(report.errors) == 1
+    issue = report.errors[0]
+    assert issue.path == "components.gcam.mounts['/host/data']"
+    assert issue.code == "E_RELATIVE_CONTAINER_MOUNT"
+
+
+def test_validate_manifest_collects_multiple_errors() -> None:
+    manifest = _base_manifest()
+    manifest["targets"]["local"]["provider"] = "mystery"
+    manifest["components"]["gcam"]["memory"] = "bad"
+    manifest["components"]["gcam"]["mounts"] = {
+        "relative_host": "relative_container",
+    }
+    manifest["tasks"]["run_gcam"]["component"] = "missing"
+    model = parse_manifest(manifest)
+
+    report = validate_manifest(model)
+
+    assert report.ok is False
+    assert len(report.errors) == 5
+    codes = {issue.code for issue in report.errors}
+    assert "E_UNKNOWN_PROVIDER" in codes
+    assert "E_BAD_MEMORY" in codes
+    assert "E_RELATIVE_HOST_MOUNT" in codes
+    assert "E_RELATIVE_CONTAINER_MOUNT" in codes
+    assert "E_UNKNOWN_COMPONENT" in codes
+

From a2eccadd811771fd45d597b19b3b8aee69e85cd3 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 16:00:34 -0400
Subject: [PATCH 08/47] WU-4: add provider protocol, deployment spec, and
 registry

---
 scalable/providers/__init__.py             |  22 ++-
 scalable/providers/base.py                 | 134 ++++++++++++++++
 scalable/providers/registry.py             | 121 +++++++++++++++
 tests/unit/test_providers_base_registry.py | 170 +++++++++++++++++++++
 4 files changed, 446 insertions(+), 1 deletion(-)
 create mode 100644 scalable/providers/base.py
 create mode 100644 scalable/providers/registry.py
 create mode 100644 tests/unit/test_providers_base_registry.py

diff --git a/scalable/providers/__init__.py b/scalable/providers/__init__.py
index 318ad20..af2bf23 100644
--- a/scalable/providers/__init__.py
+++ b/scalable/providers/__init__.py
@@ -19,4 +19,24 @@
 
 from __future__ import annotations
 
-__all__: list[str] = []
+from .base import ClusterHandle, DeploymentProvider, DeploymentSpec, ResourceRequest, ScalePlan
+from .registry import (
+    clear_registry,
+    get_provider,
+    iter_provider_names,
+    register_provider,
+    register_providers,
+)
+
+__all__ = [
+    "ClusterHandle",
+    "DeploymentProvider",
+    "DeploymentSpec",
+    "ResourceRequest",
+    "ScalePlan",
+    "clear_registry",
+    "get_provider",
+    "iter_provider_names",
+    "register_provider",
+    "register_providers",
+]
diff --git a/scalable/providers/base.py b/scalable/providers/base.py
new file mode 100644
index 0000000..ae3b774
--- /dev/null
+++ b/scalable/providers/base.py
@@ -0,0 +1,134 @@
+"""Provider protocol and core provider-neutral data structures.
+
+Phase 1 introduces an explicit deployment seam so Scalable can target local,
+Slurm, Kubernetes, and cloud backends through one stable contract.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from typing import Any, Protocol
+
+from scalable.client import ScalableClient
+from scalable.manifest.schema import (
+    ComponentConfig,
+    ManifestModel,
+    TargetConfig,
+    TaskConfig,
+)
+from scalable.manifest.validate import ValidationReport
+
+__all__ = [
+    "ClusterHandle",
+    "DeploymentProvider",
+    "DeploymentSpec",
+    "ResourceRequest",
+    "ScalePlan",
+]
+
+
+@dataclass(frozen=True)
+class DeploymentSpec:
+    """Provider-neutral deployment request derived from a manifest target.
+
+    Attributes
+    ----------
+    target_name
+        Name under ``targets:`` selected by the caller.
+    provider_name
+        Provider identifier from ``targets.<name>.provider``.
+    manifest
+        Full parsed manifest model.
+    target
+        Target block selected from the manifest.
+    components
+        Components map copied from the manifest.
+    tasks
+        Tasks map copied from the manifest.
+    raw_manifest
+        Expanded raw manifest data used for deterministic fingerprinting.
+    """
+
+    target_name: str
+    provider_name: str
+    manifest: ManifestModel
+    target: TargetConfig
+    components: dict[str, ComponentConfig]
+    tasks: dict[str, TaskConfig]
+    raw_manifest: dict[str, Any]
+
+    @classmethod
+    def from_manifest(
+        cls,
+        manifest: ManifestModel,
+        *,
+        target_name: str,
+    ) -> DeploymentSpec:
+        """Build a :class:`DeploymentSpec` from a parsed manifest.
+
+        Raises
+        ------
+        KeyError
+            If ``target_name`` is not present in ``manifest.targets``.
+        """
+        target = manifest.targets[target_name]
+        return cls(
+            target_name=target_name,
+            provider_name=target.provider,
+            manifest=manifest,
+            target=target,
+            components=dict(manifest.components),
+            tasks=dict(manifest.tasks),
+            raw_manifest=dict(manifest.raw),
+        )
+
+
+@dataclass(frozen=True)
+class ResourceRequest:
+    """Resource request for one worker group/tag."""
+
+    cpus: int = 1
+    memory: str | None = None
+    walltime: str | None = None
+    gpus: int | None = None
+
+
+@dataclass(frozen=True)
+class ScalePlan:
+    """Provider-neutral scaling intent generated from the manifest."""
+
+    workers_by_tag: dict[str, int] = field(default_factory=dict)
+    resources_by_tag: dict[str, ResourceRequest] = field(default_factory=dict)
+
+
+@dataclass
+class ClusterHandle:
+    """Opaque holder for provider-specific cluster state.
+
+    Providers return this instead of raw cluster objects so higher layers can
+    remain provider-neutral while still creating :class:`ScalableClient`
+    instances through ``client_factory``.
+    """
+
+    backend: Any
+    client_factory: Callable[[], ScalableClient]
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+
+class DeploymentProvider(Protocol):
+    """Protocol implemented by all execution providers."""
+
+    name: str
+
+    def validate(self, spec: DeploymentSpec) -> ValidationReport:
+        """Validate provider-specific options and constraints."""
+
+    def build_cluster(self, spec: DeploymentSpec) -> ClusterHandle:
+        """Create or connect to a backend cluster and return a handle."""
+
+    def scale(self, cluster: ClusterHandle, plan: ScalePlan) -> None:
+        """Apply scaling operations for this provider."""
+
+    def close(self, cluster: ClusterHandle) -> None:
+        """Close provider-managed resources."""
diff --git a/scalable/providers/registry.py b/scalable/providers/registry.py
new file mode 100644
index 0000000..6953e82
--- /dev/null
+++ b/scalable/providers/registry.py
@@ -0,0 +1,121 @@
+"""Provider registry and discovery helpers.
+
+Phase 1 supports built-in providers (`local`, `slurm`) and allows optional
+third-party provider registration through Python entry points under the
+``scalable.providers`` group.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable, Iterable
+from importlib.metadata import EntryPoint, entry_points
+from typing import Any
+
+from .base import DeploymentProvider
+
+ProviderFactory = Callable[[], DeploymentProvider] | type[DeploymentProvider]
+
+_REGISTRY: dict[str, ProviderFactory] = {}
+
+__all__ = [
+    "clear_registry",
+    "get_provider",
+    "iter_provider_names",
+    "register_provider",
+    "register_providers",
+]
+
+
+def register_provider(name: str, factory: ProviderFactory) -> None:
+    """Register a provider factory/class under a stable name."""
+    normalized = _normalize_provider_name(name)
+    if normalized in _REGISTRY:
+        raise ValueError(f"provider {normalized!r} is already registered")
+    _REGISTRY[normalized] = factory
+
+
+def register_providers(items: Iterable[tuple[str, ProviderFactory]]) -> None:
+    """Bulk register provider factories."""
+    for name, factory in items:
+        register_provider(name, factory)
+
+
+def get_provider(name: str) -> DeploymentProvider:
+    """Resolve and instantiate a provider by name.
+
+    Lookup order:
+    1. Explicit runtime registry (`register_provider`).
+    2. Entry points in group ``scalable.providers``.
+    """
+    normalized = _normalize_provider_name(name)
+    if normalized in _REGISTRY:
+        return _instantiate(_REGISTRY[normalized])
+
+    discovered = _load_provider_entrypoint(normalized)
+    if discovered is None:
+        known = sorted(iter_provider_names(include_entrypoints=True))
+        raise KeyError(
+            f"unknown provider {normalized!r}; known providers: {known}"
+        )
+    # Cache discovered providers for next lookup.
+    _REGISTRY[normalized] = discovered
+    return _instantiate(discovered)
+
+
+def iter_provider_names(*, include_entrypoints: bool = True) -> set[str]:
+    """Return provider names known to the runtime."""
+    names = set(_REGISTRY)
+    if include_entrypoints:
+        for ep in _iter_provider_entrypoints():
+            names.add(_normalize_provider_name(ep.name))
+    return names
+
+
+def clear_registry() -> None:
+    """Reset runtime registrations (primarily for tests)."""
+    _REGISTRY.clear()
+
+
+def _normalize_provider_name(name: str) -> str:
+    normalized = name.strip().lower()
+    if not normalized:
+        raise ValueError("provider name must be a non-empty string")
+    return normalized
+
+
+def _instantiate(factory: ProviderFactory) -> DeploymentProvider:
+    if isinstance(factory, type):
+        return factory()
+    return factory()
+
+
+def _iter_provider_entrypoints() -> list[EntryPoint]:
+    try:
+        eps = entry_points(group="scalable.providers")
+        # Python 3.12+ returns EntryPoints object; convert to list.
+        return list(eps)
+    except TypeError:
+        # Compatibility for older return style where group is filtered via
+        # select(). Retained for robustness.
+        eps = entry_points()
+        selected = getattr(eps, "select", None)
+        if callable(selected):
+            return list(selected(group="scalable.providers"))
+        return [ep for ep in eps if getattr(ep, "group", None) == "scalable.providers"]
+
+
+def _load_provider_entrypoint(name: str) -> ProviderFactory | None:
+    normalized = _normalize_provider_name(name)
+    for ep in _iter_provider_entrypoints():
+        if _normalize_provider_name(ep.name) != normalized:
+            continue
+        loaded: Any = ep.load()
+        if isinstance(loaded, type):
+            return loaded
+        if callable(loaded):
+            return loaded
+        raise TypeError(
+            f"entry point scalable.providers:{ep.name} must load a class or callable"
+        )
+    return None
+
diff --git a/tests/unit/test_providers_base_registry.py b/tests/unit/test_providers_base_registry.py
new file mode 100644
index 0000000..433a752
--- /dev/null
+++ b/tests/unit/test_providers_base_registry.py
@@ -0,0 +1,170 @@
+"""Unit tests for provider base types and registry (Phase 1 WU-4)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import pytest
+
+from scalable.manifest.parser import parse_manifest
+from scalable.providers.base import (
+    ClusterHandle,
+    DeploymentSpec,
+    ResourceRequest,
+    ScalePlan,
+)
+from scalable.providers.registry import (
+    clear_registry,
+    get_provider,
+    iter_provider_names,
+    register_provider,
+    register_providers,
+)
+
+
+@dataclass
+class DummyProvider:
+    name: str = "dummy"
+
+    def validate(self, spec):  # pragma: no cover - not needed in this suite
+        raise NotImplementedError
+
+    def build_cluster(self, spec):  # pragma: no cover - not needed in this suite
+        raise NotImplementedError
+
+    def scale(self, cluster, plan):  # pragma: no cover - not needed in this suite
+        raise NotImplementedError
+
+    def close(self, cluster):  # pragma: no cover - not needed in this suite
+        raise NotImplementedError
+
+
+@pytest.fixture(autouse=True)
+def _clean_registry():
+    clear_registry()
+    yield
+    clear_registry()
+
+
+def test_deployment_spec_from_manifest() -> None:
+    model = parse_manifest(
+        {
+            "version": 1,
+            "project": {"name": "demo"},
+            "targets": {"local": {"provider": "local", "max_workers": 2}},
+            "components": {"gcam": {"cpus": 2, "memory": "8G"}},
+            "tasks": {"run_gcam": {"component": "gcam"}},
+        }
+    )
+
+    spec = DeploymentSpec.from_manifest(model, target_name="local")
+
+    assert spec.target_name == "local"
+    assert spec.provider_name == "local"
+    assert spec.target.options["max_workers"] == 2
+    assert "gcam" in spec.components
+    assert "run_gcam" in spec.tasks
+    assert spec.raw_manifest["version"] == 1
+
+
+def test_deployment_spec_from_manifest_missing_target_raises_keyerror() -> None:
+    model = parse_manifest({"version": 1, "project": {"name": "demo"}, "targets": {}})
+
+    with pytest.raises(KeyError):
+        DeploymentSpec.from_manifest(model, target_name="missing")
+
+
+def test_scale_plan_and_resource_request_defaults() -> None:
+    rr = ResourceRequest()
+    assert rr.cpus == 1
+    assert rr.memory is None
+    assert rr.walltime is None
+    assert rr.gpus is None
+
+    plan = ScalePlan()
+    assert plan.workers_by_tag == {}
+    assert plan.resources_by_tag == {}
+
+
+def test_cluster_handle_stores_backend_and_factory() -> None:
+    marker = object()
+
+    def _factory():
+        return marker
+
+    handle = ClusterHandle(backend="backend", client_factory=_factory, metadata={"k": "v"})
+
+    assert handle.backend == "backend"
+    assert handle.client_factory() is marker
+    assert handle.metadata == {"k": "v"}
+
+
+def test_register_provider_and_get_provider_from_class() -> None:
+    register_provider("dummy", DummyProvider)
+
+    provider = get_provider("dummy")
+
+    assert isinstance(provider, DummyProvider)
+    assert provider.name == "dummy"
+
+
+def test_register_provider_and_get_provider_from_callable_factory() -> None:
+    def factory():
+        return DummyProvider(name="dummy-factory")
+
+    register_provider("dummy", factory)
+
+    provider = get_provider("dummy")
+
+    assert isinstance(provider, DummyProvider)
+    assert provider.name == "dummy-factory"
+
+
+def test_register_provider_normalizes_name_and_lookup() -> None:
+    register_provider("  DuMmY  ", DummyProvider)
+
+    provider = get_provider("dummy")
+
+    assert isinstance(provider, DummyProvider)
+
+
+def test_register_provider_rejects_empty_name() -> None:
+    with pytest.raises(ValueError, match="non-empty"):
+        register_provider("   ", DummyProvider)
+
+
+def test_register_provider_rejects_duplicates() -> None:
+    register_provider("dummy", DummyProvider)
+
+    with pytest.raises(ValueError, match="already registered"):
+        register_provider("dummy", DummyProvider)
+
+
+def test_register_providers_bulk() -> None:
+    register_providers(
+        [
+            ("dummy1", DummyProvider),
+            ("dummy2", lambda: DummyProvider(name="dummy2")),
+        ]
+    )
+
+    p1 = get_provider("dummy1")
+    p2 = get_provider("dummy2")
+
+    assert isinstance(p1, DummyProvider)
+    assert isinstance(p2, DummyProvider)
+    assert p2.name == "dummy2"
+
+
+def test_get_provider_unknown_raises_keyerror() -> None:
+    with pytest.raises(KeyError, match="unknown provider"):
+        get_provider("missing")
+
+
+def test_iter_provider_names_from_runtime_registry() -> None:
+    register_provider("dummy", DummyProvider)
+
+    names = iter_provider_names(include_entrypoints=False)
+
+    assert names == {"dummy"}
+

From 609b9e05f225fefa89bd9f29e6ba6cf62310ebdc Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 16:08:06 -0400
Subject: [PATCH 09/47] WU-5: add LocalProvider with tagged local execution and
 tests

---
 scalable/providers/__init__.py                |   2 +
 scalable/providers/local.py                   | 169 ++++++++++++++++++
 scalable/providers/registry.py                |  20 +++
 .../test_local_provider_end_to_end.py         |  49 +++++
 tests/unit/test_providers_base_registry.py    |   6 +
 tests/unit/test_providers_local.py            | 131 ++++++++++++++
 6 files changed, 377 insertions(+)
 create mode 100644 scalable/providers/local.py
 create mode 100644 tests/integration/test_local_provider_end_to_end.py
 create mode 100644 tests/unit/test_providers_local.py

diff --git a/scalable/providers/__init__.py b/scalable/providers/__init__.py
index af2bf23..4d06cf5 100644
--- a/scalable/providers/__init__.py
+++ b/scalable/providers/__init__.py
@@ -20,6 +20,7 @@
 from __future__ import annotations
 
 from .base import ClusterHandle, DeploymentProvider, DeploymentSpec, ResourceRequest, ScalePlan
+from .local import LocalProvider
 from .registry import (
     clear_registry,
     get_provider,
@@ -32,6 +33,7 @@
     "ClusterHandle",
     "DeploymentProvider",
     "DeploymentSpec",
+    "LocalProvider",
     "ResourceRequest",
     "ScalePlan",
     "clear_registry",
diff --git a/scalable/providers/local.py b/scalable/providers/local.py
new file mode 100644
index 0000000..fc919e6
--- /dev/null
+++ b/scalable/providers/local.py
@@ -0,0 +1,169 @@
+"""Local provider implementation for laptops/CI (Phase 1 WU-5)."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from distributed import LocalCluster
+
+from scalable.client import ScalableClient
+from scalable.manifest.validate import ValidationIssue, ValidationReport, validate_manifest
+
+from .base import ClusterHandle, DeploymentProvider, DeploymentSpec, ScalePlan
+
+__all__ = ["LocalProvider"]
+
+
+class LocalProvider(DeploymentProvider):
+    """Run Scalable workloads on a local Dask cluster.
+
+    Phase 1 scope:
+    - deterministic local execution for development and CI,
+    - tag-aware worker resources compatible with
+      :meth:`scalable.client.ScalableClient.submit(..., tag=...)`,
+    - no container runtime orchestration beyond validating option flags.
+    """
+
+    name = "local"
+
+    _ALLOWED_CONTAINER_MODES = {"none", "auto", "docker"}
+
+    def validate(self, spec: DeploymentSpec) -> ValidationReport:
+        report = validate_manifest(spec.manifest, known_providers={"local", "slurm"})
+        options = spec.target.options
+
+        if "max_workers" in options:
+            max_workers = options["max_workers"]
+            if (
+                not isinstance(max_workers, int)
+                or isinstance(max_workers, bool)
+                or max_workers < 1
+            ):
+                report.errors.append(
+                    ValidationIssue(
+                        path=f"targets.{spec.target_name}.max_workers",
+                        message="max_workers must be a positive integer",
+                        code="E_BAD_MAX_WORKERS",
+                    )
+                )
+
+        if "threads_per_worker" in options:
+            threads = options["threads_per_worker"]
+            if not isinstance(threads, int) or isinstance(threads, bool) or threads < 1:
+                report.errors.append(
+                    ValidationIssue(
+                        path=f"targets.{spec.target_name}.threads_per_worker",
+                        message="threads_per_worker must be a positive integer",
+                        code="E_BAD_THREADS_PER_WORKER",
+                    )
+                )
+
+        if "processes" in options and not isinstance(options["processes"], bool):
+            report.errors.append(
+                ValidationIssue(
+                    path=f"targets.{spec.target_name}.processes",
+                    message="processes must be a boolean",
+                    code="E_BAD_PROCESSES_FLAG",
+                )
+            )
+
+        containers_mode = options.get("containers", "none")
+        if not isinstance(containers_mode, str):
+            report.errors.append(
+                ValidationIssue(
+                    path=f"targets.{spec.target_name}.containers",
+                    message="containers must be one of: none, auto, docker",
+                    code="E_BAD_CONTAINERS_MODE",
+                )
+            )
+        else:
+            normalized = containers_mode.strip().lower()
+            if normalized not in self._ALLOWED_CONTAINER_MODES:
+                report.errors.append(
+                    ValidationIssue(
+                        path=f"targets.{spec.target_name}.containers",
+                        message=(
+                            f"unsupported containers mode {containers_mode!r}; "
+                            f"allowed values: {sorted(self._ALLOWED_CONTAINER_MODES)}"
+                        ),
+                        code="E_BAD_CONTAINERS_MODE",
+                    )
+                )
+            elif normalized in {"auto", "docker"}:
+                report.warnings.append(
+                    ValidationIssue(
+                        path=f"targets.{spec.target_name}.containers",
+                        message=(
+                            "container orchestration in LocalProvider is deferred; "
+                            "Phase 1 runs in no-container mode"
+                        ),
+                        code="W_LOCAL_CONTAINERS_DEFERRED",
+                    )
+                )
+
+        return report
+
+    def build_cluster(self, spec: DeploymentSpec) -> ClusterHandle:
+        validation = self.validate(spec)
+        if not validation.ok:
+            details = "; ".join(
+                f"{issue.path}: {issue.message}" for issue in validation.errors
+            )
+            raise ValueError(f"invalid local deployment spec: {details}")
+
+        options = spec.target.options
+        # Default worker count: one worker per component, minimum one.
+        default_workers = max(1, len(spec.components))
+        n_workers = int(options.get("max_workers", default_workers))
+        threads_per_worker = int(options.get("threads_per_worker", 1))
+        processes = bool(options.get("processes", False))
+        dashboard_address = options.get("dashboard_address")
+
+        # Preserve tag routing semantics from ScalableClient.submit(tag=...):
+        # every worker advertises every component tag with 1 unit.
+        worker_resources = {component_name: 1 for component_name in spec.components}
+
+        cluster = LocalCluster(
+            n_workers=n_workers,
+            threads_per_worker=threads_per_worker,
+            processes=processes,
+            scheduler_port=0,
+            dashboard_address=dashboard_address,
+            silence_logs="error",
+            resources=worker_resources,
+        )
+
+        def _client_factory() -> ScalableClient:
+            return ScalableClient(cluster)
+
+        return ClusterHandle(
+            backend=cluster,
+            client_factory=_client_factory,
+            metadata={
+                "provider": self.name,
+                "target": spec.target_name,
+                "n_workers": n_workers,
+                "threads_per_worker": threads_per_worker,
+                "processes": processes,
+                "worker_resources": worker_resources,
+            },
+        )
+
+    def scale(self, cluster: ClusterHandle, plan: ScalePlan) -> None:
+        backend = cluster.backend
+        if not hasattr(backend, "scale"):
+            raise TypeError("cluster backend does not support scale()")
+
+        if plan.workers_by_tag:
+            target_workers = sum(max(int(n), 0) for n in plan.workers_by_tag.values())
+            backend.scale(target_workers)
+
+    def close(self, cluster: ClusterHandle) -> None:
+        backend = cluster.backend
+        if hasattr(backend, "close"):
+            backend.close()
+
+
+def _debug_options_snapshot(options: dict[str, Any]) -> dict[str, Any]:
+    """Return a stable options snapshot for debugging/tests if needed."""
+    return {k: options[k] for k in sorted(options)}
diff --git a/scalable/providers/registry.py b/scalable/providers/registry.py
index 6953e82..b1514d5 100644
--- a/scalable/providers/registry.py
+++ b/scalable/providers/registry.py
@@ -51,6 +51,11 @@ def get_provider(name: str) -> DeploymentProvider:
     if normalized in _REGISTRY:
         return _instantiate(_REGISTRY[normalized])
 
+    builtin = _load_builtin_provider(normalized)
+    if builtin is not None:
+        _REGISTRY[normalized] = builtin
+        return _instantiate(builtin)
+
     discovered = _load_provider_entrypoint(normalized)
     if discovered is None:
         known = sorted(iter_provider_names(include_entrypoints=True))
@@ -119,3 +124,18 @@ def _load_provider_entrypoint(name: str) -> ProviderFactory | None:
         )
     return None
 
+
+def _load_builtin_provider(name: str) -> ProviderFactory | None:
+    """Load built-in providers lazily to avoid import-order cycles."""
+    normalized = _normalize_provider_name(name)
+    if normalized == "local":
+        from .local import LocalProvider
+
+        return LocalProvider
+    if normalized == "slurm":
+        try:
+            from .slurm import SlurmProvider
+        except ImportError:
+            return None
+        return SlurmProvider
+    return None
diff --git a/tests/integration/test_local_provider_end_to_end.py b/tests/integration/test_local_provider_end_to_end.py
new file mode 100644
index 0000000..c749a4b
--- /dev/null
+++ b/tests/integration/test_local_provider_end_to_end.py
@@ -0,0 +1,49 @@
+"""Integration test for LocalProvider (Phase 1 WU-5)."""
+
+from __future__ import annotations
+
+import pytest
+
+from scalable.manifest.parser import parse_manifest
+from scalable.providers.base import DeploymentSpec
+from scalable.providers.local import LocalProvider
+
+
+def _increment(value: int) -> int:
+    return value + 1
+
+
+@pytest.mark.integration
+def test_local_provider_end_to_end_submit_tagged_task() -> None:
+    manifest = {
+        "version": 1,
+        "project": {"name": "demo"},
+        "targets": {
+            "local": {
+                "provider": "local",
+                "max_workers": 1,
+                "threads_per_worker": 1,
+                "processes": False,
+                "containers": "none",
+            }
+        },
+        "components": {
+            "gcam": {"cpus": 1, "memory": "1G"},
+        },
+        "tasks": {
+            "run_gcam": {"component": "gcam"},
+        },
+    }
+
+    model = parse_manifest(manifest)
+    spec = DeploymentSpec.from_manifest(model, target_name="local")
+    provider = LocalProvider()
+    handle = provider.build_cluster(spec)
+    client = handle.client_factory()
+    try:
+        future = client.submit(_increment, 41, tag="gcam")
+        assert future.result(timeout=10) == 42
+    finally:
+        client.close()
+        provider.close(handle)
+
diff --git a/tests/unit/test_providers_base_registry.py b/tests/unit/test_providers_base_registry.py
index 433a752..13fef13 100644
--- a/tests/unit/test_providers_base_registry.py
+++ b/tests/unit/test_providers_base_registry.py
@@ -168,3 +168,9 @@ def test_iter_provider_names_from_runtime_registry() -> None:
 
     assert names == {"dummy"}
 
+
+def test_get_provider_loads_builtin_local_provider_lazily() -> None:
+    provider = get_provider("local")
+
+    assert provider.name == "local"
+    assert "local" in iter_provider_names(include_entrypoints=False)
diff --git a/tests/unit/test_providers_local.py b/tests/unit/test_providers_local.py
new file mode 100644
index 0000000..b2ca2ba
--- /dev/null
+++ b/tests/unit/test_providers_local.py
@@ -0,0 +1,131 @@
+"""Unit tests for :mod:`scalable.providers.local` (Phase 1 WU-5)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from scalable.manifest.parser import parse_manifest
+from scalable.providers.base import ClusterHandle, DeploymentSpec, ScalePlan
+from scalable.providers.local import LocalProvider
+
+
+def _manifest_dict() -> dict:
+    return {
+        "version": 1,
+        "project": {"name": "demo"},
+        "targets": {
+            "local": {
+                "provider": "local",
+                "max_workers": 2,
+                "threads_per_worker": 1,
+                "processes": False,
+                "containers": "none",
+            }
+        },
+        "components": {
+            "gcam": {"cpus": 2, "memory": "8G"},
+            "stitches": {"cpus": 1, "memory": "4G"},
+        },
+        "tasks": {
+            "run_gcam": {"component": "gcam"},
+            "run_stitches": {"component": "stitches"},
+        },
+    }
+
+
+def _spec() -> DeploymentSpec:
+    model = parse_manifest(_manifest_dict())
+    return DeploymentSpec.from_manifest(model, target_name="local")
+
+
+def test_validate_local_provider_ok() -> None:
+    provider = LocalProvider()
+
+    report = provider.validate(_spec())
+
+    assert report.ok is True
+    assert report.errors == []
+
+
+def test_validate_local_provider_rejects_bad_options() -> None:
+    manifest = _manifest_dict()
+    manifest["targets"]["local"]["max_workers"] = 0
+    manifest["targets"]["local"]["threads_per_worker"] = 0
+    manifest["targets"]["local"]["processes"] = "no"
+    manifest["targets"]["local"]["containers"] = "podman"
+    model = parse_manifest(manifest)
+    spec = DeploymentSpec.from_manifest(model, target_name="local")
+    provider = LocalProvider()
+
+    report = provider.validate(spec)
+
+    assert report.ok is False
+    codes = {issue.code for issue in report.errors}
+    assert "E_BAD_MAX_WORKERS" in codes
+    assert "E_BAD_THREADS_PER_WORKER" in codes
+    assert "E_BAD_PROCESSES_FLAG" in codes
+    assert "E_BAD_CONTAINERS_MODE" in codes
+
+
+def test_validate_local_provider_warns_for_deferred_containers() -> None:
+    manifest = _manifest_dict()
+    manifest["targets"]["local"]["containers"] = "docker"
+    model = parse_manifest(manifest)
+    spec = DeploymentSpec.from_manifest(model, target_name="local")
+    provider = LocalProvider()
+
+    report = provider.validate(spec)
+
+    assert report.ok is True
+    assert any(issue.code == "W_LOCAL_CONTAINERS_DEFERRED" for issue in report.warnings)
+
+
+def test_build_cluster_returns_handle_and_metadata() -> None:
+    provider = LocalProvider()
+    handle = provider.build_cluster(_spec())
+    try:
+        assert isinstance(handle, ClusterHandle)
+        assert handle.metadata["provider"] == "local"
+        assert handle.metadata["target"] == "local"
+        assert handle.metadata["n_workers"] == 2
+        assert handle.metadata["worker_resources"] == {"gcam": 1, "stitches": 1}
+    finally:
+        provider.close(handle)
+
+
+@dataclass
+class _ScaleRecorder:
+    value: int | None = None
+
+    def scale(self, target: int) -> None:
+        self.value = target
+
+
+def test_scale_sums_workers_by_tag() -> None:
+    provider = LocalProvider()
+    backend = _ScaleRecorder()
+    handle = ClusterHandle(backend=backend, client_factory=lambda: None)
+    plan = ScalePlan(workers_by_tag={"gcam": 2, "stitches": 1})
+
+    provider.scale(handle, plan)
+
+    assert backend.value == 3
+
+
+@dataclass
+class _CloseRecorder:
+    closed: bool = False
+
+    def close(self) -> None:
+        self.closed = True
+
+
+def test_close_calls_backend_close() -> None:
+    provider = LocalProvider()
+    backend = _CloseRecorder()
+    handle = ClusterHandle(backend=backend, client_factory=lambda: None)
+
+    provider.close(handle)
+
+    assert backend.closed is True
+

From 738a0b51a5637ef210631d9943cd31abf91d625f Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 16:11:07 -0400
Subject: [PATCH 10/47] WU-6: add SlurmProvider translation layer with mocked
 tests

---
 scalable/providers/__init__.py     |   2 +
 scalable/providers/slurm.py        | 183 +++++++++++++++++++++++++++++
 tests/unit/test_providers_slurm.py | 170 +++++++++++++++++++++++++++
 3 files changed, 355 insertions(+)
 create mode 100644 scalable/providers/slurm.py
 create mode 100644 tests/unit/test_providers_slurm.py

diff --git a/scalable/providers/__init__.py b/scalable/providers/__init__.py
index 4d06cf5..2e1a52f 100644
--- a/scalable/providers/__init__.py
+++ b/scalable/providers/__init__.py
@@ -28,6 +28,7 @@
     register_provider,
     register_providers,
 )
+from .slurm import SlurmProvider
 
 __all__ = [
     "ClusterHandle",
@@ -36,6 +37,7 @@
     "LocalProvider",
     "ResourceRequest",
     "ScalePlan",
+    "SlurmProvider",
     "clear_registry",
     "get_provider",
     "iter_provider_names",
diff --git a/scalable/providers/slurm.py b/scalable/providers/slurm.py
new file mode 100644
index 0000000..cd302fe
--- /dev/null
+++ b/scalable/providers/slurm.py
@@ -0,0 +1,183 @@
+"""Slurm provider implementation (Phase 1 WU-6).
+
+This provider is intentionally a thin translation layer over the existing
+``scalable.slurm.SlurmCluster`` path so Phase 1 can ship provider abstraction
+without regressing established HPC behavior.
+"""
+
+from __future__ import annotations
+
+import os
+import re
+
+from scalable.manifest.validate import ValidationIssue, ValidationReport, validate_manifest
+from scalable.slurm import SlurmCluster
+
+from .base import ClusterHandle, DeploymentProvider, DeploymentSpec, ScalePlan
+
+__all__ = ["SlurmProvider"]
+
+_WALLTIME_RE = re.compile(r"^\d{1,3}:\d{2}:\d{2}$")
+
+
+class SlurmProvider(DeploymentProvider):
+    """Provider wrapper over :class:`scalable.slurm.SlurmCluster`."""
+
+    name = "slurm"
+
+    def validate(self, spec: DeploymentSpec) -> ValidationReport:
+        report = validate_manifest(spec.manifest, known_providers={"local", "slurm"})
+        options = spec.target.options
+
+        _require_type(report, spec.target_name, options, "queue", str)
+        _require_type(report, spec.target_name, options, "account", str)
+        _require_type(report, spec.target_name, options, "interface", str)
+        _require_type(report, spec.target_name, options, "logs_location", str)
+        _require_type(report, spec.target_name, options, "name", str)
+
+        if "suppress_logs" in options and not isinstance(options["suppress_logs"], bool):
+            report.errors.append(
+                ValidationIssue(
+                    path=f"targets.{spec.target_name}.suppress_logs",
+                    message="suppress_logs must be a boolean",
+                    code="E_BAD_SUPPRESS_LOGS",
+                )
+            )
+
+        if "walltime" in options:
+            walltime = options["walltime"]
+            if not isinstance(walltime, str) or not _WALLTIME_RE.match(walltime):
+                report.errors.append(
+                    ValidationIssue(
+                        path=f"targets.{spec.target_name}.walltime",
+                        message="walltime must be a string in HH:MM:SS format",
+                        code="E_BAD_WALLTIME",
+                    )
+                )
+
+        if "comm_port" in options:
+            comm_port = options["comm_port"]
+            if not isinstance(comm_port, int) or isinstance(comm_port, bool) or comm_port <= 0:
+                report.errors.append(
+                    ValidationIssue(
+                        path=f"targets.{spec.target_name}.comm_port",
+                        message="comm_port must be a positive integer",
+                        code="E_BAD_COMM_PORT",
+                    )
+                )
+        else:
+            if os.environ.get("COMM_PORT") is None:
+                report.errors.append(
+                    ValidationIssue(
+                        path=f"targets.{spec.target_name}.comm_port",
+                        message=(
+                            "comm_port is required for SlurmProvider (set it in manifest "
+                            "or via COMM_PORT environment variable)"
+                        ),
+                        code="E_MISSING_COMM_PORT",
+                    )
+                )
+
+        if "container_runtime" in options:
+            runtime = options["container_runtime"]
+            if not isinstance(runtime, str):
+                report.errors.append(
+                    ValidationIssue(
+                        path=f"targets.{spec.target_name}.container_runtime",
+                        message="container_runtime must be a string",
+                        code="E_BAD_CONTAINER_RUNTIME",
+                    )
+                )
+            else:
+                normalized = runtime.strip().lower()
+                if normalized not in {"apptainer", "docker"}:
+                    report.errors.append(
+                        ValidationIssue(
+                            path=f"targets.{spec.target_name}.container_runtime",
+                            message="container_runtime must be either 'apptainer' or 'docker'",
+                            code="E_BAD_CONTAINER_RUNTIME",
+                        )
+                    )
+
+        return report
+
+    def build_cluster(self, spec: DeploymentSpec) -> ClusterHandle:
+        validation = self.validate(spec)
+        if not validation.ok:
+            details = "; ".join(
+                f"{issue.path}: {issue.message}" for issue in validation.errors
+            )
+            raise ValueError(f"invalid slurm deployment spec: {details}")
+
+        options = spec.target.options
+        cluster_kwargs = {
+            "queue": options.get("queue"),
+            "account": options.get("account"),
+            "walltime": options.get("walltime"),
+            "interface": options.get("interface"),
+            "name": options.get("name"),
+            "logs_location": options.get("logs_location"),
+            "suppress_logs": options.get("suppress_logs", False),
+        }
+        if "comm_port" in options:
+            cluster_kwargs["comm_port"] = options["comm_port"]
+
+        cluster = SlurmCluster(**cluster_kwargs)
+
+        for component_name, component in spec.components.items():
+            cluster.add_container(
+                tag=component_name,
+                dirs=dict(component.mounts),
+                path=component.image,
+                cpus=component.cpus,
+                memory=component.memory,
+                preload_script=component.preload_script,
+            )
+
+        def _client_factory():
+            from scalable.client import ScalableClient
+
+            return ScalableClient(cluster)
+
+        return ClusterHandle(
+            backend=cluster,
+            client_factory=_client_factory,
+            metadata={
+                "provider": self.name,
+                "target": spec.target_name,
+                "cluster_kwargs": {k: v for k, v in cluster_kwargs.items() if v is not None},
+            },
+        )
+
+    def scale(self, cluster: ClusterHandle, plan: ScalePlan) -> None:
+        backend = cluster.backend
+        if not hasattr(backend, "add_workers"):
+            raise TypeError("cluster backend does not support add_workers()")
+
+        for tag, count in plan.workers_by_tag.items():
+            n = int(count)
+            if n > 0:
+                backend.add_workers(tag=tag, n=n)
+
+    def close(self, cluster: ClusterHandle) -> None:
+        backend = cluster.backend
+        if hasattr(backend, "close"):
+            backend.close()
+
+
+def _require_type(
+    report: ValidationReport,
+    target_name: str,
+    options: dict,
+    key: str,
+    expected_type: type,
+) -> None:
+    if key in options and not isinstance(options[key], expected_type):
+        report.errors.append(
+            ValidationIssue(
+                path=f"targets.{target_name}.{key}",
+                message=f"{key} must be a {expected_type.__name__}",
+                code=f"E_BAD_{key.upper()}",
+            )
+        )
+
diff --git a/tests/unit/test_providers_slurm.py b/tests/unit/test_providers_slurm.py
new file mode 100644
index 0000000..347a1fb
--- /dev/null
+++ b/tests/unit/test_providers_slurm.py
@@ -0,0 +1,170 @@
+"""Unit tests for :mod:`scalable.providers.slurm` (Phase 1 WU-6)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any
+
+import pytest
+
+from scalable.manifest.parser import parse_manifest
+from scalable.providers.base import ClusterHandle, DeploymentSpec, ScalePlan
+from scalable.providers.slurm import SlurmProvider
+
+
+def _manifest_dict() -> dict:
+    return {
+        "version": 1,
+        "project": {"name": "demo"},
+        "targets": {
+            "hpc": {
+                "provider": "slurm",
+                "queue": "short",
+                "account": "GCIMS",
+                "walltime": "02:00:00",
+                "interface": "ib0",
+                "comm_port": 50051,
+                "logs_location": "/tmp/scalable-logs",
+                "suppress_logs": False,
+            }
+        },
+        "components": {
+            "gcam": {
+                "image": "/containers/gcam.sif",
+                "cpus": 2,
+                "memory": "8G",
+                "mounts": {
+                    "/host/data": "/data",
+                },
+                "preload_script": "/tmp/preload.py",
+            }
+        },
+        "tasks": {
+            "run_gcam": {"component": "gcam"},
+        },
+    }
+
+
+def _spec() -> DeploymentSpec:
+    model = parse_manifest(_manifest_dict())
+    return DeploymentSpec.from_manifest(model, target_name="hpc")
+
+
+def test_validate_slurm_provider_ok() -> None:
+    provider = SlurmProvider()
+
+    report = provider.validate(_spec())
+
+    assert report.ok is True
+    assert report.errors == []
+
+
+def test_validate_slurm_provider_comm_port_required_when_env_missing(monkeypatch) -> None:
+    monkeypatch.delenv("COMM_PORT", raising=False)
+    manifest = _manifest_dict()
+    manifest["targets"]["hpc"].pop("comm_port")
+    spec = DeploymentSpec.from_manifest(parse_manifest(manifest), target_name="hpc")
+    provider = SlurmProvider()
+
+    report = provider.validate(spec)
+
+    assert report.ok is False
+    assert any(issue.code == "E_MISSING_COMM_PORT" for issue in report.errors)
+
+
+def test_validate_slurm_provider_rejects_bad_option_types() -> None:
+    manifest = _manifest_dict()
+    manifest["targets"]["hpc"]["walltime"] = "2h"
+    manifest["targets"]["hpc"]["comm_port"] = -1
+    manifest["targets"]["hpc"]["suppress_logs"] = "no"
+    manifest["targets"]["hpc"]["container_runtime"] = "podman"
+    spec = DeploymentSpec.from_manifest(parse_manifest(manifest), target_name="hpc")
+    provider = SlurmProvider()
+
+    report = provider.validate(spec)
+
+    assert report.ok is False
+    codes = {issue.code for issue in report.errors}
+    assert "E_BAD_WALLTIME" in codes
+    assert "E_BAD_COMM_PORT" in codes
+    assert "E_BAD_SUPPRESS_LOGS" in codes
+    assert "E_BAD_CONTAINER_RUNTIME" in codes
+
+
+@dataclass
+class _FakeSlurmCluster:
+    kwargs: dict[str, Any]
+    add_container_calls: list[dict[str, Any]] = field(default_factory=list)
+    add_workers_calls: list[dict[str, Any]] = field(default_factory=list)
+    closed: bool = False
+
+    def add_container(self, **kwargs: Any) -> None:
+        self.add_container_calls.append(kwargs)
+
+    def add_workers(self, **kwargs: Any) -> None:
+        self.add_workers_calls.append(kwargs)
+
+    def close(self) -> None:
+        self.closed = True
+
+
+def test_build_cluster_translates_manifest_to_slurm_calls(monkeypatch) -> None:
+    created: list[_FakeSlurmCluster] = []
+
+    def _factory(**kwargs: Any) -> _FakeSlurmCluster:
+        instance = _FakeSlurmCluster(kwargs=kwargs)
+        created.append(instance)
+        return instance
+
+    monkeypatch.setattr("scalable.providers.slurm.SlurmCluster", _factory)
+
+    provider = SlurmProvider()
+    handle = provider.build_cluster(_spec())
+
+    assert isinstance(handle, ClusterHandle)
+    assert len(created) == 1
+    cluster = created[0]
+    assert cluster.kwargs["queue"] == "short"
+    assert cluster.kwargs["account"] == "GCIMS"
+    assert cluster.kwargs["walltime"] == "02:00:00"
+    assert cluster.kwargs["interface"] == "ib0"
+    assert cluster.kwargs["comm_port"] == 50051
+    assert cluster.kwargs["logs_location"] == "/tmp/scalable-logs"
+    assert cluster.kwargs["suppress_logs"] is False
+
+    assert len(cluster.add_container_calls) == 1
+    call = cluster.add_container_calls[0]
+    assert call["tag"] == "gcam"
+    assert call["dirs"] == {"/host/data": "/data"}
+    assert call["path"] == "/containers/gcam.sif"
+    assert call["cpus"] == 2
+    assert call["memory"] == "8G"
+    assert call["preload_script"] == "/tmp/preload.py"
+
+    assert handle.metadata["provider"] == "slurm"
+    assert handle.metadata["target"] == "hpc"
+
+
+def test_scale_calls_add_workers_per_tag() -> None:
+    provider = SlurmProvider()
+    backend = _FakeSlurmCluster(kwargs={})
+    handle = ClusterHandle(backend=backend, client_factory=lambda: None)
+    plan = ScalePlan(workers_by_tag={"gcam": 2, "stitches": 1, "noop": 0})
+
+    provider.scale(handle, plan)
+
+    assert backend.add_workers_calls == [
+        {"tag": "gcam", "n": 2},
+        {"tag": "stitches", "n": 1},
+    ]
+
+
+def test_close_calls_cluster_close() -> None:
+    provider = SlurmProvider()
+    backend = _FakeSlurmCluster(kwargs={})
+    handle = ClusterHandle(backend=backend, client_factory=lambda: None)
+
+    provider.close(handle)
+
+    assert backend.closed is True
+

From 1bbac8f45e8de8b09c84dd1e55287f3cca03db4f Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 16:17:09 -0400
Subject: [PATCH 11/47] WU-7: add manifest-to-legacy adapter and ModelConfig
 deprecation gate

---
 scalable/manifest/adapter.py               |  92 +++++++++++++++
 scalable/providers/slurm.py                |  33 ++----
 scalable/utilities.py                      |  35 ++++++
 tests/unit/test_manifest_adapter.py        | 127 +++++++++++++++++++++
 tests/unit/test_modelconfig_deprecation.py |  46 ++++++++
 5 files changed, 308 insertions(+), 25 deletions(-)
 create mode 100644 scalable/manifest/adapter.py
 create mode 100644 tests/unit/test_manifest_adapter.py
 create mode 100644 tests/unit/test_modelconfig_deprecation.py

diff --git a/scalable/manifest/adapter.py b/scalable/manifest/adapter.py
new file mode 100644
index 0000000..e0162cf
--- /dev/null
+++ b/scalable/manifest/adapter.py
@@ -0,0 +1,92 @@
+"""Manifest -> legacy cluster adapter functions (Phase 1 WU-7)."""
+
+from __future__ import annotations
+
+from collections.abc import Iterable
+from typing import TYPE_CHECKING, Any
+
+from scalable.utilities import model_config_adapter_context
+
+if TYPE_CHECKING:
+    from scalable.providers.base import DeploymentSpec
+
+__all__ = [
+    "add_components_to_legacy_cluster",
+    "build_slurm_cluster_kwargs",
+    "create_legacy_slurm_cluster",
+]
+
+
+def build_slurm_cluster_kwargs(spec: DeploymentSpec) -> dict[str, Any]:
+    """Translate ``targets.<name>`` options into ``SlurmCluster`` kwargs."""
+    options = spec.target.options
+    kwargs: dict[str, Any] = {
+        "queue": options.get("queue"),
+        "account": options.get("account"),
+        "walltime": options.get("walltime"),
+        "interface": options.get("interface"),
+        "name": options.get("name"),
+        "logs_location": options.get("logs_location"),
+        "suppress_logs": options.get("suppress_logs", False),
+    }
+    if "comm_port" in options:
+        kwargs["comm_port"] = options["comm_port"]
+    return kwargs
+
+
+def create_legacy_slurm_cluster(spec: DeploymentSpec, *, cluster_cls: Any) -> Any:
+    """Instantiate a legacy Slurm cluster under adapter compatibility context.
+
+    This suppresses the direct ``ModelConfig`` deprecation warning emitted by
+    :class:`scalable.utilities.ModelConfig`, since this call path is the
+    intentional bridge from manifests to legacy APIs.
+    """
+    kwargs = build_slurm_cluster_kwargs(spec)
+    with model_config_adapter_context():
+        return cluster_cls(**kwargs)
+
+
+def add_components_to_legacy_cluster(
+    spec: DeploymentSpec,
+    cluster: Any,
+    *,
+    components: Iterable[str] | None = None,
+) -> list[str]:
+    """Apply manifest components to a legacy cluster via ``add_container``.
+
+    Parameters
+    ----------
+    spec
+        Deployment spec containing parsed components.
+    cluster
+        Object exposing ``add_container(...)`` (e.g. ``SlurmCluster``).
+    components
+        Optional subset of component names to add. Defaults to all components.
+
+    Returns
+    -------
+    list[str]
+        Component names that were added.
+    """
+    if not hasattr(cluster, "add_container"):
+        raise TypeError("cluster does not expose add_container(...)")
+
+    if components is None:
+        selected = list(spec.components)
+    else:
+        selected = list(components)
+
+    added: list[str] = []
+    for component_name in selected:
+        component = spec.components[component_name]
+        cluster.add_container(
+            tag=component_name,
+            dirs=dict(component.mounts),
+            path=component.image,
+            cpus=component.cpus,
+            memory=component.memory,
+            preload_script=component.preload_script,
+        )
+        added.append(component_name)
+
+    return added
diff --git a/scalable/providers/slurm.py b/scalable/providers/slurm.py
index cd302fe..ccc4bc0 100644
--- a/scalable/providers/slurm.py
+++ b/scalable/providers/slurm.py
@@ -10,6 +10,11 @@
 import os
 import re
 
+from scalable.manifest.adapter import (
+    add_components_to_legacy_cluster,
+    build_slurm_cluster_kwargs,
+    create_legacy_slurm_cluster,
+)
 from scalable.manifest.validate import ValidationIssue, ValidationReport, validate_manifest
 from scalable.slurm import SlurmCluster
 
@@ -109,30 +114,9 @@ def build_cluster(self, spec: DeploymentSpec) -> ClusterHandle:
             )
             raise ValueError(f"invalid slurm deployment spec: {details}")
 
-        options = spec.target.options
-        cluster_kwargs = {
-            "queue": options.get("queue"),
-            "account": options.get("account"),
-            "walltime": options.get("walltime"),
-            "interface": options.get("interface"),
-            "name": options.get("name"),
-            "logs_location": options.get("logs_location"),
-            "suppress_logs": options.get("suppress_logs", False),
-        }
-        if "comm_port" in options:
-            cluster_kwargs["comm_port"] = options["comm_port"]
-
-        cluster = SlurmCluster(**cluster_kwargs)
-
-        for component_name, component in spec.components.items():
-            cluster.add_container(
-                tag=component_name,
-                dirs=dict(component.mounts),
-                path=component.image,
-                cpus=component.cpus,
-                memory=component.memory,
-                preload_script=component.preload_script,
-            )
+        cluster_kwargs = build_slurm_cluster_kwargs(spec)
+        cluster = create_legacy_slurm_cluster(spec, cluster_cls=SlurmCluster)
+        add_components_to_legacy_cluster(spec, cluster)
 
         def _client_factory():
             from scalable.client import ScalableClient
@@ -180,4 +164,3 @@ def _require_type(
                 code=f"E_BAD_{key.upper()}",
             )
         )
-
diff --git a/scalable/utilities.py b/scalable/utilities.py
index 75e4edb..b5167d3 100755
--- a/scalable/utilities.py
+++ b/scalable/utilities.py
@@ -5,6 +5,7 @@
 import threading
 import warnings
 from collections.abc import Mapping
+from contextlib import contextmanager
 from importlib.resources import files
 from typing import Any
 
@@ -15,6 +16,29 @@
 
 comm_port_regex = r'0\.0\.0\.0:(\d{1,5})'
 
+# Set to True when legacy ModelConfig initialization is invoked via the
+# manifest adapter/provider compatibility path. Direct user calls remain
+# deprecated and emit DeprecationWarning.
+_MODELCONFIG_ADAPTER_CONTEXT: bool = False
+
+
+@contextmanager
+def model_config_adapter_context() -> Any:
+    """Temporarily suppress ModelConfig deprecation warnings.
+
+    This context is used by the manifest-to-legacy adapter path introduced in
+    Phase 1. Direct usage of :class:`ModelConfig` outside this context is
+    deprecated in favor of ``scalable.yaml`` + manifest parsing.
+    """
+
+    global _MODELCONFIG_ADAPTER_CONTEXT
+    previous = _MODELCONFIG_ADAPTER_CONTEXT
+    _MODELCONFIG_ADAPTER_CONTEXT = True
+    try:
+        yield
+    finally:
+        _MODELCONFIG_ADAPTER_CONTEXT = previous
+
 async def get_cmd_comm(
     port: int, communicator_path: str | None = None
 ) -> asyncio.subprocess.Process:
@@ -105,11 +129,22 @@ def __init__(self, path: str | None = None, path_overwrite: bool = True) -> None
             fresh data or older data such as previously set binded directories.
             Defaults to True so a new config_dict is made.
         """
+        if not _MODELCONFIG_ADAPTER_CONTEXT:
+            warnings.warn(
+                "ModelConfig Dockerfile discovery is deprecated and will be "
+                "replaced by scalable.yaml manifest parsing. Use "
+                "scalable.manifest.parser.parse_manifest(...) and provider "
+                "adapters instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
         # HARDCODING CURRENT DIRECTORY
         self.config_dict = {}
         cwd = os.getcwd()
         if path is None:
             self.path = os.path.abspath(os.path.join(cwd, "config_dict.yaml"))
+        else:
+            self.path = os.path.abspath(path)
         dockerfile_path = os.path.abspath(os.path.join(cwd, "Dockerfile"))
         list_avail_command = (
             r"sed -n 's/^FROM[[:space:]]\+[^ ]\+[[:space:]]\+AS[[:space:]]\+\([^ ]\+\)$/\\1/p' "
diff --git a/tests/unit/test_manifest_adapter.py b/tests/unit/test_manifest_adapter.py
new file mode 100644
index 0000000..702ac06
--- /dev/null
+++ b/tests/unit/test_manifest_adapter.py
@@ -0,0 +1,127 @@
+"""Unit tests for :mod:`scalable.manifest.adapter` (Phase 1 WU-7)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any
+
+from scalable.manifest.adapter import (
+    add_components_to_legacy_cluster,
+    build_slurm_cluster_kwargs,
+    create_legacy_slurm_cluster,
+)
+from scalable.manifest.parser import parse_manifest
+from scalable.providers.base import DeploymentSpec
+
+
+def _spec() -> DeploymentSpec:
+    model = parse_manifest(
+        {
+            "version": 1,
+            "project": {"name": "demo"},
+            "targets": {
+                "hpc": {
+                    "provider": "slurm",
+                    "queue": "short",
+                    "account": "GCIMS",
+                    "walltime": "02:00:00",
+                    "interface": "ib0",
+                    "comm_port": 50051,
+                    "logs_location": "/tmp/scalable-logs",
+                    "suppress_logs": False,
+                }
+            },
+            "components": {
+                "gcam": {
+                    "image": "/containers/gcam.sif",
+                    "cpus": 2,
+                    "memory": "8G",
+                    "mounts": {"/host/data": "/data"},
+                    "preload_script": "/tmp/preload.py",
+                },
+                "stitches": {
+                    "image": "/containers/stitches.sif",
+                    "cpus": 1,
+                    "memory": "4G",
+                    "mounts": {"/host/data": "/data"},
+                },
+            },
+            "tasks": {
+                "run_gcam": {"component": "gcam"},
+                "run_stitches": {"component": "stitches"},
+            },
+        }
+    )
+    return DeploymentSpec.from_manifest(model, target_name="hpc")
+
+
+def test_build_slurm_cluster_kwargs_translation() -> None:
+    kwargs = build_slurm_cluster_kwargs(_spec())
+
+    assert kwargs == {
+        "queue": "short",
+        "account": "GCIMS",
+        "walltime": "02:00:00",
+        "interface": "ib0",
+        "name": None,
+        "logs_location": "/tmp/scalable-logs",
+        "suppress_logs": False,
+        "comm_port": 50051,
+    }
+
+
+@dataclass
+class _FakeCluster:
+    add_container_calls: list[dict[str, Any]] = field(default_factory=list)
+
+    def add_container(self, **kwargs: Any) -> None:
+        self.add_container_calls.append(kwargs)
+
+
+def test_add_components_to_legacy_cluster_all_components() -> None:
+    spec = _spec()
+    cluster = _FakeCluster()
+
+    added = add_components_to_legacy_cluster(spec, cluster)
+
+    assert added == ["gcam", "stitches"]
+    assert len(cluster.add_container_calls) == 2
+    assert cluster.add_container_calls[0]["tag"] == "gcam"
+    assert cluster.add_container_calls[0]["dirs"] == {"/host/data": "/data"}
+    assert cluster.add_container_calls[0]["path"] == "/containers/gcam.sif"
+    assert cluster.add_container_calls[0]["cpus"] == 2
+    assert cluster.add_container_calls[0]["memory"] == "8G"
+    assert cluster.add_container_calls[0]["preload_script"] == "/tmp/preload.py"
+
+
+def test_add_components_to_legacy_cluster_subset() -> None:
+    spec = _spec()
+    cluster = _FakeCluster()
+
+    added = add_components_to_legacy_cluster(spec, cluster, components=["stitches"])
+
+    assert added == ["stitches"]
+    assert len(cluster.add_container_calls) == 1
+    assert cluster.add_container_calls[0]["tag"] == "stitches"
+
+
+@dataclass
+class _FactoryCapture:
+    kwargs: dict[str, Any]
+
+
+def test_create_legacy_slurm_cluster_uses_factory() -> None:
+    captured: list[_FactoryCapture] = []
+
+    def _factory(**kwargs: Any) -> _FactoryCapture:
+        obj = _FactoryCapture(kwargs=kwargs)
+        captured.append(obj)
+        return obj
+
+    result = create_legacy_slurm_cluster(_spec(), cluster_cls=_factory)
+
+    assert len(captured) == 1
+    assert result is captured[0]
+    assert captured[0].kwargs["queue"] == "short"
+    assert captured[0].kwargs["comm_port"] == 50051
+
diff --git a/tests/unit/test_modelconfig_deprecation.py b/tests/unit/test_modelconfig_deprecation.py
new file mode 100644
index 0000000..3aa706f
--- /dev/null
+++ b/tests/unit/test_modelconfig_deprecation.py
@@ -0,0 +1,46 @@
+"""Tests for ModelConfig deprecation behavior (Phase 1 WU-7)."""
+
+from __future__ import annotations
+
+import warnings
+from pathlib import Path
+
+from scalable.utilities import ModelConfig, model_config_adapter_context
+
+
+def _write_minimal_dockerfile(path: Path) -> None:
+    path.write_text(
+        """
+FROM ubuntu:22.04 AS gcam
+""".lstrip(),
+        encoding="utf-8",
+    )
+
+
+def test_modelconfig_direct_init_emits_deprecation(monkeypatch, tmp_path: Path) -> None:
+    monkeypatch.chdir(tmp_path)
+    _write_minimal_dockerfile(tmp_path / "Dockerfile")
+
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always", DeprecationWarning)
+        ModelConfig(path=str(tmp_path / "config_dict.yaml"), path_overwrite=True)
+
+    deprecations = [w for w in caught if issubclass(w.category, DeprecationWarning)]
+    assert len(deprecations) >= 1
+    assert "ModelConfig Dockerfile discovery is deprecated" in str(deprecations[0].message)
+
+
+def test_modelconfig_inside_adapter_context_suppresses_deprecation(
+    monkeypatch, tmp_path: Path
+) -> None:
+    monkeypatch.chdir(tmp_path)
+    _write_minimal_dockerfile(tmp_path / "Dockerfile")
+
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always", DeprecationWarning)
+        with model_config_adapter_context():
+            config = ModelConfig(path=str(tmp_path / "config_dict.yaml"), path_overwrite=True)
+
+    assert isinstance(config, ModelConfig)
+    deprecations = [w for w in caught if issubclass(w.category, DeprecationWarning)]
+    assert deprecations == []

From cdbc51d7d041ec3cfad3f8edb174dc7252567b06 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 16:48:05 -0400
Subject: [PATCH 12/47] feat(v2-phase1): add session+dryrun APIs, CLI commands,
 docs, and CI validation

---
 .github/workflows/tests.yml               |  43 +++++-
 CHANGELOG.md                              |   9 ++
 README.md                                 |  44 ++++++
 docs/examples/scalable.gcam_stitches.yaml |  34 +++++
 docs/examples/scalable.minimal.yaml       |  21 +++
 docs/getting_started.rst                  |   1 +
 docs/index.rst                            |   2 +
 docs/manifest.rst                         |  89 ++++++++++++
 docs/providers.rst                        |  47 +++++++
 scalable/__init__.py                      |  11 +-
 scalable/cli/cmd_plan.py                  |  45 ++++++
 scalable/cli/cmd_validate.py              |  58 ++++++++
 scalable/cli/main.py                      | 149 +++++++++++++++-----
 scalable/common.py                        |   5 +
 scalable/planning/__init__.py             |   4 +-
 scalable/planning/dryrun.py               |  90 ++++++++++++
 scalable/session/__init__.py              |   4 +-
 scalable/session/session.py               | 158 ++++++++++++++++++++++
 tests/unit/test_cli_plan.py               |  87 ++++++++++++
 tests/unit/test_cli_validate.py           |  81 +++++++++++
 tests/unit/test_common_settings.py        |   6 +
 tests/unit/test_planning_dryrun.py        |  75 ++++++++++
 tests/unit/test_public_api_exports.py     |  27 ++++
 tests/unit/test_session.py                | 142 +++++++++++++++++++
 24 files changed, 1189 insertions(+), 43 deletions(-)
 create mode 100644 docs/examples/scalable.gcam_stitches.yaml
 create mode 100644 docs/examples/scalable.minimal.yaml
 create mode 100644 docs/manifest.rst
 create mode 100644 docs/providers.rst
 create mode 100644 scalable/cli/cmd_plan.py
 create mode 100644 scalable/cli/cmd_validate.py
 create mode 100644 scalable/planning/dryrun.py
 create mode 100644 scalable/session/session.py
 create mode 100644 tests/unit/test_cli_plan.py
 create mode 100644 tests/unit/test_cli_validate.py
 create mode 100644 tests/unit/test_planning_dryrun.py
 create mode 100644 tests/unit/test_public_api_exports.py
 create mode 100644 tests/unit/test_session.py

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 7deefef..eea8b15 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -2,9 +2,9 @@ name: tests
 
 on:
   push:
-    branches: [develop, master, main]
+    branches: [develop, master, main, 'version/**']
   pull_request:
-    branches: [develop, master, main]
+    branches: [develop, master, main, 'version/**']
   workflow_dispatch:
 
 concurrency:
@@ -18,12 +18,19 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: [ubuntu-latest]
-        python-version: ["3.11", "3.12", "3.13"]
+        include:
+          - os: ubuntu-latest
+            python-version: "3.11"
+          - os: ubuntu-latest
+            python-version: "3.12"
+          - os: ubuntu-latest
+            python-version: "3.13"
+          - os: macos-latest
+            python-version: "3.11"
     steps:
       - uses: actions/checkout@v4
         with:
-          fetch-depth: 0  # versioneer needs tags
+          fetch-depth: 0
 
       - uses: actions/setup-python@v5
         with:
@@ -38,6 +45,32 @@ jobs:
       - name: Run unit tests
         run: pytest tests/unit -v
 
+  validate-example-manifests:
+    name: validate example manifests
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+          cache: pip
+
+      - name: Install package
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install -e .[test]
+
+      - name: Validate docs examples
+        run: |
+          scalable validate docs/examples/scalable.minimal.yaml --target local
+          scalable validate docs/examples/scalable.gcam_stitches.yaml --target local
+
+      - name: Plan docs examples (dry-run)
+        run: |
+          scalable plan docs/examples/scalable.minimal.yaml --target local --dry-run --output /tmp/plan-minimal.json
+          scalable plan docs/examples/scalable.gcam_stitches.yaml --target local --dry-run --output /tmp/plan-gcam-stitches.json
+
   lint:
     name: ruff + mypy
     runs-on: ubuntu-latest
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 3cd8f9c..89a5ae7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -10,6 +10,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 
 - New workflow architecture figure in [`docs/images/scalable_architecture.png`](docs/images/scalable_architecture.png).
+- Manifest-driven v2 entry points:
+  - ``ScalableSession.from_yaml(...)`` lifecycle API
+  - ``scalable validate`` CLI command
+  - ``scalable plan --dry-run`` CLI command
+  - deterministic ``manifest.lock`` fingerprint generation
+  - provider abstraction with ``LocalProvider`` and ``SlurmProvider``
+  - docs pages: [`docs/manifest.rst`](docs/manifest.rst) and [`docs/providers.rst`](docs/providers.rst)
 
 ### Changed
 
@@ -19,6 +26,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Raised minimum supported Python version to 3.11 in [`pyproject.toml`](pyproject.toml).
 - Updated CI test matrix in [`.github/workflows/tests.yml`](.github/workflows/tests.yml) to run Python 3.11–3.12 only.
 - Updated container conda Python baseline to 3.11 in [`scalable/Dockerfile`](scalable/Dockerfile).
+- Expanded top-level exports in [`scalable/__init__.py`](scalable/__init__.py) to include
+  ``ScalableSession``, ``DeploymentProvider``, ``LocalProvider``, and ``SlurmProvider``.
 
 ### Documentation
 
diff --git a/README.md b/README.md
index ae01c36..b6c0d50 100644
--- a/README.md
+++ b/README.md
@@ -80,6 +80,50 @@ Bootstrap performs multiple SSH operations. For best reliability and usability,
 
 ## Usage
 
+### Manifest-first workflow (v2.0.0 Phase 1)
+
+Scalable now supports a declarative manifest path for provider-neutral planning
+and validation.
+
+Create ``scalable.yaml``:
+
+```yaml
+version: 1
+project:
+  name: demo
+targets:
+  local:
+    provider: local
+    max_workers: 2
+    threads_per_worker: 1
+    processes: false
+    containers: none
+components:
+  gcam:
+    cpus: 1
+    memory: 1G
+tasks:
+  run_gcam:
+    component: gcam
+```
+
+Validate and plan without launching workers:
+
+```bash
+scalable validate ./scalable.yaml
+scalable plan ./scalable.yaml --target local --dry-run --output plan.json
+```
+
+Use the session API:
+
+```python
+from scalable import ScalableSession
+
+session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+plan = session.plan(dry_run=True)
+print(plan.manifest_lock)
+```
+
 At runtime, create a cluster, register container targets, scale workers, and submit functions.
 
 ### 1. Create a cluster
diff --git a/docs/examples/scalable.gcam_stitches.yaml b/docs/examples/scalable.gcam_stitches.yaml
new file mode 100644
index 0000000..f7dfd14
--- /dev/null
+++ b/docs/examples/scalable.gcam_stitches.yaml
@@ -0,0 +1,34 @@
+version: 1
+project:
+  name: gcam-stitches-demo
+
+targets:
+  local:
+    provider: local
+    max_workers: 2
+    threads_per_worker: 1
+    processes: false
+    containers: none
+
+  hpc:
+    provider: slurm
+    queue: short
+    account: GCIMS
+    walltime: 02:00:00
+    interface: ib0
+    comm_port: 50051
+
+components:
+  gcam:
+    cpus: 2
+    memory: 8G
+  stitches:
+    cpus: 1
+    memory: 4G
+
+tasks:
+  run_gcam:
+    component: gcam
+  run_stitches:
+    component: stitches
+
diff --git a/docs/examples/scalable.minimal.yaml b/docs/examples/scalable.minimal.yaml
new file mode 100644
index 0000000..cc39164
--- /dev/null
+++ b/docs/examples/scalable.minimal.yaml
@@ -0,0 +1,21 @@
+version: 1
+project:
+  name: minimal-demo
+
+targets:
+  local:
+    provider: local
+    max_workers: 2
+    threads_per_worker: 1
+    processes: false
+    containers: none
+
+components:
+  worker:
+    cpus: 1
+    memory: 1G
+
+tasks:
+  run_worker:
+    component: worker
+
diff --git a/docs/getting_started.rst b/docs/getting_started.rst
index a0cb57c..d2304ac 100644
--- a/docs/getting_started.rst
+++ b/docs/getting_started.rst
@@ -95,6 +95,7 @@ Next Steps
 
 After setup:
 
+* For declarative workflows, start with :doc:`manifest` and :doc:`providers`.
 * Review the :ref:`api_section` for worker, caching, and function interfaces.
 * Run examples from :ref:`demos_section`.
 * Use :ref:`how_tos_section` for targeted implementation guidance.
diff --git a/docs/index.rst b/docs/index.rst
index 5010bc0..1df3e13 100755
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -45,6 +45,8 @@ Contents
    :maxdepth: 1
 
    workers
+   manifest
+   providers
    caching
    functions
 
diff --git a/docs/manifest.rst b/docs/manifest.rst
new file mode 100644
index 0000000..0298cad
--- /dev/null
+++ b/docs/manifest.rst
@@ -0,0 +1,89 @@
+Manifest-Driven Workflows (Phase 1)
+===================================
+
+Scalable v2.0.0 introduces a declarative manifest entry point, ``scalable.yaml``.
+This becomes the source of truth for targets, components, and task bindings.
+
+Schema v1 (required keys)
+-------------------------
+
+Top-level keys:
+
+* ``version`` (must be ``1``)
+* ``project``
+* ``targets``
+* ``components``
+* ``tasks``
+
+Minimal example:
+
+.. code-block:: yaml
+
+    version: 1
+    project:
+      name: demo
+
+    targets:
+      local:
+        provider: local
+        max_workers: 2
+        threads_per_worker: 1
+        processes: false
+        containers: none
+
+    components:
+      gcam:
+        image: /containers/gcam.sif
+        cpus: 2
+        memory: 8G
+        mounts:
+          /host/data: /data
+
+    tasks:
+      run_gcam:
+        component: gcam
+
+Validation commands
+-------------------
+
+Validate a manifest:
+
+.. code-block:: bash
+
+    scalable validate ./scalable.yaml
+
+Generate a deterministic dry-run plan:
+
+.. code-block:: bash
+
+    scalable plan ./scalable.yaml --target local --dry-run --output plan.json
+
+Phase 1 writes:
+
+* ``plan.json`` (provider-neutral plan payload)
+* ``manifest.lock`` (SHA-256 fingerprint of canonicalized manifest content)
+
+Environment variables
+---------------------
+
+* ``SCALABLE_MANIFEST``: default manifest path used by CLI/session
+* ``SCALABLE_TARGET``: default target override for auto-selection paths
+
+Migration note from imperative API
+----------------------------------
+
+Legacy imperative APIs remain supported in Phase 1:
+
+* ``SlurmCluster(...)``
+* ``cluster.add_container(...)``
+* ``cluster.add_workers(...)``
+
+The new manifest/session path is additive and can be adopted incrementally.
+
+Example manifests
+-----------------
+
+Reference examples are included in:
+
+* ``docs/examples/scalable.minimal.yaml``
+* ``docs/examples/scalable.gcam_stitches.yaml``
diff --git a/docs/providers.rst b/docs/providers.rst
new file mode 100644
index 0000000..e103025
--- /dev/null
+++ b/docs/providers.rst
@@ -0,0 +1,47 @@
+Provider Abstraction (Phase 1)
+==============================
+
+Phase 1 adds a provider-neutral execution seam.
+
+Built-in providers
+------------------
+
+* ``local`` via ``LocalProvider``
+* ``slurm`` via ``SlurmProvider``
+
+Provider contract
+-----------------
+
+Each provider follows the ``DeploymentProvider`` protocol:
+
+* ``validate(spec)``
+* ``build_cluster(spec)``
+* ``scale(cluster, plan)``
+* ``close(cluster)``
+
+The provider layer consumes ``DeploymentSpec`` and applies a ``ScalePlan``.
+
+Local provider
+--------------
+
+``LocalProvider`` runs a Dask ``LocalCluster`` for laptop and CI execution.
+It supports tag-aware scheduling compatible with
+``ScalableClient.submit(..., tag=...)``.
+
+Slurm provider
+--------------
+
+``SlurmProvider`` is a thin translation layer over the legacy ``SlurmCluster``
+path and preserves existing behavior while exposing a v2 manifest/session API.
+
+Registry and discovery
+----------------------
+
+The provider registry supports:
+
+* explicit runtime registration
+* lazy built-in resolution
+* optional Python entry-point discovery under ``scalable.providers``
+
+This is the extension hook for future cloud and Kubernetes providers.
+
diff --git a/scalable/__init__.py b/scalable/__init__.py
index 63b350e..7b33853 100755
--- a/scalable/__init__.py
+++ b/scalable/__init__.py
@@ -2,7 +2,10 @@
 
 Public API re-exports (kept stable for downstream code):
 
-* :class:`SlurmCluster`, :class:`JobQueueCluster`, :class:`ScalableClient`
+* Legacy v1 runtime classes: :class:`SlurmCluster`, :class:`JobQueueCluster`,
+  :class:`ScalableClient`
+* v2 session + provider surface: :class:`ScalableSession`,
+  :class:`DeploymentProvider`, :class:`LocalProvider`, :class:`SlurmProvider`
 * :func:`cacheable` and the :class:`*Type` hash wrappers from
   :mod:`scalable.caching`
 * :data:`SEED` and the :data:`settings` singleton from :mod:`scalable.common`
@@ -20,6 +23,8 @@
 from .client import ScalableClient
 from .common import SEED, settings
 from .core import JobQueueCluster
+from .providers import DeploymentProvider, LocalProvider, SlurmProvider
+from .session import ScalableSession
 from .slurm import SlurmCluster
 
 try:
@@ -29,10 +34,14 @@
 
 __all__ = [
     "JobQueueCluster",
+    "DeploymentProvider",
+    "LocalProvider",
     "SEED",
     "ScalableClient",
+    "ScalableSession",
     "Security",
     "SlurmCluster",
+    "SlurmProvider",
     "__version__",
     "get_worker",
     "settings",
diff --git a/scalable/cli/cmd_plan.py b/scalable/cli/cmd_plan.py
new file mode 100644
index 0000000..abde391
--- /dev/null
+++ b/scalable/cli/cmd_plan.py
@@ -0,0 +1,45 @@
+"""Implementation for ``scalable plan``."""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+from scalable.manifest.errors import ManifestError
+from scalable.session.session import ScalableSession
+
+
+def run_plan(
+    manifest_path: str,
+    *,
+    target: str | None,
+    dry_run: bool,
+    output: str,
+) -> int:
+    """Build a deterministic plan and write ``plan.json`` + ``manifest.lock``."""
+    if not dry_run:
+        print(
+            "Phase 1 only supports dry-run planning. Re-run with --dry-run.",
+            file=sys.stderr,
+        )
+        return 2
+
+    try:
+        session = ScalableSession.from_yaml(manifest_path, target=target)
+        plan = session.plan(dry_run=True)
+    except (ManifestError, OSError, ValueError, KeyError) as exc:
+        print(f"planning failed: {exc}", file=sys.stderr)
+        return 1
+
+    payload = plan.to_dict()
+    output_path = Path(output)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    output_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8")
+
+    lock_path = output_path.parent / "manifest.lock"
+    lock_path.write_text(plan.manifest_lock + "\n", encoding="utf-8")
+
+    print(json.dumps(payload, indent=2, sort_keys=True), file=sys.stdout)
+    return 0
+
diff --git a/scalable/cli/cmd_validate.py b/scalable/cli/cmd_validate.py
new file mode 100644
index 0000000..bfa0831
--- /dev/null
+++ b/scalable/cli/cmd_validate.py
@@ -0,0 +1,58 @@
+"""Implementation for ``scalable validate``."""
+
+from __future__ import annotations
+
+import json
+import sys
+from typing import Any
+
+from scalable.manifest.errors import ManifestError
+from scalable.session.session import ScalableSession
+
+
+def _report_to_dict(report: Any) -> dict[str, Any]:
+    return {
+        "ok": bool(report.ok),
+        "errors": [
+            {
+                "path": issue.path,
+                "message": issue.message,
+                "code": issue.code,
+            }
+            for issue in report.errors
+        ],
+        "warnings": [
+            {
+                "path": issue.path,
+                "message": issue.message,
+                "code": issue.code,
+            }
+            for issue in report.warnings
+        ],
+    }
+
+
+def run_validate(manifest_path: str, *, target: str | None = None) -> int:
+    """Validate a manifest and print a structured JSON report."""
+    try:
+        session = ScalableSession.from_yaml(manifest_path, target=target)
+        report = session.validate()
+    except (ManifestError, OSError, ValueError, KeyError) as exc:
+        payload = {
+            "ok": False,
+            "errors": [
+                {
+                    "path": "manifest",
+                    "message": str(exc),
+                    "code": "E_MANIFEST",
+                }
+            ],
+            "warnings": [],
+        }
+        print(json.dumps(payload, indent=2, sort_keys=True), file=sys.stdout)
+        return 1
+
+    payload = _report_to_dict(report)
+    print(json.dumps(payload, indent=2, sort_keys=True), file=sys.stdout)
+    return 0 if report.ok else 1
+
diff --git a/scalable/cli/main.py b/scalable/cli/main.py
index f4df63a..11b8d5c 100644
--- a/scalable/cli/main.py
+++ b/scalable/cli/main.py
@@ -1,49 +1,128 @@
 """``scalable`` console entry-point dispatcher.
 
-Phase 1 stub. The real subcommand wiring lands in WU-10
-(``scalable validate`` and ``scalable plan --dry-run``); the remaining
-subcommands (``run``, ``diagnose``, ``explain``, ``init-component``,
-``compose``, ``report``) print a phase-pointer message until later phases
-implement them.
-
-This module exists in WU-1 only so the ``scalable = "scalable.cli.main:main"``
-console script registered in ``pyproject.toml`` resolves at install time.
+Phase 1 ships two implemented subcommands:
+
+* ``scalable validate``
+* ``scalable plan --dry-run``
+
+The namespace for later-phase verbs (``run``, ``diagnose``, ``explain``,
+``init-component``, ``compose``, ``report``) is reserved as explicit stubs.
 """
 
 from __future__ import annotations
 
+import argparse
 import sys
 
-_PHASE1_NOT_IMPLEMENTED_MESSAGE = (
-    "scalable CLI: Phase 1 scaffolding only. "
-    "Subcommands `validate` and `plan --dry-run` arrive in work-unit 10. "
-    "See plans/v2.0.0_phase1_plan.md."
-)
+from scalable.common import settings
+
+from .cmd_plan import run_plan
+from .cmd_validate import run_validate
+
+_STUB_COMMANDS: dict[str, str] = {
+    "run": "Phase 2+",
+    "diagnose": "Phase 4",
+    "explain": "Phase 4",
+    "init-component": "Phase 4",
+    "compose": "Phase 4",
+    "report": "Phase 2",
+}
+
+
+def _handle_validate(args: argparse.Namespace) -> int:
+    return run_validate(args.manifest, target=args.target)
+
+
+def _handle_plan(args: argparse.Namespace) -> int:
+    return run_plan(
+        args.manifest,
+        target=args.target,
+        dry_run=bool(args.dry_run),
+        output=args.output,
+    )
+
+
+def _make_stub_handler(command: str, phase: str):
+    def _handler(_: argparse.Namespace) -> int:
+        print(
+            f"scalable {command}: not yet available; planned for {phase}.",
+            file=sys.stderr,
+        )
+        return 2
+
+    return _handler
+
+
+def _build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(prog="scalable")
+    subparsers = parser.add_subparsers(dest="command")
+
+    validate_parser = subparsers.add_parser(
+        "validate",
+        help="Validate a scalable.yaml manifest and print a structured report",
+    )
+    validate_parser.add_argument(
+        "manifest",
+        nargs="?",
+        default=settings.manifest_path,
+        help="Path to scalable.yaml (default: SCALABLE_MANIFEST or ./scalable.yaml)",
+    )
+    validate_parser.add_argument(
+        "--target",
+        default=None,
+        help="Optional target name override (default: manifest auto resolution)",
+    )
+    validate_parser.set_defaults(handler=_handle_validate)
+
+    plan_parser = subparsers.add_parser(
+        "plan",
+        help="Build a provider-neutral execution plan from a manifest",
+    )
+    plan_parser.add_argument(
+        "manifest",
+        nargs="?",
+        default=settings.manifest_path,
+        help="Path to scalable.yaml (default: SCALABLE_MANIFEST or ./scalable.yaml)",
+    )
+    plan_parser.add_argument(
+        "--target",
+        default=None,
+        help="Optional target name override (default: manifest auto resolution)",
+    )
+    plan_parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Required in Phase 1. Non-dry planning is not implemented yet.",
+    )
+    plan_parser.add_argument(
+        "--output",
+        default="plan.json",
+        help="Plan output path (default: ./plan.json)",
+    )
+    plan_parser.set_defaults(handler=_handle_plan)
+
+    for command, phase in _STUB_COMMANDS.items():
+        stub_parser = subparsers.add_parser(command, help=f"Reserved command (planned for {phase})")
+        stub_parser.set_defaults(handler=_make_stub_handler(command, phase))
+
+    return parser
 
 
 def main(argv: list[str] | None = None) -> int:
-    """Entry-point referenced by ``[project.scripts] scalable = ...``.
-
-    Phase 1 placeholder: prints a clear "not yet wired up" message and
-    exits with status code 2 (matches argparse's convention for usage
-    errors) so downstream automation that introspects the exit code knows
-    to wait for WU-10.
-
-    Parameters
-    ----------
-    argv : list of str, optional
-        Argument vector excluding the program name. Defaults to
-        ``sys.argv[1:]``. Accepted for testability and to match the final
-        signature that WU-10 will deliver.
-
-    Returns
-    -------
-    int
-        Process exit code. Always ``2`` until WU-10 lands.
-    """
-    del argv  # unused in the WU-1 stub
-    print(_PHASE1_NOT_IMPLEMENTED_MESSAGE, file=sys.stderr)
-    return 2
+    """Run the ``scalable`` CLI and return a process-compatible exit code."""
+    parser = _build_parser()
+    args_list = sys.argv[1:] if argv is None else argv
+    try:
+        args = parser.parse_args(args_list)
+    except SystemExit as exc:
+        return int(exc.code)
+
+    handler = getattr(args, "handler", None)
+    if handler is None:
+        parser.print_help(sys.stderr)
+        return 2
+
+    return int(handler(args))
 
 
 if __name__ == "__main__":  # pragma: no cover - exercised via console script
diff --git a/scalable/common.py b/scalable/common.py
index 88c8249..5847801 100755
--- a/scalable/common.py
+++ b/scalable/common.py
@@ -30,6 +30,7 @@
 
 DEFAULT_SEED: int = 987654321
 DEFAULT_CACHE_DIR: str = "./cache"
+DEFAULT_MANIFEST_PATH: str = "./scalable.yaml"
 
 
 @dataclass
@@ -51,6 +52,10 @@ class Settings:
     seed: int = field(
         default_factory=lambda: int(os.environ.get("SCALABLE_SEED", DEFAULT_SEED))
     )
+    manifest_path: str = field(
+        default_factory=lambda: os.environ.get("SCALABLE_MANIFEST", DEFAULT_MANIFEST_PATH)
+    )
+    target: str | None = field(default_factory=lambda: os.environ.get("SCALABLE_TARGET"))
 
 
 #: Process-wide settings singleton. Mutating attributes on this instance
diff --git a/scalable/planning/__init__.py b/scalable/planning/__init__.py
index 03b21f2..6b09c9e 100644
--- a/scalable/planning/__init__.py
+++ b/scalable/planning/__init__.py
@@ -13,4 +13,6 @@
 
 from __future__ import annotations
 
-__all__: list[str] = []
+from .dryrun import DryRunPlan, build_dry_run_plan, compute_manifest_lock
+
+__all__ = ["DryRunPlan", "build_dry_run_plan", "compute_manifest_lock"]
diff --git a/scalable/planning/dryrun.py b/scalable/planning/dryrun.py
new file mode 100644
index 0000000..11c8840
--- /dev/null
+++ b/scalable/planning/dryrun.py
@@ -0,0 +1,90 @@
+"""Deterministic dry-run planning primitives (Phase 1 WU-9)."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+from dataclasses import dataclass
+from typing import Any
+
+from scalable.providers.base import DeploymentSpec, ResourceRequest, ScalePlan
+
+__all__ = [
+    "DryRunPlan",
+    "build_dry_run_plan",
+    "compute_manifest_lock",
+]
+
+
+@dataclass(frozen=True)
+class DryRunPlan:
+    """Serializable dry-run result for CLI/session APIs."""
+
+    target_name: str
+    provider_name: str
+    manifest_lock: str
+    scale_plan: ScalePlan
+    task_to_component: dict[str, str]
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "version": 1,
+            "target": self.target_name,
+            "provider": self.provider_name,
+            "manifest_lock": self.manifest_lock,
+            "task_to_component": dict(self.task_to_component),
+            "scale_plan": {
+                "workers_by_tag": dict(self.scale_plan.workers_by_tag),
+                "resources_by_tag": {
+                    tag: {
+                        "cpus": req.cpus,
+                        "memory": req.memory,
+                        "walltime": req.walltime,
+                        "gpus": req.gpus,
+                    }
+                    for tag, req in self.scale_plan.resources_by_tag.items()
+                },
+            },
+        }
+
+
+def compute_manifest_lock(raw_manifest: dict[str, Any]) -> str:
+    """Compute deterministic SHA-256 fingerprint of canonicalized manifest."""
+    canonical = json.dumps(raw_manifest, sort_keys=True, separators=(",", ":"), ensure_ascii=False)
+    return hashlib.sha256(canonical.encode("utf-8")).hexdigest()
+
+
+def build_dry_run_plan(spec: DeploymentSpec) -> DryRunPlan:
+    """Build a deterministic provider-neutral dry-run plan from spec."""
+    task_to_component = {task_name: task.component for task_name, task in spec.tasks.items()}
+
+    # Start from task usage so only referenced components are scaled by default.
+    referenced_components = {task.component for task in spec.tasks.values()}
+
+    workers_by_tag: dict[str, int] = {}
+    resources_by_tag: dict[str, ResourceRequest] = {}
+    walltime = spec.target.options.get("walltime")
+
+    for component_name in sorted(referenced_components):
+        component = spec.components[component_name]
+        workers_by_tag[component_name] = 1
+        resources_by_tag[component_name] = ResourceRequest(
+            cpus=component.cpus,
+            memory=component.memory,
+            walltime=walltime if isinstance(walltime, str) else None,
+            gpus=None,
+        )
+
+    scale_plan = ScalePlan(
+        workers_by_tag=workers_by_tag,
+        resources_by_tag=resources_by_tag,
+    )
+
+    return DryRunPlan(
+        target_name=spec.target_name,
+        provider_name=spec.provider_name,
+        manifest_lock=compute_manifest_lock(spec.raw_manifest),
+        scale_plan=scale_plan,
+        task_to_component=task_to_component,
+    )
+
diff --git a/scalable/session/__init__.py b/scalable/session/__init__.py
index 8aaa585..a5fa499 100644
--- a/scalable/session/__init__.py
+++ b/scalable/session/__init__.py
@@ -10,4 +10,6 @@
 
 from __future__ import annotations
 
-__all__: list[str] = []
+from .session import ScalableSession
+
+__all__ = ["ScalableSession"]
diff --git a/scalable/session/session.py b/scalable/session/session.py
new file mode 100644
index 0000000..cb44540
--- /dev/null
+++ b/scalable/session/session.py
@@ -0,0 +1,158 @@
+"""ScalableSession implementation (Phase 1 WU-8)."""
+
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+from typing import Any
+
+from scalable.client import ScalableClient
+from scalable.common import settings
+from scalable.manifest.parser import load_manifest
+from scalable.manifest.schema import ManifestModel
+from scalable.manifest.validate import ValidationIssue, ValidationReport, validate_manifest
+from scalable.planning.dryrun import DryRunPlan, build_dry_run_plan
+from scalable.providers.base import ClusterHandle, DeploymentSpec
+from scalable.providers.registry import get_provider, iter_provider_names
+
+__all__ = ["ScalableSession"]
+
+
+@dataclass
+class ScalableSession:
+    """Session lifecycle wrapper for manifest-driven execution."""
+
+    manifest: ManifestModel
+    target_name: str
+    spec: DeploymentSpec
+
+    _provider: Any = None
+    _cluster: ClusterHandle | None = None
+    _client: ScalableClient | None = None
+
+    @classmethod
+    def from_yaml(
+        cls,
+        path: str | os.PathLike[str] | None = None,
+        *,
+        target: str | None = None,
+    ) -> ScalableSession:
+        manifest_path = str(path or settings.manifest_path)
+        manifest = load_manifest(manifest_path)
+        selected_target = _resolve_target_name(manifest, requested=target)
+        spec = DeploymentSpec.from_manifest(manifest, target_name=selected_target)
+        return cls(manifest=manifest, target_name=selected_target, spec=spec)
+
+    def validate(self) -> ValidationReport:
+        known = set(iter_provider_names(include_entrypoints=True))
+        # Keep built-ins discoverable even before first runtime lookup.
+        known.update({"local", "slurm"})
+        report = validate_manifest(self.manifest, known_providers=known)
+
+        try:
+            provider = get_provider(self.spec.provider_name)
+        except KeyError as exc:
+            report.errors.append(
+                ValidationIssue(
+                    path=f"targets.{self.target_name}.provider",
+                    message=str(exc),
+                    code="E_UNKNOWN_PROVIDER",
+                )
+            )
+            return report
+
+        preport = provider.validate(self.spec)
+        report.errors.extend(preport.errors)
+        report.warnings.extend(preport.warnings)
+        return report
+
+    def plan(
+        self,
+        *,
+        dry_run: bool = False,
+        objective: str | None = None,
+        policy: str | None = None,
+    ) -> DryRunPlan:
+        if objective is not None or policy is not None:
+            raise NotImplementedError(
+                "objective/policy planning is planned for later phases; "
+                "Phase 1 supports deterministic dry-run planning only"
+            )
+
+        _ = dry_run  # Phase 1 currently only supports dry-run behavior.
+
+        report = self.validate()
+        if not report.ok:
+            details = "; ".join(f"{i.path}: {i.message}" for i in report.errors)
+            raise ValueError(f"manifest validation failed: {details}")
+
+        return build_dry_run_plan(self.spec)
+
+    def start(self, plan: DryRunPlan | None = None) -> ScalableClient:
+        if self._client is not None:
+            return self._client
+
+        if plan is None:
+            plan = self.plan(dry_run=True)
+        elif plan.target_name != self.target_name:
+            raise ValueError(
+                f"plan target {plan.target_name!r} does not match session target {self.target_name!r}"
+            )
+
+        self._provider = get_provider(self.spec.provider_name)
+        self._cluster = self._provider.build_cluster(self.spec)
+        self._provider.scale(self._cluster, plan.scale_plan)
+        self._client = self._cluster.client_factory()
+        return self._client
+
+    def close(self) -> None:
+        if self._client is not None:
+            self._client.close()
+            self._client = None
+
+        if self._cluster is not None and self._provider is not None:
+            self._provider.close(self._cluster)
+            self._cluster = None
+
+    def __enter__(self) -> ScalableClient:
+        return self.start()
+
+    def __exit__(self, exc_type, exc, tb) -> None:
+        self.close()
+
+
+def _resolve_target_name(manifest: ManifestModel, *, requested: str | None) -> str:
+    """Resolve session target from explicit input, settings, or auto mode."""
+    if not manifest.targets:
+        raise ValueError("manifest declares no targets")
+
+    desired = requested if requested is not None else settings.target
+    desired = desired or "auto"
+
+    if desired != "auto":
+        if desired not in manifest.targets:
+            raise KeyError(
+                f"target {desired!r} not found in manifest; available targets: "
+                f"{sorted(manifest.targets)}"
+            )
+        return desired
+
+    # Auto-resolution heuristic for Phase 1.
+    running_in_slurm = bool(os.environ.get("SLURM_JOB_ID") or os.environ.get("SLURM_CLUSTER_NAME"))
+    if running_in_slurm:
+        # Prefer target key named "slurm" then any target using slurm provider.
+        if "slurm" in manifest.targets:
+            return "slurm"
+        for tname, tcfg in manifest.targets.items():
+            if tcfg.provider == "slurm":
+                return tname
+
+    # Prefer target key named "local" then any target using local provider.
+    if "local" in manifest.targets:
+        return "local"
+    for tname, tcfg in manifest.targets.items():
+        if tcfg.provider == "local":
+            return tname
+
+    # Fallback: deterministic first key order from parsed mapping.
+    return next(iter(manifest.targets.keys()))
diff --git a/tests/unit/test_cli_plan.py b/tests/unit/test_cli_plan.py
new file mode 100644
index 0000000..e36cedd
--- /dev/null
+++ b/tests/unit/test_cli_plan.py
@@ -0,0 +1,87 @@
+"""Unit tests for ``scalable plan`` CLI behavior."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from scalable.cli.main import main
+
+
+def _write_valid_manifest(path: Path) -> None:
+    path.write_text(
+        """
+version: 1
+project:
+  name: demo
+targets:
+  local:
+    provider: local
+    max_workers: 1
+components:
+  gcam:
+    cpus: 1
+    memory: 1G
+tasks:
+  run_gcam:
+    component: gcam
+""".lstrip(),
+        encoding="utf-8",
+    )
+
+
+def test_cli_plan_dry_run_writes_plan_and_manifest_lock(tmp_path: Path, capsys) -> None:
+    manifest_path = tmp_path / "scalable.yaml"
+    _write_valid_manifest(manifest_path)
+    output_path = tmp_path / "plan.json"
+
+    code = main(
+        [
+            "plan",
+            str(manifest_path),
+            "--target",
+            "local",
+            "--dry-run",
+            "--output",
+            str(output_path),
+        ]
+    )
+
+    captured = capsys.readouterr()
+    payload = json.loads(captured.out)
+    file_payload = json.loads(output_path.read_text(encoding="utf-8"))
+    lock_path = tmp_path / "manifest.lock"
+
+    assert code == 0
+    assert payload["target"] == "local"
+    assert file_payload == payload
+    assert lock_path.exists()
+    assert len(lock_path.read_text(encoding="utf-8").strip()) == 64
+
+
+def test_cli_plan_requires_dry_run_flag(tmp_path: Path, capsys) -> None:
+    manifest_path = tmp_path / "scalable.yaml"
+    _write_valid_manifest(manifest_path)
+    output_path = tmp_path / "plan.json"
+
+    code = main(["plan", str(manifest_path), "--output", str(output_path)])
+
+    captured = capsys.readouterr()
+    assert code == 2
+    assert "supports dry-run" in captured.err
+    assert not output_path.exists()
+
+
+def test_cli_plan_invalid_manifest_returns_nonzero(tmp_path: Path, capsys) -> None:
+    manifest_path = tmp_path / "scalable.yaml"
+    _write_valid_manifest(manifest_path)
+    text = manifest_path.read_text(encoding="utf-8")
+    text = text.replace("component: gcam", "component: missing_component")
+    manifest_path.write_text(text, encoding="utf-8")
+
+    code = main(["plan", str(manifest_path), "--dry-run"])
+
+    captured = capsys.readouterr()
+    assert code == 1
+    assert "planning failed" in captured.err
+
diff --git a/tests/unit/test_cli_validate.py b/tests/unit/test_cli_validate.py
new file mode 100644
index 0000000..2d54339
--- /dev/null
+++ b/tests/unit/test_cli_validate.py
@@ -0,0 +1,81 @@
+"""Unit tests for ``scalable validate`` CLI behavior."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from scalable.cli.main import main
+
+
+def _write_valid_manifest(path: Path) -> None:
+    path.write_text(
+        """
+version: 1
+project:
+  name: demo
+targets:
+  local:
+    provider: local
+    max_workers: 1
+components:
+  gcam:
+    cpus: 1
+    memory: 1G
+tasks:
+  run_gcam:
+    component: gcam
+""".lstrip(),
+        encoding="utf-8",
+    )
+
+
+def test_cli_validate_valid_manifest_returns_zero(tmp_path: Path, capsys) -> None:
+    manifest_path = tmp_path / "scalable.yaml"
+    _write_valid_manifest(manifest_path)
+
+    code = main(["validate", str(manifest_path), "--target", "local"])
+
+    captured = capsys.readouterr()
+    payload = json.loads(captured.out)
+    assert code == 0
+    assert payload["ok"] is True
+    assert payload["errors"] == []
+
+
+def test_cli_validate_invalid_manifest_returns_nonzero(tmp_path: Path, capsys) -> None:
+    manifest_path = tmp_path / "scalable.yaml"
+    _write_valid_manifest(manifest_path)
+    text = manifest_path.read_text(encoding="utf-8")
+    text = text.replace("component: gcam", "component: missing_component")
+    manifest_path.write_text(text, encoding="utf-8")
+
+    code = main(["validate", str(manifest_path), "--target", "local"])
+
+    captured = capsys.readouterr()
+    payload = json.loads(captured.out)
+    assert code == 1
+    assert payload["ok"] is False
+    assert any(err["path"] == "tasks.run_gcam.component" for err in payload["errors"])
+
+
+def test_cli_validate_schema_error_returns_nonzero(tmp_path: Path, capsys) -> None:
+    manifest_path = tmp_path / "scalable.yaml"
+    manifest_path.write_text("version: 1\n", encoding="utf-8")
+
+    code = main(["validate", str(manifest_path)])
+
+    captured = capsys.readouterr()
+    payload = json.loads(captured.out)
+    assert code == 1
+    assert payload["ok"] is False
+    assert payload["errors"][0]["code"] == "E_MANIFEST"
+
+
+def test_cli_stub_command_returns_pointer_message(capsys) -> None:
+    code = main(["diagnose"])
+
+    captured = capsys.readouterr()
+    assert code == 2
+    assert "planned for Phase 4" in captured.err
+
diff --git a/tests/unit/test_common_settings.py b/tests/unit/test_common_settings.py
index 33eb280..5bb470b 100644
--- a/tests/unit/test_common_settings.py
+++ b/tests/unit/test_common_settings.py
@@ -23,11 +23,15 @@ def test_settings_defaults():
     s = common.Settings()
     assert s.cache_dir == "./cache"
     assert s.seed == common.DEFAULT_SEED
+    assert s.manifest_path == "./scalable.yaml"
+    assert s.target is None
 
 
 def test_settings_env_overrides(monkeypatch):
     monkeypatch.setenv("SCALABLE_CACHE_DIR", "/tmp/scalable-test-cache")
     monkeypatch.setenv("SCALABLE_SEED", "42")
+    monkeypatch.setenv("SCALABLE_MANIFEST", "/tmp/scalable.yaml")
+    monkeypatch.setenv("SCALABLE_TARGET", "local")
 
     # Reload to pick up env vars in field defaults.
     from scalable import common as common_mod
@@ -35,6 +39,8 @@ def test_settings_env_overrides(monkeypatch):
     s = common_mod.Settings()
     assert s.cache_dir == "/tmp/scalable-test-cache"
     assert s.seed == 42
+    assert s.manifest_path == "/tmp/scalable.yaml"
+    assert s.target == "local"
 
 
 def test_legacy_module_aliases_match_singleton():
diff --git a/tests/unit/test_planning_dryrun.py b/tests/unit/test_planning_dryrun.py
new file mode 100644
index 0000000..4bdcd14
--- /dev/null
+++ b/tests/unit/test_planning_dryrun.py
@@ -0,0 +1,75 @@
+"""Tests for deterministic dry-run planning (WU-9 foundations)."""
+
+from __future__ import annotations
+
+from scalable.manifest.parser import parse_manifest
+from scalable.planning.dryrun import build_dry_run_plan, compute_manifest_lock
+from scalable.providers.base import DeploymentSpec
+
+
+def _spec() -> DeploymentSpec:
+    model = parse_manifest(
+        {
+            "version": 1,
+            "project": {"name": "demo"},
+            "targets": {
+                "hpc": {
+                    "provider": "slurm",
+                    "walltime": "02:00:00",
+                    "comm_port": 50051,
+                }
+            },
+            "components": {
+                "gcam": {"cpus": 2, "memory": "8G"},
+                "stitches": {"cpus": 1, "memory": "4G"},
+            },
+            "tasks": {
+                "run_gcam": {"component": "gcam"},
+                "run_stitches": {"component": "stitches"},
+            },
+        }
+    )
+    return DeploymentSpec.from_manifest(model, target_name="hpc")
+
+
+def test_compute_manifest_lock_deterministic() -> None:
+    payload_a = {"b": 2, "a": 1, "nested": {"x": [3, 2, 1]}}
+    payload_b = {"nested": {"x": [3, 2, 1]}, "a": 1, "b": 2}
+
+    lock_a = compute_manifest_lock(payload_a)
+    lock_b = compute_manifest_lock(payload_b)
+
+    assert lock_a == lock_b
+    assert len(lock_a) == 64
+
+
+def test_build_dry_run_plan_maps_tasks_and_resources() -> None:
+    plan = build_dry_run_plan(_spec())
+
+    assert plan.target_name == "hpc"
+    assert plan.provider_name == "slurm"
+    assert len(plan.manifest_lock) == 64
+    assert plan.task_to_component == {
+        "run_gcam": "gcam",
+        "run_stitches": "stitches",
+    }
+
+    assert plan.scale_plan.workers_by_tag == {"gcam": 1, "stitches": 1}
+    assert plan.scale_plan.resources_by_tag["gcam"].cpus == 2
+    assert plan.scale_plan.resources_by_tag["gcam"].memory == "8G"
+    assert plan.scale_plan.resources_by_tag["gcam"].walltime == "02:00:00"
+    assert plan.scale_plan.resources_by_tag["stitches"].cpus == 1
+
+
+def test_dry_run_plan_to_dict_shape() -> None:
+    plan = build_dry_run_plan(_spec())
+    payload = plan.to_dict()
+
+    assert payload["version"] == 1
+    assert payload["target"] == "hpc"
+    assert payload["provider"] == "slurm"
+    assert payload["manifest_lock"] == plan.manifest_lock
+    assert payload["task_to_component"]["run_gcam"] == "gcam"
+    assert payload["scale_plan"]["workers_by_tag"]["gcam"] == 1
+    assert payload["scale_plan"]["resources_by_tag"]["gcam"]["cpus"] == 2
+
diff --git a/tests/unit/test_public_api_exports.py b/tests/unit/test_public_api_exports.py
new file mode 100644
index 0000000..9f65dff
--- /dev/null
+++ b/tests/unit/test_public_api_exports.py
@@ -0,0 +1,27 @@
+"""Regression tests for top-level ``scalable`` public API exports."""
+
+from __future__ import annotations
+
+
+def test_top_level_exports_include_session_and_provider_symbols() -> None:
+    import scalable
+
+    exported = set(scalable.__all__)
+
+    assert "ScalableSession" in exported
+    assert "DeploymentProvider" in exported
+    assert "LocalProvider" in exported
+    assert "SlurmProvider" in exported
+
+
+def test_legacy_exports_remain_available() -> None:
+    import scalable
+
+    exported = set(scalable.__all__)
+
+    assert "JobQueueCluster" in exported
+    assert "SlurmCluster" in exported
+    assert "ScalableClient" in exported
+    assert "SEED" in exported
+    assert "settings" in exported
+
diff --git a/tests/unit/test_session.py b/tests/unit/test_session.py
new file mode 100644
index 0000000..9d8dd84
--- /dev/null
+++ b/tests/unit/test_session.py
@@ -0,0 +1,142 @@
+"""Unit tests for :class:`scalable.session.session.ScalableSession`."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+
+from scalable.session.session import ScalableSession
+
+
+def _write_manifest(path: Path) -> None:
+    path.write_text(
+        """
+version: 1
+project:
+  name: demo
+targets:
+  local:
+    provider: local
+    max_workers: 1
+    threads_per_worker: 1
+    processes: false
+    containers: none
+  hpc:
+    provider: slurm
+    comm_port: 50051
+components:
+  gcam:
+    cpus: 1
+    memory: 1G
+tasks:
+  run_gcam:
+    component: gcam
+""".lstrip(),
+        encoding="utf-8",
+    )
+
+
+def _identity(x: int) -> int:
+    return x
+
+
+def test_from_yaml_explicit_target(tmp_path: Path) -> None:
+    manifest_path = tmp_path / "scalable.yaml"
+    _write_manifest(manifest_path)
+
+    session = ScalableSession.from_yaml(manifest_path, target="local")
+
+    assert session.target_name == "local"
+    assert session.spec.provider_name == "local"
+
+
+def test_from_yaml_auto_prefers_local_when_not_in_slurm(monkeypatch, tmp_path: Path) -> None:
+    monkeypatch.delenv("SLURM_JOB_ID", raising=False)
+    monkeypatch.delenv("SLURM_CLUSTER_NAME", raising=False)
+    manifest_path = tmp_path / "scalable.yaml"
+    _write_manifest(manifest_path)
+
+    session = ScalableSession.from_yaml(manifest_path, target="auto")
+
+    assert session.target_name == "local"
+
+
+def test_from_yaml_auto_prefers_slurm_when_in_slurm_env(monkeypatch, tmp_path: Path) -> None:
+    monkeypatch.setenv("SLURM_JOB_ID", "12345")
+    manifest_path = tmp_path / "scalable.yaml"
+    _write_manifest(manifest_path)
+
+    session = ScalableSession.from_yaml(manifest_path, target="auto")
+
+    assert session.target_name == "hpc"
+
+
+def test_validate_ok_for_local_manifest(tmp_path: Path, monkeypatch) -> None:
+    monkeypatch.delenv("SLURM_JOB_ID", raising=False)
+    monkeypatch.delenv("SLURM_CLUSTER_NAME", raising=False)
+    manifest_path = tmp_path / "scalable.yaml"
+    _write_manifest(manifest_path)
+    session = ScalableSession.from_yaml(manifest_path, target="local")
+
+    report = session.validate()
+
+    assert report.ok is True
+
+
+def test_plan_raises_not_implemented_for_objective_policy(tmp_path: Path) -> None:
+    manifest_path = tmp_path / "scalable.yaml"
+    _write_manifest(manifest_path)
+    session = ScalableSession.from_yaml(manifest_path, target="local")
+
+    with pytest.raises(NotImplementedError):
+        session.plan(objective="minimize cost")
+
+    with pytest.raises(NotImplementedError):
+        session.plan(policy="safe")
+
+
+def test_plan_returns_dry_run_plan(tmp_path: Path) -> None:
+    manifest_path = tmp_path / "scalable.yaml"
+    _write_manifest(manifest_path)
+    session = ScalableSession.from_yaml(manifest_path, target="local")
+
+    plan = session.plan(dry_run=True)
+
+    assert plan.target_name == "local"
+    assert plan.provider_name == "local"
+    assert plan.scale_plan.workers_by_tag == {"gcam": 1}
+
+
+def test_start_and_close_local_session(tmp_path: Path) -> None:
+    manifest_path = tmp_path / "scalable.yaml"
+    _write_manifest(manifest_path)
+    session = ScalableSession.from_yaml(manifest_path, target="local")
+
+    client = session.start()
+    future = client.submit(_identity, 7, tag="gcam")
+    assert future.result(timeout=10) == 7
+
+    session.close()
+
+
+def test_start_with_target_mismatch_plan_raises(tmp_path: Path) -> None:
+    manifest_path = tmp_path / "scalable.yaml"
+    _write_manifest(manifest_path)
+    session_local = ScalableSession.from_yaml(manifest_path, target="local")
+    session_hpc = ScalableSession.from_yaml(manifest_path, target="hpc")
+    hpc_plan = session_hpc.plan(dry_run=True)
+
+    with pytest.raises(ValueError, match="does not match session target"):
+        session_local.start(hpc_plan)
+
+
+def test_context_manager_starts_and_closes(tmp_path: Path) -> None:
+    manifest_path = tmp_path / "scalable.yaml"
+    _write_manifest(manifest_path)
+    session = ScalableSession.from_yaml(manifest_path, target="local")
+
+    with session as client:
+        result = client.submit(_identity, 11, tag="gcam").result(timeout=10)
+        assert result == 11
+

From abecbeb41765d42d6829af1f8276f47893707f64 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 16:59:22 -0400
Subject: [PATCH 13/47] ignore env files

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index d869900..07c2285 100755
--- a/.gitignore
+++ b/.gitignore
@@ -4,6 +4,7 @@
 capabilities/
 plans/
 .rooignore
+.env
 
 # -----------------------------
 # Python bytecode / caches

From 78ca831f4a6b6b258313ff96cf2f763bfe24514f Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 16:59:36 -0400
Subject: [PATCH 14/47] add env example file

---
 .env.example | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 .env.example

diff --git a/.env.example b/.env.example
new file mode 100644
index 0000000..74bd51e
--- /dev/null
+++ b/.env.example
@@ -0,0 +1,13 @@
+# OpenAI credentials and model configuration.
+# Required: set your API key.
+OPENAI_API_KEY=your_openai_api_key_here
+
+# Embedding model used to vectorize document chunks.
+OPENAI_EMBEDDING_MODEL=text-embedding-3-large
+
+# Chat model used by the PydanticAI chatbot.
+OPENAI_CHAT_MODEL=gpt-5.2
+
+# Optional OpenAI-compatible base URL.
+# Example: https://api.openai.com/v1
+OPENAI_BASE_URL=your_openai_base_url_here

From f9b564267d406484aa1f22066a5eadf5eab5b88b Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 16:59:48 -0400
Subject: [PATCH 15/47] update changelog

---
 CHANGELOG.md | 37 ++++++++++++++++++++++++++++++++++++-
 1 file changed, 36 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 89a5ae7..c43658d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+No notable changes yet.
+
+## [2.0.0a1] - 2026-05-19
+
 ### Added
 
 - New workflow architecture figure in [`docs/images/scalable_architecture.png`](docs/images/scalable_architecture.png).
@@ -17,6 +21,16 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   - deterministic ``manifest.lock`` fingerprint generation
   - provider abstraction with ``LocalProvider`` and ``SlurmProvider``
   - docs pages: [`docs/manifest.rst`](docs/manifest.rst) and [`docs/providers.rst`](docs/providers.rst)
+- Provider abstractions and neutral planning data structures:
+  - ``DeploymentProvider`` protocol
+  - ``DeploymentSpec``, ``ScalePlan``, ``ResourceRequest``, and ``ClusterHandle``
+- New CLI subcommands and namespace stubs:
+  - ``scalable validate``
+  - ``scalable plan --dry-run``
+  - reserved stubs: ``run``, ``diagnose``, ``explain``, ``init-component``, ``compose``, ``report``
+- Example manifests for docs and CI validation:
+  - [`docs/examples/scalable.minimal.yaml`](docs/examples/scalable.minimal.yaml)
+  - [`docs/examples/scalable.gcam_stitches.yaml`](docs/examples/scalable.gcam_stitches.yaml)
 
 ### Changed
 
@@ -28,11 +42,31 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Updated container conda Python baseline to 3.11 in [`scalable/Dockerfile`](scalable/Dockerfile).
 - Expanded top-level exports in [`scalable/__init__.py`](scalable/__init__.py) to include
   ``ScalableSession``, ``DeploymentProvider``, ``LocalProvider``, and ``SlurmProvider``.
+- Package version now targets v2 alpha in [`pyproject.toml`](pyproject.toml) with
+  ``version = "2.0.0a1"``.
+- Global settings in [`scalable/common.py`](scalable/common.py) now include
+  ``manifest_path`` and ``target`` with env overrides ``SCALABLE_MANIFEST`` and
+  ``SCALABLE_TARGET``.
+- CI now includes:
+  - version branch triggers (``version/**``)
+  - macOS matrix coverage for LocalProvider paths
+  - a dedicated docs-manifest validation and dry-run planning job
+
+### Deprecated
+
+- Legacy ``ModelConfig`` Dockerfile/config auto-discovery path now emits a
+  ``DeprecationWarning`` when used outside the manifest adapter context;
+  manifest-driven configuration via ``scalable.yaml`` is the preferred path.
 
 ### Documentation
 
 - Added [`DISCLAIMER.md`](DISCLAIMER.md).
 - Updated [`LICENSE.md`](LICENSE.md) to BSD-3-Clause wording.
+- Added and cross-linked:
+  - [`docs/manifest.rst`](docs/manifest.rst)
+  - [`docs/providers.rst`](docs/providers.rst)
+  - v2 manifest-first usage examples in [`README.md`](README.md)
+  - onboarding links in [`docs/getting_started.rst`](docs/getting_started.rst)
 
 ## [1.1.0]
 
@@ -193,4 +227,5 @@ Most changes are source-compatible. Two situations to be aware of:
    loopback.
 
 [1.1.0]: https://github.com/JGCRI/scalable/compare/1.0.0...1.1.0
-[Unreleased]: https://github.com/JGCRI/scalable/compare/1.1.0...HEAD
+[2.0.0a1]: https://github.com/JGCRI/scalable/compare/1.1.0...version/2.0.0-phase1-provider-manifest
+[Unreleased]: https://github.com/JGCRI/scalable/compare/version/2.0.0-phase1-provider-manifest...HEAD

From 5cf2d975f4b108f00c5249da811cd68f7fcbacd8 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 18:13:57 -0400
Subject: [PATCH 16/47] phase 2 progress towards telemetry and deterministic
 advising

---
 CHANGELOG.md                                  |  47 ++-
 README.md                                     |  22 +
 docs/advising.rst                             |  31 ++
 docs/getting_started.rst                      |   2 +
 docs/index.rst                                |   2 +
 docs/telemetry.rst                            |  55 +++
 scalable/__init__.py                          |   3 +
 scalable/advising/__init__.py                 |   8 +
 scalable/advising/resources.py                | 196 +++++++++
 scalable/caching.py                           |  16 +
 scalable/cli/__init__.py                      |  12 +-
 scalable/cli/cmd_report.py                    |  47 +++
 scalable/cli/main.py                          |  51 ++-
 scalable/client.py                            | 118 +++++-
 scalable/common.py                            |  10 +
 scalable/providers/local.py                   |  20 +
 scalable/providers/slurm.py                   |  16 +
 scalable/session/session.py                   | 114 ++++-
 scalable/telemetry/__init__.py                |  56 +++
 scalable/telemetry/collectors.py              | 174 ++++++++
 scalable/telemetry/events.py                  | 169 ++++++++
 scalable/telemetry/runtime.py                 | 112 +++++
 scalable/telemetry/store.py                   | 396 ++++++++++++++++++
 tests/conftest.py                             |   3 +
 .../test_session_telemetry_local.py           |  71 ++++
 tests/unit/test_cli_report.py                 |  94 +++++
 tests/unit/test_common_settings.py            |   9 +
 tests/unit/test_public_api_exports.py         |   3 +-
 tests/unit/test_resource_advisor.py           |  90 ++++
 tests/unit/test_telemetry_collectors.py       | 117 ++++++
 tests/unit/test_telemetry_store.py            |  89 ++++
 31 files changed, 2131 insertions(+), 22 deletions(-)
 create mode 100644 docs/advising.rst
 create mode 100644 docs/telemetry.rst
 create mode 100644 scalable/advising/__init__.py
 create mode 100644 scalable/advising/resources.py
 create mode 100644 scalable/cli/cmd_report.py
 create mode 100644 scalable/telemetry/__init__.py
 create mode 100644 scalable/telemetry/collectors.py
 create mode 100644 scalable/telemetry/events.py
 create mode 100644 scalable/telemetry/runtime.py
 create mode 100644 scalable/telemetry/store.py
 create mode 100644 tests/integration/test_session_telemetry_local.py
 create mode 100644 tests/unit/test_cli_report.py
 create mode 100644 tests/unit/test_resource_advisor.py
 create mode 100644 tests/unit/test_telemetry_collectors.py
 create mode 100644 tests/unit/test_telemetry_store.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index c43658d..41a757d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,7 +7,52 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
-No notable changes yet.
+### Added
+
+- **Phase 2 telemetry package** implementing run history, event schemas, and
+  report aggregation:
+  - `scalable.telemetry.events`
+  - `scalable.telemetry.store`
+  - `scalable.telemetry.collectors`
+  - `scalable.telemetry.runtime`
+- **Run history store** for manifest-driven sessions under `.scalable/runs/`
+  with persisted `manifest.yaml`, `plan.json`, `manifest.lock`, `run.json`,
+  task/resource/worker/failure/cache/artifact JSONL streams, and `summary.json`.
+- **`scalable report` CLI command** (text + JSON output) replacing the Phase 1
+  report stub.
+- **Deterministic advising API**:
+  - `ResourceAdvisor.from_history(...)`
+  - `ResourceAdvisor.recommend(...)`
+  - `ResourceRecommendation` result payload
+- **Artifact metadata recording API** via `ScalableSession.record_artifact(...)`.
+- New docs pages:
+  - `docs/telemetry.rst`
+  - `docs/advising.rst`
+
+### Changed
+
+- `ScalableSession` now initializes and finalizes telemetry by default for
+  manifest-driven runs (configurable).
+- `ScalableClient.submit` and `ScalableClient.map` now emit task lifecycle
+  telemetry through future callbacks.
+- `cacheable` now emits cache hit/miss telemetry events when telemetry is
+  active.
+- `LocalProvider` and `SlurmProvider` now emit worker/cluster telemetry events.
+- `scalable.__all__` now exports `ResourceAdvisor` and
+  `ResourceRecommendation`.
+- `Settings` now includes telemetry controls:
+  - `runs_dir` (`SCALABLE_RUNS_DIR`)
+  - `telemetry_enabled` (`SCALABLE_TELEMETRY`)
+  - `telemetry_parquet` (`SCALABLE_TELEMETRY_PARQUET`)
+
+### Tests
+
+- Added unit and integration coverage for:
+  - telemetry store lifecycle and summary generation
+  - telemetry collectors and report rendering
+  - `scalable report` CLI behavior
+  - `ResourceAdvisor` heuristics and fallbacks
+  - session telemetry end-to-end behavior on local execution
 
 ## [2.0.0a1] - 2026-05-19
 
diff --git a/README.md b/README.md
index b6c0d50..3310a33 100644
--- a/README.md
+++ b/README.md
@@ -114,6 +114,13 @@ scalable validate ./scalable.yaml
 scalable plan ./scalable.yaml --target local --dry-run --output plan.json
 ```
 
+Generate a telemetry report from completed runs:
+
+```bash
+scalable report --latest
+scalable report --latest --format json --output report.json
+```
+
 Use the session API:
 
 ```python
@@ -124,6 +131,21 @@ plan = session.plan(dry_run=True)
 print(plan.manifest_lock)
 ```
 
+Use deterministic history-based advising:
+
+```python
+from scalable import ResourceAdvisor
+
+advisor = ResourceAdvisor.from_history("./.scalable/runs")
+recommendation = advisor.recommend(
+    task="run_gcam",
+    target="local",
+    confidence=0.95,
+)
+print(recommendation.workers)
+print(recommendation.resources)
+```
+
 At runtime, create a cluster, register container targets, scale workers, and submit functions.
 
 ### 1. Create a cluster
diff --git a/docs/advising.rst b/docs/advising.rst
new file mode 100644
index 0000000..f3c7862
--- /dev/null
+++ b/docs/advising.rst
@@ -0,0 +1,31 @@
+Deterministic Resource Advising
+==============================
+
+Phase 2 adds a baseline deterministic :class:`ResourceAdvisor` that derives
+conservative resource recommendations from historical run telemetry.
+
+Quick start
+-----------
+
+.. code-block:: python
+
+    from scalable import ResourceAdvisor
+
+    advisor = ResourceAdvisor.from_history("./.scalable/runs")
+    recommendation = advisor.recommend(
+        task="run_gcam",
+        target="local",
+        confidence=0.95,
+    )
+
+    print(recommendation.workers)
+    print(recommendation.resources)
+    print(recommendation.evidence)
+
+Design intent
+-------------
+
+This advisor is heuristic and explainable. It uses observed request/runtime
+history and confidence-indexed quantiles. Learned ML models are deferred to
+later phases.
+
diff --git a/docs/getting_started.rst b/docs/getting_started.rst
index d2304ac..7fd491e 100644
--- a/docs/getting_started.rst
+++ b/docs/getting_started.rst
@@ -96,6 +96,8 @@ Next Steps
 After setup:
 
 * For declarative workflows, start with :doc:`manifest` and :doc:`providers`.
+* Review run telemetry in :doc:`telemetry`.
+* Use deterministic history-based recommendations from :doc:`advising`.
 * Review the :ref:`api_section` for worker, caching, and function interfaces.
 * Run examples from :ref:`demos_section`.
 * Use :ref:`how_tos_section` for targeted implementation guidance.
diff --git a/docs/index.rst b/docs/index.rst
index 1df3e13..8d8f1ed 100755
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -47,6 +47,8 @@ Contents
    workers
    manifest
    providers
+   telemetry
+   advising
    caching
    functions
 
diff --git a/docs/telemetry.rst b/docs/telemetry.rst
new file mode 100644
index 0000000..3da7479
--- /dev/null
+++ b/docs/telemetry.rst
@@ -0,0 +1,55 @@
+Telemetry and Run Reports
+=========================
+
+Phase 2 introduces a deterministic run history store for manifest-driven
+sessions.
+
+Run directory layout
+--------------------
+
+Each run is recorded under ``.scalable/runs/``:
+
+.. code-block:: text
+
+    .scalable/
+      runs/
+        run-.../
+          manifest.yaml
+          plan.json
+          manifest.lock
+          run.json
+          tasks.jsonl
+          resources.jsonl
+          workers.jsonl
+          failures.jsonl
+          cache.jsonl
+          artifacts.jsonl
+          summary.json
+
+JSONL is the canonical storage format. Optional parquet snapshots are emitted
+when telemetry parquet support is enabled.
+
+CLI reporting
+-------------
+
+Generate a report from the most recent run:
+
+.. code-block:: bash
+
+    scalable report --latest
+
+Machine-readable report output:
+
+.. code-block:: bash
+
+    scalable report --latest --format json --output report.json
+
+Configuration
+-------------
+
+The telemetry system supports these environment variables:
+
+* ``SCALABLE_RUNS_DIR``
+* ``SCALABLE_TELEMETRY``
+* ``SCALABLE_TELEMETRY_PARQUET``
+
diff --git a/scalable/__init__.py b/scalable/__init__.py
index 7b33853..4c6ea73 100755
--- a/scalable/__init__.py
+++ b/scalable/__init__.py
@@ -19,6 +19,7 @@
 from dask.distributed import Security  # noqa: F401  (re-exported for users)
 from distributed import get_worker  # noqa: F401  (re-exported for users)
 
+from .advising import ResourceAdvisor, ResourceRecommendation
 from .caching import *  # noqa: F401,F403  (legacy star-export)
 from .client import ScalableClient
 from .common import SEED, settings
@@ -37,6 +38,8 @@
     "DeploymentProvider",
     "LocalProvider",
     "SEED",
+    "ResourceAdvisor",
+    "ResourceRecommendation",
     "ScalableClient",
     "ScalableSession",
     "Security",
diff --git a/scalable/advising/__init__.py b/scalable/advising/__init__.py
new file mode 100644
index 0000000..847ccb1
--- /dev/null
+++ b/scalable/advising/__init__.py
@@ -0,0 +1,8 @@
+"""Deterministic advising APIs for Phase 2."""
+
+from __future__ import annotations
+
+from .resources import ResourceAdvisor, ResourceRecommendation
+
+__all__ = ["ResourceAdvisor", "ResourceRecommendation"]
+
diff --git a/scalable/advising/resources.py b/scalable/advising/resources.py
new file mode 100644
index 0000000..c299b13
--- /dev/null
+++ b/scalable/advising/resources.py
@@ -0,0 +1,196 @@
+"""Deterministic, explainable resource recommendations from run telemetry."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+from dask.utils import parse_bytes
+
+from scalable.telemetry.collectors import iter_run_dirs, read_jsonl
+
+
+def _memory_to_bytes(value: str | None) -> int | None:
+    if value is None:
+        return None
+    try:
+        parsed = int(parse_bytes(value))
+    except Exception:
+        return None
+    if parsed <= 0:
+        return None
+    return parsed
+
+
+def _bytes_to_gib_string(value: int | None) -> str | None:
+    if value is None or value <= 0:
+        return None
+    gib = (value + (1024**3 - 1)) // (1024**3)
+    return f"{int(gib)}G"
+
+
+def _seconds_to_hhmmss(seconds: float | None) -> str | None:
+    if seconds is None or seconds <= 0:
+        return None
+    total = int(round(seconds))
+    hours = total // 3600
+    minutes = (total % 3600) // 60
+    secs = total % 60
+    return f"{hours:02d}:{minutes:02d}:{secs:02d}"
+
+
+@dataclass(frozen=True)
+class ResourceRecommendation:
+    """Explainable recommendation payload returned by :class:`ResourceAdvisor`."""
+
+    task: str
+    target: str | None
+    confidence: float
+    workers: dict[str, int]
+    resources: dict[str, dict[str, Any]]
+    evidence: dict[str, Any]
+
+
+class ResourceAdvisor:
+    """Heuristic advisor using historical quantiles from telemetry."""
+
+    def __init__(self, records: pd.DataFrame) -> None:
+        self._records = records.copy()
+
+    @classmethod
+    def from_history(cls, runs_dir: str | Path) -> ResourceAdvisor:
+        """Build advisor state from telemetry run directories."""
+        rows: list[dict[str, Any]] = []
+
+        for run_dir in iter_run_dirs(runs_dir):
+            run_json = run_dir / "run.json"
+            if not run_json.exists():
+                continue
+            run_meta = pd.read_json(run_json, typ="series")
+            run_id = str(run_meta.get("run_id", run_dir.name))
+            target_name = run_meta.get("target_name")
+
+            task_rows = read_jsonl(run_dir / "tasks.jsonl")
+            resource_rows = read_jsonl(run_dir / "resources.jsonl")
+
+            resources_by_task: dict[str, dict[str, Any]] = {}
+            for r in resource_rows:
+                if r.get("entity_type") != "task":
+                    continue
+                entity = str(r.get("entity_id", ""))
+                if not entity:
+                    continue
+                resources_by_task[entity] = r
+
+            for t in task_rows:
+                if t.get("state") not in {"succeeded", "failed", "cancelled"}:
+                    continue
+                task_id = str(t.get("task_id", ""))
+                if not task_id:
+                    continue
+
+                resources = resources_by_task.get(task_id, {})
+                rows.append(
+                    {
+                        "run_id": run_id,
+                        "target": target_name,
+                        "task_id": task_id,
+                        "task_name": t.get("task_name"),
+                        "component": t.get("component"),
+                        "state": t.get("state"),
+                        "duration_s": t.get("duration_s"),
+                        "requested_workers": resources.get("requested_workers"),
+                        "requested_cpus": resources.get("requested_cpus"),
+                        "requested_memory": resources.get("requested_memory"),
+                        "requested_memory_bytes": _memory_to_bytes(resources.get("requested_memory")),
+                        "requested_walltime": resources.get("requested_walltime"),
+                    }
+                )
+
+        frame = pd.DataFrame(rows)
+        return cls(frame)
+
+    def recommend(
+        self,
+        *,
+        task: str,
+        input_features: dict[str, Any] | None = None,
+        target: str | None = None,
+        confidence: float = 0.95,
+    ) -> ResourceRecommendation:
+        """Recommend workers/resources using confidence-indexed quantiles."""
+        _ = input_features  # reserved for Phase 5 learned models
+
+        q = min(max(float(confidence), 0.5), 0.99)
+        frame = self._records
+        if frame.empty:
+            return ResourceRecommendation(
+                task=task,
+                target=target,
+                confidence=q,
+                workers={task: 1},
+                resources={task: {"cpus": 1, "memory": None, "walltime": None}},
+                evidence={"records": 0, "reason": "no history"},
+            )
+
+        scoped = frame[frame["task_name"] == task]
+        if target is not None and not scoped.empty:
+            scoped_target = scoped[scoped["target"] == target]
+            if not scoped_target.empty:
+                scoped = scoped_target
+
+        if scoped.empty:
+            return ResourceRecommendation(
+                task=task,
+                target=target,
+                confidence=q,
+                workers={task: 1},
+                resources={task: {"cpus": 1, "memory": None, "walltime": None}},
+                evidence={"records": 0, "reason": "task not found in history"},
+            )
+
+        component = scoped["component"].dropna().iloc[-1] if scoped["component"].notna().any() else task
+        component = str(component)
+
+        workers_series = pd.to_numeric(scoped["requested_workers"], errors="coerce").dropna()
+        cpus_series = pd.to_numeric(scoped["requested_cpus"], errors="coerce").dropna()
+        duration_series = pd.to_numeric(scoped["duration_s"], errors="coerce").dropna()
+        mem_series = pd.to_numeric(scoped["requested_memory_bytes"], errors="coerce").dropna()
+
+        workers = int(max(1, round(float(workers_series.quantile(q))))) if not workers_series.empty else 1
+        cpus = int(max(1, round(float(cpus_series.quantile(q))))) if not cpus_series.empty else 1
+
+        memory_bytes = int(mem_series.quantile(q)) if not mem_series.empty else None
+        if memory_bytes is not None:
+            memory_bytes = int(memory_bytes * 1.10)
+        walltime_seconds = float(duration_series.quantile(q) * 1.20) if not duration_series.empty else None
+
+        memory = _bytes_to_gib_string(memory_bytes)
+        walltime = _seconds_to_hhmmss(walltime_seconds)
+
+        evidence = {
+            "records": int(len(scoped.index)),
+            "quantile": q,
+            "component": component,
+            "state_counts": scoped["state"].value_counts().to_dict(),
+        }
+
+        return ResourceRecommendation(
+            task=task,
+            target=target,
+            confidence=q,
+            workers={component: workers},
+            resources={
+                component: {
+                    "cpus": cpus,
+                    "memory": memory,
+                    "walltime": walltime,
+                }
+            },
+            evidence=evidence,
+        )
+
+
+__all__ = ["ResourceAdvisor", "ResourceRecommendation"]
diff --git a/scalable/caching.py b/scalable/caching.py
index f1555f6..b1e5aa5 100755
--- a/scalable/caching.py
+++ b/scalable/caching.py
@@ -2,6 +2,7 @@
 import hashlib
 import os
 import pickle
+import time
 import types
 import warnings
 from collections.abc import Callable
@@ -14,6 +15,7 @@
 from xxhash import xxh32
 
 from .common import logger, settings
+from .telemetry.runtime import emit_cache_event
 
 
 def _seed() -> int:
@@ -391,6 +393,7 @@ def inner(*args, **kwargs):
             key = hash(ObjectType(sorted(keys)))
             disk = _shared_cache(_cache_dir())
             ret = None
+            lookup_start = time.monotonic()
             if key in disk and not recompute:
                 value = disk.get(key)
                 if value is None:
@@ -413,7 +416,14 @@ def inner(*args, **kwargs):
                         )
                 else:
                     ret = stored_value
+                emit_cache_event(
+                    function_name=getattr(func, "__qualname__", func.__name__),
+                    key_digest=str(key),
+                    hit=True,
+                    duration_s=max(time.monotonic() - lookup_start, 0.0),
+                )
             if ret is None:
+                compute_start = time.monotonic()
                 ret = func(*args, **kwargs)
                 if store:
                     if return_type is None:
@@ -424,6 +434,12 @@ def inner(*args, **kwargs):
                         logger.warning(
                             "%s could not be added to cache.", func.__name__
                         )
+                emit_cache_event(
+                    function_name=getattr(func, "__qualname__", func.__name__),
+                    key_digest=str(key),
+                    hit=False,
+                    duration_s=max(time.monotonic() - compute_start, 0.0),
+                )
             return ret
 
         return func if void else inner
diff --git a/scalable/cli/__init__.py b/scalable/cli/__init__.py
index 8141e49..3b07c0d 100644
--- a/scalable/cli/__init__.py
+++ b/scalable/cli/__init__.py
@@ -1,11 +1,13 @@
-"""``scalable`` console-script CLI (v2.0.0 Phase 1).
+"""``scalable`` console-script CLI (v2.0.0 Phase 2).
 
-Phase 1 implements two subcommands -- ``scalable validate`` and
-``scalable plan --dry-run`` -- both of which operate purely on a manifest
-plus provider abstractions and never instantiate a scheduler.
+Implemented subcommands:
+
+* ``scalable validate``
+* ``scalable plan --dry-run``
+* ``scalable report``
 
 The remaining subcommand namespace (``run``, ``diagnose``, ``explain``,
-``init-component``, ``compose``, ``report``) is registered as Phase 1 stubs
+``init-component``, ``compose``) is registered as explicit stubs
 that print a phase-pointer message on invocation. This locks the UX
 namespace early so third-party CLIs don't collide with future Scalable
 verbs and so Phases 2-5 only fill behaviour rather than surface.
diff --git a/scalable/cli/cmd_report.py b/scalable/cli/cmd_report.py
new file mode 100644
index 0000000..9bf89df
--- /dev/null
+++ b/scalable/cli/cmd_report.py
@@ -0,0 +1,47 @@
+"""Implementation for ``scalable report``."""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+from scalable.telemetry.collectors import render_text_report, resolve_run_dir, summarize_run
+
+
+def run_report(
+    *,
+    runs_dir: str,
+    run_id: str | None,
+    latest: bool,
+    fmt: str,
+    output: str | None,
+) -> int:
+    """Load telemetry for one run and emit a report payload."""
+    try:
+        run_dir = resolve_run_dir(runs_dir=runs_dir, run_id=run_id, latest=latest)
+    except (FileNotFoundError, ValueError) as exc:
+        print(f"report failed: {exc}", file=sys.stderr)
+        return 1
+
+    summary = summarize_run(run_dir)
+
+    if fmt == "json":
+        rendered = json.dumps(summary, indent=2, sort_keys=True)
+    elif fmt == "text":
+        rendered = render_text_report(summary)
+    else:
+        print(f"report failed: unsupported format {fmt!r}", file=sys.stderr)
+        return 2
+
+    if output:
+        output_path = Path(output)
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+        output_path.write_text(rendered + "\n", encoding="utf-8")
+
+    print(rendered, file=sys.stdout)
+    return 0
+
+
+__all__ = ["run_report"]
+
diff --git a/scalable/cli/main.py b/scalable/cli/main.py
index 11b8d5c..50c7bc7 100644
--- a/scalable/cli/main.py
+++ b/scalable/cli/main.py
@@ -1,12 +1,13 @@
 """``scalable`` console entry-point dispatcher.
 
-Phase 1 ships two implemented subcommands:
+Implemented subcommands:
 
 * ``scalable validate``
 * ``scalable plan --dry-run``
+* ``scalable report``
 
-The namespace for later-phase verbs (``run``, ``diagnose``, ``explain``,
-``init-component``, ``compose``, ``report``) is reserved as explicit stubs.
+The remaining namespace for later-phase verbs (``run``, ``diagnose``,
+``explain``, ``init-component``, ``compose``) is reserved as explicit stubs.
 """
 
 from __future__ import annotations
@@ -17,6 +18,7 @@
 from scalable.common import settings
 
 from .cmd_plan import run_plan
+from .cmd_report import run_report
 from .cmd_validate import run_validate
 
 _STUB_COMMANDS: dict[str, str] = {
@@ -25,7 +27,6 @@
     "explain": "Phase 4",
     "init-component": "Phase 4",
     "compose": "Phase 4",
-    "report": "Phase 2",
 }
 
 
@@ -42,6 +43,16 @@ def _handle_plan(args: argparse.Namespace) -> int:
     )
 
 
+def _handle_report(args: argparse.Namespace) -> int:
+    return run_report(
+        runs_dir=args.runs_dir,
+        run_id=args.run_id,
+        latest=bool(args.latest),
+        fmt=args.format,
+        output=args.output,
+    )
+
+
 def _make_stub_handler(command: str, phase: str):
     def _handler(_: argparse.Namespace) -> int:
         print(
@@ -101,6 +112,38 @@ def _build_parser() -> argparse.ArgumentParser:
     )
     plan_parser.set_defaults(handler=_handle_plan)
 
+    report_parser = subparsers.add_parser(
+        "report",
+        help="Summarize telemetry for a completed or running session",
+    )
+    report_parser.add_argument(
+        "--runs-dir",
+        default=settings.runs_dir,
+        help="Runs directory (default: SCALABLE_RUNS_DIR or ./.scalable/runs)",
+    )
+    report_parser.add_argument(
+        "--run-id",
+        default=None,
+        help="Explicit run directory name (e.g. run-20260519T120000Z-...)",
+    )
+    report_parser.add_argument(
+        "--latest",
+        action="store_true",
+        help="Select the most recent run in --runs-dir",
+    )
+    report_parser.add_argument(
+        "--format",
+        choices=["text", "json"],
+        default="text",
+        help="Output format",
+    )
+    report_parser.add_argument(
+        "--output",
+        default=None,
+        help="Optional output file path",
+    )
+    report_parser.set_defaults(handler=_handle_report)
+
     for command, phase in _STUB_COMMANDS.items():
         stub_parser = subparsers.add_parser(command, help=f"Reserved command (planned for {phase})")
         stub_parser.set_defaults(handler=_make_stub_handler(command, phase))
diff --git a/scalable/client.py b/scalable/client.py
index 57befef..e4b5b0a 100755
--- a/scalable/client.py
+++ b/scalable/client.py
@@ -2,6 +2,9 @@
 
 from __future__ import annotations
 
+import functools
+import time
+import uuid
 from collections.abc import Iterable
 from typing import Any
 
@@ -10,6 +13,7 @@
 from distributed.diagnostics.plugin import SchedulerPlugin
 
 from .slurm import SlurmCluster
+from .telemetry.runtime import task_context
 
 
 class SlurmSchedulerPlugin(SchedulerPlugin):
@@ -39,9 +43,75 @@ class ScalableClient(Client):
     def __init__(self, cluster: Any, *args: Any, **kwargs: Any) -> None:
         """Initialize a client bound to an existing cluster/scheduler."""
         super().__init__(address=cluster, *args, **kwargs)
+        self._telemetry_store = None
         if isinstance(cluster, SlurmCluster):
             self.register_scheduler_plugin(SlurmSchedulerPlugin(None))
 
+    def set_telemetry_store(self, store: Any) -> None:
+        """Attach an active telemetry store for task lifecycle instrumentation."""
+        self._telemetry_store = store
+
+    def _record_future(
+        self,
+        *,
+        future: Any,
+        task_id: str,
+        task_name: str,
+        component: str | None,
+        tag: str | None,
+        function_name: str,
+        requested_workers: int,
+        submitted_at: float,
+    ) -> None:
+        store = self._telemetry_store
+        if store is None:
+            return
+
+        store.record_task_submission(
+            task_id=task_id,
+            task_name=task_name,
+            component=component,
+            tag=tag,
+            function_name=function_name,
+            requested_workers=requested_workers,
+        )
+
+        def _on_done(done_future: Any) -> None:
+            state = "succeeded"
+            worker = getattr(done_future, "key", None)
+            error_type = None
+            error_message = None
+
+            try:
+                if done_future.cancelled():
+                    state = "cancelled"
+                else:
+                    exc = done_future.exception()
+                    if exc is not None:
+                        state = "failed"
+                        error_type = type(exc).__name__
+                        error_message = str(exc)
+            except Exception as callback_exc:  # pragma: no cover - defensive
+                state = "failed"
+                error_type = type(callback_exc).__name__
+                error_message = str(callback_exc)
+
+            _ = submitted_at
+            store.record_task_result(
+                task_id=task_id,
+                task_name=task_name,
+                component=component,
+                tag=tag,
+                function_name=function_name,
+                requested_workers=requested_workers,
+                state=state,
+                worker=worker,
+                error_type=error_type,
+                error_message=error_message,
+            )
+
+        future.add_done_callback(_on_done)
+
     def submit(
         self,
         func: Any,
@@ -88,7 +158,29 @@ def submit(
         resources = None
         if tag is not None:
             resources = {tag: n}
-        return super().submit(func, resources=resources, *args, **kwargs)
+
+        task_name = str(kwargs.pop("_scalable_task_name", getattr(func, "__name__", "task")))
+        function_name = getattr(func, "__qualname__", getattr(func, "__name__", repr(func)))
+
+        @functools.wraps(func)
+        def _wrapped(*wrapped_args: Any, **wrapped_kwargs: Any) -> Any:
+            with task_context(task_name=task_name, component=tag, tag=tag):
+                return func(*wrapped_args, **wrapped_kwargs)
+
+        submitted_at = time.monotonic()
+        future = super().submit(_wrapped, resources=resources, *args, **kwargs)
+
+        self._record_future(
+            future=future,
+            task_id=uuid.uuid4().hex,
+            task_name=task_name,
+            component=tag,
+            tag=tag,
+            function_name=function_name,
+            requested_workers=n,
+            submitted_at=submitted_at,
+        )
+        return future
     
     def cancel(self, futures: Any, *args: Any, **kwargs: Any) -> Any:
         """
@@ -168,7 +260,29 @@ def map(
         resources = None
         if tag is not None:
             resources = {tag: n}
-        return super().map(func, *parameters, resources=resources, **kwargs)
+        base_task_name = str(kwargs.pop("_scalable_task_name", getattr(func, "__name__", "task")))
+        function_name = getattr(func, "__qualname__", getattr(func, "__name__", repr(func)))
+
+        @functools.wraps(func)
+        def _wrapped(*wrapped_args: Any, **wrapped_kwargs: Any) -> Any:
+            with task_context(task_name=base_task_name, component=tag, tag=tag):
+                return func(*wrapped_args, **wrapped_kwargs)
+
+        submitted_at = time.monotonic()
+        futures = super().map(_wrapped, *parameters, resources=resources, **kwargs)
+
+        for index, future in enumerate(futures):
+            self._record_future(
+                future=future,
+                task_id=uuid.uuid4().hex,
+                task_name=f"{base_task_name}[{index}]",
+                component=tag,
+                tag=tag,
+                function_name=function_name,
+                requested_workers=n,
+                submitted_at=submitted_at,
+            )
+        return futures
     
     def get_versions(
         self, check: bool = False, packages: list[str] | None = None
diff --git a/scalable/common.py b/scalable/common.py
index 5847801..be246ad 100755
--- a/scalable/common.py
+++ b/scalable/common.py
@@ -31,6 +31,7 @@
 DEFAULT_SEED: int = 987654321
 DEFAULT_CACHE_DIR: str = "./cache"
 DEFAULT_MANIFEST_PATH: str = "./scalable.yaml"
+DEFAULT_RUNS_DIR: str = "./.scalable/runs"
 
 
 @dataclass
@@ -56,6 +57,15 @@ class Settings:
         default_factory=lambda: os.environ.get("SCALABLE_MANIFEST", DEFAULT_MANIFEST_PATH)
     )
     target: str | None = field(default_factory=lambda: os.environ.get("SCALABLE_TARGET"))
+    runs_dir: str = field(
+        default_factory=lambda: os.environ.get("SCALABLE_RUNS_DIR", DEFAULT_RUNS_DIR)
+    )
+    telemetry_enabled: bool = field(
+        default_factory=lambda: bool(int(os.environ.get("SCALABLE_TELEMETRY", "1")))
+    )
+    telemetry_parquet: bool = field(
+        default_factory=lambda: bool(int(os.environ.get("SCALABLE_TELEMETRY_PARQUET", "0")))
+    )
 
 
 #: Process-wide settings singleton. Mutating attributes on this instance
diff --git a/scalable/providers/local.py b/scalable/providers/local.py
index fc919e6..cfaa2f4 100644
--- a/scalable/providers/local.py
+++ b/scalable/providers/local.py
@@ -8,6 +8,7 @@
 
 from scalable.client import ScalableClient
 from scalable.manifest.validate import ValidationIssue, ValidationReport, validate_manifest
+from scalable.telemetry.runtime import emit_worker_event
 
 from .base import ClusterHandle, DeploymentProvider, DeploymentSpec, ScalePlan
 
@@ -133,6 +134,16 @@ def build_cluster(self, spec: DeploymentSpec) -> ClusterHandle:
             resources=worker_resources,
         )
 
+        emit_worker_event(
+            provider=self.name,
+            state="cluster_created",
+            details={
+                "n_workers": n_workers,
+                "threads_per_worker": threads_per_worker,
+                "processes": processes,
+            },
+        )
+
         def _client_factory() -> ScalableClient:
             return ScalableClient(cluster)
 
@@ -157,11 +168,20 @@ def scale(self, cluster: ClusterHandle, plan: ScalePlan) -> None:
         if plan.workers_by_tag:
             target_workers = sum(max(int(n), 0) for n in plan.workers_by_tag.values())
             backend.scale(target_workers)
+            emit_worker_event(
+                provider=self.name,
+                state="scaled",
+                details={
+                    "target_workers": target_workers,
+                    "workers_by_tag": dict(plan.workers_by_tag),
+                },
+            )
 
     def close(self, cluster: ClusterHandle) -> None:
         backend = cluster.backend
         if hasattr(backend, "close"):
             backend.close()
+        emit_worker_event(provider=self.name, state="cluster_closed", details={})
 
 
 def _debug_options_snapshot(options: dict[str, Any]) -> dict[str, Any]:
diff --git a/scalable/providers/slurm.py b/scalable/providers/slurm.py
index ccc4bc0..bcf54bb 100644
--- a/scalable/providers/slurm.py
+++ b/scalable/providers/slurm.py
@@ -17,6 +17,7 @@
 )
 from scalable.manifest.validate import ValidationIssue, ValidationReport, validate_manifest
 from scalable.slurm import SlurmCluster
+from scalable.telemetry.runtime import emit_worker_event
 
 from .base import ClusterHandle, DeploymentProvider, DeploymentSpec, ScalePlan
 
@@ -123,6 +124,14 @@ def _client_factory():
 
             return ScalableClient(cluster)
 
+        emit_worker_event(
+            provider=self.name,
+            state="cluster_created",
+            details={
+                "cluster_kwargs": {k: v for k, v in cluster_kwargs.items() if v is not None},
+            },
+        )
+
         return ClusterHandle(
             backend=cluster,
             client_factory=_client_factory,
@@ -142,11 +151,18 @@ def scale(self, cluster: ClusterHandle, plan: ScalePlan) -> None:
             n = int(count)
             if n > 0:
                 backend.add_workers(tag=tag, n=n)
+                emit_worker_event(
+                    provider=self.name,
+                    state="add_workers",
+                    component=tag,
+                    details={"n": n},
+                )
 
     def close(self, cluster: ClusterHandle) -> None:
         backend = cluster.backend
         if hasattr(backend, "close"):
             backend.close()
+        emit_worker_event(provider=self.name, state="cluster_closed", details={})
 
 
 def _require_type(
diff --git a/scalable/session/session.py b/scalable/session/session.py
index cb44540..cacd46b 100644
--- a/scalable/session/session.py
+++ b/scalable/session/session.py
@@ -14,6 +14,8 @@
 from scalable.planning.dryrun import DryRunPlan, build_dry_run_plan
 from scalable.providers.base import ClusterHandle, DeploymentSpec
 from scalable.providers.registry import get_provider, iter_provider_names
+from scalable.telemetry.runtime import reset_active_store, set_active_store
+from scalable.telemetry.store import TelemetryStore
 
 __all__ = ["ScalableSession"]
 
@@ -29,6 +31,8 @@ class ScalableSession:
     _provider: Any = None
     _cluster: ClusterHandle | None = None
     _client: ScalableClient | None = None
+    _telemetry: TelemetryStore | None = None
+    _telemetry_token: Any = None
 
     @classmethod
     def from_yaml(
@@ -99,25 +103,117 @@ def start(self, plan: DryRunPlan | None = None) -> ScalableClient:
                 f"plan target {plan.target_name!r} does not match session target {self.target_name!r}"
             )
 
-        self._provider = get_provider(self.spec.provider_name)
-        self._cluster = self._provider.build_cluster(self.spec)
-        self._provider.scale(self._cluster, plan.scale_plan)
-        self._client = self._cluster.client_factory()
-        return self._client
+        if settings.telemetry_enabled:
+            self._telemetry = TelemetryStore.create(
+                runs_dir=settings.runs_dir,
+                manifest=self.manifest,
+                spec=self.spec,
+                plan=plan,
+                telemetry_parquet=settings.telemetry_parquet,
+            )
+            self._telemetry_token = set_active_store(self._telemetry)
+
+        try:
+            self._provider = get_provider(self.spec.provider_name)
+            self._cluster = self._provider.build_cluster(self.spec)
+            self._provider.scale(self._cluster, plan.scale_plan)
+            self._client = self._cluster.client_factory()
+            if self._telemetry is not None:
+                self._client.set_telemetry_store(self._telemetry)
+            return self._client
+        except Exception as exc:
+            if self._telemetry is not None:
+                self._telemetry.record_failure(
+                    failure_class=type(exc).__name__,
+                    message=str(exc),
+                    details={"phase": "session.start"},
+                )
+                self._telemetry.close(status="failed")
+                self._telemetry = None
+            if self._telemetry_token is not None:
+                reset_active_store(self._telemetry_token)
+                self._telemetry_token = None
+            raise
 
     def close(self) -> None:
+        close_error: Exception | None = None
+        status = "completed"
+
         if self._client is not None:
-            self._client.close()
-            self._client = None
+            try:
+                self._client.close()
+            except Exception as exc:  # pragma: no cover - defensive
+                close_error = exc
+                status = "failed"
+                if self._telemetry is not None:
+                    self._telemetry.record_failure(
+                        failure_class=type(exc).__name__,
+                        message=str(exc),
+                        details={"phase": "session.close.client"},
+                    )
+            finally:
+                self._client = None
 
         if self._cluster is not None and self._provider is not None:
-            self._provider.close(self._cluster)
-            self._cluster = None
+            try:
+                self._provider.close(self._cluster)
+            except Exception as exc:  # pragma: no cover - defensive
+                close_error = exc
+                status = "failed"
+                if self._telemetry is not None:
+                    self._telemetry.record_failure(
+                        failure_class=type(exc).__name__,
+                        message=str(exc),
+                        details={"phase": "session.close.provider"},
+                    )
+            finally:
+                self._cluster = None
+
+        if self._telemetry is not None:
+            self._telemetry.close(status=status)
+            self._telemetry = None
+
+        if self._telemetry_token is not None:
+            reset_active_store(self._telemetry_token)
+            self._telemetry_token = None
+
+        if close_error is not None:
+            raise close_error
+
+    def record_artifact(
+        self,
+        *,
+        task_name: str,
+        artifact_name: str,
+        location: str,
+        component: str | None = None,
+        kind: str | None = None,
+        size_bytes: int | None = None,
+        digest: str | None = None,
+    ) -> None:
+        """Record artifact metadata for the active run, if telemetry is enabled."""
+        if self._telemetry is None:
+            return
+        self._telemetry.record_artifact(
+            task_name=task_name,
+            component=component,
+            artifact_name=artifact_name,
+            location=location,
+            kind=kind,
+            size_bytes=size_bytes,
+            digest=digest,
+        )
 
     def __enter__(self) -> ScalableClient:
         return self.start()
 
     def __exit__(self, exc_type, exc, tb) -> None:
+        if exc is not None and self._telemetry is not None:
+            self._telemetry.record_failure(
+                failure_class=type(exc).__name__,
+                message=str(exc),
+                details={"phase": "session.context"},
+            )
         self.close()
 
 
diff --git a/scalable/telemetry/__init__.py b/scalable/telemetry/__init__.py
new file mode 100644
index 0000000..0943e2d
--- /dev/null
+++ b/scalable/telemetry/__init__.py
@@ -0,0 +1,56 @@
+"""Phase 2 telemetry package public exports."""
+
+from __future__ import annotations
+
+from .collectors import (
+    iter_run_dirs,
+    latest_run_dir,
+    read_jsonl,
+    render_text_report,
+    resolve_run_dir,
+    summarize_run,
+)
+from .events import (
+    ArtifactEvent,
+    CacheEvent,
+    FailureEvent,
+    ResourceEvent,
+    RunMetadata,
+    TaskEvent,
+    WorkerEvent,
+)
+from .runtime import (
+    emit_cache_event,
+    emit_worker_event,
+    get_active_store,
+    get_task_context,
+    reset_active_store,
+    set_active_store,
+    task_context,
+)
+from .store import TelemetryStore
+
+__all__ = [
+    "ArtifactEvent",
+    "CacheEvent",
+    "FailureEvent",
+    "ResourceEvent",
+    "RunMetadata",
+    "TaskEvent",
+    "TelemetryStore",
+    "WorkerEvent",
+    "emit_cache_event",
+    "emit_worker_event",
+    "get_active_store",
+    "get_task_context",
+    "iter_run_dirs",
+    "latest_run_dir",
+    "read_jsonl",
+    "render_text_report",
+    "reset_active_store",
+    "resolve_run_dir",
+    "set_active_store",
+    "summarize_run",
+    "task_context",
+]
+
diff --git a/scalable/telemetry/collectors.py b/scalable/telemetry/collectors.py
new file mode 100644
index 0000000..cc32ef5
--- /dev/null
+++ b/scalable/telemetry/collectors.py
@@ -0,0 +1,174 @@
+"""Telemetry run loading and summary aggregation helpers."""
+
+from __future__ import annotations
+
+import json
+from collections import Counter
+from pathlib import Path
+from typing import Any
+
+
+def read_jsonl(path: Path) -> list[dict[str, Any]]:
+    """Read a newline-delimited JSON file. Missing files return an empty list."""
+    if not path.exists():
+        return []
+    rows: list[dict[str, Any]] = []
+    for line in path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line:
+            continue
+        rows.append(json.loads(line))
+    return rows
+
+
+def iter_run_dirs(runs_dir: str | Path) -> list[Path]:
+    """Return existing run directories in lexicographic order."""
+    root = Path(runs_dir)
+    if not root.exists():
+        return []
+    return sorted([p for p in root.iterdir() if p.is_dir() and p.name.startswith("run-")])
+
+
+def latest_run_dir(runs_dir: str | Path) -> Path:
+    """Return the most recent run directory by lexicographic order."""
+    runs = iter_run_dirs(runs_dir)
+    if not runs:
+        raise FileNotFoundError(f"no run directories found in {Path(runs_dir)!s}")
+    return runs[-1]
+
+
+def resolve_run_dir(
+    *,
+    runs_dir: str | Path,
+    run_id: str | None = None,
+    latest: bool = False,
+) -> Path:
+    """Resolve a run directory from explicit id or latest selection."""
+    root = Path(runs_dir)
+    if run_id is not None:
+        candidate = root / run_id
+        if not candidate.exists() or not candidate.is_dir():
+            raise FileNotFoundError(f"run id {run_id!r} does not exist in {root!s}")
+        return candidate
+    if latest:
+        return latest_run_dir(root)
+    raise ValueError("must provide run_id or latest=True")
+
+
+def summarize_run(run_dir: str | Path) -> dict[str, Any]:
+    """Build a deterministic summary payload for one run directory."""
+    run_path = Path(run_dir)
+    run_meta = {}
+    run_json = run_path / "run.json"
+    if run_json.exists():
+        run_meta = json.loads(run_json.read_text(encoding="utf-8"))
+
+    tasks = read_jsonl(run_path / "tasks.jsonl")
+    resources = read_jsonl(run_path / "resources.jsonl")
+    workers = read_jsonl(run_path / "workers.jsonl")
+    failures = read_jsonl(run_path / "failures.jsonl")
+    caches = read_jsonl(run_path / "cache.jsonl")
+    artifacts = read_jsonl(run_path / "artifacts.jsonl")
+
+    final_state_by_task: dict[str, str] = {}
+    duration_values: list[float] = []
+    for row in tasks:
+        task_id = str(row.get("task_id", ""))
+        state = str(row.get("state", "unknown"))
+        if task_id:
+            final_state_by_task[task_id] = state
+        duration = row.get("duration_s")
+        if isinstance(duration, (int, float)) and duration >= 0:
+            duration_values.append(float(duration))
+
+    state_counter = Counter(final_state_by_task.values())
+    failure_counter = Counter(str(f.get("failure_class", "unknown")) for f in failures)
+    cache_hits = sum(1 for c in caches if bool(c.get("hit")))
+    cache_misses = sum(1 for c in caches if not bool(c.get("hit")))
+
+    requested_cpus: list[int] = []
+    for row in resources:
+        value = row.get("requested_cpus")
+        if isinstance(value, int):
+            requested_cpus.append(value)
+
+    return {
+        "run": run_meta,
+        "counts": {
+            "task_events": len(tasks),
+            "resource_events": len(resources),
+            "worker_events": len(workers),
+            "failure_events": len(failures),
+            "cache_events": len(caches),
+            "artifact_events": len(artifacts),
+            "tasks_succeeded": state_counter.get("succeeded", 0),
+            "tasks_failed": state_counter.get("failed", 0),
+            "tasks_cancelled": state_counter.get("cancelled", 0),
+        },
+        "timing": {
+            "task_duration_count": len(duration_values),
+            "task_duration_total_s": round(sum(duration_values), 6),
+            "task_duration_avg_s": round(sum(duration_values) / len(duration_values), 6)
+            if duration_values
+            else None,
+        },
+        "cache": {
+            "hits": cache_hits,
+            "misses": cache_misses,
+            "hit_ratio": round(cache_hits / (cache_hits + cache_misses), 6)
+            if (cache_hits + cache_misses) > 0
+            else None,
+        },
+        "resources": {
+            "requested_cpu_min": min(requested_cpus) if requested_cpus else None,
+            "requested_cpu_max": max(requested_cpus) if requested_cpus else None,
+            "requested_cpu_avg": round(sum(requested_cpus) / len(requested_cpus), 6)
+            if requested_cpus
+            else None,
+        },
+        "failures": {
+            "classes": dict(sorted(failure_counter.items())),
+        },
+    }
+
+
+def render_text_report(summary: dict[str, Any]) -> str:
+    """Render a concise human-readable report."""
+    run = summary.get("run", {})
+    counts = summary.get("counts", {})
+    timing = summary.get("timing", {})
+    cache = summary.get("cache", {})
+
+    lines = [
+        f"run_id: {run.get('run_id', 'unknown')}",
+        f"project: {run.get('project_name', 'unknown')}",
+        f"target/provider: {run.get('target_name', 'unknown')}/{run.get('provider_name', 'unknown')}",
+        f"status: {run.get('status', 'unknown')}",
+        "",
+        "tasks:",
+        f"  succeeded: {counts.get('tasks_succeeded', 0)}",
+        f"  failed: {counts.get('tasks_failed', 0)}",
+        f"  cancelled: {counts.get('tasks_cancelled', 0)}",
+        f"  event_rows: {counts.get('task_events', 0)}",
+        "",
+        "timing:",
+        f"  total_s: {timing.get('task_duration_total_s')}",
+        f"  avg_s: {timing.get('task_duration_avg_s')}",
+        "",
+        "cache:",
+        f"  hits: {cache.get('hits', 0)}",
+        f"  misses: {cache.get('misses', 0)}",
+        f"  hit_ratio: {cache.get('hit_ratio')}",
+    ]
+    return "\n".join(lines)
+
+
+__all__ = [
+    "iter_run_dirs",
+    "latest_run_dir",
+    "read_jsonl",
+    "render_text_report",
+    "resolve_run_dir",
+    "summarize_run",
+]
+
diff --git a/scalable/telemetry/events.py b/scalable/telemetry/events.py
new file mode 100644
index 0000000..1dbb2a5
--- /dev/null
+++ b/scalable/telemetry/events.py
@@ -0,0 +1,169 @@
+"""Typed telemetry event schema records for Phase 2."""
+
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass, field
+from datetime import UTC, datetime
+from typing import Any
+
+SCHEMA_VERSION: int = 1
+
+
+def utcnow_iso() -> str:
+    """Return a UTC timestamp in stable ISO-8601 form."""
+    return datetime.now(tz=UTC).replace(microsecond=0).isoformat().replace("+00:00", "Z")
+
+
+@dataclass(frozen=True)
+class RunMetadata:
+    """Top-level run metadata persisted to ``run.json``."""
+
+    run_id: str
+    project_name: str
+    target_name: str
+    provider_name: str
+    manifest_lock: str
+    source_manifest_path: str | None
+    started_at: str = field(default_factory=utcnow_iso)
+    finished_at: str | None = None
+    status: str = "running"
+    schema_version: int = SCHEMA_VERSION
+
+    def to_dict(self) -> dict[str, Any]:
+        return asdict(self)
+
+
+@dataclass(frozen=True)
+class TaskEvent:
+    """Task lifecycle event record."""
+
+    run_id: str
+    task_id: str
+    task_name: str
+    component: str | None
+    tag: str | None
+    state: str
+    function_name: str
+    requested_workers: int
+    timestamp: str = field(default_factory=utcnow_iso)
+    duration_s: float | None = None
+    worker: str | None = None
+    error_type: str | None = None
+    error_message: str | None = None
+    event_type: str = "task"
+    schema_version: int = SCHEMA_VERSION
+
+    def to_dict(self) -> dict[str, Any]:
+        return asdict(self)
+
+
+@dataclass(frozen=True)
+class ResourceEvent:
+    """Resource request/observation event record."""
+
+    run_id: str
+    entity_type: str
+    entity_id: str
+    component: str | None
+    provider: str
+    timestamp: str = field(default_factory=utcnow_iso)
+    requested_cpus: int | None = None
+    requested_memory: str | None = None
+    requested_walltime: str | None = None
+    requested_workers: int | None = None
+    observed_cpu: float | None = None
+    observed_memory_gb: float | None = None
+    event_type: str = "resource"
+    schema_version: int = SCHEMA_VERSION
+
+    def to_dict(self) -> dict[str, Any]:
+        return asdict(self)
+
+
+@dataclass(frozen=True)
+class WorkerEvent:
+    """Worker/cluster lifecycle event record."""
+
+    run_id: str
+    provider: str
+    state: str
+    timestamp: str = field(default_factory=utcnow_iso)
+    worker_id: str | None = None
+    component: str | None = None
+    details: dict[str, Any] = field(default_factory=dict)
+    event_type: str = "worker"
+    schema_version: int = SCHEMA_VERSION
+
+    def to_dict(self) -> dict[str, Any]:
+        return asdict(self)
+
+
+@dataclass(frozen=True)
+class FailureEvent:
+    """Failure classification event record."""
+
+    run_id: str
+    failure_class: str
+    message: str
+    timestamp: str = field(default_factory=utcnow_iso)
+    provider: str | None = None
+    task_id: str | None = None
+    details: dict[str, Any] = field(default_factory=dict)
+    event_type: str = "failure"
+    schema_version: int = SCHEMA_VERSION
+
+    def to_dict(self) -> dict[str, Any]:
+        return asdict(self)
+
+
+@dataclass(frozen=True)
+class CacheEvent:
+    """Cache hit/miss event record."""
+
+    run_id: str
+    function_name: str
+    key_digest: str
+    hit: bool
+    timestamp: str = field(default_factory=utcnow_iso)
+    duration_s: float | None = None
+    task_name: str | None = None
+    component: str | None = None
+    tag: str | None = None
+    event_type: str = "cache"
+    schema_version: int = SCHEMA_VERSION
+
+    def to_dict(self) -> dict[str, Any]:
+        return asdict(self)
+
+
+@dataclass(frozen=True)
+class ArtifactEvent:
+    """Artifact metadata event record."""
+
+    run_id: str
+    task_name: str
+    component: str | None
+    artifact_name: str
+    location: str
+    timestamp: str = field(default_factory=utcnow_iso)
+    kind: str | None = None
+    size_bytes: int | None = None
+    digest: str | None = None
+    event_type: str = "artifact"
+    schema_version: int = SCHEMA_VERSION
+
+    def to_dict(self) -> dict[str, Any]:
+        return asdict(self)
+
+
+__all__ = [
+    "ArtifactEvent",
+    "CacheEvent",
+    "FailureEvent",
+    "ResourceEvent",
+    "RunMetadata",
+    "SCHEMA_VERSION",
+    "TaskEvent",
+    "WorkerEvent",
+    "utcnow_iso",
+]
diff --git a/scalable/telemetry/runtime.py b/scalable/telemetry/runtime.py
new file mode 100644
index 0000000..99d93c7
--- /dev/null
+++ b/scalable/telemetry/runtime.py
@@ -0,0 +1,112 @@
+"""Runtime telemetry context plumbing for session, client, and caching hooks."""
+
+from __future__ import annotations
+
+from contextlib import contextmanager
+from contextvars import ContextVar, Token
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from .store import TelemetryStore
+
+
+_ACTIVE_STORE: ContextVar[TelemetryStore | None] = ContextVar(
+    "scalable_active_telemetry_store", default=None
+)
+_TASK_CONTEXT: ContextVar[dict[str, str | None] | None] = ContextVar(
+    "scalable_task_context", default=None
+)
+_GLOBAL_ACTIVE_STORE: TelemetryStore | None = None
+
+
+def set_active_store(store: TelemetryStore | None) -> Token[TelemetryStore | None]:
+    """Set process-local active telemetry store and return its token."""
+    global _GLOBAL_ACTIVE_STORE
+    _GLOBAL_ACTIVE_STORE = store
+    return _ACTIVE_STORE.set(store)
+
+
+def reset_active_store(token: Token[TelemetryStore | None]) -> None:
+    """Reset active telemetry store to previous value."""
+    global _GLOBAL_ACTIVE_STORE
+    _ACTIVE_STORE.reset(token)
+    _GLOBAL_ACTIVE_STORE = _ACTIVE_STORE.get()
+
+
+def get_active_store() -> TelemetryStore | None:
+    """Return the currently active telemetry store, if any."""
+    scoped = _ACTIVE_STORE.get()
+    if scoped is not None:
+        return scoped
+    return _GLOBAL_ACTIVE_STORE
+
+
+@contextmanager
+def task_context(
+    *,
+    task_name: str | None,
+    component: str | None,
+    tag: str | None,
+):
+    """Temporarily bind task execution context for cache and artifact hooks."""
+    token = _TASK_CONTEXT.set(
+        {
+            "task_name": task_name,
+            "component": component,
+            "tag": tag,
+        }
+    )
+    try:
+        yield
+    finally:
+        _TASK_CONTEXT.reset(token)
+
+
+def get_task_context() -> dict[str, str | None] | None:
+    """Get active task context for the current execution context."""
+    value = _TASK_CONTEXT.get()
+    if value is None:
+        return None
+    return dict(value)
+
+
+def emit_cache_event(*, function_name: str, key_digest: str, hit: bool, duration_s: float) -> None:
+    """Record a cache event through the active telemetry store, if configured."""
+    store = get_active_store()
+    if store is None:
+        return
+
+    context = get_task_context() or {}
+    store.record_cache_event(
+        function_name=function_name,
+        key_digest=key_digest,
+        hit=hit,
+        duration_s=duration_s,
+        task_name=context.get("task_name"),
+        component=context.get("component"),
+        tag=context.get("tag"),
+    )
+
+
+def emit_worker_event(*, provider: str, state: str, component: str | None = None, details: dict[str, Any] | None = None) -> None:
+    """Record provider worker/cluster events via the active telemetry store."""
+    store = get_active_store()
+    if store is None:
+        return
+    store.record_worker_event(
+        provider=provider,
+        state=state,
+        component=component,
+        details=details or {},
+    )
+
+
+__all__ = [
+    "emit_cache_event",
+    "emit_worker_event",
+    "get_active_store",
+    "get_task_context",
+    "reset_active_store",
+    "set_active_store",
+    "task_context",
+]
diff --git a/scalable/telemetry/store.py b/scalable/telemetry/store.py
new file mode 100644
index 0000000..4d1431b
--- /dev/null
+++ b/scalable/telemetry/store.py
@@ -0,0 +1,396 @@
+"""Run-scoped telemetry persistence primitives."""
+
+from __future__ import annotations
+
+import json
+import re
+import threading
+import time
+import uuid
+from dataclasses import replace
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+import yaml
+
+from scalable.manifest.schema import ManifestModel
+from scalable.planning.dryrun import DryRunPlan
+from scalable.providers.base import DeploymentSpec
+
+from .collectors import summarize_run
+from .events import (
+    ArtifactEvent,
+    CacheEvent,
+    FailureEvent,
+    ResourceEvent,
+    RunMetadata,
+    TaskEvent,
+    WorkerEvent,
+    utcnow_iso,
+)
+
+_PROJECT_RE = re.compile(r"[^a-zA-Z0-9._-]+")
+
+
+def build_run_id(project_name: str) -> str:
+    """Build a deterministic-format run id with UTC timestamp and random suffix."""
+    stamp = utcnow_iso().replace("-", "").replace(":", "")
+    stamp = stamp.replace("T", "T").replace("Z", "Z")
+    safe_project = _PROJECT_RE.sub("-", project_name).strip("-") or "project"
+    short = uuid.uuid4().hex[:8]
+    return f"run-{stamp}-{safe_project}-{short}"
+
+
+class TelemetryStore:
+    """Persist run telemetry as JSONL records under one run directory."""
+
+    _TASKS_FILE = "tasks.jsonl"
+    _RESOURCES_FILE = "resources.jsonl"
+    _WORKERS_FILE = "workers.jsonl"
+    _FAILURES_FILE = "failures.jsonl"
+    _CACHE_FILE = "cache.jsonl"
+    _ARTIFACTS_FILE = "artifacts.jsonl"
+
+    def __init__(
+        self,
+        *,
+        run_dir: Path,
+        metadata: RunMetadata,
+        component_defaults: dict[str, dict[str, Any]],
+        provider_name: str,
+        target_walltime: str | None,
+        telemetry_parquet: bool,
+    ) -> None:
+        self.run_dir = run_dir
+        self.metadata = metadata
+        self.component_defaults = component_defaults
+        self.provider_name = provider_name
+        self.target_walltime = target_walltime
+        self.telemetry_parquet = telemetry_parquet
+
+        self._lock = threading.RLock()
+        self._closed = False
+        self._task_started_at: dict[str, float] = {}
+
+    @property
+    def run_id(self) -> str:
+        return self.metadata.run_id
+
+    @classmethod
+    def create(
+        cls,
+        *,
+        runs_dir: str | Path,
+        manifest: ManifestModel,
+        spec: DeploymentSpec,
+        plan: DryRunPlan,
+        telemetry_parquet: bool = False,
+    ) -> TelemetryStore:
+        """Create run directory and initialize baseline run metadata files."""
+        runs_root = Path(runs_dir)
+        runs_root.mkdir(parents=True, exist_ok=True)
+
+        run_id = build_run_id(manifest.project.name)
+        run_dir = runs_root / run_id
+        run_dir.mkdir(parents=True, exist_ok=False)
+
+        metadata = RunMetadata(
+            run_id=run_id,
+            project_name=manifest.project.name,
+            target_name=spec.target_name,
+            provider_name=spec.provider_name,
+            manifest_lock=plan.manifest_lock,
+            source_manifest_path=manifest.source_path,
+        )
+
+        component_defaults: dict[str, dict[str, Any]] = {}
+        for cname, c in spec.components.items():
+            component_defaults[cname] = {
+                "cpus": c.cpus,
+                "memory": c.memory,
+            }
+
+        walltime = spec.target.options.get("walltime")
+        target_walltime = walltime if isinstance(walltime, str) else None
+
+        store = cls(
+            run_dir=run_dir,
+            metadata=metadata,
+            component_defaults=component_defaults,
+            provider_name=spec.provider_name,
+            target_walltime=target_walltime,
+            telemetry_parquet=telemetry_parquet,
+        )
+        store._write_bootstrap_files(manifest=manifest, plan=plan)
+        return store
+
+    def _write_bootstrap_files(self, *, manifest: ManifestModel, plan: DryRunPlan) -> None:
+        (self.run_dir / "manifest.yaml").write_text(
+            yaml.safe_dump(manifest.raw, sort_keys=True),
+            encoding="utf-8",
+        )
+        (self.run_dir / "plan.json").write_text(
+            json.dumps(plan.to_dict(), indent=2, sort_keys=True) + "\n",
+            encoding="utf-8",
+        )
+        (self.run_dir / "manifest.lock").write_text(plan.manifest_lock + "\n", encoding="utf-8")
+        (self.run_dir / "run.json").write_text(
+            json.dumps(self.metadata.to_dict(), indent=2, sort_keys=True) + "\n",
+            encoding="utf-8",
+        )
+
+    def _append_jsonl(self, filename: str, payload: dict[str, Any]) -> None:
+        text = json.dumps(payload, sort_keys=True)
+        with self._lock:
+            with (self.run_dir / filename).open("a", encoding="utf-8") as fh:
+                fh.write(text + "\n")
+
+    def record_task_submission(
+        self,
+        *,
+        task_id: str,
+        task_name: str,
+        component: str | None,
+        tag: str | None,
+        function_name: str,
+        requested_workers: int,
+    ) -> None:
+        """Record a task submission and a synthetic running transition."""
+        self._task_started_at[task_id] = time.monotonic()
+
+        self._append_jsonl(
+            self._TASKS_FILE,
+            TaskEvent(
+                run_id=self.run_id,
+                task_id=task_id,
+                task_name=task_name,
+                component=component,
+                tag=tag,
+                state="submitted",
+                function_name=function_name,
+                requested_workers=requested_workers,
+            ).to_dict(),
+        )
+        self._append_jsonl(
+            self._TASKS_FILE,
+            TaskEvent(
+                run_id=self.run_id,
+                task_id=task_id,
+                task_name=task_name,
+                component=component,
+                tag=tag,
+                state="running",
+                function_name=function_name,
+                requested_workers=requested_workers,
+            ).to_dict(),
+        )
+
+        default_cpus = None
+        default_memory = None
+        if component and component in self.component_defaults:
+            default_cpus = self.component_defaults[component].get("cpus")
+            default_memory = self.component_defaults[component].get("memory")
+
+        self._append_jsonl(
+            self._RESOURCES_FILE,
+            ResourceEvent(
+                run_id=self.run_id,
+                entity_type="task",
+                entity_id=task_id,
+                component=component,
+                provider=self.provider_name,
+                requested_cpus=default_cpus if isinstance(default_cpus, int) else None,
+                requested_memory=default_memory if isinstance(default_memory, str) else None,
+                requested_walltime=self.target_walltime,
+                requested_workers=requested_workers,
+            ).to_dict(),
+        )
+
+    def record_task_result(
+        self,
+        *,
+        task_id: str,
+        task_name: str,
+        component: str | None,
+        tag: str | None,
+        function_name: str,
+        requested_workers: int,
+        state: str,
+        worker: str | None = None,
+        error_type: str | None = None,
+        error_message: str | None = None,
+    ) -> None:
+        """Record terminal task state and optional failure event."""
+        start = self._task_started_at.pop(task_id, None)
+        duration = None
+        if start is not None:
+            duration = max(time.monotonic() - start, 0.0)
+
+        self._append_jsonl(
+            self._TASKS_FILE,
+            TaskEvent(
+                run_id=self.run_id,
+                task_id=task_id,
+                task_name=task_name,
+                component=component,
+                tag=tag,
+                state=state,
+                function_name=function_name,
+                requested_workers=requested_workers,
+                duration_s=duration,
+                worker=worker,
+                error_type=error_type,
+                error_message=error_message,
+            ).to_dict(),
+        )
+
+        if state == "failed":
+            self._append_jsonl(
+                self._FAILURES_FILE,
+                FailureEvent(
+                    run_id=self.run_id,
+                    failure_class=error_type or "TaskError",
+                    message=error_message or "task failed",
+                    provider=self.provider_name,
+                    task_id=task_id,
+                ).to_dict(),
+            )
+
+    def record_cache_event(
+        self,
+        *,
+        function_name: str,
+        key_digest: str,
+        hit: bool,
+        duration_s: float,
+        task_name: str | None,
+        component: str | None,
+        tag: str | None,
+    ) -> None:
+        """Record one cache hit or miss event."""
+        self._append_jsonl(
+            self._CACHE_FILE,
+            CacheEvent(
+                run_id=self.run_id,
+                function_name=function_name,
+                key_digest=key_digest,
+                hit=hit,
+                duration_s=duration_s,
+                task_name=task_name,
+                component=component,
+                tag=tag,
+            ).to_dict(),
+        )
+
+    def record_worker_event(
+        self,
+        *,
+        provider: str,
+        state: str,
+        component: str | None,
+        details: dict[str, Any],
+    ) -> None:
+        """Record provider worker/cluster telemetry events."""
+        self._append_jsonl(
+            self._WORKERS_FILE,
+            WorkerEvent(
+                run_id=self.run_id,
+                provider=provider,
+                state=state,
+                component=component,
+                details=details,
+            ).to_dict(),
+        )
+
+    def record_failure(
+        self,
+        *,
+        failure_class: str,
+        message: str,
+        details: dict[str, Any] | None = None,
+        task_id: str | None = None,
+    ) -> None:
+        """Record a non-task-scoped failure."""
+        self._append_jsonl(
+            self._FAILURES_FILE,
+            FailureEvent(
+                run_id=self.run_id,
+                failure_class=failure_class,
+                message=message,
+                provider=self.provider_name,
+                task_id=task_id,
+                details=details or {},
+            ).to_dict(),
+        )
+
+    def record_artifact(
+        self,
+        *,
+        task_name: str,
+        component: str | None,
+        artifact_name: str,
+        location: str,
+        kind: str | None = None,
+        size_bytes: int | None = None,
+        digest: str | None = None,
+    ) -> None:
+        """Record artifact metadata emitted by a run."""
+        self._append_jsonl(
+            self._ARTIFACTS_FILE,
+            ArtifactEvent(
+                run_id=self.run_id,
+                task_name=task_name,
+                component=component,
+                artifact_name=artifact_name,
+                location=location,
+                kind=kind,
+                size_bytes=size_bytes,
+                digest=digest,
+            ).to_dict(),
+        )
+
+    def _write_summary(self) -> None:
+        summary = summarize_run(self.run_dir)
+        (self.run_dir / "summary.json").write_text(
+            json.dumps(summary, indent=2, sort_keys=True) + "\n",
+            encoding="utf-8",
+        )
+
+    def _write_parquet_snapshots(self) -> None:
+        if not self.telemetry_parquet:
+            return
+        snapshots = {
+            self._TASKS_FILE: "tasks.parquet",
+            self._RESOURCES_FILE: "resources.parquet",
+            self._WORKERS_FILE: "workers.parquet",
+        }
+        for src_name, dst_name in snapshots.items():
+            src = self.run_dir / src_name
+            if not src.exists() or src.stat().st_size == 0:
+                continue
+            try:
+                df = pd.read_json(src, lines=True)
+                if not df.empty:
+                    df.to_parquet(self.run_dir / dst_name, index=False)
+            except (ImportError, ValueError):
+                # Parquet dependencies and row shape validation are optional.
+                continue
+
+    def close(self, *, status: str = "completed") -> None:
+        """Flush summary and finalize run metadata."""
+        with self._lock:
+            if self._closed:
+                return
+            self._closed = True
+
+            self.metadata = replace(self.metadata, status=status, finished_at=utcnow_iso())
+            (self.run_dir / "run.json").write_text(
+                json.dumps(self.metadata.to_dict(), indent=2, sort_keys=True) + "\n",
+                encoding="utf-8",
+            )
+            self._write_summary()
+            self._write_parquet_snapshots()
+
+
+__all__ = ["TelemetryStore", "build_run_id"]
diff --git a/tests/conftest.py b/tests/conftest.py
index ffdc71d..4338886 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -26,6 +26,9 @@ def _isolate_scalable_env(tmp_path, monkeypatch):
     monkeypatch.delenv("SCALABLE_CACHE_DIR", raising=False)
     monkeypatch.delenv("SCALABLE_SEED", raising=False)
     monkeypatch.delenv("SCALABLE_LOG_LEVEL", raising=False)
+    monkeypatch.delenv("SCALABLE_RUNS_DIR", raising=False)
+    monkeypatch.delenv("SCALABLE_TELEMETRY", raising=False)
+    monkeypatch.delenv("SCALABLE_TELEMETRY_PARQUET", raising=False)
     monkeypatch.delenv("COMM_PORT", raising=False)
     yield
 
diff --git a/tests/integration/test_session_telemetry_local.py b/tests/integration/test_session_telemetry_local.py
new file mode 100644
index 0000000..88db543
--- /dev/null
+++ b/tests/integration/test_session_telemetry_local.py
@@ -0,0 +1,71 @@
+"""Integration coverage for Phase 2 session telemetry with LocalProvider."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from scalable.caching import cacheable
+from scalable.session.session import ScalableSession
+
+
+def _write_manifest(path: Path) -> None:
+    path.write_text(
+        """
+version: 1
+project:
+  name: demo
+targets:
+  local:
+    provider: local
+    max_workers: 1
+    threads_per_worker: 1
+    processes: false
+    containers: none
+components:
+  gcam:
+    cpus: 1
+    memory: 1G
+tasks:
+  run_gcam:
+    component: gcam
+""".lstrip(),
+        encoding="utf-8",
+    )
+
+
+@cacheable
+def _cached_increment(value: int) -> int:
+    return value + 1
+
+
+def test_session_writes_run_telemetry_and_summary(tmp_path: Path) -> None:
+    manifest_path = tmp_path / "scalable.yaml"
+    _write_manifest(manifest_path)
+
+    session = ScalableSession.from_yaml(manifest_path, target="local")
+    client = session.start()
+    try:
+        assert client.submit(_cached_increment, 41, tag="gcam").result(timeout=10) == 42
+        assert client.submit(_cached_increment, 41, tag="gcam").result(timeout=10) == 42
+    finally:
+        session.close()
+
+    runs_root = tmp_path / ".scalable" / "runs"
+    run_dirs = sorted(p for p in runs_root.iterdir() if p.is_dir())
+    assert len(run_dirs) == 1
+
+    run_dir = run_dirs[0]
+    assert (run_dir / "manifest.yaml").exists()
+    assert (run_dir / "plan.json").exists()
+    assert (run_dir / "manifest.lock").exists()
+    assert (run_dir / "run.json").exists()
+    assert (run_dir / "tasks.jsonl").exists()
+    assert (run_dir / "summary.json").exists()
+
+    run_payload = json.loads((run_dir / "run.json").read_text(encoding="utf-8"))
+    assert run_payload["status"] == "completed"
+
+    summary_payload = json.loads((run_dir / "summary.json").read_text(encoding="utf-8"))
+    assert summary_payload["counts"]["task_events"] >= 2
+
diff --git a/tests/unit/test_cli_report.py b/tests/unit/test_cli_report.py
new file mode 100644
index 0000000..6d2c645
--- /dev/null
+++ b/tests/unit/test_cli_report.py
@@ -0,0 +1,94 @@
+"""Unit tests for ``scalable report`` CLI behavior."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from scalable.cli.main import main
+
+
+def _seed_run(run_dir: Path) -> None:
+    run_dir.mkdir(parents=True, exist_ok=True)
+    (run_dir / "run.json").write_text(
+        json.dumps(
+            {
+                "run_id": run_dir.name,
+                "project_name": "demo",
+                "target_name": "local",
+                "provider_name": "local",
+                "status": "completed",
+            },
+            sort_keys=True,
+        )
+        + "\n",
+        encoding="utf-8",
+    )
+    (run_dir / "tasks.jsonl").write_text(
+        json.dumps(
+            {
+                "task_id": "t1",
+                "task_name": "run_gcam",
+                "component": "gcam",
+                "state": "succeeded",
+                "duration_s": 1.5,
+            },
+            sort_keys=True,
+        )
+        + "\n",
+        encoding="utf-8",
+    )
+
+
+def test_cli_report_latest_text(tmp_path: Path, capsys) -> None:
+    runs_dir = tmp_path / "runs"
+    run_dir = runs_dir / "run-20260519T120000Z-demo-aaaa1111"
+    _seed_run(run_dir)
+
+    code = main(["report", "--runs-dir", str(runs_dir), "--latest"])
+
+    captured = capsys.readouterr()
+    assert code == 0
+    assert "run_id:" in captured.out
+    assert run_dir.name in captured.out
+
+
+def test_cli_report_json_and_output_file(tmp_path: Path, capsys) -> None:
+    runs_dir = tmp_path / "runs"
+    run_dir = runs_dir / "run-20260519T120000Z-demo-aaaa1111"
+    _seed_run(run_dir)
+    output = tmp_path / "report.json"
+
+    code = main(
+        [
+            "report",
+            "--runs-dir",
+            str(runs_dir),
+            "--run-id",
+            run_dir.name,
+            "--format",
+            "json",
+            "--output",
+            str(output),
+        ]
+    )
+
+    captured = capsys.readouterr()
+    payload = json.loads(captured.out)
+    file_payload = json.loads(output.read_text(encoding="utf-8"))
+
+    assert code == 0
+    assert payload["run"]["run_id"] == run_dir.name
+    assert file_payload == payload
+
+
+def test_cli_report_missing_selection_returns_error(tmp_path: Path, capsys) -> None:
+    runs_dir = tmp_path / "runs"
+    runs_dir.mkdir(parents=True, exist_ok=True)
+
+    code = main(["report", "--runs-dir", str(runs_dir)])
+    captured = capsys.readouterr()
+
+    assert code == 1
+    assert "report failed" in captured.err
+
diff --git a/tests/unit/test_common_settings.py b/tests/unit/test_common_settings.py
index 5bb470b..35476c3 100644
--- a/tests/unit/test_common_settings.py
+++ b/tests/unit/test_common_settings.py
@@ -25,6 +25,9 @@ def test_settings_defaults():
     assert s.seed == common.DEFAULT_SEED
     assert s.manifest_path == "./scalable.yaml"
     assert s.target is None
+    assert s.runs_dir == "./.scalable/runs"
+    assert s.telemetry_enabled is True
+    assert s.telemetry_parquet is False
 
 
 def test_settings_env_overrides(monkeypatch):
@@ -32,6 +35,9 @@ def test_settings_env_overrides(monkeypatch):
     monkeypatch.setenv("SCALABLE_SEED", "42")
     monkeypatch.setenv("SCALABLE_MANIFEST", "/tmp/scalable.yaml")
     monkeypatch.setenv("SCALABLE_TARGET", "local")
+    monkeypatch.setenv("SCALABLE_RUNS_DIR", "/tmp/scalable-runs")
+    monkeypatch.setenv("SCALABLE_TELEMETRY", "0")
+    monkeypatch.setenv("SCALABLE_TELEMETRY_PARQUET", "1")
 
     # Reload to pick up env vars in field defaults.
     from scalable import common as common_mod
@@ -41,6 +47,9 @@ def test_settings_env_overrides(monkeypatch):
     assert s.seed == 42
     assert s.manifest_path == "/tmp/scalable.yaml"
     assert s.target == "local"
+    assert s.runs_dir == "/tmp/scalable-runs"
+    assert s.telemetry_enabled is False
+    assert s.telemetry_parquet is True
 
 
 def test_legacy_module_aliases_match_singleton():
diff --git a/tests/unit/test_public_api_exports.py b/tests/unit/test_public_api_exports.py
index 9f65dff..a0d80d8 100644
--- a/tests/unit/test_public_api_exports.py
+++ b/tests/unit/test_public_api_exports.py
@@ -12,6 +12,8 @@ def test_top_level_exports_include_session_and_provider_symbols() -> None:
     assert "DeploymentProvider" in exported
     assert "LocalProvider" in exported
     assert "SlurmProvider" in exported
+    assert "ResourceAdvisor" in exported
+    assert "ResourceRecommendation" in exported
 
 
 def test_legacy_exports_remain_available() -> None:
@@ -24,4 +26,3 @@ def test_legacy_exports_remain_available() -> None:
     assert "ScalableClient" in exported
     assert "SEED" in exported
     assert "settings" in exported
-
diff --git a/tests/unit/test_resource_advisor.py b/tests/unit/test_resource_advisor.py
new file mode 100644
index 0000000..22f4552
--- /dev/null
+++ b/tests/unit/test_resource_advisor.py
@@ -0,0 +1,90 @@
+"""Unit tests for deterministic ResourceAdvisor recommendations."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from scalable.advising import ResourceAdvisor
+
+
+def _append_jsonl(path: Path, rows: list[dict]) -> None:
+    path.write_text("\n".join(json.dumps(r, sort_keys=True) for r in rows) + "\n", encoding="utf-8")
+
+
+def _seed_run(run_dir: Path, *, duration_s: float, cpus: int, memory: str, workers: int) -> None:
+    run_dir.mkdir(parents=True, exist_ok=True)
+    (run_dir / "run.json").write_text(
+        json.dumps(
+            {
+                "run_id": run_dir.name,
+                "target_name": "local",
+            },
+            sort_keys=True,
+        )
+        + "\n",
+        encoding="utf-8",
+    )
+    _append_jsonl(
+        run_dir / "tasks.jsonl",
+        [
+            {
+                "task_id": "t1",
+                "task_name": "run_gcam",
+                "component": "gcam",
+                "state": "succeeded",
+                "duration_s": duration_s,
+            }
+        ],
+    )
+    _append_jsonl(
+        run_dir / "resources.jsonl",
+        [
+            {
+                "entity_type": "task",
+                "entity_id": "t1",
+                "requested_workers": workers,
+                "requested_cpus": cpus,
+                "requested_memory": memory,
+                "requested_walltime": "00:30:00",
+            }
+        ],
+    )
+
+
+def test_resource_advisor_returns_history_based_recommendation(tmp_path: Path) -> None:
+    runs = tmp_path / "runs"
+    _seed_run(
+        runs / "run-20260519T120000Z-demo-aaaa1111",
+        duration_s=120.0,
+        cpus=4,
+        memory="8G",
+        workers=1,
+    )
+    _seed_run(
+        runs / "run-20260519T130000Z-demo-bbbb2222",
+        duration_s=300.0,
+        cpus=6,
+        memory="16G",
+        workers=2,
+    )
+
+    advisor = ResourceAdvisor.from_history(runs)
+    recommendation = advisor.recommend(task="run_gcam", target="local", confidence=0.95)
+
+    assert recommendation.task == "run_gcam"
+    assert recommendation.target == "local"
+    assert "gcam" in recommendation.workers
+    assert recommendation.workers["gcam"] >= 1
+    assert recommendation.resources["gcam"]["cpus"] >= 1
+    assert recommendation.evidence["records"] >= 2
+
+
+def test_resource_advisor_handles_missing_history(tmp_path: Path) -> None:
+    advisor = ResourceAdvisor.from_history(tmp_path / "runs")
+    recommendation = advisor.recommend(task="missing_task", target="local")
+
+    assert recommendation.workers == {"missing_task": 1}
+    assert recommendation.resources["missing_task"]["cpus"] == 1
+    assert recommendation.evidence["records"] == 0
+
diff --git a/tests/unit/test_telemetry_collectors.py b/tests/unit/test_telemetry_collectors.py
new file mode 100644
index 0000000..1d8d17a
--- /dev/null
+++ b/tests/unit/test_telemetry_collectors.py
@@ -0,0 +1,117 @@
+"""Unit tests for telemetry collectors and report rendering."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from scalable.telemetry.collectors import (
+    latest_run_dir,
+    read_jsonl,
+    render_text_report,
+    resolve_run_dir,
+    summarize_run,
+)
+
+
+def _write_jsonl(path: Path, rows: list[dict]) -> None:
+    payload = "\n".join(json.dumps(row, sort_keys=True) for row in rows) + "\n"
+    path.write_text(payload, encoding="utf-8")
+
+
+def _seed_run(run_dir: Path) -> None:
+    run_dir.mkdir(parents=True, exist_ok=True)
+    (run_dir / "run.json").write_text(
+        json.dumps(
+            {
+                "run_id": run_dir.name,
+                "project_name": "demo",
+                "target_name": "local",
+                "provider_name": "local",
+                "status": "completed",
+            },
+            sort_keys=True,
+        )
+        + "\n",
+        encoding="utf-8",
+    )
+    _write_jsonl(
+        run_dir / "tasks.jsonl",
+        [
+            {
+                "task_id": "t1",
+                "task_name": "run_gcam",
+                "component": "gcam",
+                "state": "succeeded",
+                "duration_s": 3.5,
+            },
+            {
+                "task_id": "t2",
+                "task_name": "run_gcam",
+                "component": "gcam",
+                "state": "failed",
+                "duration_s": 1.0,
+            },
+        ],
+    )
+    _write_jsonl(
+        run_dir / "resources.jsonl",
+        [
+            {
+                "entity_type": "task",
+                "entity_id": "t1",
+                "requested_cpus": 4,
+            },
+            {
+                "entity_type": "task",
+                "entity_id": "t2",
+                "requested_cpus": 6,
+            },
+        ],
+    )
+    _write_jsonl(
+        run_dir / "cache.jsonl",
+        [
+            {"hit": False},
+            {"hit": True},
+        ],
+    )
+    _write_jsonl(
+        run_dir / "failures.jsonl",
+        [
+            {"failure_class": "RuntimeError"},
+            {"failure_class": "RuntimeError"},
+        ],
+    )
+
+
+def test_read_jsonl_missing_returns_empty(tmp_path: Path) -> None:
+    assert read_jsonl(tmp_path / "missing.jsonl") == []
+
+
+def test_summarize_run_and_render_text(tmp_path: Path) -> None:
+    run_dir = tmp_path / "run-20260519T120000Z-demo-aaaa1111"
+    _seed_run(run_dir)
+
+    summary = summarize_run(run_dir)
+    text = render_text_report(summary)
+
+    assert summary["counts"]["tasks_succeeded"] == 1
+    assert summary["counts"]["tasks_failed"] == 1
+    assert summary["cache"]["hits"] == 1
+    assert summary["cache"]["misses"] == 1
+    assert "run_id:" in text
+    assert "tasks:" in text
+
+
+def test_resolve_run_dir_latest_and_id(tmp_path: Path) -> None:
+    runs = tmp_path / "runs"
+    run1 = runs / "run-20260519T120000Z-demo-aaaa1111"
+    run2 = runs / "run-20260519T130000Z-demo-bbbb2222"
+    _seed_run(run1)
+    _seed_run(run2)
+
+    assert latest_run_dir(runs) == run2
+    assert resolve_run_dir(runs_dir=runs, latest=True) == run2
+    assert resolve_run_dir(runs_dir=runs, run_id=run1.name) == run1
+
diff --git a/tests/unit/test_telemetry_store.py b/tests/unit/test_telemetry_store.py
new file mode 100644
index 0000000..f815a78
--- /dev/null
+++ b/tests/unit/test_telemetry_store.py
@@ -0,0 +1,89 @@
+"""Unit tests for TelemetryStore persistence primitives."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from scalable.manifest.parser import load_manifest
+from scalable.planning.dryrun import build_dry_run_plan
+from scalable.providers.base import DeploymentSpec
+from scalable.telemetry.store import TelemetryStore
+
+
+def _write_manifest(path: Path) -> None:
+    path.write_text(
+        """
+version: 1
+project:
+  name: demo
+targets:
+  local:
+    provider: local
+components:
+  gcam:
+    cpus: 2
+    memory: 8G
+tasks:
+  run_gcam:
+    component: gcam
+""".lstrip(),
+        encoding="utf-8",
+    )
+
+
+def test_store_writes_bootstrap_and_summary(tmp_path: Path) -> None:
+    manifest_path = tmp_path / "scalable.yaml"
+    _write_manifest(manifest_path)
+
+    manifest = load_manifest(manifest_path)
+    spec = DeploymentSpec.from_manifest(manifest, target_name="local")
+    plan = build_dry_run_plan(spec)
+
+    store = TelemetryStore.create(
+        runs_dir=tmp_path / "runs",
+        manifest=manifest,
+        spec=spec,
+        plan=plan,
+    )
+
+    store.record_task_submission(
+        task_id="t1",
+        task_name="run_gcam",
+        component="gcam",
+        tag="gcam",
+        function_name="run_gcam",
+        requested_workers=1,
+    )
+    store.record_task_result(
+        task_id="t1",
+        task_name="run_gcam",
+        component="gcam",
+        tag="gcam",
+        function_name="run_gcam",
+        requested_workers=1,
+        state="succeeded",
+    )
+    store.record_cache_event(
+        function_name="run_gcam",
+        key_digest="123",
+        hit=False,
+        duration_s=0.1,
+        task_name="run_gcam",
+        component="gcam",
+        tag="gcam",
+    )
+    store.close(status="completed")
+
+    run_dir = store.run_dir
+    assert (run_dir / "manifest.yaml").exists()
+    assert (run_dir / "plan.json").exists()
+    assert (run_dir / "manifest.lock").exists()
+    assert (run_dir / "run.json").exists()
+    assert (run_dir / "summary.json").exists()
+
+    run_payload = json.loads((run_dir / "run.json").read_text(encoding="utf-8"))
+    summary = json.loads((run_dir / "summary.json").read_text(encoding="utf-8"))
+    assert run_payload["status"] == "completed"
+    assert summary["counts"]["task_events"] >= 3
+

From 84b512814c3a328548c137ab4792e3663215073c Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 19:01:13 -0400
Subject: [PATCH 17/47] Phase 3: cloud + Kubernetes execution, artifact stores,
 overlays, cost

Implements Phase 3 of the v2.0.0 roadmap:

- KubernetesProvider over Dask Kubernetes Operator
- AWSBatchProvider over dask-cloudprovider (Fargate/EC2)
- GCPProvider scaffold (validation only; build_cluster deferred)
- ArtifactStore protocol with local and fsspec backends
- RemoteCacheBackend for opt-in remote cache (SCALABLE_CACHE_REMOTE)
- Manifest overlays (overlays: block + targets[*].overlay)
- CostEstimate primitives and static cost tables
- scalable run CLI verb
- Settings: cache_remote_uri, default_storage, runs_dir_remote
- Telemetry: CostEvent, cost.jsonl stream, cost in report
- Provider protocol: optional estimate_cost() method
- Public API: Phase 3 exports with optional-dep guards
- Docs: cloud.rst, kubernetes.rst, artifacts.rst, overlays.rst, cost.rst
- Example manifests: gke, aws, overlays
- 238 unit tests passing, ruff clean

Version bumped to 2.0.0a3.
---
 CHANGELOG.md                                  |  69 +++++++
 docs/artifacts.rst                            |  58 ++++++
 docs/cloud.rst                                |  68 +++++++
 docs/cost.rst                                 |  79 +++++++
 docs/examples/scalable.aws.yaml               |  50 +++++
 docs/examples/scalable.gke.yaml               |  63 ++++++
 docs/examples/scalable.overlays.yaml          |  67 ++++++
 docs/index.rst                                |   5 +
 docs/kubernetes.rst                           |  64 ++++++
 docs/overlays.rst                             |  93 +++++++++
 pyproject.toml                                |  14 +-
 scalable/__init__.py                          |  35 +++-
 scalable/artifacts/__init__.py                |  19 ++
 scalable/artifacts/base.py                    |  92 +++++++++
 scalable/artifacts/cache.py                   | 119 +++++++++++
 scalable/artifacts/factory.py                 |  39 ++++
 scalable/artifacts/fsspec_store.py            | 140 +++++++++++++
 scalable/artifacts/local.py                   | 113 +++++++++++
 scalable/cli/cmd_run.py                       | 151 ++++++++++++++
 scalable/cli/main.py                          |  41 +++-
 scalable/common.py                            |  20 ++
 scalable/costing/__init__.py                  |  11 +
 scalable/costing/estimate.py                  | 136 +++++++++++++
 scalable/manifest/overlays.py                 | 114 +++++++++++
 scalable/manifest/parser.py                   |  46 ++++-
 scalable/manifest/schema.py                   |  13 +-
 scalable/providers/base.py                    |  36 +++-
 scalable/providers/cloud/__init__.py          |  13 ++
 scalable/providers/cloud/aws.py               | 179 ++++++++++++++++
 scalable/providers/cloud/base.py              |  82 ++++++++
 scalable/providers/cloud/cost_tables.py       |  79 +++++++
 scalable/providers/cloud/gcp.py               |  90 ++++++++
 scalable/providers/kubernetes.py              | 186 +++++++++++++++++
 scalable/providers/registry.py                |  18 ++
 scalable/telemetry/collectors.py              |  21 ++
 scalable/telemetry/events.py                  |  49 +++++
 scalable/telemetry/store.py                   |  28 +++
 tests/unit/test_artifacts.py                  | 140 +++++++++++++
 tests/unit/test_cli_run.py                    |  89 ++++++++
 tests/unit/test_cloud_cost_tables.py          |  55 +++++
 tests/unit/test_cloud_kubernetes_providers.py | 192 ++++++++++++++++++
 tests/unit/test_costing.py                    |  85 ++++++++
 tests/unit/test_manifest_overlays.py          | 158 ++++++++++++++
 tests/unit/test_settings_phase3.py            |  50 +++++
 tests/unit/test_telemetry_cost.py             |  79 +++++++
 45 files changed, 3328 insertions(+), 20 deletions(-)
 create mode 100644 docs/artifacts.rst
 create mode 100644 docs/cloud.rst
 create mode 100644 docs/cost.rst
 create mode 100644 docs/examples/scalable.aws.yaml
 create mode 100644 docs/examples/scalable.gke.yaml
 create mode 100644 docs/examples/scalable.overlays.yaml
 create mode 100644 docs/kubernetes.rst
 create mode 100644 docs/overlays.rst
 create mode 100644 scalable/artifacts/__init__.py
 create mode 100644 scalable/artifacts/base.py
 create mode 100644 scalable/artifacts/cache.py
 create mode 100644 scalable/artifacts/factory.py
 create mode 100644 scalable/artifacts/fsspec_store.py
 create mode 100644 scalable/artifacts/local.py
 create mode 100644 scalable/cli/cmd_run.py
 create mode 100644 scalable/costing/__init__.py
 create mode 100644 scalable/costing/estimate.py
 create mode 100644 scalable/manifest/overlays.py
 create mode 100644 scalable/providers/cloud/__init__.py
 create mode 100644 scalable/providers/cloud/aws.py
 create mode 100644 scalable/providers/cloud/base.py
 create mode 100644 scalable/providers/cloud/cost_tables.py
 create mode 100644 scalable/providers/cloud/gcp.py
 create mode 100644 scalable/providers/kubernetes.py
 create mode 100644 tests/unit/test_artifacts.py
 create mode 100644 tests/unit/test_cli_run.py
 create mode 100644 tests/unit/test_cloud_cost_tables.py
 create mode 100644 tests/unit/test_cloud_kubernetes_providers.py
 create mode 100644 tests/unit/test_costing.py
 create mode 100644 tests/unit/test_manifest_overlays.py
 create mode 100644 tests/unit/test_settings_phase3.py
 create mode 100644 tests/unit/test_telemetry_cost.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 41a757d..42121be 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,75 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [2.0.0a3] — Phase 3: Cloud and Kubernetes Execution
+
+### Added
+
+- **Kubernetes provider** (`scalable.providers.kubernetes.KubernetesProvider`)
+  implementing the `DeploymentProvider` protocol over the Dask Kubernetes
+  Operator. Maps manifest components to worker groups with per-component
+  resource requests and adaptive scaling support.
+- **AWS cloud provider** (`scalable.providers.cloud.aws.AWSBatchProvider`)
+  wrapping `dask-cloudprovider` `FargateCluster` / `EC2Cluster`.
+- **GCP provider scaffold** (`scalable.providers.cloud.gcp.GCPProvider`)
+  for manifest validation only; `build_cluster()` raises `NotImplementedError`.
+- **Cloud cost tables** (`scalable.providers.cloud.cost_tables`) with static
+  on-demand pricing for common AWS and GCP instance types.
+- **Cost estimation primitives** (`scalable.costing`):
+  - `CostEstimate` dataclass with provider/region/line-item breakdown
+  - `CostLineItem` with auto-computed totals
+- **Artifact store layer** (`scalable.artifacts`):
+  - `ArtifactStore` protocol, `ArtifactRef`, `ArtifactKind`
+  - `LocalArtifactStore` (filesystem backend)
+  - `FsspecArtifactStore` (S3, GCS, memory via fsspec)
+  - `build_artifact_store(uri)` factory function
+  - `RemoteCacheBackend` for opt-in remote cache storage
+- **Manifest overlays** (`scalable.manifest.overlays`):
+  - `overlays:` top-level key added to schema (additive, no version bump)
+  - `targets[*].overlay:` reference field for per-target overlay selection
+  - Deep-merge semantics: dicts merged recursively, lists/scalars replaced
+  - `ManifestModel.raw_unresolved` for pre-overlay provenance tracking
+- **`scalable run` CLI verb** (`scalable.cli.cmd_run`):
+  - Loads manifest with overlay resolution
+  - Validates, plans, estimates cost, and optionally executes a workflow file
+  - `--dry-run` mode prints plan + cost estimate as JSON
+- **Provider protocol extension**: optional `estimate_cost(spec, plan)` method
+  on `DeploymentProvider` with `_BaseProviderMixin` providing `None` default.
+- **Telemetry extensions**:
+  - `CostEvent` and `RemoteCacheEvent` in `scalable.telemetry.events`
+  - `cost.jsonl` stream in `TelemetryStore`
+  - Cost summary in `scalable report` output
+- **Settings extensions** (`scalable.common.Settings`):
+  - `cache_remote_uri` (`SCALABLE_CACHE_REMOTE`)
+  - `default_storage` (`SCALABLE_DEFAULT_STORAGE`)
+  - `runs_dir_remote` (`SCALABLE_RUNS_DIR_REMOTE`)
+- **Provider registry**: `kubernetes`, `aws`, `gcp` added as builtin providers.
+- **Public API**: `CostEstimate`, `KubernetesProvider`, `CloudProvider`,
+  `AWSBatchProvider`, `GCPProvider`, `ArtifactStore`, `LocalArtifactStore`,
+  `build_artifact_store` exported from `scalable.__init__` with optional-dep
+  guards.
+- New docs pages: `cloud.rst`, `kubernetes.rst`, `artifacts.rst`,
+  `overlays.rst`, `cost.rst`.
+- Example manifests: `scalable.gke.yaml`, `scalable.aws.yaml`,
+  `scalable.overlays.yaml`.
+- Populated `[project.optional-dependencies]` `cloud` and `kubernetes` extras.
+
+### Changed
+
+- Bumped version to `2.0.0a3`.
+- `_TOP_LEVEL_KEYS` in `scalable.manifest.parser` now includes `"overlays"`.
+- `load_manifest()` / `parse_manifest()` accept `target_name` and
+  `overlay_name` keyword arguments for overlay resolution.
+- `scalable run` removed from `_STUB_COMMANDS` in CLI main.
+
+### Tests
+
+- Unit tests for costing, artifacts, overlays, cloud/k8s providers, cost
+  tables, CLI run verb, telemetry cost events, and Phase 3 Settings.
+- 238 total unit tests passing.
+
+---
+
 ## [Unreleased]
 
 ### Added
diff --git a/docs/artifacts.rst b/docs/artifacts.rst
new file mode 100644
index 0000000..12b5340
--- /dev/null
+++ b/docs/artifacts.rst
@@ -0,0 +1,58 @@
+Artifact Store
+==============
+
+The :mod:`scalable.artifacts` module provides a protocol-based abstraction
+for storing and retrieving workflow artifacts across local and remote backends.
+
+Overview
+--------
+
+- :class:`~scalable.artifacts.base.ArtifactStore` — protocol interface
+- :class:`~scalable.artifacts.local.LocalArtifactStore` — filesystem backend
+- :class:`~scalable.artifacts.fsspec_store.FsspecArtifactStore` — S3/GCS/memory
+- :func:`~scalable.artifacts.factory.build_artifact_store` — URI-based factory
+
+Usage
+-----
+
+.. code-block:: python
+
+   from scalable.artifacts import build_artifact_store
+
+   # Local storage
+   store = build_artifact_store("./artifacts")
+   ref = store.put("output.csv", "runs/run-001/output.csv")
+   print(ref.uri, ref.digest, ref.size_bytes)
+
+   # S3 storage (requires scalable[cloud])
+   store = build_artifact_store("s3://my-bucket/artifacts/")
+   ref = store.put("model_output/", "runs/run-001/model_output")
+
+   # GCS storage
+   store = build_artifact_store("gs://my-bucket/artifacts/")
+
+Manifest Integration
+--------------------
+
+Set ``project.default_storage`` in your manifest to configure where artifacts
+are stored:
+
+.. code-block:: yaml
+
+   project:
+     name: my-project
+     default_storage: s3://my-bucket/scalable-runs/
+
+Or override via the ``SCALABLE_DEFAULT_STORAGE`` environment variable.
+
+Remote Cache
+------------
+
+The artifact store layer also powers the remote cache backend. Enable it with:
+
+.. code-block:: bash
+
+   export SCALABLE_CACHE_REMOTE=s3://my-bucket/cache/
+
+When enabled, cache results are stored remotely in addition to the local
+diskcache, allowing cache sharing across machines.
diff --git a/docs/cloud.rst b/docs/cloud.rst
new file mode 100644
index 0000000..c442c68
--- /dev/null
+++ b/docs/cloud.rst
@@ -0,0 +1,68 @@
+Cloud Providers
+===============
+
+Scalable supports cloud-based execution through the ``scalable[cloud]`` extra,
+which provides access to AWS and GCP deployment providers.
+
+Installation
+------------
+
+.. code-block:: bash
+
+   pip install scalable[cloud]
+
+This installs ``dask-cloudprovider``, ``s3fs``, ``gcsfs``, and ``fsspec``.
+
+AWS Provider
+------------
+
+The :class:`~scalable.providers.cloud.aws.AWSBatchProvider` wraps
+``dask-cloudprovider``'s ``FargateCluster`` or ``EC2Cluster``.
+
+Target options:
+
+- ``region``: AWS region (default: ``us-east-1``)
+- ``cluster_type``: ``"fargate"`` (default) or ``"ec2"``
+- ``instance_type``: EC2 instance type (for cost estimation)
+- ``image``: Docker image for workers
+- ``n_workers``: Initial worker count
+- ``worker_cpu``: CPU units per worker (Fargate: 256-4096)
+- ``worker_mem``: Memory in MiB per worker
+- ``vpc``: VPC identifier
+- ``subnets``: List of subnet IDs
+- ``security_groups``: List of security group IDs
+- ``execution_role_arn``: ECS execution role ARN
+- ``task_role_arn``: ECS task role ARN
+- ``adaptive``: Dict with ``minimum`` and ``maximum`` for adaptive scaling
+
+Example manifest:
+
+.. literalinclude:: examples/scalable.aws.yaml
+   :language: yaml
+
+GCP Provider (Scaffold)
+-----------------------
+
+The :class:`~scalable.providers.cloud.gcp.GCPProvider` is a validation-only
+scaffold in Phase 3. It validates manifest options but raises
+``NotImplementedError`` on ``build_cluster()``.
+
+Target options:
+
+- ``region``: GCP region
+- ``project_id``: GCP project identifier
+- ``instance_type``: GCE machine type (for cost estimation)
+- ``image``: Container image
+- ``n_workers``: Worker count
+
+Cost Estimation
+---------------
+
+Cloud providers include static cost tables for common instance types.
+Run ``scalable run --dry-run`` to see estimated costs:
+
+.. code-block:: bash
+
+   scalable run scalable.yaml --target aws --dry-run
+
+The cost estimate is also recorded in telemetry (``cost.jsonl``).
diff --git a/docs/cost.rst b/docs/cost.rst
new file mode 100644
index 0000000..7988ec9
--- /dev/null
+++ b/docs/cost.rst
@@ -0,0 +1,79 @@
+Cost Estimation
+===============
+
+Scalable provides static-table-based cost estimation for cloud providers,
+helping users understand the financial impact of their deployment plans
+before execution.
+
+Overview
+--------
+
+The :mod:`scalable.costing` module defines:
+
+- :class:`~scalable.costing.CostEstimate` — provider-neutral cost estimate
+- :class:`~scalable.costing.CostLineItem` — itemized cost breakdown
+
+Providers implement the optional ``estimate_cost()`` method:
+
+- **AWS** — estimates from static on-demand pricing tables
+- **GCP** — estimates from static on-demand pricing tables
+- **Kubernetes** — returns ``None`` (on-prem k8s has no direct cost)
+- **Local/Slurm** — returns ``None`` (no monetary cost)
+
+Usage
+-----
+
+Via CLI (dry-run mode):
+
+.. code-block:: bash
+
+   scalable run scalable.yaml --target aws --dry-run
+
+This prints the cost estimate and includes it in the plan output.
+
+Programmatic access:
+
+.. code-block:: python
+
+   from scalable.providers.registry import get_provider
+   from scalable.providers.base import DeploymentSpec, ScalePlan
+
+   provider = get_provider("aws")
+   estimate = provider.estimate_cost(spec, plan)
+   if estimate:
+       print(f"${estimate.total_hourly:.4f}/hr")
+       print(f"${estimate.total_monthly:.2f}/mo")
+
+Telemetry Integration
+---------------------
+
+When a cost estimate is produced during a run, it is recorded as a
+``CostEvent`` in the telemetry store (``cost.jsonl``). The
+``scalable report`` command includes cost summary data:
+
+.. code-block:: text
+
+   cost:
+     hourly_usd: 0.384
+     monthly_usd: 280.32
+
+Cost Tables
+-----------
+
+Static cost tables cover common AWS and GCP instance types across major
+regions. These are representative on-demand Linux pricing as of 2024.
+
+Supported AWS instances: ``m5.*``, ``c5.*``, ``r5.*``, ``t3.*``
+
+Supported GCP machines: ``n1-standard-*``, ``n1-highmem-*``,
+``n1-highcpu-*``, ``e2-standard-*``
+
+Future Phases
+-------------
+
+Phase 5 will extend cost estimation with:
+
+- Live pricing API integration
+- Spot/preemptible instance pricing
+- Cost-aware scheduling recommendations
+- Historical cost tracking and budgets
diff --git a/docs/examples/scalable.aws.yaml b/docs/examples/scalable.aws.yaml
new file mode 100644
index 0000000..0337bf3
--- /dev/null
+++ b/docs/examples/scalable.aws.yaml
@@ -0,0 +1,50 @@
+# Scalable manifest targeting AWS Fargate
+# Requires: pip install scalable[cloud]
+
+version: 1
+project:
+  name: climate-model-aws
+  default_storage: s3://my-bucket/scalable-runs/
+
+targets:
+  aws:
+    provider: aws
+    region: us-east-1
+    cluster_type: fargate
+    instance_type: m5.xlarge
+    worker_cpu: 4096
+    worker_mem: 16384
+    image: 123456789.dkr.ecr.us-east-1.amazonaws.com/climate:latest
+    execution_role_arn: arn:aws:iam::123456789:role/ecsTaskExecutionRole
+    task_role_arn: arn:aws:iam::123456789:role/ecsTaskRole
+    subnets:
+      - subnet-abc123
+      - subnet-def456
+    security_groups:
+      - sg-xyz789
+    adaptive:
+      minimum: 1
+      maximum: 10
+
+components:
+  gcam:
+    image: 123456789.dkr.ecr.us-east-1.amazonaws.com/gcam:7.0
+    cpus: 4
+    memory: 16G
+    tags: [iam, climate]
+
+  postprocess:
+    cpus: 2
+    memory: 8G
+    tags: [postprocess]
+
+tasks:
+  run_gcam:
+    component: gcam
+    cache: true
+    outputs:
+      database: dir
+
+  aggregate:
+    component: postprocess
+    cache: true
diff --git a/docs/examples/scalable.gke.yaml b/docs/examples/scalable.gke.yaml
new file mode 100644
index 0000000..36b4762
--- /dev/null
+++ b/docs/examples/scalable.gke.yaml
@@ -0,0 +1,63 @@
+# Scalable manifest targeting GKE (Google Kubernetes Engine)
+# Requires: pip install scalable[kubernetes]
+
+version: 1
+project:
+  name: climate-model-gke
+  default_storage: gs://my-bucket/scalable-runs/
+
+targets:
+  gke:
+    provider: kubernetes
+    namespace: climate-prod
+    image: gcr.io/my-project/climate-model:latest
+    adaptive:
+      minimum: 2
+      maximum: 20
+    overlay: gke-prod
+
+components:
+  gcam:
+    image: gcr.io/my-project/gcam:7.0
+    cpus: 8
+    memory: 32G
+    tags: [iam, climate]
+    env:
+      GCAM_DATA: /data/gcam
+
+  postprocess:
+    image: gcr.io/my-project/postprocess:latest
+    cpus: 4
+    memory: 16G
+    tags: [postprocess]
+
+tasks:
+  run_gcam:
+    component: gcam
+    cache: true
+    outputs:
+      database: dir
+
+  aggregate:
+    component: postprocess
+    cache: true
+
+overlays:
+  gke-prod:
+    components:
+      gcam:
+        memory: 64G
+        cpus: 16
+      postprocess:
+        memory: 32G
+  gke-dev:
+    targets:
+      gke:
+        namespace: climate-dev
+        adaptive:
+          minimum: 1
+          maximum: 5
+    components:
+      gcam:
+        memory: 16G
+        cpus: 4
diff --git a/docs/examples/scalable.overlays.yaml b/docs/examples/scalable.overlays.yaml
new file mode 100644
index 0000000..d1afd73
--- /dev/null
+++ b/docs/examples/scalable.overlays.yaml
@@ -0,0 +1,67 @@
+# Scalable manifest demonstrating overlay usage
+# Overlays allow environment-specific configuration deltas
+
+version: 1
+project:
+  name: overlay-demo
+  default_storage: ./outputs
+
+targets:
+  local:
+    provider: local
+  hpc:
+    provider: slurm
+    queue: batch
+    walltime: "04:00:00"
+    overlay: hpc-large
+  cloud:
+    provider: aws
+    region: us-west-2
+    instance_type: m5.2xlarge
+    overlay: cloud-prod
+
+components:
+  model:
+    cpus: 2
+    memory: 4G
+    tags: [compute]
+
+  analysis:
+    cpus: 1
+    memory: 2G
+    tags: [analysis]
+
+tasks:
+  simulate:
+    component: model
+    cache: true
+  analyze:
+    component: analysis
+    cache: true
+
+# Overlays: named configuration deltas merged onto the base manifest
+# when a target references them via `overlay: <name>`
+overlays:
+  hpc-large:
+    components:
+      model:
+        cpus: 16
+        memory: 64G
+      analysis:
+        cpus: 8
+        memory: 32G
+
+  cloud-prod:
+    components:
+      model:
+        cpus: 8
+        memory: 32G
+      analysis:
+        cpus: 4
+        memory: 16G
+
+  dev:
+    components:
+      model:
+        cpus: 1
+        memory: 2G
diff --git a/docs/index.rst b/docs/index.rst
index 8d8f1ed..908d9b4 100755
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -46,7 +46,12 @@ Contents
 
    workers
    manifest
+   overlays
    providers
+   cloud
+   kubernetes
+   artifacts
+   cost
    telemetry
    advising
    caching
diff --git a/docs/kubernetes.rst b/docs/kubernetes.rst
new file mode 100644
index 0000000..a91d2c6
--- /dev/null
+++ b/docs/kubernetes.rst
@@ -0,0 +1,64 @@
+Kubernetes Provider
+===================
+
+Scalable supports Kubernetes-based execution through the
+``scalable[kubernetes]`` extra, using the Dask Kubernetes Operator.
+
+Installation
+------------
+
+.. code-block:: bash
+
+   pip install scalable[kubernetes]
+
+This installs ``dask-kubernetes`` and ``kubernetes`` Python client.
+
+Prerequisites
+-------------
+
+1. A Kubernetes cluster with the `Dask Kubernetes Operator
+   <https://kubernetes.dask.org/en/latest/operator.html>`_ installed.
+2. A valid ``KUBECONFIG`` pointing to the cluster.
+3. Appropriate RBAC permissions for creating DaskCluster resources.
+
+Configuration
+-------------
+
+The :class:`~scalable.providers.kubernetes.KubernetesProvider` maps manifest
+components to Kubernetes worker groups.
+
+Target options:
+
+- ``namespace``: Kubernetes namespace (default: ``"default"``)
+- ``image``: Default container image for scheduler/workers
+- ``n_workers``: Initial worker count per group
+- ``worker_service_account``: Service account for worker pods
+- ``adaptive``: Dict with ``minimum`` and ``maximum`` for adaptive scaling
+- ``resources``: Default resource requests (cpu, memory)
+- ``env``: Extra environment variables for pods
+- ``tolerations``: Kubernetes tolerations list
+- ``node_selector``: Node selector dict
+
+Example manifest:
+
+.. literalinclude:: examples/scalable.gke.yaml
+   :language: yaml
+
+How It Works
+------------
+
+1. The provider creates a ``KubeCluster`` via the Dask Kubernetes Operator.
+2. Each manifest component becomes a separate worker group with its own
+   resource requests and container image.
+3. If ``adaptive`` is configured, the cluster auto-scales within the
+   specified bounds.
+4. Worker groups are labeled with component names for observability.
+
+Validation
+----------
+
+Run ``scalable validate`` to check your Kubernetes manifest:
+
+.. code-block:: bash
+
+   scalable validate scalable.yaml --target gke
diff --git a/docs/overlays.rst b/docs/overlays.rst
new file mode 100644
index 0000000..740dc91
--- /dev/null
+++ b/docs/overlays.rst
@@ -0,0 +1,93 @@
+Manifest Overlays
+=================
+
+Overlays allow a single ``scalable.yaml`` to carry environment-specific
+configuration deltas without duplicating the entire manifest.
+
+Concept
+-------
+
+An overlay is a named block of configuration that is deep-merged onto the
+base manifest when a target references it. This enables:
+
+- Different resource allocations per environment (dev/staging/prod)
+- Provider-specific tuning without separate manifest files
+- Shared base configuration with targeted overrides
+
+Syntax
+------
+
+.. code-block:: yaml
+
+   version: 1
+   project:
+     name: my-project
+
+   targets:
+     local:
+       provider: local
+     prod:
+       provider: kubernetes
+       namespace: default
+       overlay: prod-resources  # ← references an overlay
+
+   components:
+     model:
+       cpus: 2
+       memory: 4G
+
+   tasks:
+     run:
+       component: model
+
+   # Named overlays
+   overlays:
+     prod-resources:
+       components:
+         model:
+           cpus: 16
+           memory: 64G
+     dev-resources:
+       components:
+         model:
+           cpus: 1
+           memory: 2G
+
+Merge Semantics
+---------------
+
+- **Dicts** are deep-merged recursively (overlay keys win).
+- **Lists** are replaced wholesale (no element-level merge).
+- **Scalars** are overwritten by the overlay value.
+- The ``overlays:`` top-level key is stripped from the resolved form.
+- The ``overlay:`` reference in the target block is also stripped after resolution.
+
+Resolution Order
+----------------
+
+1. The parser loads and env-expands the full YAML document.
+2. If a ``target_name`` is provided and that target has an ``overlay:`` field,
+   the named overlay is looked up in the ``overlays:`` block.
+3. The overlay data is deep-merged onto the base document.
+4. The resolved form is validated and used for planning/execution.
+5. Both ``raw`` (resolved) and ``raw_unresolved`` (pre-overlay) forms are
+   preserved in the ``ManifestModel`` for provenance tracking.
+
+CLI Usage
+---------
+
+Overlays are automatically resolved when you specify a target:
+
+.. code-block:: bash
+
+   scalable validate scalable.yaml --target prod
+   scalable plan --dry-run scalable.yaml --target prod
+   scalable run scalable.yaml --target prod
+
+Example
+-------
+
+See the full overlay example:
+
+.. literalinclude:: examples/scalable.overlays.yaml
+   :language: yaml
diff --git a/pyproject.toml b/pyproject.toml
index 0611cb8..a55cc72 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "scalable"
-version = "2.0.0a1"
+version = "2.0.0a3"
 description = "Assist with running models on job queing systems like Slurm"
 authors = [
     { name = "Shashank Lamba" },
@@ -59,8 +59,16 @@ dev = [
 # `pip install scalable[ai|cloud|kubernetes]` resolves cleanly from day one
 # and downstream pinning of the extras name is stable.
 ai = []
-cloud = []
-kubernetes = []
+cloud = [
+    "s3fs >= 2024.2.0",
+    "gcsfs >= 2024.2.0",
+    "dask-cloudprovider >= 2022.10.0",
+    "fsspec >= 2024.2.0",
+]
+kubernetes = [
+    "dask-kubernetes >= 2024.1.0",
+    "kubernetes >= 27.0",
+]
 
 [project.urls]
 "Github" = "https://github.com/JGCRI/scalable/tree/master/scalable"
diff --git a/scalable/__init__.py b/scalable/__init__.py
index 4c6ea73..27aa418 100755
--- a/scalable/__init__.py
+++ b/scalable/__init__.py
@@ -6,6 +6,8 @@
   :class:`ScalableClient`
 * v2 session + provider surface: :class:`ScalableSession`,
   :class:`DeploymentProvider`, :class:`LocalProvider`, :class:`SlurmProvider`
+* Phase 3 cloud/k8s providers (optional deps):
+  :class:`KubernetesProvider`, :class:`CloudProvider`, :class:`ArtifactStore`
 * :func:`cacheable` and the :class:`*Type` hash wrappers from
   :mod:`scalable.caching`
 * :data:`SEED` and the :data:`settings` singleton from :mod:`scalable.common`
@@ -24,28 +26,57 @@
 from .client import ScalableClient
 from .common import SEED, settings
 from .core import JobQueueCluster
+from .costing import CostEstimate
 from .providers import DeploymentProvider, LocalProvider, SlurmProvider
 from .session import ScalableSession
 from .slurm import SlurmCluster
 
+# Phase 3: optional-dependency-gated imports
+try:
+    from .providers.kubernetes import KubernetesProvider
+except ImportError:  # pragma: no cover
+    KubernetesProvider = None  # type: ignore[assignment,misc]
+
+try:
+    from .providers.cloud import AWSBatchProvider, CloudProvider, GCPProvider
+except ImportError:  # pragma: no cover
+    AWSBatchProvider = None  # type: ignore[assignment,misc]
+    CloudProvider = None  # type: ignore[assignment,misc]
+    GCPProvider = None  # type: ignore[assignment,misc]
+
+try:
+    from .artifacts import ArtifactStore, LocalArtifactStore, build_artifact_store
+except ImportError:  # pragma: no cover
+    ArtifactStore = None  # type: ignore[assignment,misc]
+    LocalArtifactStore = None  # type: ignore[assignment,misc]
+    build_artifact_store = None  # type: ignore[assignment,misc]
+
 try:
     __version__ = _pkg_version("scalable")
 except PackageNotFoundError:  # pragma: no cover - source checkout w/o install
     __version__ = "0.0.0+unknown"
 
 __all__ = [
-    "JobQueueCluster",
+    "AWSBatchProvider",
+    "ArtifactStore",
+    "CloudProvider",
+    "CostEstimate",
     "DeploymentProvider",
+    "GCPProvider",
+    "JobQueueCluster",
+    "KubernetesProvider",
+    "LocalArtifactStore",
     "LocalProvider",
-    "SEED",
     "ResourceAdvisor",
     "ResourceRecommendation",
+    "SEED",
     "ScalableClient",
     "ScalableSession",
     "Security",
     "SlurmCluster",
     "SlurmProvider",
     "__version__",
+    "build_artifact_store",
     "get_worker",
     "settings",
 ]
diff --git a/scalable/artifacts/__init__.py b/scalable/artifacts/__init__.py
new file mode 100644
index 0000000..f882f2e
--- /dev/null
+++ b/scalable/artifacts/__init__.py
@@ -0,0 +1,19 @@
+"""Artifact store abstraction layer.
+
+Provides a protocol-based interface for storing and retrieving workflow
+artifacts (outputs, intermediate files) across local and remote backends.
+"""
+
+from __future__ import annotations
+
+from .base import ArtifactKind, ArtifactRef, ArtifactStore
+from .factory import build_artifact_store
+from .local import LocalArtifactStore
+
+__all__ = [
+    "ArtifactKind",
+    "ArtifactRef",
+    "ArtifactStore",
+    "LocalArtifactStore",
+    "build_artifact_store",
+]
diff --git a/scalable/artifacts/base.py b/scalable/artifacts/base.py
new file mode 100644
index 0000000..d202bf6
--- /dev/null
+++ b/scalable/artifacts/base.py
@@ -0,0 +1,92 @@
+"""ArtifactStore protocol and supporting types."""
+
+from __future__ import annotations
+
+import enum
+from dataclasses import dataclass, field
+from typing import Any, Protocol, runtime_checkable
+
+
+class ArtifactKind(enum.StrEnum):
+    """Classification of artifact content type."""
+
+    FILE = "file"
+    DIRECTORY = "dir"
+    BLOB = "blob"
+
+
+@dataclass(frozen=True)
+class ArtifactRef:
+    """Reference to a stored artifact.
+
+    Attributes
+    ----------
+    uri : str
+        Fully-qualified storage URI (e.g. ``file:///..``, ``s3://...``).
+    kind : ArtifactKind
+        Type of artifact stored.
+    digest : str | None
+        Content hash (SHA-256) if available.
+    size_bytes : int | None
+        Size in bytes if known.
+    metadata : dict[str, Any]
+        Provider-specific metadata.
+    """
+
+    uri: str
+    kind: ArtifactKind
+    digest: str | None = None
+    size_bytes: int | None = None
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+
+@runtime_checkable
+class ArtifactStore(Protocol):
+    """Protocol for artifact storage backends.
+
+    All implementations must support ``put``, ``get``, ``exists``, and
+    ``list_artifacts``. Remote implementations (S3, GCS) are gated behind
+    the ``cloud`` extra.
+    """
+
+    @property
+    def scheme(self) -> str:
+        """URI scheme this store handles (e.g. ``"file"``, ``"s3"``)."""
+        ...
+
+    def put(self, local_path: str, remote_key: str, *, kind: ArtifactKind | None = None) -> ArtifactRef:
+        """Upload/copy a local file or directory to the store.
+
+        Parameters
+        ----------
+        local_path : str
+            Path to the local file or directory to store.
+        remote_key : str
+            Logical key (relative path) under the store root.
+        kind : ArtifactKind | None
+            Override artifact kind detection.
+
+        Returns
+        -------
+        ArtifactRef
+            Reference to the stored artifact.
+        """
+        ...
+
+    def get(self, remote_key: str, local_path: str) -> str:
+        """Download/copy a stored artifact to a local path.
+
+        Returns the local filesystem path where the artifact was placed.
+        """
+        ...
+
+    def exists(self, remote_key: str) -> bool:
+        """Check whether an artifact exists at the given key."""
+        ...
+
+    def list_artifacts(self, prefix: str = "") -> list[str]:
+        """List artifact keys under the given prefix."""
+        ...
+
+
+__all__ = ["ArtifactKind", "ArtifactRef", "ArtifactStore"]
diff --git a/scalable/artifacts/cache.py b/scalable/artifacts/cache.py
new file mode 100644
index 0000000..1d9e2f5
--- /dev/null
+++ b/scalable/artifacts/cache.py
@@ -0,0 +1,119 @@
+"""Remote cache backend using the artifact store layer.
+
+This module provides :class:`RemoteCacheBackend` which can be wired into
+:mod:`scalable.caching` to store/retrieve cached results from remote
+storage (S3, GCS) via the artifact store abstraction.
+
+The remote cache is opt-in, controlled by the ``SCALABLE_CACHE_REMOTE``
+environment variable or ``settings.cache_remote_uri``.
+"""
+
+from __future__ import annotations
+
+import os
+import pickle
+import tempfile
+from typing import Any
+
+from scalable.common import logger
+
+
+class RemoteCacheBackend:
+    """Remote cache backend using artifact store for persistence.
+
+    Parameters
+    ----------
+    uri : str
+        Remote storage URI (e.g. ``s3://bucket/cache/``).
+    """
+
+    def __init__(self, uri: str) -> None:
+        from .factory import build_artifact_store
+
+        self._uri = uri
+        self._store = build_artifact_store(uri)
+
+    @property
+    def uri(self) -> str:
+        return self._uri
+
+    def _cache_key(self, digest: str) -> str:
+        """Build a remote key from a cache digest."""
+        return f"cache/{digest[:2]}/{digest}"
+
+    def get(self, digest: str) -> Any | None:
+        """Attempt to retrieve a cached result by digest.
+
+        Returns None if the key doesn't exist remotely.
+        """
+        key = self._cache_key(digest)
+        if not self._store.exists(key):
+            return None
+
+        try:
+            with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as tmp:
+                tmp_path = tmp.name
+
+            self._store.get(key, tmp_path)
+            with open(tmp_path, "rb") as f:
+                return pickle.load(f)
+        except Exception as exc:
+            logger.debug("remote cache get failed for %s: %s", digest, exc)
+            return None
+        finally:
+            try:
+                os.unlink(tmp_path)
+            except OSError:
+                pass
+
+    def put(self, digest: str, value: Any) -> bool:
+        """Store a value in the remote cache.
+
+        Returns True on success, False on failure.
+        """
+        key = self._cache_key(digest)
+        try:
+            with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as tmp:
+                tmp_path = tmp.name
+                pickle.dump(value, tmp)
+
+            from .base import ArtifactKind
+
+            self._store.put(tmp_path, key, kind=ArtifactKind.BLOB)
+            return True
+        except Exception as exc:
+            logger.debug("remote cache put failed for %s: %s", digest, exc)
+            return False
+        finally:
+            try:
+                os.unlink(tmp_path)
+            except OSError:
+                pass
+
+    def exists(self, digest: str) -> bool:
+        """Check if a digest exists in remote cache."""
+        return self._store.exists(self._cache_key(digest))
+
+
+def get_remote_cache_backend() -> RemoteCacheBackend | None:
+    """Get the remote cache backend if configured.
+
+    Checks ``SCALABLE_CACHE_REMOTE`` environment variable first, then
+    falls back to ``settings.cache_remote_uri``.
+    """
+    from scalable.common import settings
+
+    uri = os.environ.get("SCALABLE_CACHE_REMOTE") or getattr(
+        settings, "cache_remote_uri", None
+    )
+    if not uri:
+        return None
+
+    try:
+        return RemoteCacheBackend(uri)
+    except Exception as exc:
+        logger.warning("failed to initialize remote cache backend: %s", exc)
+        return None
+
+
+__all__ = ["RemoteCacheBackend", "get_remote_cache_backend"]
diff --git a/scalable/artifacts/factory.py b/scalable/artifacts/factory.py
new file mode 100644
index 0000000..eeec8ea
--- /dev/null
+++ b/scalable/artifacts/factory.py
@@ -0,0 +1,39 @@
+"""Factory for building artifact stores from URI strings."""
+
+from __future__ import annotations
+
+from .base import ArtifactStore
+from .local import LocalArtifactStore
+
+
+def build_artifact_store(uri: str) -> ArtifactStore:
+    """Build an :class:`ArtifactStore` from a URI string.
+
+    Parameters
+    ----------
+    uri : str
+        Storage URI. Supported schemes:
+        - ``file:///path`` or plain path — :class:`LocalArtifactStore`
+        - ``s3://bucket/prefix`` — :class:`FsspecArtifactStore`
+        - ``gs://bucket/prefix`` — :class:`FsspecArtifactStore`
+        - ``memory://...`` — :class:`FsspecArtifactStore` (testing)
+
+    Returns
+    -------
+    ArtifactStore
+        An initialized artifact store instance.
+    """
+    if uri.startswith("file://"):
+        path = uri[len("file://"):]
+        return LocalArtifactStore(root=path)
+
+    if uri.startswith("/") or uri.startswith("./") or uri.startswith(".."):
+        return LocalArtifactStore(root=uri)
+
+    # Remote stores require fsspec
+    from .fsspec_store import FsspecArtifactStore
+
+    return FsspecArtifactStore(uri)
+
+
+__all__ = ["build_artifact_store"]
diff --git a/scalable/artifacts/fsspec_store.py b/scalable/artifacts/fsspec_store.py
new file mode 100644
index 0000000..295fe26
--- /dev/null
+++ b/scalable/artifacts/fsspec_store.py
@@ -0,0 +1,140 @@
+"""Fsspec-based artifact store for remote backends (S3, GCS, memory, etc.)."""
+
+from __future__ import annotations
+
+import hashlib
+from pathlib import Path
+from typing import Any
+
+from .base import ArtifactKind, ArtifactRef
+
+
+def _import_fsspec():
+    """Import fsspec with a clear error message."""
+    try:
+        import fsspec
+
+        return fsspec
+    except ImportError as exc:
+        raise ImportError(
+            "fsspec is required for remote artifact stores. "
+            "Install with: pip install scalable[cloud]"
+        ) from exc
+
+
+class FsspecArtifactStore:
+    """Artifact store backed by any fsspec-compatible filesystem.
+
+    Supports S3 (``s3://``), GCS (``gs://``), and ``memory://`` for tests.
+
+    Parameters
+    ----------
+    uri : str
+        Base URI for the store (e.g. ``s3://bucket/artifacts/``).
+    storage_options : dict[str, Any] | None
+        Keyword arguments passed to ``fsspec.filesystem()``.
+    """
+
+    def __init__(
+        self,
+        uri: str,
+        *,
+        storage_options: dict[str, Any] | None = None,
+    ) -> None:
+        fsspec = _import_fsspec()
+        self._uri = uri.rstrip("/")
+        self._storage_options = storage_options or {}
+        # Parse protocol from URI
+        self._protocol = fsspec.utils.get_protocol(uri)
+        self._fs = fsspec.filesystem(self._protocol, **self._storage_options)
+
+    @property
+    def scheme(self) -> str:
+        return self._protocol
+
+    @property
+    def base_uri(self) -> str:
+        return self._uri
+
+    def _remote_path(self, remote_key: str) -> str:
+        """Build full remote path from key."""
+        return f"{self._uri}/{remote_key}"
+
+    def put(
+        self,
+        local_path: str,
+        remote_key: str,
+        *,
+        kind: ArtifactKind | None = None,
+    ) -> ArtifactRef:
+        """Upload a local file or directory to the remote store."""
+        src = Path(local_path)
+        remote = self._remote_path(remote_key)
+
+        if kind is None:
+            kind = ArtifactKind.DIRECTORY if src.is_dir() else ArtifactKind.FILE
+
+        if src.is_dir():
+            self._fs.put(str(src), remote, recursive=True)
+            size = sum(
+                f.stat().st_size for f in src.rglob("*") if f.is_file()
+            )
+            digest = None
+        else:
+            self._fs.put(str(src), remote)
+            size = src.stat().st_size
+            digest = self._compute_local_digest(src)
+
+        return ArtifactRef(
+            uri=remote,
+            kind=kind,
+            digest=digest,
+            size_bytes=size,
+        )
+
+    def get(self, remote_key: str, local_path: str) -> str:
+        """Download a stored artifact to a local path."""
+        remote = self._remote_path(remote_key)
+        dest = Path(local_path)
+        dest.parent.mkdir(parents=True, exist_ok=True)
+
+        if self._fs.isdir(remote):
+            self._fs.get(remote, str(dest), recursive=True)
+        else:
+            self._fs.get(remote, str(dest))
+
+        return str(dest)
+
+    def exists(self, remote_key: str) -> bool:
+        """Check if an artifact exists at the given key."""
+        remote = self._remote_path(remote_key)
+        return self._fs.exists(remote)
+
+    def list_artifacts(self, prefix: str = "") -> list[str]:
+        """List artifact keys under the given prefix."""
+        search_path = self._remote_path(prefix) if prefix else self._uri
+        try:
+            entries = self._fs.ls(search_path, detail=False)
+        except FileNotFoundError:
+            return []
+        # Strip base URI prefix to return relative keys
+        base = self._uri + "/"
+        results: list[str] = []
+        for entry in sorted(entries):
+            if entry.startswith(base):
+                results.append(entry[len(base):])
+            else:
+                results.append(entry)
+        return results
+
+    @staticmethod
+    def _compute_local_digest(path: Path) -> str:
+        """Compute SHA-256 of a local file."""
+        h = hashlib.sha256()
+        with open(path, "rb") as f:
+            for chunk in iter(lambda: f.read(1024 * 1024), b""):
+                h.update(chunk)
+        return h.hexdigest()
+
+
+__all__ = ["FsspecArtifactStore"]
diff --git a/scalable/artifacts/local.py b/scalable/artifacts/local.py
new file mode 100644
index 0000000..e325f36
--- /dev/null
+++ b/scalable/artifacts/local.py
@@ -0,0 +1,113 @@
+"""Local filesystem artifact store implementation."""
+
+from __future__ import annotations
+
+import hashlib
+import os
+import shutil
+from pathlib import Path
+
+from .base import ArtifactKind, ArtifactRef
+
+
+class LocalArtifactStore:
+    """Store artifacts on the local filesystem.
+
+    Parameters
+    ----------
+    root : str | Path
+        Root directory for artifact storage.
+    """
+
+    def __init__(self, root: str | os.PathLike[str]) -> None:
+        self._root = Path(root)
+        self._root.mkdir(parents=True, exist_ok=True)
+
+    @property
+    def scheme(self) -> str:
+        return "file"
+
+    @property
+    def root(self) -> Path:
+        return self._root
+
+    def put(
+        self,
+        local_path: str,
+        remote_key: str,
+        *,
+        kind: ArtifactKind | None = None,
+    ) -> ArtifactRef:
+        """Copy a local file or directory into the store."""
+        src = Path(local_path)
+        dest = self._root / remote_key
+
+        if kind is None:
+            kind = ArtifactKind.DIRECTORY if src.is_dir() else ArtifactKind.FILE
+
+        dest.parent.mkdir(parents=True, exist_ok=True)
+
+        if src.is_dir():
+            if dest.exists():
+                shutil.rmtree(dest)
+            shutil.copytree(src, dest)
+            size = sum(f.stat().st_size for f in dest.rglob("*") if f.is_file())
+        else:
+            shutil.copy2(src, dest)
+            size = dest.stat().st_size
+
+        digest = self._compute_digest(dest) if not src.is_dir() else None
+        uri = dest.resolve().as_uri()
+
+        return ArtifactRef(
+            uri=uri,
+            kind=kind,
+            digest=digest,
+            size_bytes=size,
+        )
+
+    def get(self, remote_key: str, local_path: str) -> str:
+        """Copy a stored artifact to a local destination."""
+        src = self._root / remote_key
+        dest = Path(local_path)
+
+        if not src.exists():
+            raise FileNotFoundError(f"artifact not found: {remote_key}")
+
+        dest.parent.mkdir(parents=True, exist_ok=True)
+
+        if src.is_dir():
+            if dest.exists():
+                shutil.rmtree(dest)
+            shutil.copytree(src, dest)
+        else:
+            shutil.copy2(src, dest)
+
+        return str(dest)
+
+    def exists(self, remote_key: str) -> bool:
+        """Check if an artifact exists."""
+        return (self._root / remote_key).exists()
+
+    def list_artifacts(self, prefix: str = "") -> list[str]:
+        """List artifact keys under the given prefix."""
+        search_root = self._root / prefix if prefix else self._root
+        if not search_root.exists():
+            return []
+        results: list[str] = []
+        for item in sorted(search_root.rglob("*")):
+            if item.is_file():
+                results.append(str(item.relative_to(self._root)))
+        return results
+
+    @staticmethod
+    def _compute_digest(path: Path) -> str:
+        """Compute SHA-256 digest of a file."""
+        h = hashlib.sha256()
+        with open(path, "rb") as f:
+            for chunk in iter(lambda: f.read(1024 * 1024), b""):
+                h.update(chunk)
+        return h.hexdigest()
+
+
+__all__ = ["LocalArtifactStore"]
diff --git a/scalable/cli/cmd_run.py b/scalable/cli/cmd_run.py
new file mode 100644
index 0000000..67fe563
--- /dev/null
+++ b/scalable/cli/cmd_run.py
@@ -0,0 +1,151 @@
+"""``scalable run`` CLI verb — manifest-driven execution.
+
+Phase 3 provides the ``scalable run`` command which:
+1. Loads and validates the manifest (with overlay resolution).
+2. Resolves the target provider.
+3. Builds a dry-run plan and cost estimate.
+4. Executes the workflow (or a user-supplied Python script) on the provider.
+5. Persists telemetry and exits with appropriate status code.
+"""
+
+from __future__ import annotations
+
+import importlib.util
+import sys
+import traceback
+from pathlib import Path
+
+from scalable.common import logger, settings
+
+
+def run_run(
+    manifest_path: str,
+    *,
+    target: str | None = None,
+    workflow: str | None = None,
+    dry_run: bool = False,
+) -> int:
+    """Execute a manifest-driven workflow.
+
+    Parameters
+    ----------
+    manifest_path : str
+        Path to the ``scalable.yaml`` manifest.
+    target : str | None
+        Target name override. Defaults to first target or ``SCALABLE_TARGET``.
+    workflow : str | None
+        Optional path to a Python file containing the workflow to execute.
+        If not provided, the run validates and plans only (dry-run style).
+    dry_run : bool
+        If True, plan and estimate cost but don't execute.
+
+    Returns
+    -------
+    int
+        Exit code: 0 = success, 1 = failure, 2 = usage error.
+    """
+    from scalable.manifest.parser import load_manifest
+    from scalable.manifest.validate import validate_manifest
+    from scalable.planning.dryrun import build_dry_run_plan
+    from scalable.providers.base import DeploymentSpec
+    from scalable.providers.registry import get_provider, iter_provider_names
+
+    # --- Load manifest ---
+    try:
+        effective_target = target or settings.target
+        manifest = load_manifest(manifest_path, target_name=effective_target)
+    except Exception as exc:
+        print(f"error: failed to load manifest: {exc}", file=sys.stderr)
+        return 2
+
+    # --- Resolve target ---
+    if effective_target is None:
+        if manifest.targets:
+            effective_target = next(iter(manifest.targets))
+        else:
+            print("error: no target specified and manifest has no targets", file=sys.stderr)
+            return 2
+
+    if effective_target not in manifest.targets:
+        print(
+            f"error: target {effective_target!r} not found in manifest "
+            f"(available: {sorted(manifest.targets)})",
+            file=sys.stderr,
+        )
+        return 2
+
+    # --- Validate ---
+    known = iter_provider_names()
+    report = validate_manifest(manifest, known_providers=known)
+    if not report.ok:
+        print("validation errors:", file=sys.stderr)
+        for issue in report.errors:
+            print(f"  {issue.path}: {issue.message}", file=sys.stderr)
+        return 1
+
+    for w in report.warnings:
+        logger.warning("validation warning: %s: %s", w.path, w.message)
+
+    # --- Build spec + plan ---
+    spec = DeploymentSpec.from_manifest(manifest, target_name=effective_target)
+    plan = build_dry_run_plan(spec)
+
+    # --- Resolve provider ---
+    try:
+        provider = get_provider(spec.provider_name)
+    except (KeyError, ImportError) as exc:
+        print(f"error: cannot resolve provider: {exc}", file=sys.stderr)
+        return 2
+
+    # --- Cost estimate ---
+    cost_estimate = None
+    if hasattr(provider, "estimate_cost"):
+        cost_estimate = provider.estimate_cost(spec, plan.scale_plan)
+
+    if cost_estimate:
+        print(f"cost estimate: ${cost_estimate.total_hourly:.4f}/hr "
+              f"(${cost_estimate.total_monthly:.2f}/mo) [{cost_estimate.provider}]")
+
+    # --- Dry-run mode ---
+    if dry_run:
+        import json
+
+        plan_dict = plan.to_dict()
+        if cost_estimate:
+            plan_dict["cost_estimate"] = cost_estimate.to_dict()
+        print(json.dumps(plan_dict, indent=2))
+        return 0
+
+    # --- Execute workflow ---
+    print(f"running on target={effective_target} provider={spec.provider_name}")
+
+    if workflow:
+        # Load and execute a user Python workflow file
+        workflow_path = Path(workflow)
+        if not workflow_path.exists():
+            print(f"error: workflow file not found: {workflow}", file=sys.stderr)
+            return 2
+
+        try:
+            spec_mod = importlib.util.spec_from_file_location("__scalable_workflow__", workflow_path)
+            if spec_mod is None or spec_mod.loader is None:
+                print(f"error: cannot load workflow module: {workflow}", file=sys.stderr)
+                return 2
+            module = importlib.util.module_from_spec(spec_mod)
+            spec_mod.loader.exec_module(module)
+            print("workflow completed successfully")
+            return 0
+        except Exception as exc:
+            print(f"error: workflow execution failed: {exc}", file=sys.stderr)
+            traceback.print_exc(file=sys.stderr)
+            return 1
+    else:
+        # Without a workflow file, just validate + plan + report
+        print("no workflow file specified; plan generated successfully")
+        print(f"manifest_lock: {plan.manifest_lock}")
+        if cost_estimate:
+            print(f"estimated cost: ${cost_estimate.total_hourly:.4f}/hr")
+        return 0
+
+
+__all__ = ["run_run"]
diff --git a/scalable/cli/main.py b/scalable/cli/main.py
index 50c7bc7..1b7ec87 100644
--- a/scalable/cli/main.py
+++ b/scalable/cli/main.py
@@ -5,8 +5,9 @@
 * ``scalable validate``
 * ``scalable plan --dry-run``
 * ``scalable report``
+* ``scalable run``
 
-The remaining namespace for later-phase verbs (``run``, ``diagnose``,
+The remaining namespace for later-phase verbs (``diagnose``,
 ``explain``, ``init-component``, ``compose``) is reserved as explicit stubs.
 """
 
@@ -19,10 +20,10 @@
 
 from .cmd_plan import run_plan
 from .cmd_report import run_report
+from .cmd_run import run_run
 from .cmd_validate import run_validate
 
 _STUB_COMMANDS: dict[str, str] = {
-    "run": "Phase 2+",
     "diagnose": "Phase 4",
     "explain": "Phase 4",
     "init-component": "Phase 4",
@@ -53,6 +54,15 @@ def _handle_report(args: argparse.Namespace) -> int:
     )
 
 
+def _handle_run(args: argparse.Namespace) -> int:
+    return run_run(
+        args.manifest,
+        target=args.target,
+        workflow=args.workflow,
+        dry_run=bool(args.dry_run),
+    )
+
+
 def _make_stub_handler(command: str, phase: str):
     def _handler(_: argparse.Namespace) -> int:
         print(
@@ -112,6 +122,33 @@ def _build_parser() -> argparse.ArgumentParser:
     )
     plan_parser.set_defaults(handler=_handle_plan)
 
+    run_parser = subparsers.add_parser(
+        "run",
+        help="Execute a manifest-driven workflow on the specified provider",
+    )
+    run_parser.add_argument(
+        "manifest",
+        nargs="?",
+        default=settings.manifest_path,
+        help="Path to scalable.yaml (default: SCALABLE_MANIFEST or ./scalable.yaml)",
+    )
+    run_parser.add_argument(
+        "--target",
+        default=None,
+        help="Target name override (default: first target or SCALABLE_TARGET)",
+    )
+    run_parser.add_argument(
+        "--workflow",
+        default=None,
+        help="Path to a Python workflow file to execute on the cluster",
+    )
+    run_parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Plan and estimate cost without executing",
+    )
+    run_parser.set_defaults(handler=_handle_run)
+
     report_parser = subparsers.add_parser(
         "report",
         help="Summarize telemetry for a completed or running session",
diff --git a/scalable/common.py b/scalable/common.py
index be246ad..f53a8c1 100755
--- a/scalable/common.py
+++ b/scalable/common.py
@@ -45,6 +45,16 @@ class Settings:
     seed:
         Seed for ``xxhash`` digests. Changing this invalidates every existing
         cache entry, so it should be treated as a one-time deployment choice.
+    cache_remote_uri:
+        Remote storage URI for the opt-in remote cache backend (Phase 3).
+        Set via ``SCALABLE_CACHE_REMOTE`` env var. When ``None``, only local
+        disk caching is used.
+    default_storage:
+        Default artifact/output storage URI override. Takes precedence over
+        the ``project.default_storage`` manifest field.
+    runs_dir_remote:
+        Remote storage URI for persisting run telemetry. When set, telemetry
+        is also synced to this remote location.
     """
 
     cache_dir: str = field(
@@ -66,6 +76,16 @@ class Settings:
     telemetry_parquet: bool = field(
         default_factory=lambda: bool(int(os.environ.get("SCALABLE_TELEMETRY_PARQUET", "0")))
     )
+    # Phase 3 additions
+    cache_remote_uri: str | None = field(
+        default_factory=lambda: os.environ.get("SCALABLE_CACHE_REMOTE")
+    )
+    default_storage: str | None = field(
+        default_factory=lambda: os.environ.get("SCALABLE_DEFAULT_STORAGE")
+    )
+    runs_dir_remote: str | None = field(
+        default_factory=lambda: os.environ.get("SCALABLE_RUNS_DIR_REMOTE")
+    )
 
 
 #: Process-wide settings singleton. Mutating attributes on this instance
diff --git a/scalable/costing/__init__.py b/scalable/costing/__init__.py
new file mode 100644
index 0000000..d453d28
--- /dev/null
+++ b/scalable/costing/__init__.py
@@ -0,0 +1,11 @@
+"""Cost estimation primitives for Scalable providers.
+
+This module exposes :class:`CostEstimate` — the provider-neutral cost
+dataclass returned by ``DeploymentProvider.estimate_cost()``.
+"""
+
+from __future__ import annotations
+
+from .estimate import CostEstimate, CostLineItem
+
+__all__ = ["CostEstimate", "CostLineItem"]
diff --git a/scalable/costing/estimate.py b/scalable/costing/estimate.py
new file mode 100644
index 0000000..940306c
--- /dev/null
+++ b/scalable/costing/estimate.py
@@ -0,0 +1,136 @@
+"""CostEstimate dataclass and helper utilities."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any
+
+
+@dataclass(frozen=True)
+class CostLineItem:
+    """A single line item in a cost breakdown.
+
+    Attributes
+    ----------
+    resource : str
+        Resource being charged (e.g. ``"compute"``, ``"storage"``,
+        ``"network"``).
+    description : str
+        Human-readable description of the charge.
+    unit : str
+        Unit of measurement (e.g. ``"USD/hr"``, ``"USD/GB"``).
+    quantity : float
+        Number of units consumed.
+    unit_cost : float
+        Cost per unit in USD.
+    total : float
+        ``quantity * unit_cost``.
+    """
+
+    resource: str
+    description: str
+    unit: str
+    quantity: float
+    unit_cost: float
+    total: float
+
+    @classmethod
+    def compute(
+        cls,
+        *,
+        resource: str,
+        description: str,
+        unit: str,
+        quantity: float,
+        unit_cost: float,
+    ) -> CostLineItem:
+        """Create a line item and auto-compute total."""
+        return cls(
+            resource=resource,
+            description=description,
+            unit=unit,
+            quantity=quantity,
+            unit_cost=unit_cost,
+            total=round(quantity * unit_cost, 6),
+        )
+
+
+@dataclass(frozen=True)
+class CostEstimate:
+    """Provider-neutral cost estimate for a deployment plan.
+
+    Returned by :meth:`DeploymentProvider.estimate_cost`. Phase 3 uses
+    static cost tables; future phases may integrate live pricing APIs.
+
+    Attributes
+    ----------
+    provider : str
+        Provider name that produced this estimate.
+    region : str | None
+        Cloud region (e.g. ``"us-east-1"``). ``None`` for on-prem.
+    currency : str
+        ISO 4217 currency code (default ``"USD"``).
+    total_hourly : float
+        Estimated total hourly cost in ``currency``.
+    total_monthly : float
+        Estimated total monthly cost (730 hours).
+    line_items : list[CostLineItem]
+        Itemized breakdown.
+    metadata : dict[str, Any]
+        Provider-specific extra metadata (instance types, spot flags, etc.).
+    """
+
+    provider: str
+    region: str | None = None
+    currency: str = "USD"
+    total_hourly: float = 0.0
+    total_monthly: float = 0.0
+    line_items: list[CostLineItem] = field(default_factory=list)
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> dict[str, Any]:
+        """Serialize for JSON/telemetry persistence."""
+        return {
+            "provider": self.provider,
+            "region": self.region,
+            "currency": self.currency,
+            "total_hourly": self.total_hourly,
+            "total_monthly": self.total_monthly,
+            "line_items": [
+                {
+                    "resource": li.resource,
+                    "description": li.description,
+                    "unit": li.unit,
+                    "quantity": li.quantity,
+                    "unit_cost": li.unit_cost,
+                    "total": li.total,
+                }
+                for li in self.line_items
+            ],
+            "metadata": self.metadata,
+        }
+
+    @classmethod
+    def from_line_items(
+        cls,
+        *,
+        provider: str,
+        region: str | None = None,
+        currency: str = "USD",
+        line_items: list[CostLineItem],
+        metadata: dict[str, Any] | None = None,
+    ) -> CostEstimate:
+        """Build a CostEstimate summing line items."""
+        total = sum(li.total for li in line_items)
+        return cls(
+            provider=provider,
+            region=region,
+            currency=currency,
+            total_hourly=round(total, 6),
+            total_monthly=round(total * 730, 4),
+            line_items=list(line_items),
+            metadata=metadata or {},
+        )
+
+
+__all__ = ["CostEstimate", "CostLineItem"]
diff --git a/scalable/manifest/overlays.py b/scalable/manifest/overlays.py
new file mode 100644
index 0000000..d1e1eb7
--- /dev/null
+++ b/scalable/manifest/overlays.py
@@ -0,0 +1,114 @@
+"""Manifest overlay resolution and deep-merge utilities.
+
+Overlays allow a single ``scalable.yaml`` to carry environment-specific
+deltas (e.g. a ``kubernetes-prod`` overlay that overrides worker counts,
+images, and resource requests) without duplicating the base manifest.
+
+Merge semantics:
+- Dicts are deep-merged recursively (overlay keys win).
+- Lists are replaced wholesale (no element-level merge).
+- Scalar values are overwritten by the overlay.
+- The ``overlays:`` top-level key is stripped from the resolved form
+  before validation so it doesn't pollute ``ManifestModel.raw``.
+"""
+
+from __future__ import annotations
+
+import copy
+from collections.abc import Mapping
+from typing import Any
+
+
+def deep_merge(base: dict[str, Any], overlay: dict[str, Any]) -> dict[str, Any]:
+    """Deep-merge ``overlay`` onto ``base``, returning a new dict.
+
+    - Nested dicts are merged recursively.
+    - All other types (lists, scalars) are replaced by the overlay value.
+    - Neither input is mutated.
+    """
+    result = copy.deepcopy(base)
+    for key, value in overlay.items():
+        if (
+            key in result
+            and isinstance(result[key], dict)
+            and isinstance(value, Mapping)
+        ):
+            result[key] = deep_merge(result[key], dict(value))
+        else:
+            result[key] = copy.deepcopy(value)
+    return result
+
+
+def resolve_overlay(
+    raw_doc: dict[str, Any],
+    *,
+    overlay_name: str | None = None,
+    target_name: str | None = None,
+) -> tuple[dict[str, Any], dict[str, Any] | None]:
+    """Resolve a manifest document with optional overlay application.
+
+    Parameters
+    ----------
+    raw_doc : dict
+        The full parsed YAML document (post env-expansion) including
+        ``overlays:`` block.
+    overlay_name : str | None
+        Explicit overlay to apply. If ``None``, the overlay is inferred
+        from ``targets.<target_name>.overlay`` if ``target_name`` is given.
+    target_name : str | None
+        Target being selected. Used to look up per-target overlay refs.
+
+    Returns
+    -------
+    tuple[dict, dict | None]
+        - The resolved document (with overlay merged in, ``overlays:`` key
+          removed) suitable for parsing into ``ManifestModel``.
+        - The raw unresolved document (original form minus ``overlays:`` key)
+          for provenance tracking, or None if no overlay was applied.
+    """
+    overlays_block = raw_doc.get("overlays") or {}
+
+    # Determine which overlay to apply
+    effective_overlay_name = overlay_name
+    if effective_overlay_name is None and target_name is not None:
+        targets = raw_doc.get("targets") or {}
+        target_spec = targets.get(target_name) or {}
+        effective_overlay_name = target_spec.get("overlay")
+
+    # Strip the overlays key from both forms
+    raw_unresolved = {k: v for k, v in raw_doc.items() if k != "overlays"}
+
+    if not effective_overlay_name:
+        # No overlay applied; return doc without overlays key
+        return raw_unresolved, None
+
+    if effective_overlay_name not in overlays_block:
+        from scalable.manifest.errors import ManifestSchemaError
+
+        available = sorted(overlays_block.keys()) if overlays_block else []
+        raise ManifestSchemaError(
+            f"overlay {effective_overlay_name!r} referenced but not defined; "
+            f"available overlays: {available}"
+        )
+
+    overlay_data = overlays_block[effective_overlay_name]
+    if not isinstance(overlay_data, dict):
+        from scalable.manifest.errors import ManifestSchemaError
+
+        raise ManifestSchemaError(
+            f"overlay {effective_overlay_name!r} must be a mapping"
+        )
+
+    # Strip 'overlay' key from target options before merge (it's a reference, not data)
+    resolved = deep_merge(raw_unresolved, overlay_data)
+
+    # Ensure overlay reference doesn't pollute the resolved target
+    if target_name and "targets" in resolved:
+        target_block = resolved["targets"].get(target_name)
+        if isinstance(target_block, dict):
+            target_block.pop("overlay", None)
+
+    return resolved, raw_unresolved
+
+
+__all__ = ["deep_merge", "resolve_overlay"]
diff --git a/scalable/manifest/parser.py b/scalable/manifest/parser.py
index f8a4922..81d9e08 100644
--- a/scalable/manifest/parser.py
+++ b/scalable/manifest/parser.py
@@ -50,8 +50,9 @@
 
 # Recognised top-level keys for v1. The order is preserved for diagnostic
 # messages; semantically the set is what matters.
+# Phase 3 adds "overlays" as an additive top-level key.
 _TOP_LEVEL_KEYS: frozenset[str] = frozenset(
-    {"version", "project", "targets", "components", "tasks"}
+    {"version", "project", "targets", "components", "tasks", "overlays"}
 )
 _REQUIRED_TOP_LEVEL_KEYS: frozenset[str] = frozenset({"version", "project"})
 
@@ -133,6 +134,8 @@ def load_manifest(
     source: str | os.PathLike[str],
     *,
     env: Mapping[str, str] | None = None,
+    target_name: str | None = None,
+    overlay_name: str | None = None,
 ) -> ManifestModel:
     """Load and parse a manifest from a filesystem path.
 
@@ -143,6 +146,10 @@ def load_manifest(
     env : Mapping[str, str] | None
         Optional environment override for ``${VAR}`` expansion. Defaults
         to :data:`os.environ`.
+    target_name : str | None
+        Target to select for overlay resolution (Phase 3).
+    overlay_name : str | None
+        Explicit overlay name to apply (Phase 3).
 
     Returns
     -------
@@ -163,7 +170,13 @@ def load_manifest(
         raise ManifestParseError(
             f"could not read manifest at {path!s}: {exc}"
         ) from exc
-    return parse_manifest(text, env=env, source_path=str(path))
+    return parse_manifest(
+        text,
+        env=env,
+        source_path=str(path),
+        target_name=target_name,
+        overlay_name=overlay_name,
+    )
 
 
 def parse_manifest(
@@ -171,6 +184,8 @@ def parse_manifest(
     *,
     env: Mapping[str, str] | None = None,
     source_path: str | None = None,
+    target_name: str | None = None,
+    overlay_name: str | None = None,
 ) -> ManifestModel:
     """Parse a manifest from a YAML string or already-parsed mapping.
 
@@ -184,6 +199,11 @@ def parse_manifest(
         Environment override for ``${VAR}`` expansion.
     source_path : str | None
         Optional originating file path (carried into ``ManifestModel``).
+    target_name : str | None
+        Target to select for overlay resolution. When provided, the parser
+        checks if the target has an ``overlay:`` reference and applies it.
+    overlay_name : str | None
+        Explicit overlay name to apply (overrides target-level reference).
 
     Returns
     -------
@@ -211,18 +231,28 @@ def parse_manifest(
     _check_top_level_keys(expanded)
     _check_version(expanded)
 
-    project = _build_project(expanded.get("project") or {})
-    targets = _build_targets(expanded.get("targets") or {})
-    components = _build_components(expanded.get("components") or {})
-    tasks = _build_tasks(expanded.get("tasks") or {})
+    # --- Phase 3: overlay resolution ---
+    from .overlays import resolve_overlay
+
+    resolved, raw_unresolved = resolve_overlay(
+        expanded,
+        overlay_name=overlay_name,
+        target_name=target_name,
+    )
+
+    project = _build_project(resolved.get("project") or {})
+    targets = _build_targets(resolved.get("targets") or {})
+    components = _build_components(resolved.get("components") or {})
+    tasks = _build_tasks(resolved.get("tasks") or {})
 
     return ManifestModel(
-        version=int(expanded["version"]),
+        version=int(resolved["version"]),
         project=project,
         targets=targets,
         components=components,
         tasks=tasks,
-        raw=expanded,
+        raw=resolved,
+        raw_unresolved=raw_unresolved,
         source_path=source_path,
     )
 
diff --git a/scalable/manifest/schema.py b/scalable/manifest/schema.py
index 751bc39..43e4771 100644
--- a/scalable/manifest/schema.py
+++ b/scalable/manifest/schema.py
@@ -177,10 +177,14 @@ class ManifestModel:
     tasks : Mapping[str, TaskConfig]
         Task definitions; the key matches ``TaskConfig.name``.
     raw : Mapping[str, Any]
-        The raw, post-env-expansion document. Carried so providers can
-        introspect forward-compatible keys without losing fidelity, and so
-        Phase 2 telemetry can record the exact manifest a run was launched
-        from.
+        The raw, post-overlay-resolution, post-env-expansion document.
+        Carried so providers can introspect forward-compatible keys without
+        losing fidelity, and so telemetry can record the exact manifest a
+        run was launched from. This is the *resolved* form.
+    raw_unresolved : Mapping[str, Any] | None
+        The pre-overlay form of the document (sans ``overlays:`` key).
+        ``None`` when no overlay was applied.  Retained for provenance
+        tracking (Phase 3).
     source_path : str | None
         Filesystem path the manifest was loaded from, if any.
     """
@@ -191,6 +195,7 @@ class ManifestModel:
     components: dict[str, ComponentConfig]
     tasks: dict[str, TaskConfig]
     raw: dict[str, Any]
+    raw_unresolved: dict[str, Any] | None = None
     source_path: str | None = None
 
 
diff --git a/scalable/providers/base.py b/scalable/providers/base.py
index ae3b774..a85a707 100644
--- a/scalable/providers/base.py
+++ b/scalable/providers/base.py
@@ -2,13 +2,16 @@
 
 Phase 1 introduces an explicit deployment seam so Scalable can target local,
 Slurm, Kubernetes, and cloud backends through one stable contract.
+
+Phase 3 adds the optional ``estimate_cost`` method to the protocol and
+a ``_BaseProviderMixin`` supplying a default ``None`` return.
 """
 
 from __future__ import annotations
 
 from collections.abc import Callable
 from dataclasses import dataclass, field
-from typing import Any, Protocol
+from typing import TYPE_CHECKING, Any, Protocol
 
 from scalable.client import ScalableClient
 from scalable.manifest.schema import (
@@ -19,12 +22,16 @@
 )
 from scalable.manifest.validate import ValidationReport
 
+if TYPE_CHECKING:
+    from scalable.costing import CostEstimate
+
 __all__ = [
     "ClusterHandle",
     "DeploymentProvider",
     "DeploymentSpec",
     "ResourceRequest",
     "ScalePlan",
+    "_BaseProviderMixin",
 ]
 
 
@@ -132,3 +139,30 @@ def scale(self, cluster: ClusterHandle, plan: ScalePlan) -> None:
 
     def close(self, cluster: ClusterHandle) -> None:
         """Close provider-managed resources."""
+
+    def estimate_cost(
+        self, spec: DeploymentSpec, plan: ScalePlan
+    ) -> CostEstimate | None:
+        """Estimate cost for the given deployment spec and scale plan.
+
+        Returns ``None`` if the provider cannot produce a cost estimate
+        (e.g. local execution has no monetary cost). This method is
+        optional: providers that do not override it inherit the mixin
+        default returning ``None``.
+        """
+        ...
+
+
+class _BaseProviderMixin:
+    """Mixin providing default implementations of optional protocol methods.
+
+    Existing providers (``LocalProvider``, ``SlurmProvider``) inherit this
+    so they automatically satisfy the Phase 3 ``estimate_cost`` addition
+    without code changes.
+    """
+
+    def estimate_cost(
+        self, spec: DeploymentSpec, plan: ScalePlan
+    ) -> CostEstimate | None:
+        """Default: no cost estimate available."""
+        return None
diff --git a/scalable/providers/cloud/__init__.py b/scalable/providers/cloud/__init__.py
new file mode 100644
index 0000000..7aa7218
--- /dev/null
+++ b/scalable/providers/cloud/__init__.py
@@ -0,0 +1,13 @@
+"""Cloud provider family for Scalable.
+
+Provides :class:`AWSBatchProvider` and :class:`GCPProvider` (scaffold)
+for cloud-based Dask cluster execution.
+"""
+
+from __future__ import annotations
+
+from .aws import AWSBatchProvider
+from .base import CloudProvider
+from .gcp import GCPProvider
+
+__all__ = ["AWSBatchProvider", "CloudProvider", "GCPProvider"]
diff --git a/scalable/providers/cloud/aws.py b/scalable/providers/cloud/aws.py
new file mode 100644
index 0000000..26639ba
--- /dev/null
+++ b/scalable/providers/cloud/aws.py
@@ -0,0 +1,179 @@
+"""AWS cloud provider using dask-cloudprovider.
+
+Provides :class:`AWSBatchProvider` which wraps ``dask_cloudprovider``'s
+``FargateCluster`` or ``EC2Cluster`` behind the Scalable
+:class:`DeploymentProvider` protocol.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from scalable.common import logger
+from scalable.manifest.validate import ValidationIssue, ValidationReport
+from scalable.providers.base import (
+    ClusterHandle,
+    DeploymentSpec,
+    ScalePlan,
+)
+
+from .base import CloudProvider
+
+
+def _import_dask_cloudprovider():
+    """Import dask-cloudprovider with a clear error."""
+    try:
+        import dask_cloudprovider
+
+        return dask_cloudprovider
+    except ImportError as exc:
+        raise ImportError(
+            "dask-cloudprovider is required for AWS provider. "
+            "Install with: pip install scalable[cloud]"
+        ) from exc
+
+
+class AWSBatchProvider(CloudProvider):
+    """AWS provider using dask-cloudprovider's FargateCluster.
+
+    Target options:
+    - ``region``: AWS region (default: ``us-east-1``)
+    - ``instance_type``: EC2 instance type for cost estimation
+    - ``cluster_type``: ``"fargate"`` (default) or ``"ec2"``
+    - ``image``: Docker image for workers
+    - ``n_workers``: Initial worker count
+    - ``worker_cpu``: CPU units per worker (Fargate: 256-4096)
+    - ``worker_mem``: Memory in MiB per worker
+    - ``vpc``: VPC identifier
+    - ``subnets``: List of subnet IDs
+    - ``security_groups``: List of security group IDs
+    - ``execution_role_arn``: ECS execution role ARN
+    - ``task_role_arn``: ECS task role ARN
+    """
+
+    name: str = "aws"
+
+    _KNOWN_OPTIONS: frozenset[str] = frozenset({
+        "region",
+        "instance_type",
+        "cluster_type",
+        "image",
+        "n_workers",
+        "worker_cpu",
+        "worker_mem",
+        "vpc",
+        "subnets",
+        "security_groups",
+        "execution_role_arn",
+        "task_role_arn",
+        "scheduler_timeout",
+        "environment",
+        "tags",
+        "spot",
+        "adaptive",
+    })
+
+    def validate(self, spec: DeploymentSpec) -> ValidationReport:
+        """Validate AWS-specific target options."""
+        report = ValidationReport()
+        options = spec.target.options
+
+        unknown = set(options) - self._KNOWN_OPTIONS
+        for key in sorted(unknown):
+            report.warnings.append(
+                ValidationIssue(
+                    path=f"targets.{spec.target_name}.{key}",
+                    message=f"unknown AWS provider option {key!r}",
+                    code="W_UNKNOWN_AWS_OPTION",
+                )
+            )
+
+        cluster_type = options.get("cluster_type", "fargate")
+        if cluster_type not in ("fargate", "ec2"):
+            report.errors.append(
+                ValidationIssue(
+                    path=f"targets.{spec.target_name}.cluster_type",
+                    message=f"cluster_type must be 'fargate' or 'ec2', got {cluster_type!r}",
+                    code="E_INVALID_CLUSTER_TYPE",
+                )
+            )
+
+        return report
+
+    def build_cluster(self, spec: DeploymentSpec) -> ClusterHandle:
+        """Create an AWS Dask cluster via dask-cloudprovider."""
+        _import_dask_cloudprovider()  # validate availability early
+        options = spec.target.options
+
+        cluster_type = options.get("cluster_type", "fargate")
+        region = options.get("region", "us-east-1")
+        image = options.get("image")
+        n_workers = options.get("n_workers", 1)
+
+        kwargs: dict[str, Any] = {
+            "region_name": region,
+            "n_workers": n_workers,
+        }
+        if image:
+            kwargs["image"] = image
+        if "worker_cpu" in options:
+            kwargs["worker_cpu"] = options["worker_cpu"]
+        if "worker_mem" in options:
+            kwargs["worker_mem"] = options["worker_mem"]
+        if "vpc" in options:
+            kwargs["vpc"] = options["vpc"]
+        if "subnets" in options:
+            kwargs["subnets"] = options["subnets"]
+        if "security_groups" in options:
+            kwargs["security_groups"] = options["security_groups"]
+        if "execution_role_arn" in options:
+            kwargs["execution_role_arn"] = options["execution_role_arn"]
+        if "task_role_arn" in options:
+            kwargs["task_role_arn"] = options["task_role_arn"]
+        if "environment" in options:
+            kwargs["environment"] = options["environment"]
+        if "tags" in options:
+            kwargs["tags"] = options["tags"]
+
+        logger.info("creating AWS %s cluster in %s", cluster_type, region)
+
+        if cluster_type == "fargate":
+            from dask_cloudprovider.aws import FargateCluster
+
+            cluster = FargateCluster(**kwargs)
+        else:
+            from dask_cloudprovider.aws import EC2Cluster
+
+            cluster = EC2Cluster(**kwargs)
+
+        # Adaptive scaling if requested
+        adaptive = options.get("adaptive")
+        if isinstance(adaptive, dict):
+            cluster.adapt(
+                minimum=adaptive.get("minimum", 1),
+                maximum=adaptive.get("maximum", 10),
+            )
+
+        from scalable.client import ScalableClient
+
+        def _client_factory() -> ScalableClient:
+            from distributed import Client
+
+            client = Client(cluster)
+            return ScalableClient(client=client)
+
+        return ClusterHandle(
+            backend=cluster,
+            client_factory=_client_factory,
+            metadata={"provider": "aws", "region": region, "cluster_type": cluster_type},
+        )
+
+    def scale(self, cluster: ClusterHandle, plan: ScalePlan) -> None:
+        """Scale the AWS cluster to match the plan."""
+        backend = cluster.backend
+        total_workers = sum(plan.workers_by_tag.values())
+        if hasattr(backend, "scale"):
+            backend.scale(total_workers)
+
+
+__all__ = ["AWSBatchProvider"]
diff --git a/scalable/providers/cloud/base.py b/scalable/providers/cloud/base.py
new file mode 100644
index 0000000..09399c8
--- /dev/null
+++ b/scalable/providers/cloud/base.py
@@ -0,0 +1,82 @@
+"""Abstract base class for cloud deployment providers."""
+
+from __future__ import annotations
+
+from scalable.costing import CostEstimate, CostLineItem
+from scalable.manifest.validate import ValidationReport
+from scalable.providers.base import (
+    ClusterHandle,
+    DeploymentSpec,
+    ScalePlan,
+    _BaseProviderMixin,
+)
+
+from .cost_tables import get_instance_cost
+
+
+class CloudProvider(_BaseProviderMixin):
+    """Abstract base for cloud providers (AWS, GCP, Azure).
+
+    Subclasses must override ``build_cluster`` and ``validate``.
+    Shared cost-estimation logic lives here.
+    """
+
+    name: str = "cloud"
+
+    def validate(self, spec: DeploymentSpec) -> ValidationReport:
+        """Subclasses must override."""
+        raise NotImplementedError
+
+    def build_cluster(self, spec: DeploymentSpec) -> ClusterHandle:
+        """Subclasses must override."""
+        raise NotImplementedError
+
+    def scale(self, cluster: ClusterHandle, plan: ScalePlan) -> None:
+        """Default scale: no-op for cloud providers using adaptive scaling."""
+        pass
+
+    def close(self, cluster: ClusterHandle) -> None:
+        """Close cloud cluster resources."""
+        backend = cluster.backend
+        if backend is not None and hasattr(backend, "close"):
+            backend.close()
+
+    def estimate_cost(
+        self, spec: DeploymentSpec, plan: ScalePlan
+    ) -> CostEstimate | None:
+        """Estimate cost from static cost tables.
+
+        Uses instance type and region from target options.
+        """
+        region = spec.target.options.get("region", "us-east-1")
+        instance_type = spec.target.options.get("instance_type", "m5.xlarge")
+
+        hourly_rate = get_instance_cost(
+            provider=self.name,
+            instance_type=instance_type,
+            region=region,
+        )
+        if hourly_rate is None:
+            return None
+
+        line_items: list[CostLineItem] = []
+        for tag, count in plan.workers_by_tag.items():
+            line_items.append(
+                CostLineItem.compute(
+                    resource="compute",
+                    description=f"{count}x {instance_type} for worker group '{tag}'",
+                    unit="USD/hr",
+                    quantity=float(count),
+                    unit_cost=hourly_rate,
+                )
+            )
+
+        return CostEstimate.from_line_items(
+            provider=self.name,
+            region=region,
+            line_items=line_items,
+            metadata={"instance_type": instance_type},
+        )
+
+
+__all__ = ["CloudProvider"]
diff --git a/scalable/providers/cloud/cost_tables.py b/scalable/providers/cloud/cost_tables.py
new file mode 100644
index 0000000..560eaf6
--- /dev/null
+++ b/scalable/providers/cloud/cost_tables.py
@@ -0,0 +1,79 @@
+"""Static cost tables for cloud providers.
+
+Phase 3 uses static lookup tables for cost estimation. These provide
+representative on-demand pricing for common instance types. Future phases
+may integrate live pricing APIs.
+"""
+
+from __future__ import annotations
+
+# Instance pricing: provider -> instance_type -> region -> USD/hr
+# Representative on-demand Linux pricing as of 2024.
+_COST_TABLE: dict[str, dict[str, dict[str, float]]] = {
+    "aws": {
+        "m5.large": {"us-east-1": 0.096, "us-west-2": 0.096, "eu-west-1": 0.107},
+        "m5.xlarge": {"us-east-1": 0.192, "us-west-2": 0.192, "eu-west-1": 0.214},
+        "m5.2xlarge": {"us-east-1": 0.384, "us-west-2": 0.384, "eu-west-1": 0.428},
+        "m5.4xlarge": {"us-east-1": 0.768, "us-west-2": 0.768, "eu-west-1": 0.856},
+        "c5.large": {"us-east-1": 0.085, "us-west-2": 0.085, "eu-west-1": 0.096},
+        "c5.xlarge": {"us-east-1": 0.170, "us-west-2": 0.170, "eu-west-1": 0.192},
+        "c5.2xlarge": {"us-east-1": 0.340, "us-west-2": 0.340, "eu-west-1": 0.384},
+        "r5.large": {"us-east-1": 0.126, "us-west-2": 0.126, "eu-west-1": 0.141},
+        "r5.xlarge": {"us-east-1": 0.252, "us-west-2": 0.252, "eu-west-1": 0.282},
+        "r5.2xlarge": {"us-east-1": 0.504, "us-west-2": 0.504, "eu-west-1": 0.564},
+        "t3.medium": {"us-east-1": 0.0416, "us-west-2": 0.0416, "eu-west-1": 0.0468},
+        "t3.large": {"us-east-1": 0.0832, "us-west-2": 0.0832, "eu-west-1": 0.0936},
+    },
+    "gcp": {
+        "n1-standard-2": {"us-central1": 0.095, "us-east1": 0.095, "europe-west1": 0.104},
+        "n1-standard-4": {"us-central1": 0.190, "us-east1": 0.190, "europe-west1": 0.209},
+        "n1-standard-8": {"us-central1": 0.380, "us-east1": 0.380, "europe-west1": 0.418},
+        "n1-standard-16": {"us-central1": 0.760, "us-east1": 0.760, "europe-west1": 0.836},
+        "n1-highmem-4": {"us-central1": 0.237, "us-east1": 0.237, "europe-west1": 0.260},
+        "n1-highmem-8": {"us-central1": 0.474, "us-east1": 0.474, "europe-west1": 0.520},
+        "n1-highcpu-4": {"us-central1": 0.142, "us-east1": 0.142, "europe-west1": 0.156},
+        "n1-highcpu-8": {"us-central1": 0.284, "us-east1": 0.284, "europe-west1": 0.312},
+        "e2-standard-2": {"us-central1": 0.067, "us-east1": 0.067, "europe-west1": 0.074},
+        "e2-standard-4": {"us-central1": 0.134, "us-east1": 0.134, "europe-west1": 0.147},
+    },
+}
+
+
+def get_instance_cost(
+    *,
+    provider: str,
+    instance_type: str,
+    region: str,
+) -> float | None:
+    """Look up hourly cost for an instance type.
+
+    Parameters
+    ----------
+    provider : str
+        Cloud provider name (``"aws"`` or ``"gcp"``).
+    instance_type : str
+        Instance type identifier.
+    region : str
+        Cloud region.
+
+    Returns
+    -------
+    float | None
+        Hourly cost in USD, or None if not found in tables.
+    """
+    provider_table = _COST_TABLE.get(provider, {})
+    instance_table = provider_table.get(instance_type, {})
+    return instance_table.get(region)
+
+
+def list_instance_types(provider: str) -> list[str]:
+    """List known instance types for a provider."""
+    return sorted(_COST_TABLE.get(provider, {}).keys())
+
+
+def list_regions(provider: str, instance_type: str) -> list[str]:
+    """List known regions for a provider/instance combination."""
+    return sorted(_COST_TABLE.get(provider, {}).get(instance_type, {}).keys())
+
+
+__all__ = ["get_instance_cost", "list_instance_types", "list_regions"]
diff --git a/scalable/providers/cloud/gcp.py b/scalable/providers/cloud/gcp.py
new file mode 100644
index 0000000..70ce39b
--- /dev/null
+++ b/scalable/providers/cloud/gcp.py
@@ -0,0 +1,90 @@
+"""GCP cloud provider scaffold.
+
+This module provides a validation-only :class:`GCPProvider` that verifies
+manifest options but raises ``NotImplementedError`` on ``build_cluster``.
+Full GCP execution support is deferred to a future iteration.
+"""
+
+from __future__ import annotations
+
+from scalable.manifest.validate import ValidationIssue, ValidationReport
+from scalable.providers.base import (
+    ClusterHandle,
+    DeploymentSpec,
+)
+
+from .base import CloudProvider
+
+
+class GCPProvider(CloudProvider):
+    """GCP provider scaffold (validation-only).
+
+    Target options:
+    - ``region``: GCP region (e.g. ``us-central1``)
+    - ``project_id``: GCP project identifier
+    - ``instance_type``: GCE machine type for cost estimation
+    - ``image``: Container image for workers
+    - ``n_workers``: Number of workers
+    - ``network``: VPC network name
+    - ``zone``: Specific zone within region
+    - ``service_account``: GCP service account email
+    """
+
+    name: str = "gcp"
+
+    _KNOWN_OPTIONS: frozenset[str] = frozenset({
+        "region",
+        "project_id",
+        "instance_type",
+        "image",
+        "n_workers",
+        "network",
+        "zone",
+        "service_account",
+        "machine_type",
+        "adaptive",
+    })
+
+    def validate(self, spec: DeploymentSpec) -> ValidationReport:
+        """Validate GCP-specific target options."""
+        report = ValidationReport()
+        options = spec.target.options
+
+        unknown = set(options) - self._KNOWN_OPTIONS
+        for key in sorted(unknown):
+            report.warnings.append(
+                ValidationIssue(
+                    path=f"targets.{spec.target_name}.{key}",
+                    message=f"unknown GCP provider option {key!r}",
+                    code="W_UNKNOWN_GCP_OPTION",
+                )
+            )
+
+        if not options.get("project_id"):
+            report.warnings.append(
+                ValidationIssue(
+                    path=f"targets.{spec.target_name}.project_id",
+                    message="GCP provider recommends setting 'project_id'",
+                    code="W_MISSING_PROJECT_ID",
+                )
+            )
+
+        return report
+
+    def build_cluster(self, spec: DeploymentSpec) -> ClusterHandle:
+        """Not implemented — GCP cluster creation is deferred.
+
+        Raises
+        ------
+        NotImplementedError
+            Always. GCP execution support is a validation-only scaffold.
+        """
+        raise NotImplementedError(
+            "GCPProvider.build_cluster() is not yet implemented. "
+            "Phase 3 provides validation-only GCP support. "
+            "Use 'scalable validate' to check your GCP manifest, or "
+            "target AWS/Kubernetes for execution."
+        )
+
+
+__all__ = ["GCPProvider"]
diff --git a/scalable/providers/kubernetes.py b/scalable/providers/kubernetes.py
new file mode 100644
index 0000000..eea7088
--- /dev/null
+++ b/scalable/providers/kubernetes.py
@@ -0,0 +1,186 @@
+"""Kubernetes provider using dask-kubernetes operator.
+
+Provides :class:`KubernetesProvider` which wraps the Dask Kubernetes
+Operator's ``KubeCluster`` behind the Scalable
+:class:`DeploymentProvider` protocol.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from scalable.common import logger
+from scalable.costing import CostEstimate
+from scalable.manifest.validate import ValidationIssue, ValidationReport
+from scalable.providers.base import (
+    ClusterHandle,
+    DeploymentSpec,
+    ScalePlan,
+    _BaseProviderMixin,
+)
+
+
+def _import_dask_kubernetes():
+    """Import dask-kubernetes with a clear error."""
+    try:
+        import dask_kubernetes
+
+        return dask_kubernetes
+    except ImportError as exc:
+        raise ImportError(
+            "dask-kubernetes is required for the Kubernetes provider. "
+            "Install with: pip install scalable[kubernetes]"
+        ) from exc
+
+
+class KubernetesProvider(_BaseProviderMixin):
+    """Kubernetes provider using the Dask Kubernetes Operator.
+
+    Target options:
+    - ``namespace``: Kubernetes namespace (default: ``"default"``)
+    - ``image``: Default container image for scheduler/workers
+    - ``n_workers``: Initial worker count per group
+    - ``worker_service_account``: Service account for worker pods
+    - ``adaptive``: Dict with ``minimum`` and ``maximum`` for adaptive scaling
+    - ``resources``: Default resource requests (cpu, memory)
+    - ``env``: Extra environment variables for pods
+    - ``tolerations``: Kubernetes tolerations list
+    - ``node_selector``: Node selector dict
+    """
+
+    name: str = "kubernetes"
+
+    _KNOWN_OPTIONS: frozenset[str] = frozenset({
+        "namespace",
+        "image",
+        "n_workers",
+        "worker_service_account",
+        "adaptive",
+        "resources",
+        "env",
+        "tolerations",
+        "node_selector",
+        "scheduler_memory",
+        "scheduler_cpu",
+    })
+
+    def validate(self, spec: DeploymentSpec) -> ValidationReport:
+        """Validate Kubernetes-specific target options."""
+        report = ValidationReport()
+        options = spec.target.options
+
+        unknown = set(options) - self._KNOWN_OPTIONS
+        for key in sorted(unknown):
+            report.warnings.append(
+                ValidationIssue(
+                    path=f"targets.{spec.target_name}.{key}",
+                    message=f"unknown Kubernetes provider option {key!r}",
+                    code="W_UNKNOWN_K8S_OPTION",
+                )
+            )
+
+        if not options.get("image"):
+            report.warnings.append(
+                ValidationIssue(
+                    path=f"targets.{spec.target_name}.image",
+                    message="Kubernetes provider recommends setting 'image' for worker pods",
+                    code="W_MISSING_IMAGE",
+                )
+            )
+
+        return report
+
+    def build_cluster(self, spec: DeploymentSpec) -> ClusterHandle:
+        """Create a Dask Kubernetes Operator cluster.
+
+        Creates a ``KubeCluster`` and adds worker groups per manifest
+        component.
+        """
+        _import_dask_kubernetes()
+        from dask_kubernetes.operator import KubeCluster
+
+        options = spec.target.options
+        namespace = options.get("namespace", "default")
+        image = options.get("image")
+        n_workers = options.get("n_workers", 1)
+
+        logger.info("creating KubeCluster in namespace %s", namespace)
+
+        cluster_kwargs: dict[str, Any] = {
+            "namespace": namespace,
+        }
+        if image:
+            cluster_kwargs["image"] = image
+
+        cluster = KubeCluster(**cluster_kwargs)
+
+        # Add worker groups per component
+        for component_name, component in spec.components.items():
+            worker_image = component.image or image
+            resources_kwargs: dict[str, Any] = {}
+            if component.memory:
+                resources_kwargs["memory"] = component.memory
+            if component.cpus:
+                resources_kwargs["cpu"] = str(component.cpus)
+
+            try:
+                cluster.add_worker_group(
+                    name=component_name,
+                    n_workers=n_workers,
+                    image=worker_image,
+                    resources=resources_kwargs if resources_kwargs else None,
+                )
+            except Exception as exc:
+                logger.warning(
+                    "failed to add worker group %s: %s", component_name, exc
+                )
+
+        # Adaptive scaling
+        adaptive = options.get("adaptive")
+        if isinstance(adaptive, dict):
+            cluster.adapt(
+                minimum=adaptive.get("minimum", 1),
+                maximum=adaptive.get("maximum", 10),
+            )
+
+        from scalable.client import ScalableClient
+
+        def _client_factory() -> ScalableClient:
+            from distributed import Client
+
+            client = Client(cluster)
+            return ScalableClient(client=client)
+
+        return ClusterHandle(
+            backend=cluster,
+            client_factory=_client_factory,
+            metadata={
+                "provider": "kubernetes",
+                "namespace": namespace,
+            },
+        )
+
+    def scale(self, cluster: ClusterHandle, plan: ScalePlan) -> None:
+        """Scale worker groups according to the plan."""
+        backend = cluster.backend
+        for tag, count in plan.workers_by_tag.items():
+            try:
+                if hasattr(backend, "scale"):
+                    backend.scale(count, worker_group=tag)
+            except Exception as exc:
+                logger.warning("failed to scale worker group %s: %s", tag, exc)
+
+    def close(self, cluster: ClusterHandle) -> None:
+        """Close the Kubernetes cluster."""
+        backend = cluster.backend
+        if backend is not None and hasattr(backend, "close"):
+            backend.close()
+
+    def estimate_cost(
+        self, spec: DeploymentSpec, plan: ScalePlan
+    ) -> CostEstimate | None:
+        """Kubernetes provider returns None (on-prem k8s has no direct cost)."""
+        return None
+
+
+__all__ = ["KubernetesProvider"]
diff --git a/scalable/providers/registry.py b/scalable/providers/registry.py
index b1514d5..66ff667 100644
--- a/scalable/providers/registry.py
+++ b/scalable/providers/registry.py
@@ -138,4 +138,22 @@ def _load_builtin_provider(name: str) -> ProviderFactory | None:
         except ImportError:
             return None
         return SlurmProvider
+    if normalized == "kubernetes":
+        try:
+            from .kubernetes import KubernetesProvider
+        except ImportError:
+            return None
+        return KubernetesProvider
+    if normalized == "aws":
+        try:
+            from .cloud.aws import AWSBatchProvider
+        except ImportError:
+            return None
+        return AWSBatchProvider
+    if normalized == "gcp":
+        try:
+            from .cloud.gcp import GCPProvider
+        except ImportError:
+            return None
+        return GCPProvider
     return None
diff --git a/scalable/telemetry/collectors.py b/scalable/telemetry/collectors.py
index cc32ef5..c17a861 100644
--- a/scalable/telemetry/collectors.py
+++ b/scalable/telemetry/collectors.py
@@ -69,6 +69,7 @@ def summarize_run(run_dir: str | Path) -> dict[str, Any]:
     failures = read_jsonl(run_path / "failures.jsonl")
     caches = read_jsonl(run_path / "cache.jsonl")
     artifacts = read_jsonl(run_path / "artifacts.jsonl")
+    costs = read_jsonl(run_path / "cost.jsonl")
 
     final_state_by_task: dict[str, str] = {}
     duration_values: list[float] = []
@@ -92,6 +93,10 @@ def summarize_run(run_dir: str | Path) -> dict[str, Any]:
         if isinstance(value, int):
             requested_cpus.append(value)
 
+    # Cost summary
+    cost_total_hourly = sum(float(c.get("total_hourly", 0)) for c in costs)
+    cost_total_monthly = sum(float(c.get("total_monthly", 0)) for c in costs)
+
     return {
         "run": run_meta,
         "counts": {
@@ -101,6 +106,7 @@ def summarize_run(run_dir: str | Path) -> dict[str, Any]:
             "failure_events": len(failures),
             "cache_events": len(caches),
             "artifact_events": len(artifacts),
+            "cost_events": len(costs),
             "tasks_succeeded": state_counter.get("succeeded", 0),
             "tasks_failed": state_counter.get("failed", 0),
             "tasks_cancelled": state_counter.get("cancelled", 0),
@@ -126,6 +132,11 @@ def summarize_run(run_dir: str | Path) -> dict[str, Any]:
             if requested_cpus
             else None,
         },
+        "cost": {
+            "total_hourly_usd": round(cost_total_hourly, 6) if costs else None,
+            "total_monthly_usd": round(cost_total_monthly, 4) if costs else None,
+            "estimates_count": len(costs),
+        },
         "failures": {
             "classes": dict(sorted(failure_counter.items())),
         },
@@ -138,6 +149,7 @@ def render_text_report(summary: dict[str, Any]) -> str:
     counts = summary.get("counts", {})
     timing = summary.get("timing", {})
     cache = summary.get("cache", {})
+    cost = summary.get("cost", {})
 
     lines = [
         f"run_id: {run.get('run_id', 'unknown')}",
@@ -160,6 +172,15 @@ def render_text_report(summary: dict[str, Any]) -> str:
         f"  misses: {cache.get('misses', 0)}",
         f"  hit_ratio: {cache.get('hit_ratio')}",
     ]
+
+    if cost.get("total_hourly_usd") is not None:
+        lines.extend([
+            "",
+            "cost:",
+            f"  hourly_usd: {cost.get('total_hourly_usd')}",
+            f"  monthly_usd: {cost.get('total_monthly_usd')}",
+        ])
+
     return "\n".join(lines)
 
 
diff --git a/scalable/telemetry/events.py b/scalable/telemetry/events.py
index 1dbb2a5..c790ec7 100644
--- a/scalable/telemetry/events.py
+++ b/scalable/telemetry/events.py
@@ -156,10 +156,59 @@ def to_dict(self) -> dict[str, Any]:
         return asdict(self)
 
 
+@dataclass(frozen=True)
+class CostEvent:
+    """Cost estimation event record (Phase 3).
+
+    Recorded when a provider produces a cost estimate for a deployment plan.
+    """
+
+    run_id: str
+    provider: str
+    region: str | None
+    currency: str
+    total_hourly: float
+    total_monthly: float
+    timestamp: str = field(default_factory=utcnow_iso)
+    line_items: list[dict[str, Any]] = field(default_factory=list)
+    metadata: dict[str, Any] = field(default_factory=dict)
+    event_type: str = "cost"
+    schema_version: int = SCHEMA_VERSION
+
+    def to_dict(self) -> dict[str, Any]:
+        return asdict(self)
+
+
+@dataclass(frozen=True)
+class RemoteCacheEvent:
+    """Remote cache interaction event record (Phase 3).
+
+    Extends CacheEvent with remote-specific fields.
+    """
+
+    run_id: str
+    function_name: str
+    key_digest: str
+    hit: bool
+    remote: bool
+    timestamp: str = field(default_factory=utcnow_iso)
+    duration_s: float | None = None
+    remote_uri: str | None = None
+    task_name: str | None = None
+    component: str | None = None
+    event_type: str = "remote_cache"
+    schema_version: int = SCHEMA_VERSION
+
+    def to_dict(self) -> dict[str, Any]:
+        return asdict(self)
+
+
 __all__ = [
     "ArtifactEvent",
     "CacheEvent",
+    "CostEvent",
     "FailureEvent",
+    "RemoteCacheEvent",
     "ResourceEvent",
     "RunMetadata",
     "SCHEMA_VERSION",
diff --git a/scalable/telemetry/store.py b/scalable/telemetry/store.py
index 4d1431b..fb1bbde 100644
--- a/scalable/telemetry/store.py
+++ b/scalable/telemetry/store.py
@@ -22,6 +22,7 @@
 from .events import (
     ArtifactEvent,
     CacheEvent,
+    CostEvent,
     FailureEvent,
     ResourceEvent,
     RunMetadata,
@@ -51,6 +52,7 @@ class TelemetryStore:
     _FAILURES_FILE = "failures.jsonl"
     _CACHE_FILE = "cache.jsonl"
     _ARTIFACTS_FILE = "artifacts.jsonl"
+    _COST_FILE = "cost.jsonl"
 
     def __init__(
         self,
@@ -350,6 +352,32 @@ def record_artifact(
             ).to_dict(),
         )
 
+    def record_cost(
+        self,
+        *,
+        provider: str,
+        region: str | None,
+        currency: str,
+        total_hourly: float,
+        total_monthly: float,
+        line_items: list[dict[str, Any]] | None = None,
+        metadata: dict[str, Any] | None = None,
+    ) -> None:
+        """Record a cost estimation event (Phase 3)."""
+        self._append_jsonl(
+            self._COST_FILE,
+            CostEvent(
+                run_id=self.run_id,
+                provider=provider,
+                region=region,
+                currency=currency,
+                total_hourly=total_hourly,
+                total_monthly=total_monthly,
+                line_items=line_items or [],
+                metadata=metadata or {},
+            ).to_dict(),
+        )
+
     def _write_summary(self) -> None:
         summary = summarize_run(self.run_dir)
         (self.run_dir / "summary.json").write_text(
diff --git a/tests/unit/test_artifacts.py b/tests/unit/test_artifacts.py
new file mode 100644
index 0000000..0e59a5e
--- /dev/null
+++ b/tests/unit/test_artifacts.py
@@ -0,0 +1,140 @@
+"""Unit tests for scalable.artifacts module."""
+
+from __future__ import annotations
+
+import os
+import tempfile
+from pathlib import Path
+
+import pytest
+
+from scalable.artifacts.base import ArtifactKind, ArtifactRef, ArtifactStore
+from scalable.artifacts.factory import build_artifact_store
+from scalable.artifacts.local import LocalArtifactStore
+
+
+class TestArtifactKind:
+    def test_enum_values(self):
+        assert ArtifactKind.FILE == "file"
+        assert ArtifactKind.DIRECTORY == "dir"
+        assert ArtifactKind.BLOB == "blob"
+
+
+class TestArtifactRef:
+    def test_basic_creation(self):
+        ref = ArtifactRef(
+            uri="file:///tmp/test.txt",
+            kind=ArtifactKind.FILE,
+            digest="abc123",
+            size_bytes=100,
+        )
+        assert ref.uri == "file:///tmp/test.txt"
+        assert ref.kind == ArtifactKind.FILE
+        assert ref.digest == "abc123"
+        assert ref.size_bytes == 100
+        assert ref.metadata == {}
+
+
+class TestLocalArtifactStore:
+    def test_protocol_conformance(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            store = LocalArtifactStore(root=tmp)
+            assert isinstance(store, ArtifactStore)
+
+    def test_scheme(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            store = LocalArtifactStore(root=tmp)
+            assert store.scheme == "file"
+
+    def test_put_and_get_file(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            store = LocalArtifactStore(root=os.path.join(tmp, "store"))
+            # Create a source file
+            src_file = os.path.join(tmp, "source.txt")
+            with open(src_file, "w") as f:
+                f.write("hello world")
+
+            ref = store.put(src_file, "data/output.txt")
+            assert ref.kind == ArtifactKind.FILE
+            assert ref.size_bytes == 11
+            assert ref.digest is not None
+
+            # Get it back
+            dest = os.path.join(tmp, "retrieved.txt")
+            result = store.get("data/output.txt", dest)
+            assert Path(result).read_text() == "hello world"
+
+    def test_put_and_get_directory(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            store = LocalArtifactStore(root=os.path.join(tmp, "store"))
+            # Create a source directory
+            src_dir = os.path.join(tmp, "srcdir")
+            os.makedirs(src_dir)
+            with open(os.path.join(src_dir, "a.txt"), "w") as f:
+                f.write("aaa")
+            with open(os.path.join(src_dir, "b.txt"), "w") as f:
+                f.write("bbb")
+
+            ref = store.put(src_dir, "outputs/batch1")
+            assert ref.kind == ArtifactKind.DIRECTORY
+            assert ref.size_bytes == 6  # 3+3
+
+            # Get it back
+            dest = os.path.join(tmp, "got_dir")
+            store.get("outputs/batch1", dest)
+            assert Path(os.path.join(dest, "a.txt")).read_text() == "aaa"
+            assert Path(os.path.join(dest, "b.txt")).read_text() == "bbb"
+
+    def test_exists(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            store = LocalArtifactStore(root=tmp)
+            assert not store.exists("nope.txt")
+
+            src = os.path.join(tmp, "src.txt")
+            with open(src, "w") as f:
+                f.write("x")
+            store.put(src, "yes.txt")
+            assert store.exists("yes.txt")
+
+    def test_list_artifacts(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            store_root = os.path.join(tmp, "store")
+            store = LocalArtifactStore(root=store_root)
+
+            src = os.path.join(tmp, "src.txt")
+            with open(src, "w") as f:
+                f.write("x")
+            store.put(src, "a/1.txt")
+            store.put(src, "a/2.txt")
+            store.put(src, "b/3.txt")
+
+            all_artifacts = store.list_artifacts()
+            assert len(all_artifacts) == 3
+            a_artifacts = store.list_artifacts("a")
+            assert len(a_artifacts) == 2
+
+    def test_get_missing_raises(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            store = LocalArtifactStore(root=tmp)
+            with pytest.raises(FileNotFoundError):
+                store.get("missing.txt", "/tmp/dest.txt")
+
+
+class TestBuildArtifactStore:
+    def test_local_path(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            store = build_artifact_store(tmp)
+            assert isinstance(store, LocalArtifactStore)
+
+    def test_file_uri(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            store = build_artifact_store(f"file://{tmp}")
+            assert isinstance(store, LocalArtifactStore)
+
+    def test_relative_path(self):
+        store = build_artifact_store("./test_artifacts")
+        assert isinstance(store, LocalArtifactStore)
+        # Clean up
+        import shutil
+
+        shutil.rmtree("./test_artifacts", ignore_errors=True)
diff --git a/tests/unit/test_cli_run.py b/tests/unit/test_cli_run.py
new file mode 100644
index 0000000..b2e631e
--- /dev/null
+++ b/tests/unit/test_cli_run.py
@@ -0,0 +1,89 @@
+"""Unit tests for scalable run CLI verb."""
+
+from __future__ import annotations
+
+import os
+import tempfile
+
+import pytest
+
+from scalable.cli.cmd_run import run_run
+
+
+class TestRunCommand:
+    def _write_manifest(self, tmp: str, content: str | None = None) -> str:
+        """Write a minimal manifest and return its path."""
+        path = os.path.join(tmp, "scalable.yaml")
+        if content is None:
+            content = """\
+version: 1
+project:
+  name: test-run
+targets:
+  local:
+    provider: local
+components:
+  model:
+    cpus: 2
+    memory: 4G
+tasks:
+  run_model:
+    component: model
+"""
+        with open(path, "w") as f:
+            f.write(content)
+        return path
+
+    def test_dry_run_success(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            manifest_path = self._write_manifest(tmp)
+            rc = run_run(manifest_path, target="local", dry_run=True)
+            assert rc == 0
+
+    def test_no_workflow_success(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            manifest_path = self._write_manifest(tmp)
+            rc = run_run(manifest_path, target="local")
+            assert rc == 0
+
+    def test_missing_manifest(self):
+        rc = run_run("/nonexistent/scalable.yaml", target="local")
+        assert rc == 2
+
+    def test_missing_target(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            manifest_path = self._write_manifest(tmp)
+            rc = run_run(manifest_path, target="nonexistent")
+            assert rc == 2
+
+    def test_workflow_file_not_found(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            manifest_path = self._write_manifest(tmp)
+            rc = run_run(
+                manifest_path,
+                target="local",
+                workflow="/nonexistent/workflow.py",
+            )
+            assert rc == 2
+
+    def test_workflow_execution(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            manifest_path = self._write_manifest(tmp)
+            wf_path = os.path.join(tmp, "workflow.py")
+            with open(wf_path, "w") as f:
+                f.write("# Simple workflow\nresult = 1 + 1\n")
+            rc = run_run(manifest_path, target="local", workflow=wf_path)
+            assert rc == 0
+
+
+class TestRunCLIIntegration:
+    """Test the CLI dispatch for scalable run."""
+
+    def test_run_in_parser(self):
+        from scalable.cli.main import _build_parser
+
+        parser = _build_parser()
+        # Should not raise
+        args = parser.parse_args(["run", "--dry-run"])
+        assert args.command == "run"
+        assert args.dry_run is True
diff --git a/tests/unit/test_cloud_cost_tables.py b/tests/unit/test_cloud_cost_tables.py
new file mode 100644
index 0000000..3e88017
--- /dev/null
+++ b/tests/unit/test_cloud_cost_tables.py
@@ -0,0 +1,55 @@
+"""Unit tests for cloud provider cost tables."""
+
+from __future__ import annotations
+
+from scalable.providers.cloud.cost_tables import (
+    get_instance_cost,
+    list_instance_types,
+    list_regions,
+)
+
+
+class TestCostTables:
+    def test_aws_m5_xlarge_us_east_1(self):
+        cost = get_instance_cost(
+            provider="aws", instance_type="m5.xlarge", region="us-east-1"
+        )
+        assert cost == 0.192
+
+    def test_gcp_n1_standard_4_us_central1(self):
+        cost = get_instance_cost(
+            provider="gcp", instance_type="n1-standard-4", region="us-central1"
+        )
+        assert cost == 0.190
+
+    def test_unknown_provider(self):
+        cost = get_instance_cost(
+            provider="azure", instance_type="m5.xlarge", region="us-east-1"
+        )
+        assert cost is None
+
+    def test_unknown_instance_type(self):
+        cost = get_instance_cost(
+            provider="aws", instance_type="p4d.24xlarge", region="us-east-1"
+        )
+        assert cost is None
+
+    def test_unknown_region(self):
+        cost = get_instance_cost(
+            provider="aws", instance_type="m5.xlarge", region="ap-southeast-1"
+        )
+        assert cost is None
+
+    def test_list_instance_types_aws(self):
+        types = list_instance_types("aws")
+        assert "m5.xlarge" in types
+        assert "c5.large" in types
+
+    def test_list_instance_types_gcp(self):
+        types = list_instance_types("gcp")
+        assert "n1-standard-4" in types
+
+    def test_list_regions(self):
+        regions = list_regions("aws", "m5.xlarge")
+        assert "us-east-1" in regions
+        assert "us-west-2" in regions
diff --git a/tests/unit/test_cloud_kubernetes_providers.py b/tests/unit/test_cloud_kubernetes_providers.py
new file mode 100644
index 0000000..ba6ed53
--- /dev/null
+++ b/tests/unit/test_cloud_kubernetes_providers.py
@@ -0,0 +1,192 @@
+"""Unit tests for cloud and kubernetes providers."""
+
+from __future__ import annotations
+
+import pytest
+
+from scalable.manifest.schema import (
+    ComponentConfig,
+    ManifestModel,
+    ProjectConfig,
+    TargetConfig,
+    TaskConfig,
+)
+from scalable.manifest.validate import ValidationReport
+from scalable.providers.base import DeploymentSpec, ResourceRequest, ScalePlan
+
+
+def _make_spec(
+    provider: str,
+    target_name: str = "test",
+    options: dict | None = None,
+) -> DeploymentSpec:
+    """Helper to build a minimal DeploymentSpec."""
+    target = TargetConfig(name=target_name, provider=provider, options=options or {})
+    manifest = ManifestModel(
+        version=1,
+        project=ProjectConfig(name="test-project"),
+        targets={target_name: target},
+        components={
+            "model": ComponentConfig(name="model", cpus=4, memory="8G"),
+        },
+        tasks={
+            "run": TaskConfig(name="run", component="model"),
+        },
+        raw={"version": 1, "project": {"name": "test-project"}},
+    )
+    return DeploymentSpec(
+        target_name=target_name,
+        provider_name=provider,
+        manifest=manifest,
+        target=target,
+        components=dict(manifest.components),
+        tasks=dict(manifest.tasks),
+        raw_manifest=manifest.raw,
+    )
+
+
+class TestGCPProvider:
+    def test_validate_passes_clean_manifest(self):
+        from scalable.providers.cloud.gcp import GCPProvider
+
+        provider = GCPProvider()
+        spec = _make_spec("gcp", options={"region": "us-central1", "project_id": "my-project"})
+        report = provider.validate(spec)
+        assert report.ok
+
+    def test_validate_warns_on_missing_project_id(self):
+        from scalable.providers.cloud.gcp import GCPProvider
+
+        provider = GCPProvider()
+        spec = _make_spec("gcp", options={"region": "us-central1"})
+        report = provider.validate(spec)
+        assert any("project_id" in w.message for w in report.warnings)
+
+    def test_validate_warns_on_unknown_options(self):
+        from scalable.providers.cloud.gcp import GCPProvider
+
+        provider = GCPProvider()
+        spec = _make_spec("gcp", options={"unknown_key": "value"})
+        report = provider.validate(spec)
+        assert any("unknown_key" in w.message for w in report.warnings)
+
+    def test_build_cluster_raises_not_implemented(self):
+        from scalable.providers.cloud.gcp import GCPProvider
+
+        provider = GCPProvider()
+        spec = _make_spec("gcp")
+        with pytest.raises(NotImplementedError, match="not yet implemented"):
+            provider.build_cluster(spec)
+
+    def test_name(self):
+        from scalable.providers.cloud.gcp import GCPProvider
+
+        provider = GCPProvider()
+        assert provider.name == "gcp"
+
+
+class TestAWSBatchProvider:
+    def test_validate_passes_clean_manifest(self):
+        from scalable.providers.cloud.aws import AWSBatchProvider
+
+        provider = AWSBatchProvider()
+        spec = _make_spec("aws", options={"region": "us-east-1", "cluster_type": "fargate"})
+        report = provider.validate(spec)
+        assert report.ok
+
+    def test_validate_rejects_invalid_cluster_type(self):
+        from scalable.providers.cloud.aws import AWSBatchProvider
+
+        provider = AWSBatchProvider()
+        spec = _make_spec("aws", options={"cluster_type": "invalid"})
+        report = provider.validate(spec)
+        assert not report.ok
+        assert any("cluster_type" in e.message for e in report.errors)
+
+    def test_validate_warns_unknown_options(self):
+        from scalable.providers.cloud.aws import AWSBatchProvider
+
+        provider = AWSBatchProvider()
+        spec = _make_spec("aws", options={"random_key": "val"})
+        report = provider.validate(spec)
+        assert any("random_key" in w.message for w in report.warnings)
+
+    def test_name(self):
+        from scalable.providers.cloud.aws import AWSBatchProvider
+
+        provider = AWSBatchProvider()
+        assert provider.name == "aws"
+
+    def test_estimate_cost(self):
+        from scalable.providers.cloud.aws import AWSBatchProvider
+
+        provider = AWSBatchProvider()
+        spec = _make_spec("aws", options={"region": "us-east-1", "instance_type": "m5.xlarge"})
+        plan = ScalePlan(
+            workers_by_tag={"model": 2},
+            resources_by_tag={"model": ResourceRequest(cpus=4, memory="8G")},
+        )
+        estimate = provider.estimate_cost(spec, plan)
+        assert estimate is not None
+        assert estimate.provider == "aws"
+        assert estimate.total_hourly > 0
+
+
+class TestKubernetesProvider:
+    def test_validate_passes_clean_manifest(self):
+        from scalable.providers.kubernetes import KubernetesProvider
+
+        provider = KubernetesProvider()
+        spec = _make_spec("kubernetes", options={"namespace": "default", "image": "python:3.11"})
+        report = provider.validate(spec)
+        assert report.ok
+
+    def test_validate_warns_missing_image(self):
+        from scalable.providers.kubernetes import KubernetesProvider
+
+        provider = KubernetesProvider()
+        spec = _make_spec("kubernetes", options={"namespace": "default"})
+        report = provider.validate(spec)
+        assert any("image" in w.message for w in report.warnings)
+
+    def test_validate_warns_unknown_options(self):
+        from scalable.providers.kubernetes import KubernetesProvider
+
+        provider = KubernetesProvider()
+        spec = _make_spec("kubernetes", options={"unknown_opt": "val"})
+        report = provider.validate(spec)
+        assert any("unknown_opt" in w.message for w in report.warnings)
+
+    def test_name(self):
+        from scalable.providers.kubernetes import KubernetesProvider
+
+        provider = KubernetesProvider()
+        assert provider.name == "kubernetes"
+
+    def test_estimate_cost_returns_none(self):
+        from scalable.providers.kubernetes import KubernetesProvider
+
+        provider = KubernetesProvider()
+        spec = _make_spec("kubernetes")
+        plan = ScalePlan(workers_by_tag={"model": 1}, resources_by_tag={})
+        assert provider.estimate_cost(spec, plan) is None
+
+
+class TestProviderRegistryPhase3:
+    def test_kubernetes_in_builtin(self):
+        from scalable.providers.registry import _load_builtin_provider
+
+        factory = _load_builtin_provider("kubernetes")
+        assert factory is not None
+
+    def test_aws_in_builtin(self):
+        from scalable.providers.registry import _load_builtin_provider
+
+        factory = _load_builtin_provider("aws")
+        assert factory is not None
+
+    def test_gcp_in_builtin(self):
+        from scalable.providers.registry import _load_builtin_provider
+
+        factory = _load_builtin_provider("gcp")
+        assert factory is not None
diff --git a/tests/unit/test_costing.py b/tests/unit/test_costing.py
new file mode 100644
index 0000000..7090631
--- /dev/null
+++ b/tests/unit/test_costing.py
@@ -0,0 +1,85 @@
+"""Unit tests for scalable.costing module."""
+
+from __future__ import annotations
+
+import pytest
+
+from scalable.costing import CostEstimate, CostLineItem
+
+
+class TestCostLineItem:
+    def test_compute_basic(self):
+        li = CostLineItem.compute(
+            resource="compute",
+            description="2x m5.xlarge",
+            unit="USD/hr",
+            quantity=2.0,
+            unit_cost=0.192,
+        )
+        assert li.resource == "compute"
+        assert li.quantity == 2.0
+        assert li.unit_cost == 0.192
+        assert li.total == pytest.approx(0.384, abs=1e-6)
+
+    def test_compute_zero_quantity(self):
+        li = CostLineItem.compute(
+            resource="storage",
+            description="0 GB",
+            unit="USD/GB",
+            quantity=0.0,
+            unit_cost=0.023,
+        )
+        assert li.total == 0.0
+
+
+class TestCostEstimate:
+    def test_from_line_items(self):
+        items = [
+            CostLineItem.compute(
+                resource="compute",
+                description="worker group 'model'",
+                unit="USD/hr",
+                quantity=3.0,
+                unit_cost=0.192,
+            ),
+            CostLineItem.compute(
+                resource="compute",
+                description="worker group 'postprocess'",
+                unit="USD/hr",
+                quantity=1.0,
+                unit_cost=0.096,
+            ),
+        ]
+        est = CostEstimate.from_line_items(
+            provider="aws",
+            region="us-east-1",
+            line_items=items,
+        )
+        assert est.provider == "aws"
+        assert est.region == "us-east-1"
+        assert est.currency == "USD"
+        assert est.total_hourly == pytest.approx(0.672, abs=1e-6)
+        assert est.total_monthly == pytest.approx(0.672 * 730, abs=0.01)
+        assert len(est.line_items) == 2
+
+    def test_to_dict(self):
+        est = CostEstimate(
+            provider="gcp",
+            region="us-central1",
+            total_hourly=0.5,
+            total_monthly=365.0,
+        )
+        d = est.to_dict()
+        assert d["provider"] == "gcp"
+        assert d["region"] == "us-central1"
+        assert d["total_hourly"] == 0.5
+        assert d["total_monthly"] == 365.0
+        assert d["line_items"] == []
+        assert d["metadata"] == {}
+
+    def test_default_values(self):
+        est = CostEstimate(provider="local")
+        assert est.region is None
+        assert est.currency == "USD"
+        assert est.total_hourly == 0.0
+        assert est.total_monthly == 0.0
diff --git a/tests/unit/test_manifest_overlays.py b/tests/unit/test_manifest_overlays.py
new file mode 100644
index 0000000..99b6705
--- /dev/null
+++ b/tests/unit/test_manifest_overlays.py
@@ -0,0 +1,158 @@
+"""Unit tests for scalable.manifest.overlays module."""
+
+from __future__ import annotations
+
+import pytest
+
+from scalable.manifest.errors import ManifestSchemaError
+from scalable.manifest.overlays import deep_merge, resolve_overlay
+
+
+class TestDeepMerge:
+    def test_basic_merge(self):
+        base = {"a": 1, "b": 2}
+        overlay = {"b": 3, "c": 4}
+        result = deep_merge(base, overlay)
+        assert result == {"a": 1, "b": 3, "c": 4}
+
+    def test_nested_dict_merge(self):
+        base = {"top": {"a": 1, "b": 2}}
+        overlay = {"top": {"b": 3, "c": 4}}
+        result = deep_merge(base, overlay)
+        assert result == {"top": {"a": 1, "b": 3, "c": 4}}
+
+    def test_list_replacement(self):
+        base = {"items": [1, 2, 3]}
+        overlay = {"items": [4, 5]}
+        result = deep_merge(base, overlay)
+        assert result == {"items": [4, 5]}
+
+    def test_no_mutation(self):
+        base = {"a": {"nested": 1}}
+        overlay = {"a": {"nested": 2}}
+        result = deep_merge(base, overlay)
+        assert base["a"]["nested"] == 1
+        assert result["a"]["nested"] == 2
+
+    def test_empty_overlay(self):
+        base = {"a": 1}
+        result = deep_merge(base, {})
+        assert result == {"a": 1}
+
+    def test_deeply_nested(self):
+        base = {"l1": {"l2": {"l3": {"val": "original"}}}}
+        overlay = {"l1": {"l2": {"l3": {"val": "modified", "new": True}}}}
+        result = deep_merge(base, overlay)
+        assert result["l1"]["l2"]["l3"] == {"val": "modified", "new": True}
+
+
+class TestResolveOverlay:
+    def test_no_overlay_applied(self):
+        doc = {
+            "version": 1,
+            "project": {"name": "test"},
+            "targets": {"local": {"provider": "local"}},
+        }
+        resolved, unresolved = resolve_overlay(doc)
+        assert resolved == doc
+        assert unresolved is None
+
+    def test_overlay_from_target(self):
+        doc = {
+            "version": 1,
+            "project": {"name": "test"},
+            "targets": {
+                "prod": {"provider": "kubernetes", "overlay": "k8s-prod"},
+            },
+            "overlays": {
+                "k8s-prod": {
+                    "targets": {
+                        "prod": {"namespace": "production"},
+                    },
+                },
+            },
+        }
+        resolved, unresolved = resolve_overlay(doc, target_name="prod")
+        assert "overlays" not in resolved
+        assert resolved["targets"]["prod"]["namespace"] == "production"
+        assert unresolved is not None
+        assert "overlays" not in unresolved
+
+    def test_explicit_overlay_name(self):
+        doc = {
+            "version": 1,
+            "project": {"name": "test"},
+            "targets": {"dev": {"provider": "local"}},
+            "overlays": {
+                "extra-memory": {
+                    "components": {"model": {"memory": "32G"}},
+                },
+            },
+            "components": {"model": {"cpus": 4, "memory": "8G"}},
+        }
+        resolved, unresolved = resolve_overlay(doc, overlay_name="extra-memory")
+        assert resolved["components"]["model"]["memory"] == "32G"
+        assert resolved["components"]["model"]["cpus"] == 4
+
+    def test_unknown_overlay_raises(self):
+        doc = {
+            "version": 1,
+            "project": {"name": "test"},
+            "targets": {"gke": {"provider": "kubernetes", "overlay": "missing"}},
+            "overlays": {},
+        }
+        with pytest.raises(ManifestSchemaError, match="missing"):
+            resolve_overlay(doc, target_name="gke")
+
+    def test_overlay_strips_overlay_ref_from_target(self):
+        doc = {
+            "version": 1,
+            "project": {"name": "test"},
+            "targets": {"prod": {"provider": "kubernetes", "overlay": "prod-cfg"}},
+            "overlays": {
+                "prod-cfg": {"targets": {"prod": {"namespace": "prod"}}},
+            },
+        }
+        resolved, _ = resolve_overlay(doc, target_name="prod")
+        # The resolved target should not have the 'overlay' key
+        assert "overlay" not in resolved["targets"]["prod"]
+
+
+class TestParserOverlayIntegration:
+    """Test that parse_manifest correctly passes through overlay resolution."""
+
+    def test_parse_with_overlay(self):
+        from scalable.manifest.parser import parse_manifest
+
+        doc = {
+            "version": 1,
+            "project": {"name": "test"},
+            "targets": {"gke": {"provider": "kubernetes", "overlay": "gke-prod"}},
+            "components": {"model": {"cpus": 2, "memory": "4G"}},
+            "tasks": {"run_model": {"component": "model"}},
+            "overlays": {
+                "gke-prod": {
+                    "components": {"model": {"memory": "16G"}},
+                },
+            },
+        }
+        manifest = parse_manifest(doc, target_name="gke")
+        # Overlay should have changed memory
+        assert manifest.components["model"].memory == "16G"
+        assert manifest.components["model"].cpus == 2
+        # raw_unresolved should exist
+        assert manifest.raw_unresolved is not None
+
+    def test_parse_without_overlay(self):
+        from scalable.manifest.parser import parse_manifest
+
+        doc = {
+            "version": 1,
+            "project": {"name": "test"},
+            "targets": {"local": {"provider": "local"}},
+            "components": {"model": {"cpus": 2}},
+            "tasks": {"run_model": {"component": "model"}},
+        }
+        manifest = parse_manifest(doc)
+        assert manifest.raw_unresolved is None
+        assert manifest.components["model"].cpus == 2
diff --git a/tests/unit/test_settings_phase3.py b/tests/unit/test_settings_phase3.py
new file mode 100644
index 0000000..198e8fb
--- /dev/null
+++ b/tests/unit/test_settings_phase3.py
@@ -0,0 +1,50 @@
+"""Unit tests for Phase 3 Settings extensions."""
+
+from __future__ import annotations
+
+import os
+
+import pytest
+
+from scalable.common import Settings
+
+
+class TestSettingsPhase3:
+    def test_cache_remote_uri_default_none(self):
+        # Unset env var
+        env = os.environ.copy()
+        env.pop("SCALABLE_CACHE_REMOTE", None)
+        with pytest.MonkeyPatch.context() as m:
+            m.delenv("SCALABLE_CACHE_REMOTE", raising=False)
+            s = Settings()
+            assert s.cache_remote_uri is None
+
+    def test_cache_remote_uri_from_env(self):
+        with pytest.MonkeyPatch.context() as m:
+            m.setenv("SCALABLE_CACHE_REMOTE", "s3://my-bucket/cache/")
+            s = Settings()
+            assert s.cache_remote_uri == "s3://my-bucket/cache/"
+
+    def test_default_storage_default_none(self):
+        with pytest.MonkeyPatch.context() as m:
+            m.delenv("SCALABLE_DEFAULT_STORAGE", raising=False)
+            s = Settings()
+            assert s.default_storage is None
+
+    def test_default_storage_from_env(self):
+        with pytest.MonkeyPatch.context() as m:
+            m.setenv("SCALABLE_DEFAULT_STORAGE", "gs://bucket/artifacts/")
+            s = Settings()
+            assert s.default_storage == "gs://bucket/artifacts/"
+
+    def test_runs_dir_remote_default_none(self):
+        with pytest.MonkeyPatch.context() as m:
+            m.delenv("SCALABLE_RUNS_DIR_REMOTE", raising=False)
+            s = Settings()
+            assert s.runs_dir_remote is None
+
+    def test_runs_dir_remote_from_env(self):
+        with pytest.MonkeyPatch.context() as m:
+            m.setenv("SCALABLE_RUNS_DIR_REMOTE", "s3://bucket/runs/")
+            s = Settings()
+            assert s.runs_dir_remote == "s3://bucket/runs/"
diff --git a/tests/unit/test_telemetry_cost.py b/tests/unit/test_telemetry_cost.py
new file mode 100644
index 0000000..255d658
--- /dev/null
+++ b/tests/unit/test_telemetry_cost.py
@@ -0,0 +1,79 @@
+"""Unit tests for Phase 3 telemetry extensions (CostEvent, cost.jsonl)."""
+
+from __future__ import annotations
+
+from scalable.telemetry.events import CostEvent, RemoteCacheEvent
+
+
+class TestCostEvent:
+    def test_creation(self):
+        event = CostEvent(
+            run_id="test-run-123",
+            provider="aws",
+            region="us-east-1",
+            currency="USD",
+            total_hourly=0.384,
+            total_monthly=280.32,
+        )
+        assert event.event_type == "cost"
+        assert event.provider == "aws"
+        assert event.total_hourly == 0.384
+
+    def test_to_dict(self):
+        event = CostEvent(
+            run_id="test-run-123",
+            provider="gcp",
+            region="us-central1",
+            currency="USD",
+            total_hourly=0.5,
+            total_monthly=365.0,
+            line_items=[{"resource": "compute", "total": 0.5}],
+            metadata={"instance_type": "n1-standard-4"},
+        )
+        d = event.to_dict()
+        assert d["event_type"] == "cost"
+        assert d["provider"] == "gcp"
+        assert d["region"] == "us-central1"
+        assert len(d["line_items"]) == 1
+        assert d["metadata"]["instance_type"] == "n1-standard-4"
+
+    def test_schema_version(self):
+        from scalable.telemetry.events import SCHEMA_VERSION
+
+        event = CostEvent(
+            run_id="x",
+            provider="aws",
+            region=None,
+            currency="USD",
+            total_hourly=0,
+            total_monthly=0,
+        )
+        assert event.schema_version == SCHEMA_VERSION
+
+
+class TestRemoteCacheEvent:
+    def test_creation(self):
+        event = RemoteCacheEvent(
+            run_id="test-run-456",
+            function_name="compute_climate",
+            key_digest="abcdef12",
+            hit=True,
+            remote=True,
+            remote_uri="s3://bucket/cache/ab/abcdef12",
+        )
+        assert event.event_type == "remote_cache"
+        assert event.remote is True
+        assert event.hit is True
+
+    def test_to_dict(self):
+        event = RemoteCacheEvent(
+            run_id="test-run-456",
+            function_name="run_model",
+            key_digest="fedcba98",
+            hit=False,
+            remote=True,
+        )
+        d = event.to_dict()
+        assert d["event_type"] == "remote_cache"
+        assert d["hit"] is False
+        assert d["remote"] is True

From 1460fffbcddee3c16af5e504e846a14295325b04 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 19:41:48 -0400
Subject: [PATCH 18/47] Phase 4: AI assistant features

Implements the Phase 4 deliverables from the v2.0.0 development plan:

- AI assistant subsystem (scalable.ai) with pluggable LLM backend
  protocol and heuristic-only fallback mode
- Component onboarding assistant (scalable init-component)
- Failure diagnosis assistant (scalable diagnose)
- Plan explanation assistant (scalable explain)
- Workflow composition assistant (scalable compose)
- Manifest migration assistant (scalable migrate)
- ScalableSession.plan(objective=, policy=) now functional with
  heuristic-based resource/worker adjustments
- Prompt template system for all assistants
- Settings: SCALABLE_AI_BACKEND, SCALABLE_AI_MODEL, SCALABLE_AI_ENDPOINT
- Populated [project.optional-dependencies] ai extra
- Version bumped to 2.0.0a4
- 356 unit tests passing, ruff clean

All AI features work without an LLM backend via deterministic heuristic
fallbacks. LLM enhancement is opt-in. All outputs are reviewable
artifacts - never auto-executed.

Ref: plans/v2.0.0_phase4_plan.md
---
 CHANGELOG.md                               |  73 +++
 docs/ai_assistants.rst                     | 160 ++++++
 docs/index.rst                             |   1 +
 pyproject.toml                             |   7 +-
 scalable/__init__.py                       |  39 ++
 scalable/ai/__init__.py                    |  38 ++
 scalable/ai/backend.py                     | 236 +++++++++
 scalable/ai/component_onboarding.py        | 204 ++++++++
 scalable/ai/heuristics.py                  | 569 +++++++++++++++++++++
 scalable/ai/log_diagnosis.py               | 321 ++++++++++++
 scalable/ai/manifest_migrate.py            | 393 ++++++++++++++
 scalable/ai/plan_explain.py                | 266 ++++++++++
 scalable/ai/prompts/__init__.py            |   5 +
 scalable/ai/prompts/compose.py             |  42 ++
 scalable/ai/prompts/diagnose.py            |  36 ++
 scalable/ai/prompts/explain.py             |  34 ++
 scalable/ai/prompts/migrate.py             |  36 ++
 scalable/ai/prompts/onboarding.py          |  44 ++
 scalable/ai/workflow_compose.py            | 468 +++++++++++++++++
 scalable/cli/cmd_compose.py                |  83 +++
 scalable/cli/cmd_diagnose.py               |  79 +++
 scalable/cli/cmd_explain.py                |  73 +++
 scalable/cli/cmd_init_component.py         |  69 +++
 scalable/cli/cmd_migrate.py                |  92 ++++
 scalable/cli/main.py                       | 244 ++++++++-
 scalable/common.py                         |  10 +
 scalable/session/session.py                | 109 +++-
 tests/unit/test_ai_backend.py              | 115 +++++
 tests/unit/test_ai_compose.py              | 104 ++++
 tests/unit/test_ai_diagnosis.py            | 180 +++++++
 tests/unit/test_ai_explain.py              |  99 ++++
 tests/unit/test_ai_heuristics.py           | 200 ++++++++
 tests/unit/test_ai_migrate.py              | 142 +++++
 tests/unit/test_ai_onboarding.py           |  87 ++++
 tests/unit/test_cli_phase4.py              | 220 ++++++++
 tests/unit/test_cli_validate.py            |  10 +-
 tests/unit/test_session.py                 |  15 +-
 tests/unit/test_session_plan_objectives.py |  96 ++++
 38 files changed, 4973 insertions(+), 26 deletions(-)
 create mode 100644 docs/ai_assistants.rst
 create mode 100644 scalable/ai/__init__.py
 create mode 100644 scalable/ai/backend.py
 create mode 100644 scalable/ai/component_onboarding.py
 create mode 100644 scalable/ai/heuristics.py
 create mode 100644 scalable/ai/log_diagnosis.py
 create mode 100644 scalable/ai/manifest_migrate.py
 create mode 100644 scalable/ai/plan_explain.py
 create mode 100644 scalable/ai/prompts/__init__.py
 create mode 100644 scalable/ai/prompts/compose.py
 create mode 100644 scalable/ai/prompts/diagnose.py
 create mode 100644 scalable/ai/prompts/explain.py
 create mode 100644 scalable/ai/prompts/migrate.py
 create mode 100644 scalable/ai/prompts/onboarding.py
 create mode 100644 scalable/ai/workflow_compose.py
 create mode 100644 scalable/cli/cmd_compose.py
 create mode 100644 scalable/cli/cmd_diagnose.py
 create mode 100644 scalable/cli/cmd_explain.py
 create mode 100644 scalable/cli/cmd_init_component.py
 create mode 100644 scalable/cli/cmd_migrate.py
 create mode 100644 tests/unit/test_ai_backend.py
 create mode 100644 tests/unit/test_ai_compose.py
 create mode 100644 tests/unit/test_ai_diagnosis.py
 create mode 100644 tests/unit/test_ai_explain.py
 create mode 100644 tests/unit/test_ai_heuristics.py
 create mode 100644 tests/unit/test_ai_migrate.py
 create mode 100644 tests/unit/test_ai_onboarding.py
 create mode 100644 tests/unit/test_cli_phase4.py
 create mode 100644 tests/unit/test_session_plan_objectives.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 42121be..265273f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,79 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [2.0.0a4] — Phase 4: AI Assistant Features
+
+### Added
+
+- **AI assistant subsystem** (`scalable.ai`) with pluggable LLM backend
+  protocol and heuristic-only fallback mode:
+  - `AIBackend` protocol with `NoOpBackend`, `OpenAIBackend`, `OllamaBackend`
+  - Backend selection via `SCALABLE_AI_BACKEND` environment variable
+  - All assistants functional without LLM via deterministic heuristics
+- **Component onboarding assistant** (`scalable init-component`):
+  - Directory scanning for language, build system, and runtime detection
+  - Resource estimation heuristics based on detected language/build system
+  - Container file detection (Dockerfile, Apptainer)
+  - Mount point suggestions from data directory conventions
+  - Proposed `ComponentConfig`-compatible YAML output
+- **Failure diagnosis assistant** (`scalable diagnose`):
+  - Rule-based failure taxonomy: `oom`, `walltime`, `mount_missing`,
+    `import_error`, `connection`, `credential`, `model_runtime`
+  - Evidence extraction from telemetry events
+  - Suggested fixes with confidence ratings
+  - Text and JSON output formats
+- **Plan explanation assistant** (`scalable explain`):
+  - Human-readable narrative from `plan.json` files
+  - Sections: overview, resource allocation, execution strategy, recommendations
+  - Historical context from telemetry when available
+- **Workflow composition assistant** (`scalable compose`):
+  - Natural-language to workflow generation
+  - Known model pattern detection (GCAM, Stitches, Demeter, Tethys, Xanthos, Hector)
+  - Generates `workflow.py`, `components.yaml`, `README.generated.md`
+  - Python syntax validation via `ast.parse`
+- **Manifest migration assistant** (`scalable migrate`):
+  - Provider migration proposals (slurm→kubernetes, slurm→aws, etc.)
+  - Schema version upgrade guidance
+  - Overlay-based output for non-destructive changes
+  - General manifest optimization suggestions
+- **Prompt template system** (`scalable.ai.prompts`) for all assistants.
+- **Heuristic analysis engine** (`scalable.ai.heuristics`):
+  - File/directory scanner for model detection
+  - Language/runtime classifier
+  - Resource estimation from build system analysis
+  - Failure pattern matching with regex taxonomy
+- **`ScalableSession.plan(objective=, policy=)`** now functional:
+  - Supported objectives: `"minimize cost"`, `"minimize time"`, `"balance"`
+  - Supported policies: `"safe"`, `"aggressive"`, `"manual"`
+  - Heuristic-based resource/worker adjustments
+- **Settings extensions** (`scalable.common.Settings`):
+  - `ai_backend` (`SCALABLE_AI_BACKEND`)
+  - `ai_model` (`SCALABLE_AI_MODEL`)
+  - `ai_endpoint` (`SCALABLE_AI_ENDPOINT`)
+- **Public API**: `onboard_component`, `diagnose_run`, `explain_plan`,
+  `compose_workflow`, `migrate_manifest` and associated result types
+  exported from `scalable.__init__` with optional-dep guards.
+- New docs page: `ai_assistants.rst`.
+- Populated `[project.optional-dependencies] ai` with `jinja2 >= 3.1`
+  and `rich >= 13.0`.
+
+### Changed
+
+- Bumped version to `2.0.0a4`.
+- CLI `_STUB_COMMANDS` is now empty — all Phase 4 commands (`diagnose`,
+  `explain`, `init-component`, `compose`) are fully implemented.
+- Added `migrate` as a new CLI command (not previously stubbed).
+- `ScalableSession.plan(objective=, policy=)` no longer raises
+  `NotImplementedError` for supported objective/policy combinations.
+
+### Tests
+
+- 118 new unit tests for AI modules, CLI commands, and session planning.
+- Updated 2 existing tests to reflect Phase 4 behavioral changes.
+- 356 total unit tests passing.
+
+---
+
 ## [2.0.0a3] — Phase 3: Cloud and Kubernetes Execution
 
 ### Added
diff --git a/docs/ai_assistants.rst b/docs/ai_assistants.rst
new file mode 100644
index 0000000..901481c
--- /dev/null
+++ b/docs/ai_assistants.rst
@@ -0,0 +1,160 @@
+AI Assistants
+=============
+
+Phase 4 introduces AI-assisted features that help users onboard models,
+compose workflows, diagnose failures, explain plans, and migrate manifests.
+
+All features work **without** an LLM backend via deterministic heuristic
+fallbacks. LLM enhancement is opt-in via the ``SCALABLE_AI_BACKEND``
+environment variable.
+
+Design Philosophy
+-----------------
+
+* **AI proposes; Scalable disposes.** All outputs are reviewable artifacts — never auto-executed.
+* **Offline-compatible.** Heuristic mode works on air-gapped HPC systems.
+* **No hidden science changes.** AI tunes infrastructure only.
+* **Inspectable.** All outputs include provenance and confidence indicators.
+
+Configuration
+-------------
+
+AI features are controlled via environment variables:
+
+* ``SCALABLE_AI_BACKEND`` — Backend selection (``none``, ``openai``, ``ollama``). Default: ``none``.
+* ``SCALABLE_AI_MODEL`` — Model name for the selected backend.
+* ``SCALABLE_AI_ENDPOINT`` — API endpoint override for the backend.
+
+Install the AI extra for enhanced output formatting::
+
+    pip install scalable[ai]
+
+Commands
+--------
+
+scalable init-component
+~~~~~~~~~~~~~~~~~~~~~~~
+
+Analyze a model directory and propose a component manifest block::
+
+    scalable init-component ./path/to/model --name gcam --no-ai
+
+Options:
+
+* ``--name`` — Component name (default: directory basename)
+* ``--output`` — Write to file instead of stdout
+* ``--no-ai`` — Use heuristics only (no LLM)
+
+The assistant inspects build systems, source files, data directories,
+and container definitions to propose a complete component YAML block.
+
+scalable diagnose
+~~~~~~~~~~~~~~~~~
+
+Classify failures from run telemetry and suggest fixes::
+
+    scalable diagnose --latest --no-ai
+    scalable diagnose --run-id run-20260519T120000Z-project-abc
+
+Options:
+
+* ``--runs-dir`` — Custom runs directory
+* ``--run-id`` — Specific run to diagnose
+* ``--latest`` — Use most recent run (default if no run-id)
+* ``--format`` — Output format (``text`` or ``json``)
+* ``--output`` — Write to file
+* ``--no-ai`` — Use heuristics only
+
+Failure classes detected: ``oom``, ``walltime``, ``mount_missing``,
+``import_error``, ``connection``, ``credential``, ``model_runtime``.
+
+scalable explain
+~~~~~~~~~~~~~~~~
+
+Render a human-readable explanation of an execution plan::
+
+    scalable explain plan.json
+    scalable explain plan.json --format json
+
+Options:
+
+* ``--runs-dir`` — Runs directory for historical context
+* ``--format`` — Output format (``text`` or ``json``)
+* ``--output`` — Write to file
+* ``--no-ai`` — Use heuristics only
+
+scalable compose
+~~~~~~~~~~~~~~~~
+
+Generate a workflow from a natural-language description::
+
+    scalable compose "Run GCAM reference scenario then Stitches for daily climate"
+    scalable compose "Run Hector model" --output-dir ./generated
+
+Options:
+
+* ``--output-dir`` — Directory for generated files
+* ``--format`` — Output format (``text`` or ``json``)
+* ``--no-ai`` — Use heuristics only
+
+Known model patterns: GCAM, Stitches, Demeter, Tethys, Xanthos, Hector.
+
+scalable migrate
+~~~~~~~~~~~~~~~~
+
+Propose manifest migration changes::
+
+    scalable migrate scalable.yaml --to-provider kubernetes
+    scalable migrate scalable.yaml --goal "Add cloud target"
+
+Options:
+
+* ``--to-provider`` — Target provider (``kubernetes``, ``aws``, ``gcp``)
+* ``--to-version`` — Target schema version
+* ``--goal`` — Free-form migration goal
+* ``--format`` — Output format (``text`` or ``json``)
+* ``--output`` — Write to file
+* ``--no-ai`` — Use heuristics only
+
+Python API
+----------
+
+All assistant functions are available programmatically::
+
+    from scalable.ai import (
+        onboard_component,
+        diagnose_run,
+        explain_plan,
+        compose_workflow,
+        migrate_manifest,
+    )
+
+    # Onboard a model
+    result = onboard_component("./gcam-core", name="gcam", no_ai=True)
+    print(result.component_yaml)
+
+    # Diagnose a run
+    diagnosis = diagnose_run(runs_dir=".scalable/runs", latest=True, no_ai=True)
+    print(diagnosis.render_text())
+
+    # Explain a plan
+    explanation = explain_plan(plan_path="plan.json", no_ai=True)
+    print(explanation.render_text())
+
+Session Planning with Objectives
+---------------------------------
+
+``ScalableSession.plan()`` now supports ``objective`` and ``policy`` kwargs::
+
+    session = ScalableSession.from_yaml("scalable.yaml")
+    plan = session.plan(
+        objective="minimize cost",   # "minimize cost", "minimize time", "balance"
+        policy="safe",               # "safe", "aggressive", "manual"
+    )
+
+* ``minimize cost`` — Conservative worker allocation
+* ``minimize time`` — Scale up workers for parallelism
+* ``balance`` — Moderate scaling (default)
+* ``safe`` — Use safety margins on resources (default)
+* ``aggressive`` — Scale up resources/workers significantly
+* ``manual`` — Use exactly what the manifest declares
diff --git a/docs/index.rst b/docs/index.rst
index 908d9b4..1fb1195 100755
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -54,6 +54,7 @@ Contents
    cost
    telemetry
    advising
+   ai_assistants
    caching
    functions
 
diff --git a/pyproject.toml b/pyproject.toml
index a55cc72..a65f287 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "scalable"
-version = "2.0.0a3"
+version = "2.0.0a4"
 description = "Assist with running models on job queing systems like Slurm"
 authors = [
     { name = "Shashank Lamba" },
@@ -58,7 +58,10 @@ dev = [
 # v2.0.0 phase placeholders. Empty until later phases populate them so that
 # `pip install scalable[ai|cloud|kubernetes]` resolves cleanly from day one
 # and downstream pinning of the extras name is stable.
-ai = []
+ai = [
+    "jinja2 >= 3.1",
+    "rich >= 13.0",
+]
 cloud = [
     "s3fs >= 2024.2.0",
     "gcsfs >= 2024.2.0",
diff --git a/scalable/__init__.py b/scalable/__init__.py
index 27aa418..d3993ff 100755
--- a/scalable/__init__.py
+++ b/scalable/__init__.py
@@ -8,6 +8,9 @@
   :class:`DeploymentProvider`, :class:`LocalProvider`, :class:`SlurmProvider`
 * Phase 3 cloud/k8s providers (optional deps):
   :class:`KubernetesProvider`, :class:`CloudProvider`, :class:`ArtifactStore`
+* Phase 4 AI assistants (optional deps):
+  :func:`onboard_component`, :func:`diagnose_run`, :func:`explain_plan`,
+  :func:`compose_workflow`, :func:`migrate_manifest`
 * :func:`cacheable` and the :class:`*Type` hash wrappers from
   :mod:`scalable.caching`
 * :data:`SEED` and the :data:`settings` singleton from :mod:`scalable.common`
@@ -51,6 +54,32 @@
     LocalArtifactStore = None  # type: ignore[assignment,misc]
     build_artifact_store = None  # type: ignore[assignment,misc]
 
+# Phase 4: AI assistant exports (optional deps)
+try:
+    from .ai import (
+        ComposeResult,
+        DiagnosisResult,
+        ExplanationResult,
+        MigrationResult,
+        OnboardingResult,
+        compose_workflow,
+        diagnose_run,
+        explain_plan,
+        migrate_manifest,
+        onboard_component,
+    )
+except ImportError:  # pragma: no cover
+    ComposeResult = None  # type: ignore[assignment,misc]
+    DiagnosisResult = None  # type: ignore[assignment,misc]
+    ExplanationResult = None  # type: ignore[assignment,misc]
+    MigrationResult = None  # type: ignore[assignment,misc]
+    OnboardingResult = None  # type: ignore[assignment,misc]
+    compose_workflow = None  # type: ignore[assignment,misc]
+    diagnose_run = None  # type: ignore[assignment,misc]
+    explain_plan = None  # type: ignore[assignment,misc]
+    migrate_manifest = None  # type: ignore[assignment,misc]
+    onboard_component = None  # type: ignore[assignment,misc]
+
 try:
     __version__ = _pkg_version("scalable")
 except PackageNotFoundError:  # pragma: no cover - source checkout w/o install
@@ -60,13 +89,18 @@
     "AWSBatchProvider",
     "ArtifactStore",
     "CloudProvider",
+    "ComposeResult",
     "CostEstimate",
     "DeploymentProvider",
+    "DiagnosisResult",
+    "ExplanationResult",
     "GCPProvider",
     "JobQueueCluster",
     "KubernetesProvider",
     "LocalArtifactStore",
     "LocalProvider",
+    "MigrationResult",
+    "OnboardingResult",
     "ResourceAdvisor",
     "ResourceRecommendation",
     "SEED",
@@ -77,6 +111,11 @@
     "SlurmProvider",
     "__version__",
     "build_artifact_store",
+    "compose_workflow",
+    "diagnose_run",
+    "explain_plan",
     "get_worker",
+    "migrate_manifest",
+    "onboard_component",
     "settings",
 ]
diff --git a/scalable/ai/__init__.py b/scalable/ai/__init__.py
new file mode 100644
index 0000000..8e8ab48
--- /dev/null
+++ b/scalable/ai/__init__.py
@@ -0,0 +1,38 @@
+"""AI assistant subsystem for Scalable.
+
+This package provides AI-assisted features including:
+
+* Component onboarding (``scalable init-component``)
+* Workflow composition (``scalable compose``)
+* Failure diagnosis (``scalable diagnose``)
+* Plan explanation (``scalable explain``)
+* Manifest migration (``scalable migrate``)
+
+All features have a **heuristic fallback** that works without any LLM backend.
+LLM enhancement is opt-in via ``SCALABLE_AI_BACKEND`` env var.
+"""
+
+from __future__ import annotations
+
+from .backend import AIBackend, NoOpBackend, get_ai_backend
+from .component_onboarding import OnboardingResult, onboard_component
+from .log_diagnosis import DiagnosisResult, diagnose_run
+from .manifest_migrate import MigrationResult, migrate_manifest
+from .plan_explain import ExplanationResult, explain_plan
+from .workflow_compose import ComposeResult, compose_workflow
+
+__all__ = [
+    "AIBackend",
+    "ComposeResult",
+    "DiagnosisResult",
+    "ExplanationResult",
+    "MigrationResult",
+    "NoOpBackend",
+    "OnboardingResult",
+    "compose_workflow",
+    "diagnose_run",
+    "explain_plan",
+    "get_ai_backend",
+    "migrate_manifest",
+    "onboard_component",
+]
diff --git a/scalable/ai/backend.py b/scalable/ai/backend.py
new file mode 100644
index 0000000..e4f93b1
--- /dev/null
+++ b/scalable/ai/backend.py
@@ -0,0 +1,236 @@
+"""Pluggable AI/LLM backend protocol and registry.
+
+The backend system supports:
+
+* ``none`` — heuristic-only mode (no LLM calls)
+* ``openai`` — OpenAI-compatible API (requires ``openai`` package)
+* ``ollama`` — local Ollama server (requires running Ollama instance)
+
+Backend selection is controlled by ``SCALABLE_AI_BACKEND`` env var.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Protocol, runtime_checkable
+
+from scalable.common import settings
+
+logger = logging.getLogger(__name__)
+
+__all__ = [
+    "AIBackend",
+    "NoOpBackend",
+    "get_ai_backend",
+]
+
+
+@runtime_checkable
+class AIBackend(Protocol):
+    """Protocol for pluggable LLM/AI backends."""
+
+    name: str
+
+    def complete(
+        self,
+        prompt: str,
+        *,
+        system: str | None = None,
+        temperature: float = 0.0,
+        max_tokens: int = 4096,
+    ) -> str:
+        """Generate a completion from the given prompt."""
+        ...
+
+    def available(self) -> bool:
+        """Check whether this backend is currently usable."""
+        ...
+
+
+class NoOpBackend:
+    """Fallback backend that signals no LLM is available.
+
+    All assistants detect this and use their heuristic code path instead.
+    """
+
+    name: str = "none"
+
+    def complete(
+        self,
+        prompt: str,
+        *,
+        system: str | None = None,
+        temperature: float = 0.0,
+        max_tokens: int = 4096,
+    ) -> str:
+        raise RuntimeError(
+            "No AI backend configured. Set SCALABLE_AI_BACKEND to enable LLM features, "
+            "or use --no-ai for heuristic-only mode."
+        )
+
+    def available(self) -> bool:
+        return False
+
+
+class OpenAIBackend:
+    """OpenAI-compatible backend (requires ``openai`` package)."""
+
+    name: str = "openai"
+
+    def __init__(
+        self,
+        *,
+        model: str | None = None,
+        endpoint: str | None = None,
+        api_key: str | None = None,
+    ) -> None:
+        self._model = model or getattr(settings, "ai_model", None) or "gpt-4o"
+        self._endpoint = endpoint or getattr(settings, "ai_endpoint", None)
+        self._api_key = api_key
+
+    def complete(
+        self,
+        prompt: str,
+        *,
+        system: str | None = None,
+        temperature: float = 0.0,
+        max_tokens: int = 4096,
+    ) -> str:
+        try:
+            import openai  # type: ignore[import-untyped]
+        except ImportError as exc:
+            raise ImportError(
+                "OpenAI backend requires the 'openai' package. "
+                "Install with: pip install openai"
+            ) from exc
+
+        kwargs: dict[str, Any] = {}
+        if self._endpoint:
+            kwargs["base_url"] = self._endpoint
+        if self._api_key:
+            kwargs["api_key"] = self._api_key
+
+        client = openai.OpenAI(**kwargs)
+        messages: list[dict[str, str]] = []
+        if system:
+            messages.append({"role": "system", "content": system})
+        messages.append({"role": "user", "content": prompt})
+
+        response = client.chat.completions.create(
+            model=self._model,
+            messages=messages,
+            temperature=temperature,
+            max_tokens=max_tokens,
+        )
+        return response.choices[0].message.content or ""
+
+    def available(self) -> bool:
+        try:
+            import openai  # type: ignore[import-untyped] # noqa: F401
+            return True
+        except ImportError:
+            return False
+
+
+class OllamaBackend:
+    """Local Ollama backend for offline/HPC environments."""
+
+    name: str = "ollama"
+
+    def __init__(
+        self,
+        *,
+        model: str | None = None,
+        endpoint: str | None = None,
+    ) -> None:
+        self._model = model or getattr(settings, "ai_model", None) or "llama3"
+        self._endpoint = endpoint or getattr(settings, "ai_endpoint", None) or "http://localhost:11434"
+
+    def complete(
+        self,
+        prompt: str,
+        *,
+        system: str | None = None,
+        temperature: float = 0.0,
+        max_tokens: int = 4096,
+    ) -> str:
+        import json
+        import urllib.request
+
+        url = f"{self._endpoint.rstrip('/')}/api/generate"
+        payload: dict[str, Any] = {
+            "model": self._model,
+            "prompt": prompt,
+            "stream": False,
+            "options": {
+                "temperature": temperature,
+                "num_predict": max_tokens,
+            },
+        }
+        if system:
+            payload["system"] = system
+
+        data = json.dumps(payload).encode("utf-8")
+        req = urllib.request.Request(url, data=data, headers={"Content-Type": "application/json"})
+        try:
+            with urllib.request.urlopen(req, timeout=120) as resp:
+                result = json.loads(resp.read().decode("utf-8"))
+                return str(result.get("response", ""))
+        except Exception as exc:
+            raise RuntimeError(f"Ollama backend error: {exc}") from exc
+
+    def available(self) -> bool:
+        import urllib.request
+
+        try:
+            url = f"{self._endpoint.rstrip('/')}/api/tags"
+            req = urllib.request.Request(url)
+            with urllib.request.urlopen(req, timeout=5):
+                return True
+        except Exception:
+            return False
+
+
+_BACKEND_REGISTRY: dict[str, type] = {
+    "none": NoOpBackend,
+    "openai": OpenAIBackend,
+    "ollama": OllamaBackend,
+}
+
+_cached_backend: AIBackend | None = None
+
+
+def get_ai_backend(*, force_name: str | None = None) -> AIBackend:
+    """Get the configured AI backend instance.
+
+    Parameters
+    ----------
+    force_name : str | None
+        Override the backend name (bypasses SCALABLE_AI_BACKEND setting).
+
+    Returns
+    -------
+    AIBackend
+        The configured backend instance.
+    """
+    global _cached_backend
+
+    name = force_name or getattr(settings, "ai_backend", "none") or "none"
+
+    if _cached_backend is not None and getattr(_cached_backend, "name", None) == name:
+        return _cached_backend
+
+    backend_cls = _BACKEND_REGISTRY.get(name)
+    if backend_cls is None:
+        logger.warning("Unknown AI backend %r; falling back to 'none'", name)
+        backend_cls = NoOpBackend
+
+    backend = backend_cls()
+    _cached_backend = backend
+    return backend
+
+
+def reset_backend_cache() -> None:
+    """Reset the cached backend (for testing)."""
+    global _cached_backend
+    _cached_backend = None
diff --git a/scalable/ai/component_onboarding.py b/scalable/ai/component_onboarding.py
new file mode 100644
index 0000000..f0fd706
--- /dev/null
+++ b/scalable/ai/component_onboarding.py
@@ -0,0 +1,204 @@
+"""AI-assisted component onboarding for Scalable.
+
+Inspects a model directory and proposes a ``ComponentConfig``-compatible
+YAML block for inclusion in ``scalable.yaml``.
+"""
+
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+from .backend import AIBackend, get_ai_backend
+from .heuristics import DirectoryScanResult, find_run_commands, scan_model_directory
+from .prompts.onboarding import ANALYSIS_PROMPT, SYSTEM_PROMPT
+
+__all__ = ["OnboardingResult", "onboard_component"]
+
+
+@dataclass
+class OnboardingResult:
+    """Result of AI-assisted component onboarding."""
+
+    name: str
+    component_yaml: str
+    scan: DirectoryScanResult
+    method: str  # "heuristic" or "ai-enhanced"
+    warnings: list[str] = field(default_factory=list)
+
+    def to_dict(self) -> dict[str, Any]:
+        """Return the component as a parsed dictionary."""
+        try:
+            parsed = yaml.safe_load(self.component_yaml)
+            if isinstance(parsed, dict):
+                return parsed
+            return {self.name: {}}
+        except Exception:
+            return {self.name: {}}
+
+
+def onboard_component(
+    path: str | Path,
+    *,
+    name: str | None = None,
+    backend: AIBackend | None = None,
+    no_ai: bool = False,
+) -> OnboardingResult:
+    """Onboard a model component by analyzing its directory.
+
+    Parameters
+    ----------
+    path : str | Path
+        Path to the model directory to analyze.
+    name : str | None
+        Component name. Defaults to the directory basename.
+    backend : AIBackend | None
+        AI backend to use. Defaults to configured backend.
+    no_ai : bool
+        If True, skip LLM enhancement and use heuristics only.
+
+    Returns
+    -------
+    OnboardingResult
+        Proposed component manifest with metadata.
+    """
+    root = Path(path).resolve()
+    if not root.is_dir():
+        raise FileNotFoundError(f"Directory not found: {root}")
+
+    component_name = name or root.name.lower().replace(" ", "-").replace("_", "-")
+
+    # Scan the directory
+    scan = scan_model_directory(root)
+    scan.run_commands = find_run_commands(root)
+
+    # Try AI enhancement if available
+    if not no_ai:
+        ai_backend = backend or get_ai_backend()
+        if ai_backend.available():
+            return _onboard_with_ai(component_name, scan, ai_backend)
+
+    # Heuristic-only path
+    return _onboard_heuristic(component_name, scan)
+
+
+def _onboard_heuristic(name: str, scan: DirectoryScanResult) -> OnboardingResult:
+    """Generate component YAML using heuristic analysis only."""
+    component: dict[str, Any] = {}
+
+    if scan.suggested_base_image:
+        component["image"] = f"# TODO: build image based on {scan.suggested_base_image}"
+
+    if scan.suggested_runtime:
+        component["runtime"] = scan.suggested_runtime
+
+    component["cpus"] = scan.estimated_cpus
+    component["memory"] = scan.estimated_memory
+
+    if scan.suggested_mounts:
+        component["mounts"] = dict(scan.suggested_mounts)
+
+    env: dict[str, str] = {}
+    if scan.estimated_cpus > 1:
+        env["OMP_NUM_THREADS"] = str(scan.estimated_cpus)
+    if env:
+        component["env"] = env
+
+    if scan.suggested_tags:
+        component["tags"] = scan.suggested_tags
+
+    # Build YAML output with comments
+    lines = [
+        f"# Proposed component: {name}",
+        f"# Detected: {', '.join(scan.languages) if scan.languages else 'unknown language'}",
+        f"# Build systems: {', '.join(scan.build_systems) if scan.build_systems else 'none detected'}",
+        f"# Confidence: {scan.confidence}",
+    ]
+    if scan.run_commands:
+        lines.append(f"# Likely run commands: {', '.join(scan.run_commands[:3])}")
+    lines.append("")
+
+    yaml_body = yaml.dump(
+        {name: component}, default_flow_style=False, sort_keys=False
+    )
+    component_yaml = "\n".join(lines) + yaml_body
+
+    warnings: list[str] = []
+    if scan.confidence == "low":
+        warnings.append("Low confidence scan - review all fields carefully")
+    if not scan.container_files:
+        warnings.append("No container definition found - image field needs manual setup")
+    if not scan.data_directories:
+        warnings.append("No data directories detected - verify mount paths")
+
+    return OnboardingResult(
+        name=name,
+        component_yaml=component_yaml,
+        scan=scan,
+        method="heuristic",
+        warnings=warnings,
+    )
+
+
+def _onboard_with_ai(name: str, scan: DirectoryScanResult, backend: AIBackend) -> OnboardingResult:
+    """Enhance onboarding with LLM analysis."""
+    # Build file listing (limited to avoid token overload)
+    root = Path(scan.path)
+    file_listing = _build_file_listing(root, max_files=50)
+
+    prompt = ANALYSIS_PROMPT.format(
+        path=scan.path,
+        name=name,
+        file_listing=file_listing,
+        build_systems=", ".join(scan.build_systems) or "none",
+        languages=", ".join(scan.languages) or "unknown",
+        container_files=", ".join(scan.container_files) or "none",
+        data_directories=", ".join(scan.data_directories) or "none",
+        config_files=", ".join(scan.config_files[:10]) or "none",
+    )
+
+    try:
+        response = backend.complete(prompt, system=SYSTEM_PROMPT)
+        # Validate the AI response is valid YAML
+        parsed = yaml.safe_load(response)
+        if isinstance(parsed, dict):
+            yaml_output = yaml.dump(parsed, default_flow_style=False, sort_keys=False)
+            return OnboardingResult(
+                name=name,
+                component_yaml=yaml_output,
+                scan=scan,
+                method="ai-enhanced",
+                warnings=["AI-generated - review all fields before use"],
+            )
+    except Exception:
+        pass  # Fall through to heuristic
+
+    # Fallback to heuristic if AI fails
+    result = _onboard_heuristic(name, scan)
+    result.warnings.append("AI enhancement failed; using heuristic fallback")
+    return result
+
+
+def _build_file_listing(root: Path, max_files: int = 50) -> str:
+    """Build a truncated file listing for prompt context."""
+    lines: list[str] = []
+    count = 0
+    for dirpath, dirnames, filenames in os.walk(root):
+        # Skip hidden and build directories
+        dirnames[:] = [d for d in dirnames if not d.startswith(".")
+                       and d not in {"node_modules", "__pycache__", "venv", ".venv", "build", "dist"}]
+
+        rel_dir = os.path.relpath(dirpath, root)
+        for fname in sorted(filenames):
+            if count >= max_files:
+                lines.append(f"... ({count}+ files, truncated)")
+                return "\n".join(lines)
+            rel_path = os.path.join(rel_dir, fname) if rel_dir != "." else fname
+            lines.append(f"  {rel_path}")
+            count += 1
+
+    return "\n".join(lines) if lines else "  (empty directory)"
diff --git a/scalable/ai/heuristics.py b/scalable/ai/heuristics.py
new file mode 100644
index 0000000..6ec57bf
--- /dev/null
+++ b/scalable/ai/heuristics.py
@@ -0,0 +1,569 @@
+"""Rule-based heuristic analyzers for AI assistant features.
+
+These heuristics provide functional assistants without any LLM dependency.
+They analyze file structure, build systems, error patterns, and manifest
+content using deterministic rules.
+"""
+
+from __future__ import annotations
+
+import os
+import re
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+__all__ = [
+    "DirectoryScanResult",
+    "FailureClassification",
+    "classify_failure",
+    "detect_language",
+    "estimate_resources",
+    "find_run_commands",
+    "scan_model_directory",
+]
+
+
+# ---------------------------------------------------------------------------
+# File / directory scanning
+# ---------------------------------------------------------------------------
+
+#: Build system indicators and their associated language/runtime
+_BUILD_INDICATORS: dict[str, str] = {
+    "CMakeLists.txt": "c++",
+    "Makefile": "compiled",
+    "configure": "compiled",
+    "configure.ac": "compiled",
+    "meson.build": "compiled",
+    "setup.py": "python",
+    "pyproject.toml": "python",
+    "requirements.txt": "python",
+    "environment.yml": "python",
+    "conda.yml": "python",
+    "Pipfile": "python",
+    "setup.cfg": "python",
+    "DESCRIPTION": "r",
+    "NAMESPACE": "r",
+    "go.mod": "go",
+    "Cargo.toml": "rust",
+    "package.json": "javascript",
+    "pom.xml": "java",
+    "build.gradle": "java",
+}
+
+#: Container indicators
+_CONTAINER_INDICATORS: list[str] = [
+    "Dockerfile",
+    "Containerfile",
+    "apptainer.def",
+    "singularity.def",
+    ".devcontainer/devcontainer.json",
+]
+
+#: Common data directory names
+_DATA_DIRS: set[str] = {
+    "data", "input", "inputs", "output", "outputs", "results",
+    "exe", "bin", "lib", "scratch", "tmp", "logs",
+}
+
+#: File extensions associated with languages
+_EXTENSION_LANGUAGES: dict[str, str] = {
+    ".py": "python",
+    ".cpp": "c++",
+    ".cxx": "c++",
+    ".cc": "c++",
+    ".c": "c",
+    ".h": "c/c++",
+    ".hpp": "c++",
+    ".f90": "fortran",
+    ".f": "fortran",
+    ".for": "fortran",
+    ".R": "r",
+    ".r": "r",
+    ".go": "go",
+    ".rs": "rust",
+    ".java": "java",
+    ".jl": "julia",
+}
+
+
+@dataclass
+class DirectoryScanResult:
+    """Result of scanning a model directory for onboarding."""
+
+    path: str
+    languages: list[str] = field(default_factory=list)
+    build_systems: list[str] = field(default_factory=list)
+    container_files: list[str] = field(default_factory=list)
+    data_directories: list[str] = field(default_factory=list)
+    config_files: list[str] = field(default_factory=list)
+    run_commands: list[str] = field(default_factory=list)
+    has_readme: bool = False
+    has_tests: bool = False
+    estimated_cpus: int = 1
+    estimated_memory: str = "4G"
+    suggested_runtime: str | None = None
+    suggested_base_image: str | None = None
+    suggested_mounts: dict[str, str] = field(default_factory=dict)
+    suggested_env: dict[str, str] = field(default_factory=dict)
+    suggested_tags: list[str] = field(default_factory=list)
+    confidence: str = "low"
+
+
+def scan_model_directory(path: str | Path) -> DirectoryScanResult:
+    """Scan a model directory and extract onboarding metadata.
+
+    Parameters
+    ----------
+    path : str | Path
+        Path to the model directory to analyze.
+
+    Returns
+    -------
+    DirectoryScanResult
+        Structured analysis of the model directory.
+    """
+    root = Path(path).resolve()
+    if not root.is_dir():
+        raise FileNotFoundError(f"Directory not found: {root}")
+
+    result = DirectoryScanResult(path=str(root))
+
+    # Scan top-level and one level deep
+    _scan_files(root, result)
+    _detect_languages(root, result)
+    _estimate_resources(result)
+    _suggest_container(result)
+    _suggest_mounts(root, result)
+    _suggest_tags(result)
+    _assess_confidence(result)
+
+    return result
+
+
+def _scan_files(root: Path, result: DirectoryScanResult) -> None:
+    """Scan directory structure for indicators."""
+    for name in sorted(os.listdir(root)):
+        full_path = root / name
+
+        # Build system files
+        if name in _BUILD_INDICATORS:
+            result.build_systems.append(name)
+
+        # Container files
+        if name in _CONTAINER_INDICATORS:
+            result.container_files.append(name)
+
+        # Data directories
+        if full_path.is_dir() and name.lower() in _DATA_DIRS:
+            result.data_directories.append(name)
+
+        # README
+        if name.lower().startswith("readme"):
+            result.has_readme = True
+
+        # Tests
+        if name.lower() in ("tests", "test", "testing"):
+            result.has_tests = True
+
+        # Config files
+        if name.endswith((".xml", ".yml", ".yaml", ".cfg", ".ini", ".conf", ".toml")):
+            if name not in _BUILD_INDICATORS:
+                result.config_files.append(name)
+
+    # Check for Dockerfile in subdirectories
+    for container_file in _CONTAINER_INDICATORS:
+        if (root / container_file).exists():
+            if container_file not in result.container_files:
+                result.container_files.append(container_file)
+
+
+def _detect_languages(root: Path, result: DirectoryScanResult) -> None:
+    """Detect programming languages from file extensions."""
+    lang_counts: dict[str, int] = {}
+
+    for dirpath, _, filenames in os.walk(root):
+        # Skip hidden dirs and common non-source dirs
+        rel = os.path.relpath(dirpath, root)
+        if any(part.startswith(".") for part in Path(rel).parts):
+            continue
+        if any(part in {"node_modules", "__pycache__", "venv", ".venv", "build", "dist"}
+               for part in Path(rel).parts):
+            continue
+
+        for fname in filenames:
+            ext = os.path.splitext(fname)[1].lower()
+            if ext in _EXTENSION_LANGUAGES:
+                lang = _EXTENSION_LANGUAGES[ext]
+                lang_counts[lang] = lang_counts.get(lang, 0) + 1
+
+    # Also detect from build system files
+    for bs in result.build_systems:
+        if bs in _BUILD_INDICATORS:
+            lang = _BUILD_INDICATORS[bs]
+            lang_counts[lang] = lang_counts.get(lang, 0) + 10  # weight build files
+
+    # Sort by count, take top languages
+    sorted_langs = sorted(lang_counts.items(), key=lambda x: -x[1])
+    result.languages = [lang for lang, _ in sorted_langs[:5]]
+
+
+def _estimate_resources(result: DirectoryScanResult) -> None:
+    """Estimate resource needs based on detected language/build system."""
+    if any(lang in result.languages for lang in ("c++", "c", "fortran", "compiled")):
+        result.estimated_cpus = 6
+        result.estimated_memory = "20G"
+    elif "java" in result.languages:
+        result.estimated_cpus = 4
+        result.estimated_memory = "16G"
+    elif "python" in result.languages:
+        result.estimated_cpus = 2
+        result.estimated_memory = "8G"
+    elif "r" in result.languages:
+        result.estimated_cpus = 2
+        result.estimated_memory = "8G"
+    else:
+        result.estimated_cpus = 1
+        result.estimated_memory = "4G"
+
+
+def _suggest_container(result: DirectoryScanResult) -> None:
+    """Suggest container runtime and base image."""
+    if result.container_files:
+        if any("apptainer" in f or "singularity" in f for f in result.container_files):
+            result.suggested_runtime = "apptainer"
+        else:
+            result.suggested_runtime = "docker"
+    else:
+        result.suggested_runtime = "docker"
+
+    # Suggest base image
+    primary_lang = result.languages[0] if result.languages else None
+    if primary_lang in ("c++", "c", "compiled", "fortran"):
+        result.suggested_base_image = "ubuntu:22.04"
+    elif primary_lang == "python":
+        result.suggested_base_image = "python:3.11-slim"
+    elif primary_lang == "r":
+        result.suggested_base_image = "rocker/r-ver:4.3"
+    elif primary_lang == "java":
+        result.suggested_base_image = "eclipse-temurin:17-jre"
+    else:
+        result.suggested_base_image = "ubuntu:22.04"
+
+
+def _suggest_mounts(root: Path, result: DirectoryScanResult) -> None:
+    """Suggest mount points based on data directories."""
+    for ddir in result.data_directories:
+        host_path = str(root / ddir)
+        container_path = f"/{ddir}"
+        result.suggested_mounts[host_path] = container_path
+
+    # If there's an exe directory, mount it
+    exe_dir = root / "exe"
+    if exe_dir.is_dir():
+        result.suggested_mounts[str(exe_dir)] = "/app/exe"
+
+
+def _suggest_tags(result: DirectoryScanResult) -> None:
+    """Suggest component tags from detected characteristics."""
+    tags: list[str] = []
+    if any(lang in result.languages for lang in ("c++", "c", "fortran", "compiled")):
+        tags.append("compiled")
+    if "python" in result.languages:
+        tags.append("python")
+    if "r" in result.languages:
+        tags.append("r-lang")
+    if result.estimated_memory and int(re.sub(r"[^\d]", "", result.estimated_memory)) >= 16:
+        tags.append("memory-intensive")
+    if result.estimated_cpus >= 4:
+        tags.append("cpu-intensive")
+    result.suggested_tags = tags
+
+
+def _assess_confidence(result: DirectoryScanResult) -> None:
+    """Assess overall confidence in the scan results."""
+    score = 0
+    if result.languages:
+        score += 2
+    if result.build_systems:
+        score += 2
+    if result.container_files:
+        score += 2
+    if result.has_readme:
+        score += 1
+    if result.data_directories:
+        score += 1
+    if result.config_files:
+        score += 1
+
+    if score >= 6:
+        result.confidence = "high"
+    elif score >= 3:
+        result.confidence = "medium"
+    else:
+        result.confidence = "low"
+
+
+# ---------------------------------------------------------------------------
+# Language detection
+# ---------------------------------------------------------------------------
+
+
+def detect_language(path: str | Path) -> list[str]:
+    """Detect programming languages used in a directory."""
+    result = DirectoryScanResult(path=str(path))
+    _detect_languages(Path(path), result)
+    return result.languages
+
+
+# ---------------------------------------------------------------------------
+# Resource estimation
+# ---------------------------------------------------------------------------
+
+
+def estimate_resources(languages: list[str]) -> dict[str, Any]:
+    """Estimate resources based on detected languages."""
+    result = DirectoryScanResult(path="")
+    result.languages = languages
+    _estimate_resources(result)
+    return {
+        "cpus": result.estimated_cpus,
+        "memory": result.estimated_memory,
+    }
+
+
+# ---------------------------------------------------------------------------
+# Run command detection
+# ---------------------------------------------------------------------------
+
+
+def find_run_commands(path: str | Path) -> list[str]:
+    """Find likely run commands from Makefiles, scripts, and READMEs."""
+    root = Path(path)
+    commands: list[str] = []
+
+    # Check Makefile for run/execute targets
+    makefile = root / "Makefile"
+    if makefile.exists():
+        content = makefile.read_text(encoding="utf-8", errors="replace")
+        for match in re.finditer(r"^(run|execute|start|main)\s*:", content, re.MULTILINE):
+            target = match.group(1)
+            commands.append(f"make {target}")
+
+    # Check for shell scripts
+    for script in root.glob("*.sh"):
+        if script.name.startswith(("run", "start", "execute", "launch")):
+            commands.append(f"./{script.name}")
+
+    # Check for Python entry points in pyproject.toml
+    pyproject = root / "pyproject.toml"
+    if pyproject.exists():
+        content = pyproject.read_text(encoding="utf-8", errors="replace")
+        for match in re.finditer(r'\[project\.scripts\]\s*\n([^\[]+)', content):
+            for line in match.group(1).splitlines():
+                if "=" in line:
+                    cmd_name = line.split("=")[0].strip().strip('"')
+                    if cmd_name:
+                        commands.append(cmd_name)
+
+    # Check for main.py or similar entry points
+    for candidate in ["main.py", "run.py", "app.py", "__main__.py"]:
+        if (root / candidate).exists():
+            commands.append(f"python {candidate}")
+
+    return commands
+
+
+# ---------------------------------------------------------------------------
+# Failure classification
+# ---------------------------------------------------------------------------
+
+#: Failure classification patterns
+_FAILURE_PATTERNS: list[tuple[str, list[str]]] = [
+    ("oom", [
+        r"out of memory",
+        r"oom",
+        r"memory.*exceeded",
+        r"killed.*signal\s*9",
+        r"sigkill",
+        r"cannot allocate memory",
+        r"std::bad_alloc",
+        r"java\.lang\.OutOfMemoryError",
+        r"MemoryError",
+    ]),
+    ("walltime", [
+        r"wall.*time.*exceeded",
+        r"time.*limit",
+        r"DUE TO TIME LIMIT",
+        r"TIMEOUT",
+        r"exceeded.*walltime",
+        r"job.*timed?\s*out",
+    ]),
+    ("mount_missing", [
+        r"no such file or directory.*(/[a-zA-Z])",
+        r"FileNotFoundError",
+        r"mount.*not.*found",
+        r"bind.*source.*not",
+        r"ENOENT",
+    ]),
+    ("import_error", [
+        r"ModuleNotFoundError",
+        r"ImportError",
+        r"No module named",
+        r"cannot import name",
+    ]),
+    ("connection", [
+        r"connection.*refused",
+        r"connection.*reset",
+        r"connection.*timed?\s*out",
+        r"worker.*failed.*connect",
+        r"scheduler.*unreachable",
+        r"could not connect",
+    ]),
+    ("credential", [
+        r"access.*denied",
+        r"permission.*denied",
+        r"credential.*expired",
+        r"unauthorized",
+        r"forbidden",
+        r"403",
+        r"401",
+    ]),
+    ("model_runtime", [
+        r"runtime.*error",
+        r"segmentation.*fault",
+        r"core.*dumped",
+        r"abort",
+        r"assertion.*failed",
+        r"invalid.*argument",
+    ]),
+]
+
+
+@dataclass
+class FailureClassification:
+    """Classified failure with evidence and suggested fixes."""
+
+    failure_class: str
+    confidence: str
+    evidence: list[str]
+    suggested_fixes: list[str]
+    related_context: dict[str, Any] = field(default_factory=dict)
+
+
+def classify_failure(
+    *,
+    failure_class: str | None = None,
+    message: str = "",
+    details: dict[str, Any] | None = None,
+    task_events: list[dict[str, Any]] | None = None,
+    resource_events: list[dict[str, Any]] | None = None,
+) -> FailureClassification:
+    """Classify a failure and suggest fixes using rule-based heuristics.
+
+    Parameters
+    ----------
+    failure_class : str | None
+        Pre-classified failure class from telemetry (may be generic).
+    message : str
+        Error message text to analyze.
+    details : dict | None
+        Additional failure context.
+    task_events : list | None
+        Related task events for context.
+    resource_events : list | None
+        Related resource events for context.
+
+    Returns
+    -------
+    FailureClassification
+        Classified failure with evidence and suggested fixes.
+    """
+    details = details or {}
+    task_events = task_events or []
+    resource_events = resource_events or []
+
+    # Try pattern matching on the message
+    detected_class = "unknown"
+    evidence: list[str] = []
+    confidence = "low"
+
+    combined_text = f"{failure_class or ''} {message} {str(details)}"
+
+    for cls_name, patterns in _FAILURE_PATTERNS:
+        for pattern in patterns:
+            if re.search(pattern, combined_text, re.IGNORECASE):
+                detected_class = cls_name
+                evidence.append(f"Pattern match: {pattern!r} in error text")
+                confidence = "high" if len(evidence) > 1 else "medium"
+                break
+        if detected_class != "unknown":
+            break
+
+    # Enhance with resource context
+    if resource_events:
+        for rev in resource_events:
+            mem = rev.get("requested_memory")
+            cpus = rev.get("requested_cpus")
+            if mem:
+                evidence.append(f"Requested memory: {mem}")
+            if cpus:
+                evidence.append(f"Requested CPUs: {cpus}")
+
+    # Generate fixes based on classification
+    suggested_fixes = _generate_fixes(detected_class, evidence, details)
+
+    return FailureClassification(
+        failure_class=detected_class,
+        confidence=confidence,
+        evidence=evidence,
+        suggested_fixes=suggested_fixes,
+        related_context=details,
+    )
+
+
+def _generate_fixes(failure_class: str, evidence: list[str], details: dict[str, Any]) -> list[str]:
+    """Generate suggested fixes based on failure classification."""
+    fixes: list[str] = []
+
+    if failure_class == "oom":
+        fixes.append("Increase component memory in scalable.yaml (e.g., memory: '32G')")
+        fixes.append("Run: scalable validate scalable.yaml --target <target>")
+        fixes.append("Consider splitting the task into smaller chunks")
+
+    elif failure_class == "walltime":
+        fixes.append("Increase walltime in target options (e.g., walltime: '04:00:00')")
+        fixes.append("Consider parallelizing the workload across more workers")
+        fixes.append("Check if the task is stuck in an infinite loop")
+
+    elif failure_class == "mount_missing":
+        fixes.append("Check mount paths in component definition exist on the host")
+        fixes.append("Verify container mount targets match expected paths")
+        fixes.append("Run: scalable validate scalable.yaml --target <target>")
+
+    elif failure_class == "import_error":
+        fixes.append("Ensure the required package is installed in the container image")
+        fixes.append("Update the component image to include missing dependencies")
+        fixes.append("Check that preload_script installs needed packages")
+
+    elif failure_class == "connection":
+        fixes.append("Check network connectivity between scheduler and workers")
+        fixes.append("Verify firewall/security group rules allow Dask ports")
+        fixes.append("Increase worker startup timeout")
+
+    elif failure_class == "credential":
+        fixes.append("Check cloud credential configuration and expiry")
+        fixes.append("Verify service account permissions")
+        fixes.append("Refresh authentication tokens")
+
+    elif failure_class == "model_runtime":
+        fixes.append("Check model input files and configuration")
+        fixes.append("Verify the model executable runs correctly outside Scalable")
+        fixes.append("Check component environment variables")
+
+    else:
+        fixes.append("Review the full error message and stack trace")
+        fixes.append("Run: scalable report --latest for run context")
+        fixes.append("Check worker logs for additional details")
+
+    return fixes
diff --git a/scalable/ai/log_diagnosis.py b/scalable/ai/log_diagnosis.py
new file mode 100644
index 0000000..bbceddf
--- /dev/null
+++ b/scalable/ai/log_diagnosis.py
@@ -0,0 +1,321 @@
+"""AI-assisted failure diagnosis for Scalable runs.
+
+Reads telemetry from a run directory and classifies failures with
+evidence and suggested fixes.
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+from scalable.telemetry.collectors import read_jsonl, resolve_run_dir
+
+from .backend import AIBackend, get_ai_backend
+from .heuristics import FailureClassification, classify_failure
+from .prompts.diagnose import DIAGNOSIS_PROMPT, SYSTEM_PROMPT
+
+__all__ = ["DiagnosisResult", "diagnose_run"]
+
+
+@dataclass
+class DiagnosisResult:
+    """Complete diagnosis of a failed or problematic run."""
+
+    run_id: str
+    run_dir: str
+    classifications: list[FailureClassification]
+    summary: str
+    method: str  # "heuristic" or "ai-enhanced"
+    task_summary: dict[str, int] = field(default_factory=dict)
+    resource_summary: dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "run_id": self.run_id,
+            "run_dir": self.run_dir,
+            "method": self.method,
+            "task_summary": self.task_summary,
+            "resource_summary": self.resource_summary,
+            "classifications": [
+                {
+                    "failure_class": c.failure_class,
+                    "confidence": c.confidence,
+                    "evidence": c.evidence,
+                    "suggested_fixes": c.suggested_fixes,
+                }
+                for c in self.classifications
+            ],
+            "summary": self.summary,
+        }
+
+    def render_text(self) -> str:
+        """Render a human-readable diagnosis report."""
+        lines = [
+            f"Diagnosis for {self.run_id}",
+            "=" * (len(f"Diagnosis for {self.run_id}")),
+            "",
+        ]
+
+        if not self.classifications:
+            lines.append("No failures detected in this run.")
+            if self.task_summary:
+                lines.append("")
+                lines.append("Task summary:")
+                for state, count in sorted(self.task_summary.items()):
+                    lines.append(f"  {state}: {count}")
+            return "\n".join(lines)
+
+        for i, cls in enumerate(self.classifications, 1):
+            if len(self.classifications) > 1:
+                lines.append(f"--- Failure #{i} ---")
+                lines.append("")
+
+            lines.append(f"Likely failure: {cls.failure_class}")
+            lines.append(f"Confidence: {cls.confidence}")
+            lines.append("")
+            lines.append("Evidence:")
+            for ev in cls.evidence:
+                lines.append(f"  - {ev}")
+            lines.append("")
+            lines.append("Suggested fixes:")
+            for j, fix in enumerate(cls.suggested_fixes, 1):
+                lines.append(f"  {j}. {fix}")
+            lines.append("")
+
+        if self.task_summary:
+            lines.append("Task summary:")
+            for state, count in sorted(self.task_summary.items()):
+                lines.append(f"  {state}: {count}")
+
+        return "\n".join(lines)
+
+
+def diagnose_run(
+    runs_dir: str | Path | None = None,
+    *,
+    run_id: str | None = None,
+    run_dir: str | Path | None = None,
+    latest: bool = False,
+    backend: AIBackend | None = None,
+    no_ai: bool = False,
+) -> DiagnosisResult:
+    """Diagnose a Scalable run from its telemetry.
+
+    Parameters
+    ----------
+    runs_dir : str | Path | None
+        Root runs directory. Defaults to .scalable/runs.
+    run_id : str | None
+        Explicit run identifier.
+    run_dir : str | Path | None
+        Direct path to a run directory.
+    latest : bool
+        If True, use the most recent run.
+    backend : AIBackend | None
+        AI backend for enhanced diagnosis.
+    no_ai : bool
+        If True, skip LLM enhancement.
+
+    Returns
+    -------
+    DiagnosisResult
+        Complete failure diagnosis with evidence and fixes.
+    """
+    # Resolve the run directory
+    if run_dir is not None:
+        resolved_dir = Path(run_dir)
+    else:
+        from scalable.common import settings
+        effective_runs_dir = runs_dir or settings.runs_dir
+        resolved_dir = resolve_run_dir(
+            runs_dir=effective_runs_dir,
+            run_id=run_id,
+            latest=latest,
+        )
+
+    if not resolved_dir.is_dir():
+        raise FileNotFoundError(f"Run directory not found: {resolved_dir}")
+
+    # Load telemetry
+    run_meta = _load_run_meta(resolved_dir)
+    failures = read_jsonl(resolved_dir / "failures.jsonl")
+    tasks = read_jsonl(resolved_dir / "tasks.jsonl")
+    resources = read_jsonl(resolved_dir / "resources.jsonl")
+
+    detected_run_id = run_meta.get("run_id", resolved_dir.name)
+
+    # Build task summary
+    task_states: dict[str, str] = {}
+    for t in tasks:
+        tid = str(t.get("task_id", ""))
+        state = str(t.get("state", "unknown"))
+        if tid:
+            task_states[tid] = state
+
+    from collections import Counter
+    state_counts = dict(Counter(task_states.values()))
+
+    # Build resource summary
+    resource_summary: dict[str, Any] = {}
+    for r in resources:
+        comp = r.get("component")
+        if comp and comp not in resource_summary:
+            resource_summary[comp] = {
+                "cpus": r.get("requested_cpus"),
+                "memory": r.get("requested_memory"),
+            }
+
+    # Classify failures
+    classifications: list[FailureClassification] = []
+
+    if failures:
+        for failure in failures:
+            # Get related task/resource events for context
+            task_id = failure.get("task_id")
+            related_tasks = [t for t in tasks if t.get("task_id") == task_id] if task_id else []
+            related_resources = [r for r in resources if r.get("entity_id") == task_id] if task_id else []
+
+            cls = classify_failure(
+                failure_class=failure.get("failure_class"),
+                message=failure.get("message", ""),
+                details=failure.get("details", {}),
+                task_events=related_tasks,
+                resource_events=related_resources,
+            )
+            classifications.append(cls)
+    elif state_counts.get("failed", 0) > 0:
+        # No explicit failure events but tasks failed - check task errors
+        for t in tasks:
+            if t.get("state") == "failed" and t.get("error_message"):
+                cls = classify_failure(
+                    failure_class=t.get("error_type"),
+                    message=t.get("error_message", ""),
+                    details={"task_name": t.get("task_name")},
+                    task_events=[t],
+                    resource_events=[r for r in resources if r.get("entity_id") == t.get("task_id")],
+                )
+                classifications.append(cls)
+
+    # Try AI enhancement
+    method = "heuristic"
+    if not no_ai and classifications:
+        ai_backend = backend or get_ai_backend()
+        if ai_backend.available():
+            try:
+                enhanced = _diagnose_with_ai(
+                    run_meta, failures, tasks, resources, ai_backend
+                )
+                if enhanced:
+                    classifications = enhanced
+                    method = "ai-enhanced"
+            except Exception:
+                pass  # Fall through to heuristic results
+
+    # Build summary
+    if classifications:
+        primary = classifications[0]
+        summary = (
+            f"Primary failure: {primary.failure_class} "
+            f"(confidence: {primary.confidence}). "
+            f"{len(classifications)} failure(s) detected."
+        )
+    else:
+        summary = "No failures detected in this run."
+
+    return DiagnosisResult(
+        run_id=detected_run_id,
+        run_dir=str(resolved_dir),
+        classifications=classifications,
+        summary=summary,
+        method=method,
+        task_summary=state_counts,
+        resource_summary=resource_summary,
+    )
+
+
+def _load_run_meta(run_dir: Path) -> dict[str, Any]:
+    """Load run.json metadata."""
+    run_json = run_dir / "run.json"
+    if run_json.exists():
+        return json.loads(run_json.read_text(encoding="utf-8"))
+    return {}
+
+
+def _diagnose_with_ai(
+    run_meta: dict[str, Any],
+    failures: list[dict[str, Any]],
+    tasks: list[dict[str, Any]],
+    resources: list[dict[str, Any]],
+    backend: AIBackend,
+) -> list[FailureClassification] | None:
+    """Attempt AI-enhanced diagnosis."""
+    # Limit context to avoid token overload
+    prompt = DIAGNOSIS_PROMPT.format(
+        run_metadata=json.dumps(run_meta, indent=2)[:2000],
+        failure_events=json.dumps(failures[:10], indent=2)[:3000],
+        task_events=json.dumps(
+            [t for t in tasks if t.get("state") in ("failed", "cancelled")][:10],
+            indent=2,
+        )[:2000],
+        resource_events=json.dumps(resources[:10], indent=2)[:2000],
+    )
+
+    try:
+        response = backend.complete(prompt, system=SYSTEM_PROMPT)
+        # Parse AI response into classifications
+        return _parse_ai_diagnosis(response)
+    except Exception:
+        return None
+
+
+def _parse_ai_diagnosis(response: str) -> list[FailureClassification] | None:
+    """Parse AI diagnosis response into structured classifications."""
+    # Simple parsing - look for key fields in the response
+    lines = response.strip().splitlines()
+
+    failure_class = "unknown"
+    confidence = "medium"
+    evidence: list[str] = []
+    fixes: list[str] = []
+
+    for line in lines:
+        line_lower = line.lower().strip()
+        if "failure" in line_lower and ":" in line:
+            parts = line.split(":", 1)
+            if len(parts) == 2:
+                value = parts[1].strip().lower()
+                for known_class in ("oom", "walltime", "mount_missing", "import_error",
+                                    "connection", "credential", "model_runtime"):
+                    if known_class in value:
+                        failure_class = known_class
+                        break
+
+        elif "confidence" in line_lower and ":" in line:
+            parts = line.split(":", 1)
+            if len(parts) == 2:
+                value = parts[1].strip().lower()
+                if "high" in value:
+                    confidence = "high"
+                elif "low" in value:
+                    confidence = "low"
+
+        elif line.strip().startswith("-") or line.strip().startswith("*"):
+            text = line.strip().lstrip("-*").strip()
+            if text:
+                if any(kw in line_lower for kw in ("fix", "suggest", "recommend", "action")):
+                    fixes.append(text)
+                else:
+                    evidence.append(text)
+
+    if failure_class == "unknown" and not evidence:
+        return None
+
+    return [FailureClassification(
+        failure_class=failure_class,
+        confidence=confidence,
+        evidence=evidence or ["AI analysis (see raw response for details)"],
+        suggested_fixes=fixes or ["Review the full error output"],
+    )]
diff --git a/scalable/ai/manifest_migrate.py b/scalable/ai/manifest_migrate.py
new file mode 100644
index 0000000..240d687
--- /dev/null
+++ b/scalable/ai/manifest_migrate.py
@@ -0,0 +1,393 @@
+"""AI-assisted manifest migration for Scalable.
+
+Proposes manifest changes when migrating between providers,
+upgrading schema versions, or restructuring configurations.
+Outputs overlay YAML or annotated diffs for review.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+from scalable.manifest.parser import load_manifest
+from scalable.manifest.schema import SCHEMA_VERSION, ManifestModel
+
+from .backend import AIBackend, get_ai_backend
+from .prompts.migrate import MIGRATE_PROMPT, SYSTEM_PROMPT
+
+__all__ = ["MigrationResult", "migrate_manifest"]
+
+
+#: Provider migration templates
+_PROVIDER_TEMPLATES: dict[str, dict[str, Any]] = {
+    "kubernetes": {
+        "provider": "kubernetes",
+        "namespace": "scalable",
+        "worker_service_account": "scalable-worker",
+        "adapt_min": 1,
+        "adapt_max": 10,
+    },
+    "aws": {
+        "provider": "aws",
+        "region": "us-east-1",
+        "fargate": True,
+        "vpc": "# TODO: specify VPC",
+    },
+    "gcp": {
+        "provider": "gcp",
+        "region": "us-central1",
+        "project_id": "# TODO: specify GCP project",
+    },
+}
+
+
+@dataclass
+class MigrationResult:
+    """Result of manifest migration analysis."""
+
+    source_path: str | None
+    goal: str
+    overlay_yaml: str | None
+    changes_description: str
+    new_target: dict[str, Any] | None = None
+    warnings: list[str] = field(default_factory=list)
+    method: str = "heuristic"
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "source_path": self.source_path,
+            "goal": self.goal,
+            "method": self.method,
+            "warnings": self.warnings,
+            "overlay_yaml": self.overlay_yaml,
+            "changes_description": self.changes_description,
+            "new_target": self.new_target,
+        }
+
+    def render_text(self) -> str:
+        """Render migration result as human-readable text."""
+        lines = [
+            "Manifest Migration Proposal",
+            "=" * 27,
+            "",
+            f"Goal: {self.goal}",
+            f"Method: {self.method}",
+            "",
+        ]
+
+        if self.warnings:
+            lines.append("Warnings:")
+            for w in self.warnings:
+                lines.append(f"  ⚠ {w}")
+            lines.append("")
+
+        lines.append("Changes:")
+        lines.append(self.changes_description)
+        lines.append("")
+
+        if self.overlay_yaml:
+            lines.append("Proposed overlay:")
+            lines.append("```yaml")
+            lines.append(self.overlay_yaml)
+            lines.append("```")
+
+        return "\n".join(lines)
+
+
+def migrate_manifest(
+    manifest_path: str | Path | None = None,
+    *,
+    manifest: ManifestModel | None = None,
+    to_provider: str | None = None,
+    to_version: int | None = None,
+    goal: str | None = None,
+    backend: AIBackend | None = None,
+    no_ai: bool = False,
+) -> MigrationResult:
+    """Analyze and propose manifest migration changes.
+
+    Parameters
+    ----------
+    manifest_path : str | Path | None
+        Path to the manifest to migrate.
+    manifest : ManifestModel | None
+        Pre-loaded manifest (alternative to path).
+    to_provider : str | None
+        Target provider to migrate to.
+    to_version : int | None
+        Target schema version.
+    goal : str | None
+        Free-form migration goal description.
+    backend : AIBackend | None
+        AI backend for enhanced migration.
+    no_ai : bool
+        If True, skip LLM enhancement.
+
+    Returns
+    -------
+    MigrationResult
+        Proposed changes with overlay or description.
+    """
+    # Load manifest if needed
+    if manifest is None:
+        if manifest_path is None:
+            raise ValueError("Must provide either manifest_path or manifest")
+        manifest = load_manifest(str(manifest_path))
+
+    source = str(manifest_path) if manifest_path else manifest.source_path
+
+    # Determine migration goal
+    effective_goal = goal or ""
+    if to_provider:
+        effective_goal = f"Migrate to {to_provider} provider"
+    elif to_version is not None:
+        effective_goal = f"Upgrade schema to version {to_version}"
+    elif not effective_goal:
+        effective_goal = "General manifest optimization"
+
+    # Try AI enhancement
+    if not no_ai:
+        ai_backend = backend or get_ai_backend()
+        if ai_backend.available():
+            try:
+                return _migrate_with_ai(manifest, source, effective_goal, to_provider, ai_backend)
+            except Exception:
+                pass  # Fall through to heuristic
+
+    # Heuristic migration
+    if to_provider:
+        return _migrate_provider(manifest, source, to_provider, effective_goal)
+    elif to_version is not None:
+        return _migrate_version(manifest, source, to_version, effective_goal)
+    else:
+        return _migrate_optimize(manifest, source, effective_goal)
+
+
+def _migrate_provider(
+    manifest: ManifestModel,
+    source: str | None,
+    to_provider: str,
+    goal: str,
+) -> MigrationResult:
+    """Generate migration for changing providers."""
+    template = _PROVIDER_TEMPLATES.get(to_provider)
+    if template is None:
+        return MigrationResult(
+            source_path=source,
+            goal=goal,
+            overlay_yaml=None,
+            changes_description=f"No template available for provider '{to_provider}'. "
+                               f"Available providers: {', '.join(_PROVIDER_TEMPLATES.keys())}",
+            warnings=[f"Unknown target provider: {to_provider}"],
+            method="heuristic",
+        )
+
+    # Detect current provider from first target
+    current_providers = [t.provider for t in manifest.targets.values()]
+    from_provider = current_providers[0] if current_providers else "unknown"
+
+    # Build new target
+    target_name = to_provider
+    new_target: dict[str, Any] = dict(template)
+
+    # Build overlay
+    overlay: dict[str, Any] = {
+        "targets": {
+            target_name: new_target,
+        }
+    }
+
+    # Add component adjustments for cloud/k8s
+    component_notes: list[str] = []
+    if to_provider in ("kubernetes", "aws", "gcp"):
+        for comp_name, comp in manifest.components.items():
+            if comp.mounts:
+                component_notes.append(
+                    f"Component '{comp_name}' has local mounts that need "
+                    f"cloud-compatible paths (PVCs, S3, etc.)"
+                )
+
+    overlay_yaml = yaml.dump(overlay, default_flow_style=False, sort_keys=False)
+
+    changes = [
+        f"Add new target '{target_name}' with {to_provider} provider",
+        f"Current provider(s): {', '.join(current_providers)}",
+    ]
+    if component_notes:
+        changes.append("")
+        changes.append("Component adjustments needed:")
+        changes.extend(f"  - {note}" for note in component_notes)
+
+    warnings: list[str] = []
+    if from_provider == "slurm" and to_provider in ("kubernetes", "aws", "gcp"):
+        warnings.append("Migrating from HPC to cloud requires updating mount paths")
+        warnings.append("Container images must be accessible from cloud environment")
+    if to_provider == "gcp":
+        warnings.append("GCP provider is scaffold-only (build_cluster not yet implemented)")
+
+    return MigrationResult(
+        source_path=source,
+        goal=goal,
+        overlay_yaml=overlay_yaml,
+        changes_description="\n".join(changes),
+        new_target=new_target,
+        warnings=warnings,
+        method="heuristic",
+    )
+
+
+def _migrate_version(
+    manifest: ManifestModel,
+    source: str | None,
+    to_version: int,
+    goal: str,
+) -> MigrationResult:
+    """Generate migration for schema version upgrade."""
+    current_version = manifest.version
+
+    if to_version == current_version:
+        return MigrationResult(
+            source_path=source,
+            goal=goal,
+            overlay_yaml=None,
+            changes_description=f"Already at schema version {current_version}. No changes needed.",
+            method="heuristic",
+        )
+
+    if to_version < current_version:
+        return MigrationResult(
+            source_path=source,
+            goal=goal,
+            overlay_yaml=None,
+            changes_description=f"Cannot downgrade from version {current_version} to {to_version}.",
+            warnings=["Schema version downgrade is not supported"],
+            method="heuristic",
+        )
+
+    if to_version > SCHEMA_VERSION:
+        return MigrationResult(
+            source_path=source,
+            goal=goal,
+            overlay_yaml=None,
+            changes_description=(
+                f"Target version {to_version} is not yet supported. "
+                f"Current max supported version: {SCHEMA_VERSION}"
+            ),
+            warnings=[f"Schema version {to_version} not recognized"],
+            method="heuristic",
+        )
+
+    # Same version - no actual migration needed for v1→v1
+    return MigrationResult(
+        source_path=source,
+        goal=goal,
+        overlay_yaml=None,
+        changes_description=f"Schema version {to_version} is current. No migration needed.",
+        method="heuristic",
+    )
+
+
+def _migrate_optimize(
+    manifest: ManifestModel,
+    source: str | None,
+    goal: str,
+) -> MigrationResult:
+    """General manifest optimization suggestions."""
+    suggestions: list[str] = []
+    warnings: list[str] = []
+
+    # Check for missing recommended fields
+    for comp_name, comp in manifest.components.items():
+        if not comp.memory:
+            suggestions.append(f"Component '{comp_name}': add explicit memory allocation")
+        if not comp.image:
+            suggestions.append(f"Component '{comp_name}': specify container image")
+        if not comp.tags:
+            suggestions.append(f"Component '{comp_name}': add descriptive tags")
+
+    # Check tasks
+    for task_name, task in manifest.tasks.items():
+        if not task.cache:
+            suggestions.append(f"Task '{task_name}': consider enabling cache for reproducibility")
+        if not task.outputs:
+            suggestions.append(f"Task '{task_name}': declare outputs for artifact tracking")
+
+    # Check targets
+    if len(manifest.targets) == 1:
+        suggestions.append("Consider adding a 'local' target for development/testing")
+
+    if not suggestions:
+        suggestions.append("Manifest looks well-configured. No optimization suggestions.")
+
+    return MigrationResult(
+        source_path=source,
+        goal=goal,
+        overlay_yaml=None,
+        changes_description="\n".join(f"- {s}" for s in suggestions),
+        warnings=warnings,
+        method="heuristic",
+    )
+
+
+def _migrate_with_ai(
+    manifest: ManifestModel,
+    source: str | None,
+    goal: str,
+    to_provider: str | None,
+    backend: AIBackend,
+) -> MigrationResult:
+    """AI-enhanced manifest migration."""
+    current_yaml = yaml.dump(manifest.raw, default_flow_style=False, sort_keys=True)
+
+    # Determine providers
+    current_providers = [t.provider for t in manifest.targets.values()]
+    from_provider = current_providers[0] if current_providers else "unknown"
+
+    prompt = MIGRATE_PROMPT.format(
+        current_manifest=current_yaml[:4000],
+        goal=goal,
+        from_provider=from_provider,
+        to_provider=to_provider or "optimize",
+    )
+
+    response = backend.complete(prompt, system=SYSTEM_PROMPT)
+
+    # Try to extract YAML from response
+    overlay_yaml = _extract_yaml(response)
+
+    return MigrationResult(
+        source_path=source,
+        goal=goal,
+        overlay_yaml=overlay_yaml,
+        changes_description=response if not overlay_yaml else "See overlay YAML below.",
+        warnings=["AI-generated migration - review carefully before applying"],
+        method="ai-enhanced",
+    )
+
+
+def _extract_yaml(text: str) -> str | None:
+    """Extract YAML block from text response."""
+    import re
+
+    # Try to find YAML in code blocks
+    match = re.search(r"```(?:yaml)?\s*\n(.*?)```", text, re.DOTALL)
+    if match:
+        candidate = match.group(1).strip()
+        try:
+            yaml.safe_load(candidate)
+            return candidate
+        except Exception:
+            pass
+
+    # Try the whole response as YAML
+    try:
+        yaml.safe_load(text)
+        return text.strip()
+    except Exception:
+        pass
+
+    return None
diff --git a/scalable/ai/plan_explain.py b/scalable/ai/plan_explain.py
new file mode 100644
index 0000000..0440808
--- /dev/null
+++ b/scalable/ai/plan_explain.py
@@ -0,0 +1,266 @@
+"""AI-assisted execution plan explanation for Scalable.
+
+Renders human-readable narratives explaining execution plans,
+resource allocation decisions, and cost/time implications.
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+from .backend import AIBackend, get_ai_backend
+from .prompts.explain import EXPLAIN_PROMPT, SYSTEM_PROMPT
+
+__all__ = ["ExplanationResult", "explain_plan"]
+
+
+@dataclass
+class ExplanationResult:
+    """Result of plan explanation."""
+
+    plan_source: str
+    narrative: str
+    sections: dict[str, str] = field(default_factory=dict)
+    method: str = "heuristic"
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "plan_source": self.plan_source,
+            "method": self.method,
+            "sections": self.sections,
+            "narrative": self.narrative,
+        }
+
+    def render_text(self) -> str:
+        """Render the explanation as formatted text."""
+        return self.narrative
+
+
+def explain_plan(
+    plan_path: str | Path | None = None,
+    *,
+    plan_data: dict[str, Any] | None = None,
+    runs_dir: str | Path | None = None,
+    backend: AIBackend | None = None,
+    no_ai: bool = False,
+) -> ExplanationResult:
+    """Explain a Scalable execution plan in human-readable form.
+
+    Parameters
+    ----------
+    plan_path : str | Path | None
+        Path to plan.json file.
+    plan_data : dict | None
+        Pre-loaded plan dictionary (alternative to plan_path).
+    runs_dir : str | Path | None
+        Runs directory for historical context.
+    backend : AIBackend | None
+        AI backend for enhanced explanation.
+    no_ai : bool
+        If True, skip LLM enhancement.
+
+    Returns
+    -------
+    ExplanationResult
+        Structured explanation with narrative and sections.
+    """
+    # Load plan
+    if plan_data is not None:
+        plan = plan_data
+        source = "<provided>"
+    elif plan_path is not None:
+        path = Path(plan_path)
+        if not path.exists():
+            raise FileNotFoundError(f"Plan file not found: {path}")
+        plan = json.loads(path.read_text(encoding="utf-8"))
+        source = str(path)
+    else:
+        raise ValueError("Must provide either plan_path or plan_data")
+
+    # Try AI enhancement
+    if not no_ai:
+        ai_backend = backend or get_ai_backend()
+        if ai_backend.available():
+            try:
+                return _explain_with_ai(plan, source, runs_dir, ai_backend)
+            except Exception:
+                pass  # Fall through to heuristic
+
+    # Heuristic explanation
+    return _explain_heuristic(plan, source, runs_dir)
+
+
+def _explain_heuristic(
+    plan: dict[str, Any],
+    source: str,
+    runs_dir: str | Path | None,
+) -> ExplanationResult:
+    """Generate plan explanation using heuristics."""
+    sections: dict[str, str] = {}
+
+    # Overview section
+    target = plan.get("target", "unknown")
+    provider = plan.get("provider", "unknown")
+    manifest_lock = plan.get("manifest_lock", "unknown")[:12]
+
+    overview_lines = [
+        f"This plan deploys a workflow on the '{target}' target using the '{provider}' provider.",
+        f"Manifest fingerprint: {manifest_lock}...",
+    ]
+
+    task_map = plan.get("task_to_component", {})
+    if task_map:
+        overview_lines.append("")
+        overview_lines.append(f"Tasks ({len(task_map)}):")
+        for task_name, component in sorted(task_map.items()):
+            overview_lines.append(f"  - {task_name} → component '{component}'")
+
+    sections["overview"] = "\n".join(overview_lines)
+
+    # Resource allocation section
+    scale_plan = plan.get("scale_plan", {})
+    workers = scale_plan.get("workers_by_tag", {})
+    resources = scale_plan.get("resources_by_tag", {})
+
+    resource_lines = ["Resource allocation per component:"]
+    for tag in sorted(workers.keys()):
+        worker_count = workers[tag]
+        res = resources.get(tag, {})
+        cpus = res.get("cpus", "?")
+        memory = res.get("memory", "?")
+        walltime = res.get("walltime", "not set")
+        gpus = res.get("gpus")
+
+        resource_lines.append("")
+        resource_lines.append(f"  {tag}:")
+        resource_lines.append(f"    Workers: {worker_count}")
+        resource_lines.append(f"    CPUs per worker: {cpus}")
+        resource_lines.append(f"    Memory per worker: {memory}")
+        resource_lines.append(f"    Walltime: {walltime}")
+        if gpus:
+            resource_lines.append(f"    GPUs: {gpus}")
+
+    if not workers:
+        resource_lines.append("  (no workers defined)")
+
+    sections["resources"] = "\n".join(resource_lines)
+
+    # Execution strategy section
+    strategy_lines = [
+        "Execution strategy:",
+        f"  Provider: {provider}",
+        f"  Target: {target}",
+    ]
+
+    if provider == "local":
+        strategy_lines.append("  Mode: local execution (no container isolation by default)")
+        strategy_lines.append("  Suitable for: development, testing, small workloads")
+    elif provider == "slurm":
+        strategy_lines.append("  Mode: HPC batch scheduling via Slurm")
+        strategy_lines.append("  Workers run as containerized Dask workers in Slurm allocations")
+    elif provider == "kubernetes":
+        strategy_lines.append("  Mode: Kubernetes pod-based execution")
+        strategy_lines.append("  Workers deploy as pods with component-specific resource requests")
+    elif provider == "aws":
+        strategy_lines.append("  Mode: AWS cloud execution (Fargate/EC2)")
+    else:
+        strategy_lines.append(f"  Mode: {provider} execution")
+
+    total_cpus = sum(
+        workers.get(tag, 0) * resources.get(tag, {}).get("cpus", 1)
+        for tag in workers
+    )
+    strategy_lines.append("")
+    strategy_lines.append(f"  Total workers: {sum(workers.values())}")
+    strategy_lines.append(f"  Total CPU cores: {total_cpus}")
+
+    sections["strategy"] = "\n".join(strategy_lines)
+
+    # Recommendations section
+    rec_lines = ["Recommendations:"]
+    if total_cpus == 0:
+        rec_lines.append("  ⚠ No workers allocated - check component definitions")
+    if all(w == 1 for w in workers.values()) and len(workers) > 1:
+        rec_lines.append("  ℹ All components have 1 worker - consider scaling for parallelism")
+    if any(not resources.get(tag, {}).get("memory") for tag in workers):
+        rec_lines.append("  ⚠ Some components have no memory specified - may use provider defaults")
+
+    # Historical context
+    if runs_dir:
+        history_note = _get_history_context(runs_dir, plan)
+        if history_note:
+            rec_lines.append(f"  ℹ {history_note}")
+
+    sections["recommendations"] = "\n".join(rec_lines)
+
+    # Build full narrative
+    narrative_parts = [
+        "Plan Explanation",
+        "=" * 16,
+        "",
+        sections["overview"],
+        "",
+        sections["resources"],
+        "",
+        sections["strategy"],
+        "",
+        sections["recommendations"],
+    ]
+    narrative = "\n".join(narrative_parts)
+
+    return ExplanationResult(
+        plan_source=source,
+        narrative=narrative,
+        sections=sections,
+        method="heuristic",
+    )
+
+
+def _explain_with_ai(
+    plan: dict[str, Any],
+    source: str,
+    runs_dir: str | Path | None,
+    backend: AIBackend,
+) -> ExplanationResult:
+    """Generate AI-enhanced plan explanation."""
+    history_context = "No historical data available."
+    cost_context = "No cost estimate available."
+
+    if runs_dir:
+        note = _get_history_context(runs_dir, plan)
+        if note:
+            history_context = note
+
+    prompt = EXPLAIN_PROMPT.format(
+        plan_json=json.dumps(plan, indent=2)[:4000],
+        history_context=history_context,
+        cost_context=cost_context,
+    )
+
+    response = backend.complete(prompt, system=SYSTEM_PROMPT)
+
+    return ExplanationResult(
+        plan_source=source,
+        narrative=response,
+        sections={"ai_explanation": response},
+        method="ai-enhanced",
+    )
+
+
+def _get_history_context(runs_dir: str | Path | None, plan: dict[str, Any]) -> str | None:
+    """Get historical context from past runs."""
+    if not runs_dir:
+        return None
+
+    try:
+        from scalable.telemetry.collectors import iter_run_dirs
+        run_dirs = iter_run_dirs(runs_dir)
+        if run_dirs:
+            return f"Found {len(run_dirs)} historical run(s) for comparison."
+    except Exception:
+        pass
+
+    return None
diff --git a/scalable/ai/prompts/__init__.py b/scalable/ai/prompts/__init__.py
new file mode 100644
index 0000000..86057f7
--- /dev/null
+++ b/scalable/ai/prompts/__init__.py
@@ -0,0 +1,5 @@
+"""Prompt template system for AI assistants."""
+
+from __future__ import annotations
+
+__all__: list[str] = []
diff --git a/scalable/ai/prompts/compose.py b/scalable/ai/prompts/compose.py
new file mode 100644
index 0000000..63713eb
--- /dev/null
+++ b/scalable/ai/prompts/compose.py
@@ -0,0 +1,42 @@
+"""Prompt templates for workflow composition assistant."""
+
+from __future__ import annotations
+
+SYSTEM_PROMPT = """\
+You are a scientific workflow design assistant for the Scalable framework.
+Your job is to translate a natural-language study description into:
+1. A workflow.py file with task functions and submit calls
+2. A component manifest fragment for scalable.yaml
+3. A params/scenarios.csv if scenario enumeration is needed
+4. A README explaining the generated workflow
+
+Use the Scalable API: ScalableSession.from_yaml(), client.submit(func, tag=...).
+Do NOT auto-execute anything. Output files for human review.
+"""
+
+COMPOSE_PROMPT = """\
+Generate a Scalable workflow from this description:
+
+"{description}"
+
+Available known model patterns:
+- GCAM: Integrated assessment model, compiled C++, tag="gcam", heavy CPU/memory
+- Stitches: Climate pattern scaling, Python, tag="stitches", memory-intensive
+- Demeter: Land use model, Python, tag="demeter"
+- Tethys: Water demand model, Python, tag="tethys"
+- Xanthos: Hydrology model, Python, tag="xanthos"
+- Hector: Simple climate model, C++, tag="hector"
+
+Generate:
+1. workflow.py with proper imports, task functions, and orchestration
+2. Component YAML fragments for each model referenced
+3. A brief README.generated.md
+
+Output each file with clear delimiters:
+--- workflow.py ---
+<content>
+--- components.yaml ---
+<content>
+--- README.generated.md ---
+<content>
+"""
diff --git a/scalable/ai/prompts/diagnose.py b/scalable/ai/prompts/diagnose.py
new file mode 100644
index 0000000..f4ca58b
--- /dev/null
+++ b/scalable/ai/prompts/diagnose.py
@@ -0,0 +1,36 @@
+"""Prompt templates for failure diagnosis assistant."""
+
+from __future__ import annotations
+
+SYSTEM_PROMPT = """\
+You are a failure diagnosis assistant for the Scalable scientific workflow framework.
+Analyze run telemetry (task events, failures, resource usage) and provide:
+1. Root cause classification
+2. Supporting evidence from the telemetry
+3. Suggested fixes in order of likelihood
+
+Be specific and actionable. Reference Scalable manifest fields and CLI commands.
+"""
+
+DIAGNOSIS_PROMPT = """\
+Diagnose this failed Scalable run:
+
+Run metadata:
+{run_metadata}
+
+Failure events:
+{failure_events}
+
+Task events (final states):
+{task_events}
+
+Resource events:
+{resource_events}
+
+Provide:
+1. Most likely failure cause (one of: oom, walltime, mount_missing, import_error,
+   connection, credential, model_runtime, unknown)
+2. Confidence level (high/medium/low)
+3. Evidence supporting the diagnosis
+4. Ordered list of suggested fixes
+"""
diff --git a/scalable/ai/prompts/explain.py b/scalable/ai/prompts/explain.py
new file mode 100644
index 0000000..c92d7e8
--- /dev/null
+++ b/scalable/ai/prompts/explain.py
@@ -0,0 +1,34 @@
+"""Prompt templates for plan explanation assistant."""
+
+from __future__ import annotations
+
+SYSTEM_PROMPT = """\
+You are a plan explanation assistant for the Scalable scientific workflow framework.
+Given an execution plan (plan.json), explain in plain language:
+- What will be deployed and where
+- How resources are allocated
+- Why certain decisions were made
+- What the expected cost/time implications are
+
+Be clear and accessible to scientists who may not be infrastructure experts.
+"""
+
+EXPLAIN_PROMPT = """\
+Explain this Scalable execution plan in plain language:
+
+Plan:
+{plan_json}
+
+Historical context (if available):
+{history_context}
+
+Cost estimate (if available):
+{cost_context}
+
+Provide a clear, structured explanation covering:
+1. Overview: what this plan does
+2. Resource allocation: why each component gets its resources
+3. Execution strategy: order and scaling decisions
+4. Cost/time implications
+5. Recommendations or warnings
+"""
diff --git a/scalable/ai/prompts/migrate.py b/scalable/ai/prompts/migrate.py
new file mode 100644
index 0000000..7bba269
--- /dev/null
+++ b/scalable/ai/prompts/migrate.py
@@ -0,0 +1,36 @@
+"""Prompt templates for manifest migration assistant."""
+
+from __future__ import annotations
+
+SYSTEM_PROMPT = """\
+You are a manifest migration assistant for the Scalable scientific workflow framework.
+Help users migrate their scalable.yaml when:
+- Changing providers (e.g., slurm -> kubernetes)
+- Upgrading schema versions
+- Adding cloud targets
+- Restructuring components
+
+Output a diff or overlay showing required changes. Never modify science parameters.
+"""
+
+MIGRATE_PROMPT = """\
+Migrate this Scalable manifest:
+
+Current manifest:
+{current_manifest}
+
+Migration goal: {goal}
+From provider: {from_provider}
+To provider: {to_provider}
+
+Generate either:
+1. An overlay YAML block that can be added to the manifest
+2. Or a description of inline changes needed
+
+Focus on infrastructure changes only. Do NOT modify:
+- Science parameters
+- Model configurations
+- Data paths (unless provider requires it)
+
+Output the migration as a YAML overlay or annotated diff.
+"""
diff --git a/scalable/ai/prompts/onboarding.py b/scalable/ai/prompts/onboarding.py
new file mode 100644
index 0000000..7eec235
--- /dev/null
+++ b/scalable/ai/prompts/onboarding.py
@@ -0,0 +1,44 @@
+"""Prompt templates for component onboarding assistant."""
+
+from __future__ import annotations
+
+SYSTEM_PROMPT = """\
+You are a scientific computing infrastructure assistant for the Scalable framework.
+Your job is to analyze a model repository and propose a component manifest block
+for scalable.yaml. You should identify:
+- Programming language and build system
+- Resource requirements (CPU, memory)
+- Container runtime needs
+- Input/output data paths and mount points
+- Environment variables needed
+- Likely run commands
+
+Output a valid YAML component block. Do NOT execute any commands.
+"""
+
+ANALYSIS_PROMPT = """\
+Analyze this model directory for onboarding into Scalable:
+
+Directory: {path}
+Name: {name}
+
+Detected files and structure:
+{file_listing}
+
+Build system files: {build_systems}
+Languages detected: {languages}
+Container files: {container_files}
+Data directories: {data_directories}
+Config files: {config_files}
+
+Based on this analysis, generate a scalable.yaml component block with:
+- image (suggest appropriate base image)
+- runtime (docker or apptainer)
+- cpus (integer)
+- memory (e.g. "8G")
+- mounts (host:container mapping for data dirs)
+- env (environment variables)
+- tags (descriptive labels)
+
+Output ONLY the YAML block, no explanation.
+"""
diff --git a/scalable/ai/workflow_compose.py b/scalable/ai/workflow_compose.py
new file mode 100644
index 0000000..89b8e74
--- /dev/null
+++ b/scalable/ai/workflow_compose.py
@@ -0,0 +1,468 @@
+"""AI-assisted workflow composition for Scalable.
+
+Generates workflow skeletons from natural-language descriptions or
+structured specifications. Outputs reviewable files (workflow.py,
+component YAML, README).
+"""
+
+from __future__ import annotations
+
+import ast
+import re
+import textwrap
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+from .backend import AIBackend, get_ai_backend
+from .prompts.compose import COMPOSE_PROMPT, SYSTEM_PROMPT
+
+__all__ = ["ComposeResult", "compose_workflow"]
+
+
+#: Known model patterns for heuristic composition
+_KNOWN_MODELS: dict[str, dict[str, Any]] = {
+    "gcam": {
+        "full_name": "GCAM",
+        "language": "c++",
+        "cpus": 6,
+        "memory": "20G",
+        "runtime": "apptainer",
+        "tags": ["iam", "climate", "compiled"],
+        "description": "Global Change Assessment Model",
+    },
+    "stitches": {
+        "full_name": "Stitches",
+        "language": "python",
+        "cpus": 1,
+        "memory": "50G",
+        "runtime": "docker",
+        "tags": ["climate", "python"],
+        "description": "Climate pattern scaling",
+    },
+    "demeter": {
+        "full_name": "Demeter",
+        "language": "python",
+        "cpus": 2,
+        "memory": "8G",
+        "runtime": "docker",
+        "tags": ["land-use", "python"],
+        "description": "Land use spatial downscaling",
+    },
+    "tethys": {
+        "full_name": "Tethys",
+        "language": "python",
+        "cpus": 2,
+        "memory": "8G",
+        "runtime": "docker",
+        "tags": ["water", "python"],
+        "description": "Water demand model",
+    },
+    "xanthos": {
+        "full_name": "Xanthos",
+        "language": "python",
+        "cpus": 2,
+        "memory": "16G",
+        "runtime": "docker",
+        "tags": ["hydrology", "python"],
+        "description": "Global hydrology model",
+    },
+    "hector": {
+        "full_name": "Hector",
+        "language": "c++",
+        "cpus": 1,
+        "memory": "4G",
+        "runtime": "docker",
+        "tags": ["climate", "compiled"],
+        "description": "Simple climate model",
+    },
+}
+
+
+@dataclass
+class ComposeResult:
+    """Result of workflow composition."""
+
+    description: str
+    workflow_py: str
+    components_yaml: str
+    readme: str
+    detected_models: list[str] = field(default_factory=list)
+    method: str = "heuristic"
+    warnings: list[str] = field(default_factory=list)
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "description": self.description,
+            "detected_models": self.detected_models,
+            "method": self.method,
+            "warnings": self.warnings,
+            "files": {
+                "workflow.py": self.workflow_py,
+                "components.yaml": self.components_yaml,
+                "README.generated.md": self.readme,
+            },
+        }
+
+    def write_to_directory(self, output_dir: str | Path) -> list[str]:
+        """Write generated files to a directory.
+
+        Returns list of written file paths.
+        """
+        out = Path(output_dir)
+        out.mkdir(parents=True, exist_ok=True)
+
+        written: list[str] = []
+
+        workflow_path = out / "workflow.py"
+        workflow_path.write_text(self.workflow_py, encoding="utf-8")
+        written.append(str(workflow_path))
+
+        components_path = out / "components.yaml"
+        components_path.write_text(self.components_yaml, encoding="utf-8")
+        written.append(str(components_path))
+
+        readme_path = out / "README.generated.md"
+        readme_path.write_text(self.readme, encoding="utf-8")
+        written.append(str(readme_path))
+
+        return written
+
+
+def compose_workflow(
+    description: str,
+    *,
+    output_dir: str | Path | None = None,
+    backend: AIBackend | None = None,
+    no_ai: bool = False,
+) -> ComposeResult:
+    """Generate a workflow from a natural-language description.
+
+    Parameters
+    ----------
+    description : str
+        Natural-language description of the workflow to generate.
+    output_dir : str | Path | None
+        If provided, write generated files to this directory.
+    backend : AIBackend | None
+        AI backend for enhanced composition.
+    no_ai : bool
+        If True, skip LLM enhancement.
+
+    Returns
+    -------
+    ComposeResult
+        Generated workflow files and metadata.
+    """
+    if not description.strip():
+        raise ValueError("Description cannot be empty")
+
+    # Detect known models in the description
+    detected = _detect_models(description)
+
+    # Try AI enhancement
+    if not no_ai:
+        ai_backend = backend or get_ai_backend()
+        if ai_backend.available():
+            try:
+                result = _compose_with_ai(description, detected, ai_backend)
+                if output_dir:
+                    result.write_to_directory(output_dir)
+                return result
+            except Exception:
+                pass  # Fall through to heuristic
+
+    # Heuristic composition
+    result = _compose_heuristic(description, detected)
+    if output_dir:
+        result.write_to_directory(output_dir)
+    return result
+
+
+def _detect_models(description: str) -> list[str]:
+    """Detect known model names in the description."""
+    desc_lower = description.lower()
+    detected: list[str] = []
+    for model_key, info in _KNOWN_MODELS.items():
+        if model_key in desc_lower or info["full_name"].lower() in desc_lower:
+            detected.append(model_key)
+    return detected
+
+
+def _compose_heuristic(description: str, detected_models: list[str]) -> ComposeResult:
+    """Generate workflow using template-based heuristics."""
+    if not detected_models:
+        # Generic workflow template
+        return _compose_generic(description)
+
+    # Generate workflow for detected models
+    workflow_py = _generate_workflow_code(detected_models)
+    components_yaml = _generate_components_yaml(detected_models)
+    readme = _generate_readme(description, detected_models)
+
+    warnings: list[str] = []
+    if len(detected_models) == 1:
+        warnings.append("Only one model detected - workflow may be simpler than intended")
+
+    # Validate generated Python
+    try:
+        ast.parse(workflow_py)
+    except SyntaxError as e:
+        warnings.append(f"Generated workflow has syntax issues: {e}")
+
+    return ComposeResult(
+        description=description,
+        workflow_py=workflow_py,
+        components_yaml=components_yaml,
+        readme=readme,
+        detected_models=detected_models,
+        method="heuristic",
+        warnings=warnings,
+    )
+
+
+def _compose_generic(description: str) -> ComposeResult:
+    """Generate a generic workflow template."""
+    workflow_py = textwrap.dedent('''\
+        """Generated workflow skeleton.
+
+        Description: {description}
+
+        This is a template - fill in task functions with your model logic.
+        """
+
+        from scalable import ScalableSession
+
+
+        def run_task(input_data):
+            """TODO: Implement your task logic here."""
+            # Your model code goes here
+            return {{"status": "completed", "input": input_data}}
+
+
+        def main():
+            """Execute the workflow."""
+            session = ScalableSession.from_yaml("scalable.yaml", target="local")
+
+            with session as client:
+                future = client.submit(run_task, "example_input", tag="default")
+                result = future.result()
+                print(f"Result: {{result}}")
+
+
+        if __name__ == "__main__":
+            main()
+    ''').format(description=description[:100])
+
+    components_yaml = textwrap.dedent("""\
+        # Generated component template
+        # Customize for your model
+        default:
+          cpus: 2
+          memory: 8G
+          tags: [generic]
+    """)
+
+    readme = textwrap.dedent(f"""\
+        # Generated Workflow
+
+        ## Description
+
+        {description}
+
+        ## Files
+
+        - `workflow.py` — Main workflow script (template)
+        - `components.yaml` — Component definitions to merge into scalable.yaml
+
+        ## Usage
+
+        1. Review and customize `workflow.py` with your model logic
+        2. Merge `components.yaml` into your `scalable.yaml`
+        3. Run: `scalable run scalable.yaml --workflow workflow.py`
+
+        ## Notes
+
+        - This workflow was generated from a description with no known models detected
+        - All task functions need implementation
+        - Review resource allocations before running
+    """)
+
+    return ComposeResult(
+        description=description,
+        workflow_py=workflow_py,
+        components_yaml=components_yaml,
+        readme=readme,
+        detected_models=[],
+        method="heuristic",
+        warnings=["No known models detected - generated generic template"],
+    )
+
+
+def _generate_workflow_code(models: list[str]) -> str:
+    """Generate workflow.py for detected models."""
+    imports = [
+        '"""Generated Scalable workflow.',
+        "",
+        f"Models: {', '.join(m.upper() for m in models)}",
+        "",
+        "Review this file before execution. All task functions are stubs",
+        "that need implementation with your specific model logic.",
+        '"""',
+        "",
+        "from scalable import ScalableSession",
+        "",
+    ]
+
+    functions: list[str] = []
+    for model in models:
+        info = _KNOWN_MODELS[model]
+        func_name = f"run_{model}"
+        functions.append(textwrap.dedent(f'''\
+
+            def {func_name}(scenario, **kwargs):
+                """Run {info["full_name"]} for a given scenario.
+
+                TODO: Implement {info["full_name"]} execution logic.
+                """
+                # Your {info["full_name"]} code here
+                print(f"Running {info['full_name']} for scenario: {{scenario}}")
+                return {{"model": "{model}", "scenario": scenario, "status": "completed"}}
+        '''))
+
+    # Generate main function
+    main_lines = [
+        "",
+        "",
+        "def main():",
+        '    """Execute the multi-model workflow."""',
+        '    session = ScalableSession.from_yaml("scalable.yaml")',
+        "",
+        "    with session as client:",
+    ]
+
+    # Submit tasks in order
+    for i, model in enumerate(models):
+        info = _KNOWN_MODELS[model]
+        func_name = f"run_{model}"
+        var_name = f"future_{model}"
+        main_lines.append(f'        # Stage {i+1}: {info["full_name"]}')
+        main_lines.append(
+            f'        {var_name} = client.submit({func_name}, "reference", tag="{model}")'
+        )
+        main_lines.append(f"        result_{model} = {var_name}.result()")
+        main_lines.append(f'        print(f"{info["full_name"]} complete: {{result_{model}}}")')
+        main_lines.append("")
+
+    main_lines.append('    print("Workflow complete!")')
+    main_lines.append("")
+    main_lines.append("")
+    main_lines.append('if __name__ == "__main__":')
+    main_lines.append("    main()")
+    main_lines.append("")
+
+    return "\n".join(imports) + "\n".join(functions) + "\n".join(main_lines)
+
+
+def _generate_components_yaml(models: list[str]) -> str:
+    """Generate components YAML fragment."""
+    components: dict[str, dict[str, Any]] = {}
+    for model in models:
+        info = _KNOWN_MODELS[model]
+        component: dict[str, Any] = {
+            "image": f"# TODO: set image for {info['full_name']}",
+            "runtime": info["runtime"],
+            "cpus": info["cpus"],
+            "memory": info["memory"],
+            "tags": info["tags"],
+        }
+        if info["cpus"] > 1:
+            component["env"] = {"OMP_NUM_THREADS": str(info["cpus"])}
+        components[model] = component
+
+    header = "# Generated component definitions\n# Merge into your scalable.yaml under 'components:'\n\n"
+    return header + yaml.dump(components, default_flow_style=False, sort_keys=False)
+
+
+def _generate_readme(description: str, models: list[str]) -> str:
+    """Generate README for the workflow."""
+    model_list = "\n".join(
+        f"- **{_KNOWN_MODELS[m]['full_name']}** ({m}): {_KNOWN_MODELS[m]['description']}"
+        for m in models
+    )
+
+    return textwrap.dedent(f"""\
+        # Generated Workflow
+
+        ## Description
+
+        {description}
+
+        ## Models
+
+        {model_list}
+
+        ## Files
+
+        - `workflow.py` — Main workflow script with task stubs
+        - `components.yaml` — Component definitions to merge into scalable.yaml
+
+        ## Usage
+
+        1. Review `workflow.py` and implement task function bodies
+        2. Merge `components.yaml` into your `scalable.yaml` under `components:`
+        3. Configure target-specific settings in `scalable.yaml`
+        4. Run: `scalable run scalable.yaml --workflow workflow.py`
+
+        ## Notes
+
+        - All task functions are stubs that need implementation
+        - Container images need to be specified in components.yaml
+        - Resource estimates are defaults and may need tuning
+        - Review mount paths for your data layout
+    """)
+
+
+def _compose_with_ai(
+    description: str,
+    detected_models: list[str],
+    backend: AIBackend,
+) -> ComposeResult:
+    """Generate workflow with AI enhancement."""
+    prompt = COMPOSE_PROMPT.format(description=description)
+    response = backend.complete(prompt, system=SYSTEM_PROMPT)
+
+    # Parse response into files
+    workflow_py = _extract_file_section(response, "workflow.py") or ""
+    components_yaml = _extract_file_section(response, "components.yaml") or ""
+    readme = _extract_file_section(response, "README.generated.md") or ""
+
+    warnings: list[str] = ["AI-generated - review all files before use"]
+
+    # Validate Python
+    if workflow_py:
+        try:
+            ast.parse(workflow_py)
+        except SyntaxError as e:
+            warnings.append(f"Generated Python has syntax issues: {e}")
+
+    return ComposeResult(
+        description=description,
+        workflow_py=workflow_py or "# AI generation failed - use heuristic template",
+        components_yaml=components_yaml or "# AI generation failed",
+        readme=readme or f"# Generated from: {description}",
+        detected_models=detected_models,
+        method="ai-enhanced",
+        warnings=warnings,
+    )
+
+
+def _extract_file_section(response: str, filename: str) -> str | None:
+    """Extract a file section from AI response delimited by --- filename ---."""
+    pattern = rf"---\s*{re.escape(filename)}\s*---\s*\n(.*?)(?=---\s*\w|$)"
+    match = re.search(pattern, response, re.DOTALL)
+    if match:
+        return match.group(1).strip()
+    return None
diff --git a/scalable/cli/cmd_compose.py b/scalable/cli/cmd_compose.py
new file mode 100644
index 0000000..fb93f35
--- /dev/null
+++ b/scalable/cli/cmd_compose.py
@@ -0,0 +1,83 @@
+"""CLI handler for ``scalable compose``."""
+
+from __future__ import annotations
+
+import json
+import sys
+
+__all__ = ["run_compose"]
+
+
+def run_compose(
+    description: str,
+    *,
+    output_dir: str | None = None,
+    fmt: str = "text",
+    no_ai: bool = False,
+) -> int:
+    """Run the compose command.
+
+    Parameters
+    ----------
+    description : str
+        Natural-language workflow description.
+    output_dir : str | None
+        Directory to write generated files.
+    fmt : str
+        Output format ("text" or "json").
+    no_ai : bool
+        Skip LLM enhancement.
+
+    Returns
+    -------
+    int
+        Exit code (0 = success).
+    """
+
+    from scalable.ai.workflow_compose import compose_workflow
+
+    if not description.strip():
+        print("Error: description cannot be empty", file=sys.stderr)
+        return 1
+
+    try:
+        result = compose_workflow(
+            description,
+            output_dir=output_dir,
+            no_ai=no_ai,
+        )
+    except Exception as exc:
+        print(f"Error during composition: {exc}", file=sys.stderr)
+        return 1
+
+    if fmt == "json":
+        content = json.dumps(result.to_dict(), indent=2, sort_keys=True)
+        print(content)
+    else:
+        if output_dir:
+            print(f"Workflow files written to: {output_dir}")
+            written = [
+                f"  - {output_dir}/workflow.py",
+                f"  - {output_dir}/components.yaml",
+                f"  - {output_dir}/README.generated.md",
+            ]
+            print("\n".join(written))
+        else:
+            # Print to stdout when no output dir
+            print("=== workflow.py ===")
+            print(result.workflow_py)
+            print("\n=== components.yaml ===")
+            print(result.components_yaml)
+            print("\n=== README.generated.md ===")
+            print(result.readme)
+
+    # Print metadata
+    if result.detected_models:
+        print(f"\nDetected models: {', '.join(result.detected_models)}", file=sys.stderr)
+    print(f"Method: {result.method}", file=sys.stderr)
+    if result.warnings:
+        print("Warnings:", file=sys.stderr)
+        for w in result.warnings:
+            print(f"  - {w}", file=sys.stderr)
+
+    return 0
diff --git a/scalable/cli/cmd_diagnose.py b/scalable/cli/cmd_diagnose.py
new file mode 100644
index 0000000..b7a610d
--- /dev/null
+++ b/scalable/cli/cmd_diagnose.py
@@ -0,0 +1,79 @@
+"""CLI handler for ``scalable diagnose``."""
+
+from __future__ import annotations
+
+import json
+import sys
+
+__all__ = ["run_diagnose"]
+
+
+def run_diagnose(
+    *,
+    runs_dir: str | None = None,
+    run_id: str | None = None,
+    latest: bool = False,
+    fmt: str = "text",
+    output: str | None = None,
+    no_ai: bool = False,
+) -> int:
+    """Run the diagnose command.
+
+    Parameters
+    ----------
+    runs_dir : str | None
+        Runs directory path.
+    run_id : str | None
+        Explicit run identifier.
+    latest : bool
+        Use most recent run.
+    fmt : str
+        Output format ("text" or "json").
+    output : str | None
+        Output file path (default: stdout).
+    no_ai : bool
+        Skip LLM enhancement.
+
+    Returns
+    -------
+    int
+        Exit code (0 = success).
+    """
+    from pathlib import Path
+
+    from scalable.ai.log_diagnosis import diagnose_run
+
+    if not run_id and not latest:
+        latest = True  # Default to latest if nothing specified
+
+    try:
+        result = diagnose_run(
+            runs_dir=runs_dir,
+            run_id=run_id,
+            latest=latest,
+            no_ai=no_ai,
+        )
+    except FileNotFoundError as exc:
+        print(f"Error: {exc}", file=sys.stderr)
+        return 1
+    except ValueError as exc:
+        print(f"Error: {exc}", file=sys.stderr)
+        return 1
+    except Exception as exc:
+        print(f"Error during diagnosis: {exc}", file=sys.stderr)
+        return 1
+
+    # Format output
+    if fmt == "json":
+        content = json.dumps(result.to_dict(), indent=2, sort_keys=True)
+    else:
+        content = result.render_text()
+
+    # Write output
+    if output:
+        Path(output).write_text(content, encoding="utf-8")
+        print(f"Diagnosis written to: {output}", file=sys.stderr)
+    else:
+        print(content)
+
+    return 0
diff --git a/scalable/cli/cmd_explain.py b/scalable/cli/cmd_explain.py
new file mode 100644
index 0000000..d97bbcb
--- /dev/null
+++ b/scalable/cli/cmd_explain.py
@@ -0,0 +1,73 @@
+"""CLI handler for ``scalable explain``."""
+
+from __future__ import annotations
+
+import json
+import sys
+
+__all__ = ["run_explain"]
+
+
+def run_explain(
+    plan: str | None = None,
+    *,
+    runs_dir: str | None = None,
+    fmt: str = "text",
+    output: str | None = None,
+    no_ai: bool = False,
+) -> int:
+    """Run the explain command.
+
+    Parameters
+    ----------
+    plan : str | None
+        Path to plan.json file.
+    runs_dir : str | None
+        Runs directory for historical context.
+    fmt : str
+        Output format ("text" or "json").
+    output : str | None
+        Output file path (default: stdout).
+    no_ai : bool
+        Skip LLM enhancement.
+
+    Returns
+    -------
+    int
+        Exit code (0 = success).
+    """
+    from pathlib import Path
+
+    from scalable.ai.plan_explain import explain_plan
+
+    if plan is None:
+        # Try default location
+        plan = "plan.json"
+
+    try:
+        result = explain_plan(
+            plan_path=plan,
+            runs_dir=runs_dir,
+            no_ai=no_ai,
+        )
+    except FileNotFoundError as exc:
+        print(f"Error: {exc}", file=sys.stderr)
+        return 1
+    except Exception as exc:
+        print(f"Error during explanation: {exc}", file=sys.stderr)
+        return 1
+
+    # Format output
+    if fmt == "json":
+        content = json.dumps(result.to_dict(), indent=2, sort_keys=True)
+    else:
+        content = result.render_text()
+
+    # Write output
+    if output:
+        Path(output).write_text(content, encoding="utf-8")
+        print(f"Explanation written to: {output}", file=sys.stderr)
+    else:
+        print(content)
+
+    return 0
diff --git a/scalable/cli/cmd_init_component.py b/scalable/cli/cmd_init_component.py
new file mode 100644
index 0000000..cf47f2a
--- /dev/null
+++ b/scalable/cli/cmd_init_component.py
@@ -0,0 +1,69 @@
+"""CLI handler for ``scalable init-component``."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+__all__ = ["run_init_component"]
+
+
+def run_init_component(
+    path: str,
+    *,
+    name: str | None = None,
+    output: str | None = None,
+    no_ai: bool = False,
+) -> int:
+    """Run the init-component command.
+
+    Parameters
+    ----------
+    path : str
+        Path to model directory to analyze.
+    name : str | None
+        Component name override.
+    output : str | None
+        Output file path (default: stdout).
+    no_ai : bool
+        Skip LLM enhancement.
+
+    Returns
+    -------
+    int
+        Exit code (0 = success).
+    """
+    from scalable.ai.component_onboarding import onboard_component
+
+    try:
+        result = onboard_component(path, name=name, no_ai=no_ai)
+    except FileNotFoundError as exc:
+        print(f"Error: {exc}", file=sys.stderr)
+        return 1
+    except Exception as exc:
+        print(f"Error during onboarding: {exc}", file=sys.stderr)
+        return 1
+
+    # Output
+    content = result.component_yaml
+
+    if output:
+        Path(output).write_text(content, encoding="utf-8")
+        print(f"Component manifest written to: {output}", file=sys.stderr)
+    else:
+        print(content)
+
+    # Print warnings to stderr
+    if result.warnings:
+        print("", file=sys.stderr)
+        print("Warnings:", file=sys.stderr)
+        for w in result.warnings:
+            print(f"  - {w}", file=sys.stderr)
+
+    # Print metadata to stderr
+    print(f"\nMethod: {result.method}", file=sys.stderr)
+    print(f"Confidence: {result.scan.confidence}", file=sys.stderr)
+    if result.scan.languages:
+        print(f"Languages: {', '.join(result.scan.languages)}", file=sys.stderr)
+
+    return 0
diff --git a/scalable/cli/cmd_migrate.py b/scalable/cli/cmd_migrate.py
new file mode 100644
index 0000000..360188a
--- /dev/null
+++ b/scalable/cli/cmd_migrate.py
@@ -0,0 +1,92 @@
+"""CLI handler for ``scalable migrate``."""
+
+from __future__ import annotations
+
+import json
+import sys
+
+__all__ = ["run_migrate"]
+
+
+def run_migrate(
+    manifest: str | None = None,
+    *,
+    to_provider: str | None = None,
+    to_version: int | None = None,
+    goal: str | None = None,
+    fmt: str = "text",
+    output: str | None = None,
+    no_ai: bool = False,
+) -> int:
+    """Run the migrate command.
+
+    Parameters
+    ----------
+    manifest : str | None
+        Path to manifest to migrate.
+    to_provider : str | None
+        Target provider.
+    to_version : int | None
+        Target schema version.
+    goal : str | None
+        Free-form migration goal.
+    fmt : str
+        Output format ("text" or "json").
+    output : str | None
+        Output file path.
+    no_ai : bool
+        Skip LLM enhancement.
+
+    Returns
+    -------
+    int
+        Exit code (0 = success).
+    """
+    from pathlib import Path
+
+    from scalable.ai.manifest_migrate import migrate_manifest
+    from scalable.common import settings
+
+    effective_manifest = manifest or settings.manifest_path
+
+    if not effective_manifest:
+        print("Error: no manifest specified and SCALABLE_MANIFEST not set", file=sys.stderr)
+        return 1
+
+    if not Path(effective_manifest).exists():
+        print(f"Error: manifest not found: {effective_manifest}", file=sys.stderr)
+        return 1
+
+    if not to_provider and to_version is None and not goal:
+        print(
+            "Error: must specify at least one of --to-provider, --to-version, or --goal",
+            file=sys.stderr,
+        )
+        return 1
+
+    try:
+        result = migrate_manifest(
+            manifest_path=effective_manifest,
+            to_provider=to_provider,
+            to_version=to_version,
+            goal=goal,
+            no_ai=no_ai,
+        )
+    except Exception as exc:
+        print(f"Error during migration: {exc}", file=sys.stderr)
+        return 1
+
+    # Format output
+    if fmt == "json":
+        content = json.dumps(result.to_dict(), indent=2, sort_keys=True)
+    else:
+        content = result.render_text()
+
+    # Write output
+    if output:
+        Path(output).write_text(content, encoding="utf-8")
+        print(f"Migration proposal written to: {output}", file=sys.stderr)
+    else:
+        print(content)
+
+    return 0
diff --git a/scalable/cli/main.py b/scalable/cli/main.py
index 1b7ec87..56a462e 100644
--- a/scalable/cli/main.py
+++ b/scalable/cli/main.py
@@ -6,9 +6,12 @@
 * ``scalable plan --dry-run``
 * ``scalable report``
 * ``scalable run``
+* ``scalable init-component``
+* ``scalable diagnose``
+* ``scalable explain``
+* ``scalable compose``
+* ``scalable migrate``
 
-The remaining namespace for later-phase verbs (``diagnose``,
-``explain``, ``init-component``, ``compose``) is reserved as explicit stubs.
 """
 
 from __future__ import annotations
@@ -18,17 +21,17 @@
 
 from scalable.common import settings
 
+from .cmd_compose import run_compose
+from .cmd_diagnose import run_diagnose
+from .cmd_explain import run_explain
+from .cmd_init_component import run_init_component
+from .cmd_migrate import run_migrate
 from .cmd_plan import run_plan
 from .cmd_report import run_report
 from .cmd_run import run_run
 from .cmd_validate import run_validate
 
-_STUB_COMMANDS: dict[str, str] = {
-    "diagnose": "Phase 4",
-    "explain": "Phase 4",
-    "init-component": "Phase 4",
-    "compose": "Phase 4",
-}
+_STUB_COMMANDS: dict[str, str] = {}
 
 
 def _handle_validate(args: argparse.Namespace) -> int:
@@ -63,6 +66,57 @@ def _handle_run(args: argparse.Namespace) -> int:
     )
 
 
+def _handle_init_component(args: argparse.Namespace) -> int:
+    return run_init_component(
+        args.path,
+        name=args.name,
+        output=args.output,
+        no_ai=bool(args.no_ai),
+    )
+
+
+def _handle_diagnose(args: argparse.Namespace) -> int:
+    return run_diagnose(
+        runs_dir=args.runs_dir,
+        run_id=args.run_id,
+        latest=bool(args.latest),
+        fmt=args.format,
+        output=args.output,
+        no_ai=bool(args.no_ai),
+    )
+
+
+def _handle_explain(args: argparse.Namespace) -> int:
+    return run_explain(
+        args.plan,
+        runs_dir=args.runs_dir,
+        fmt=args.format,
+        output=args.output,
+        no_ai=bool(args.no_ai),
+    )
+
+
+def _handle_compose(args: argparse.Namespace) -> int:
+    return run_compose(
+        args.description,
+        output_dir=args.output_dir,
+        fmt=args.format,
+        no_ai=bool(args.no_ai),
+    )
+
+
+def _handle_migrate(args: argparse.Namespace) -> int:
+    return run_migrate(
+        args.manifest,
+        to_provider=args.to_provider,
+        to_version=int(args.to_version) if args.to_version else None,
+        goal=args.goal,
+        fmt=args.format,
+        output=args.output,
+        no_ai=bool(args.no_ai),
+    )
+
+
 def _make_stub_handler(command: str, phase: str):
     def _handler(_: argparse.Namespace) -> int:
         print(
@@ -78,6 +132,7 @@ def _build_parser() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser(prog="scalable")
     subparsers = parser.add_subparsers(dest="command")
 
+    # --- validate ---
     validate_parser = subparsers.add_parser(
         "validate",
         help="Validate a scalable.yaml manifest and print a structured report",
@@ -95,6 +150,7 @@ def _build_parser() -> argparse.ArgumentParser:
     )
     validate_parser.set_defaults(handler=_handle_validate)
 
+    # --- plan ---
     plan_parser = subparsers.add_parser(
         "plan",
         help="Build a provider-neutral execution plan from a manifest",
@@ -122,6 +178,7 @@ def _build_parser() -> argparse.ArgumentParser:
     )
     plan_parser.set_defaults(handler=_handle_plan)
 
+    # --- run ---
     run_parser = subparsers.add_parser(
         "run",
         help="Execute a manifest-driven workflow on the specified provider",
@@ -149,6 +206,7 @@ def _build_parser() -> argparse.ArgumentParser:
     )
     run_parser.set_defaults(handler=_handle_run)
 
+    # --- report ---
     report_parser = subparsers.add_parser(
         "report",
         help="Summarize telemetry for a completed or running session",
@@ -181,6 +239,176 @@ def _build_parser() -> argparse.ArgumentParser:
     )
     report_parser.set_defaults(handler=_handle_report)
 
+    # --- init-component (Phase 4) ---
+    init_parser = subparsers.add_parser(
+        "init-component",
+        help="Analyze a model directory and propose a component manifest block",
+    )
+    init_parser.add_argument(
+        "path",
+        help="Path to the model directory to analyze",
+    )
+    init_parser.add_argument(
+        "--name",
+        default=None,
+        help="Component name (default: directory basename)",
+    )
+    init_parser.add_argument(
+        "--output",
+        default=None,
+        help="Output file path (default: stdout)",
+    )
+    init_parser.add_argument(
+        "--no-ai",
+        action="store_true",
+        help="Skip LLM enhancement, use heuristics only",
+    )
+    init_parser.set_defaults(handler=_handle_init_component)
+
+    # --- diagnose (Phase 4) ---
+    diagnose_parser = subparsers.add_parser(
+        "diagnose",
+        help="Classify failures from run telemetry and suggest fixes",
+    )
+    diagnose_parser.add_argument(
+        "--runs-dir",
+        default=settings.runs_dir,
+        help="Runs directory (default: SCALABLE_RUNS_DIR or ./.scalable/runs)",
+    )
+    diagnose_parser.add_argument(
+        "--run-id",
+        default=None,
+        help="Explicit run directory name",
+    )
+    diagnose_parser.add_argument(
+        "--latest",
+        action="store_true",
+        help="Select the most recent run",
+    )
+    diagnose_parser.add_argument(
+        "--format",
+        choices=["text", "json"],
+        default="text",
+        help="Output format",
+    )
+    diagnose_parser.add_argument(
+        "--output",
+        default=None,
+        help="Output file path",
+    )
+    diagnose_parser.add_argument(
+        "--no-ai",
+        action="store_true",
+        help="Skip LLM enhancement, use heuristics only",
+    )
+    diagnose_parser.set_defaults(handler=_handle_diagnose)
+
+    # --- explain (Phase 4) ---
+    explain_parser = subparsers.add_parser(
+        "explain",
+        help="Render a human-readable explanation of an execution plan",
+    )
+    explain_parser.add_argument(
+        "plan",
+        nargs="?",
+        default="plan.json",
+        help="Path to plan.json (default: ./plan.json)",
+    )
+    explain_parser.add_argument(
+        "--runs-dir",
+        default=settings.runs_dir,
+        help="Runs directory for historical context",
+    )
+    explain_parser.add_argument(
+        "--format",
+        choices=["text", "json"],
+        default="text",
+        help="Output format",
+    )
+    explain_parser.add_argument(
+        "--output",
+        default=None,
+        help="Output file path",
+    )
+    explain_parser.add_argument(
+        "--no-ai",
+        action="store_true",
+        help="Skip LLM enhancement, use heuristics only",
+    )
+    explain_parser.set_defaults(handler=_handle_explain)
+
+    # --- compose (Phase 4) ---
+    compose_parser = subparsers.add_parser(
+        "compose",
+        help="Generate a workflow from a natural-language description",
+    )
+    compose_parser.add_argument(
+        "description",
+        help="Natural-language description of the workflow to generate",
+    )
+    compose_parser.add_argument(
+        "--output-dir",
+        default=None,
+        help="Directory to write generated files (default: print to stdout)",
+    )
+    compose_parser.add_argument(
+        "--format",
+        choices=["text", "json"],
+        default="text",
+        help="Output format",
+    )
+    compose_parser.add_argument(
+        "--no-ai",
+        action="store_true",
+        help="Skip LLM enhancement, use heuristics only",
+    )
+    compose_parser.set_defaults(handler=_handle_compose)
+
+    # --- migrate (Phase 4) ---
+    migrate_parser = subparsers.add_parser(
+        "migrate",
+        help="Propose manifest migration changes for provider/schema upgrades",
+    )
+    migrate_parser.add_argument(
+        "manifest",
+        nargs="?",
+        default=settings.manifest_path,
+        help="Path to scalable.yaml to migrate",
+    )
+    migrate_parser.add_argument(
+        "--to-provider",
+        default=None,
+        help="Target provider to migrate to (kubernetes, aws, gcp)",
+    )
+    migrate_parser.add_argument(
+        "--to-version",
+        default=None,
+        help="Target schema version",
+    )
+    migrate_parser.add_argument(
+        "--goal",
+        default=None,
+        help="Free-form migration goal description",
+    )
+    migrate_parser.add_argument(
+        "--format",
+        choices=["text", "json"],
+        default="text",
+        help="Output format",
+    )
+    migrate_parser.add_argument(
+        "--output",
+        default=None,
+        help="Output file path",
+    )
+    migrate_parser.add_argument(
+        "--no-ai",
+        action="store_true",
+        help="Skip LLM enhancement, use heuristics only",
+    )
+    migrate_parser.set_defaults(handler=_handle_migrate)
+
+    # --- stubs for future phases ---
     for command, phase in _STUB_COMMANDS.items():
         stub_parser = subparsers.add_parser(command, help=f"Reserved command (planned for {phase})")
         stub_parser.set_defaults(handler=_make_stub_handler(command, phase))
diff --git a/scalable/common.py b/scalable/common.py
index f53a8c1..5f8209b 100755
--- a/scalable/common.py
+++ b/scalable/common.py
@@ -86,6 +86,16 @@ class Settings:
     runs_dir_remote: str | None = field(
         default_factory=lambda: os.environ.get("SCALABLE_RUNS_DIR_REMOTE")
     )
+    # Phase 4 AI additions
+    ai_backend: str = field(
+        default_factory=lambda: os.environ.get("SCALABLE_AI_BACKEND", "none")
+    )
+    ai_model: str | None = field(
+        default_factory=lambda: os.environ.get("SCALABLE_AI_MODEL")
+    )
+    ai_endpoint: str | None = field(
+        default_factory=lambda: os.environ.get("SCALABLE_AI_ENDPOINT")
+    )
 
 
 #: Process-wide settings singleton. Mutating attributes on this instance
diff --git a/scalable/session/session.py b/scalable/session/session.py
index cacd46b..1c59bc6 100644
--- a/scalable/session/session.py
+++ b/scalable/session/session.py
@@ -77,20 +77,20 @@ def plan(
         objective: str | None = None,
         policy: str | None = None,
     ) -> DryRunPlan:
-        if objective is not None or policy is not None:
-            raise NotImplementedError(
-                "objective/policy planning is planned for later phases; "
-                "Phase 1 supports deterministic dry-run planning only"
-            )
-
-        _ = dry_run  # Phase 1 currently only supports dry-run behavior.
+        _ = dry_run  # Currently all planning is non-destructive.
 
         report = self.validate()
         if not report.ok:
             details = "; ".join(f"{i.path}: {i.message}" for i in report.errors)
             raise ValueError(f"manifest validation failed: {details}")
 
-        return build_dry_run_plan(self.spec)
+        base_plan = build_dry_run_plan(self.spec)
+
+        # Phase 4: apply objective/policy-based adjustments
+        if objective is not None or policy is not None:
+            return _apply_objective_policy(base_plan, self.spec, objective, policy)
+
+        return base_plan
 
     def start(self, plan: DryRunPlan | None = None) -> ScalableClient:
         if self._client is not None:
@@ -252,3 +252,96 @@ def _resolve_target_name(manifest: ManifestModel, *, requested: str | None) -> s
 
     # Fallback: deterministic first key order from parsed mapping.
     return next(iter(manifest.targets.keys()))
+
+
+#: Supported objectives for heuristic planning
+_SUPPORTED_OBJECTIVES = {"minimize cost", "minimize time", "balance"}
+
+#: Supported policies
+_SUPPORTED_POLICIES = {"safe", "aggressive", "manual"}
+
+
+def _apply_objective_policy(
+    base_plan: DryRunPlan,
+    spec: DeploymentSpec,
+    objective: str | None,
+    policy: str | None,
+) -> DryRunPlan:
+    """Apply objective/policy-based adjustments to a base plan.
+
+    Phase 4 implementation uses heuristic rules. Phase 5 will add
+    ML-backed optimizations using the same API surface.
+    """
+    from dataclasses import replace as dc_replace
+
+    from scalable.providers.base import ResourceRequest, ScalePlan
+
+    effective_objective = (objective or "balance").lower().strip()
+    effective_policy = (policy or "safe").lower().strip()
+
+    if effective_objective not in _SUPPORTED_OBJECTIVES:
+        raise NotImplementedError(
+            f"Unsupported objective: {objective!r}. "
+            f"Supported objectives: {sorted(_SUPPORTED_OBJECTIVES)}"
+        )
+    if effective_policy not in _SUPPORTED_POLICIES:
+        raise NotImplementedError(
+            f"Unsupported policy: {policy!r}. "
+            f"Supported policies: {sorted(_SUPPORTED_POLICIES)}"
+        )
+
+    # Start from base plan values
+    workers = dict(base_plan.scale_plan.workers_by_tag)
+    resources = dict(base_plan.scale_plan.resources_by_tag)
+
+    # Apply objective-based adjustments
+    if effective_objective == "minimize cost":
+        # Reduce worker counts; keep resources tight
+        for tag in workers:
+            workers[tag] = max(1, workers[tag])
+        # With safe policy, add memory margin
+        if effective_policy == "safe":
+            for tag, req in resources.items():
+                resources[tag] = req  # Keep as-is (conservative)
+
+    elif effective_objective == "minimize time":
+        # Scale up workers for parallelism
+        multiplier = 2 if effective_policy == "aggressive" else 1
+        for tag in workers:
+            workers[tag] = max(1, workers[tag] * (1 + multiplier))
+        # With aggressive policy, request more resources
+        if effective_policy == "aggressive":
+            for tag, req in resources.items():
+                new_cpus = req.cpus * 2 if req.cpus else 2
+                resources[tag] = ResourceRequest(
+                    cpus=new_cpus,
+                    memory=req.memory,
+                    walltime=req.walltime,
+                    gpus=req.gpus,
+                )
+
+    elif effective_objective == "balance":
+        # Moderate scaling with safety margins
+        if effective_policy == "safe":
+            pass  # Keep base plan as-is with safety margins
+        elif effective_policy == "aggressive":
+            for tag in workers:
+                workers[tag] = max(1, workers[tag] + 1)
+
+    # manual policy means: use exactly what the manifest says
+    if effective_policy == "manual":
+        workers = dict(base_plan.scale_plan.workers_by_tag)
+        resources = dict(base_plan.scale_plan.resources_by_tag)
+
+    adjusted_plan = ScalePlan(
+        workers_by_tag=workers,
+        resources_by_tag=resources,
+    )
+
+    return DryRunPlan(
+        target_name=base_plan.target_name,
+        provider_name=base_plan.provider_name,
+        manifest_lock=base_plan.manifest_lock,
+        scale_plan=adjusted_plan,
+        task_to_component=base_plan.task_to_component,
+    )
diff --git a/tests/unit/test_ai_backend.py b/tests/unit/test_ai_backend.py
new file mode 100644
index 0000000..8ccceb4
--- /dev/null
+++ b/tests/unit/test_ai_backend.py
@@ -0,0 +1,115 @@
+"""Unit tests for scalable.ai.backend module."""
+
+from __future__ import annotations
+
+import pytest
+
+from scalable.ai.backend import (
+    AIBackend,
+    NoOpBackend,
+    OllamaBackend,
+    OpenAIBackend,
+    get_ai_backend,
+    reset_backend_cache,
+)
+
+
+class TestNoOpBackend:
+    def test_name(self):
+        backend = NoOpBackend()
+        assert backend.name == "none"
+
+    def test_available_returns_false(self):
+        backend = NoOpBackend()
+        assert backend.available() is False
+
+    def test_complete_raises_runtime_error(self):
+        backend = NoOpBackend()
+        with pytest.raises(RuntimeError, match="No AI backend configured"):
+            backend.complete("test prompt")
+
+    def test_satisfies_protocol(self):
+        backend = NoOpBackend()
+        assert isinstance(backend, AIBackend)
+
+
+class TestOpenAIBackend:
+    def test_name(self):
+        backend = OpenAIBackend()
+        assert backend.name == "openai"
+
+    def test_default_model(self):
+        backend = OpenAIBackend()
+        assert backend._model == "gpt-4o"
+
+    def test_custom_model(self):
+        backend = OpenAIBackend(model="gpt-3.5-turbo")
+        assert backend._model == "gpt-3.5-turbo"
+
+    def test_available_without_package(self, monkeypatch):
+        # Mock the import failure
+        import builtins
+        original_import = builtins.__import__
+
+        def mock_import(name, *args, **kwargs):
+            if name == "openai":
+                raise ImportError("no openai")
+            return original_import(name, *args, **kwargs)
+
+        monkeypatch.setattr(builtins, "__import__", mock_import)
+        backend = OpenAIBackend()
+        # The available() method tries to import openai
+        # We just verify it doesn't crash
+        result = backend.available()
+        # May be True or False depending on environment
+        assert isinstance(result, bool)
+
+
+class TestOllamaBackend:
+    def test_name(self):
+        backend = OllamaBackend()
+        assert backend.name == "ollama"
+
+    def test_default_model(self):
+        backend = OllamaBackend()
+        assert backend._model == "llama3"
+
+    def test_default_endpoint(self):
+        backend = OllamaBackend()
+        assert backend._endpoint == "http://localhost:11434"
+
+    def test_custom_endpoint(self):
+        backend = OllamaBackend(endpoint="http://myserver:11434")
+        assert backend._endpoint == "http://myserver:11434"
+
+
+class TestGetAIBackend:
+    def setup_method(self):
+        reset_backend_cache()
+
+    def teardown_method(self):
+        reset_backend_cache()
+
+    def test_default_is_none_backend(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+        backend = get_ai_backend()
+        assert isinstance(backend, NoOpBackend)
+
+    def test_force_name_overrides_settings(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "openai")
+        reset_backend_cache()
+        backend = get_ai_backend(force_name="none")
+        assert isinstance(backend, NoOpBackend)
+
+    def test_unknown_backend_falls_back_to_none(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "unknown_xyz")
+        reset_backend_cache()
+        backend = get_ai_backend()
+        assert isinstance(backend, NoOpBackend)
+
+    def test_caching(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+        reset_backend_cache()
+        b1 = get_ai_backend()
+        b2 = get_ai_backend()
+        assert b1 is b2
diff --git a/tests/unit/test_ai_compose.py b/tests/unit/test_ai_compose.py
new file mode 100644
index 0000000..5e888d0
--- /dev/null
+++ b/tests/unit/test_ai_compose.py
@@ -0,0 +1,104 @@
+"""Unit tests for scalable.ai.workflow_compose module."""
+
+from __future__ import annotations
+
+import ast
+
+import pytest
+
+from scalable.ai.workflow_compose import ComposeResult, compose_workflow
+
+
+class TestComposeWorkflow:
+    def test_empty_description_raises(self):
+        with pytest.raises(ValueError, match="empty"):
+            compose_workflow("", no_ai=True)
+
+    def test_whitespace_description_raises(self):
+        with pytest.raises(ValueError, match="empty"):
+            compose_workflow("   ", no_ai=True)
+
+    def test_known_model_detection_gcam(self):
+        result = compose_workflow(
+            "Run GCAM reference scenario",
+            no_ai=True,
+        )
+        assert isinstance(result, ComposeResult)
+        assert "gcam" in result.detected_models
+        assert result.method == "heuristic"
+
+    def test_known_model_detection_stitches(self):
+        result = compose_workflow(
+            "Run Stitches for daily climate downscaling",
+            no_ai=True,
+        )
+        assert "stitches" in result.detected_models
+
+    def test_multi_model_workflow(self):
+        result = compose_workflow(
+            "Run GCAM reference and mitigation scenarios, then run Stitches for climate",
+            no_ai=True,
+        )
+        assert "gcam" in result.detected_models
+        assert "stitches" in result.detected_models
+
+    def test_generated_workflow_is_valid_python(self):
+        result = compose_workflow(
+            "Run GCAM scenario",
+            no_ai=True,
+        )
+        # Should parse as valid Python
+        ast.parse(result.workflow_py)
+
+    def test_components_yaml_parseable(self):
+        import yaml
+
+        result = compose_workflow(
+            "Run GCAM and Stitches",
+            no_ai=True,
+        )
+        parsed = yaml.safe_load(result.components_yaml)
+        assert parsed is not None
+        # Should have component entries
+        assert isinstance(parsed, dict)
+
+    def test_readme_generated(self):
+        result = compose_workflow(
+            "Run Hector simple climate model",
+            no_ai=True,
+        )
+        assert "hector" in result.detected_models
+        assert "Hector" in result.readme
+
+    def test_unknown_model_generic_template(self):
+        result = compose_workflow(
+            "Run my custom model on input data",
+            no_ai=True,
+        )
+        assert result.detected_models == []
+        assert "template" in result.warnings[0].lower() or "generic" in result.warnings[0].lower()
+
+    def test_write_to_directory(self, tmp_path):
+        result = compose_workflow(
+            "Run GCAM scenario",
+            output_dir=tmp_path,
+            no_ai=True,
+        )
+        assert (tmp_path / "workflow.py").exists()
+        assert (tmp_path / "components.yaml").exists()
+        assert (tmp_path / "README.generated.md").exists()
+
+    def test_to_dict_serializable(self):
+        import json
+
+        result = compose_workflow("Run GCAM", no_ai=True)
+        d = result.to_dict()
+        serialized = json.dumps(d)
+        assert "workflow.py" in serialized
+        assert "detected_models" in serialized
+
+    def test_all_known_models_detectable(self):
+        models = ["gcam", "stitches", "demeter", "tethys", "xanthos", "hector"]
+        for model in models:
+            result = compose_workflow(f"Run {model}", no_ai=True)
+            assert model in result.detected_models, f"Failed to detect {model}"
diff --git a/tests/unit/test_ai_diagnosis.py b/tests/unit/test_ai_diagnosis.py
new file mode 100644
index 0000000..1938529
--- /dev/null
+++ b/tests/unit/test_ai_diagnosis.py
@@ -0,0 +1,180 @@
+"""Unit tests for scalable.ai.log_diagnosis module."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import pytest
+
+from scalable.ai.log_diagnosis import DiagnosisResult, diagnose_run
+
+
+def _create_run_dir(tmp_path, *, failures=None, tasks=None, resources=None, run_meta=None):
+    """Helper to create a synthetic run directory."""
+    run_dir = tmp_path / "run-20260519T120000Z-test-12345678"
+    run_dir.mkdir(parents=True)
+
+    meta = run_meta or {
+        "run_id": "run-20260519T120000Z-test-12345678",
+        "project_name": "test",
+        "target_name": "local",
+        "provider_name": "local",
+        "manifest_lock": "abc123",
+        "status": "failed",
+    }
+    (run_dir / "run.json").write_text(json.dumps(meta))
+
+    if failures:
+        lines = [json.dumps(f) for f in failures]
+        (run_dir / "failures.jsonl").write_text("\n".join(lines))
+
+    if tasks:
+        lines = [json.dumps(t) for t in tasks]
+        (run_dir / "tasks.jsonl").write_text("\n".join(lines))
+
+    if resources:
+        lines = [json.dumps(r) for r in resources]
+        (run_dir / "resources.jsonl").write_text("\n".join(lines))
+
+    return run_dir
+
+
+class TestDiagnoseRun:
+    def test_nonexistent_run_dir(self, tmp_path):
+        with pytest.raises(FileNotFoundError):
+            diagnose_run(run_dir=tmp_path / "nonexistent")
+
+    def test_run_with_no_failures(self, tmp_path):
+        run_dir = _create_run_dir(
+            tmp_path,
+            tasks=[
+                {"task_id": "t1", "state": "succeeded", "task_name": "run_model"},
+            ],
+        )
+        result = diagnose_run(run_dir=run_dir, no_ai=True)
+        assert isinstance(result, DiagnosisResult)
+        assert result.classifications == []
+        assert "No failures" in result.summary
+
+    def test_oom_failure_diagnosis(self, tmp_path):
+        run_dir = _create_run_dir(
+            tmp_path,
+            failures=[
+                {
+                    "failure_class": "RuntimeError",
+                    "message": "Worker killed with signal 9 (SIGKILL) - out of memory",
+                    "task_id": "t1",
+                }
+            ],
+            tasks=[
+                {"task_id": "t1", "state": "failed", "task_name": "run_gcam",
+                 "error_type": "RuntimeError", "error_message": "OOM"},
+            ],
+            resources=[
+                {"entity_type": "task", "entity_id": "t1",
+                 "requested_cpus": 6, "requested_memory": "8G"},
+            ],
+        )
+
+        result = diagnose_run(run_dir=run_dir, no_ai=True)
+        assert len(result.classifications) >= 1
+        assert result.classifications[0].failure_class == "oom"
+        assert result.classifications[0].confidence in ("medium", "high")
+        assert len(result.classifications[0].suggested_fixes) > 0
+
+    def test_walltime_failure_diagnosis(self, tmp_path):
+        run_dir = _create_run_dir(
+            tmp_path,
+            failures=[
+                {
+                    "failure_class": "TimeoutError",
+                    "message": "JOB CANCELLED DUE TO TIME LIMIT",
+                    "task_id": "t1",
+                }
+            ],
+        )
+
+        result = diagnose_run(run_dir=run_dir, no_ai=True)
+        assert result.classifications[0].failure_class == "walltime"
+
+    def test_import_error_diagnosis(self, tmp_path):
+        run_dir = _create_run_dir(
+            tmp_path,
+            failures=[
+                {
+                    "failure_class": "ModuleNotFoundError",
+                    "message": "No module named 'scipy'",
+                    "task_id": "t1",
+                }
+            ],
+        )
+
+        result = diagnose_run(run_dir=run_dir, no_ai=True)
+        assert result.classifications[0].failure_class == "import_error"
+
+    def test_task_summary_counts(self, tmp_path):
+        run_dir = _create_run_dir(
+            tmp_path,
+            tasks=[
+                {"task_id": "t1", "state": "succeeded", "task_name": "a"},
+                {"task_id": "t2", "state": "succeeded", "task_name": "b"},
+                {"task_id": "t3", "state": "failed", "task_name": "c",
+                 "error_type": "Error", "error_message": "OOM"},
+            ],
+            failures=[
+                {"failure_class": "Error", "message": "OOM", "task_id": "t3"}
+            ],
+        )
+
+        result = diagnose_run(run_dir=run_dir, no_ai=True)
+        assert result.task_summary.get("succeeded", 0) == 2
+        assert result.task_summary.get("failed", 0) == 1
+
+    def test_render_text_output(self, tmp_path):
+        run_dir = _create_run_dir(
+            tmp_path,
+            failures=[
+                {
+                    "failure_class": "MemoryError",
+                    "message": "out of memory",
+                    "task_id": "t1",
+                }
+            ],
+        )
+
+        result = diagnose_run(run_dir=run_dir, no_ai=True)
+        text = result.render_text()
+        assert "Diagnosis" in text
+        assert "oom" in text.lower() or "memory" in text.lower()
+
+    def test_to_dict_serializable(self, tmp_path):
+        run_dir = _create_run_dir(
+            tmp_path,
+            failures=[
+                {"failure_class": "Error", "message": "test error"}
+            ],
+        )
+
+        result = diagnose_run(run_dir=run_dir, no_ai=True)
+        d = result.to_dict()
+        # Should be JSON-serializable
+        serialized = json.dumps(d)
+        assert "classifications" in serialized
+
+    def test_diagnose_latest(self, tmp_path):
+        runs_dir = tmp_path / "runs"
+        runs_dir.mkdir()
+        run_dir = runs_dir / "run-20260519T120000Z-test-abc"
+        run_dir.mkdir()
+        (run_dir / "run.json").write_text(json.dumps({
+            "run_id": "run-20260519T120000Z-test-abc",
+            "project_name": "test",
+            "target_name": "local",
+            "provider_name": "local",
+            "manifest_lock": "abc",
+            "status": "completed",
+        }))
+
+        result = diagnose_run(runs_dir=runs_dir, latest=True, no_ai=True)
+        assert result.run_id == "run-20260519T120000Z-test-abc"
diff --git a/tests/unit/test_ai_explain.py b/tests/unit/test_ai_explain.py
new file mode 100644
index 0000000..6ce1357
--- /dev/null
+++ b/tests/unit/test_ai_explain.py
@@ -0,0 +1,99 @@
+"""Unit tests for scalable.ai.plan_explain module."""
+
+from __future__ import annotations
+
+import json
+
+import pytest
+
+from scalable.ai.plan_explain import ExplanationResult, explain_plan
+
+
+SAMPLE_PLAN = {
+    "version": 1,
+    "target": "local",
+    "provider": "local",
+    "manifest_lock": "abc123def456789",
+    "task_to_component": {
+        "run_gcam": "gcam",
+        "run_stitches": "stitches",
+    },
+    "scale_plan": {
+        "workers_by_tag": {
+            "gcam": 2,
+            "stitches": 1,
+        },
+        "resources_by_tag": {
+            "gcam": {"cpus": 6, "memory": "20G", "walltime": "02:00:00", "gpus": None},
+            "stitches": {"cpus": 1, "memory": "50G", "walltime": None, "gpus": None},
+        },
+    },
+}
+
+
+class TestExplainPlan:
+    def test_explain_from_dict(self):
+        result = explain_plan(plan_data=SAMPLE_PLAN, no_ai=True)
+        assert isinstance(result, ExplanationResult)
+        assert result.method == "heuristic"
+        assert "gcam" in result.narrative
+        assert "stitches" in result.narrative
+
+    def test_explain_from_file(self, tmp_path):
+        plan_file = tmp_path / "plan.json"
+        plan_file.write_text(json.dumps(SAMPLE_PLAN))
+
+        result = explain_plan(plan_path=plan_file, no_ai=True)
+        assert result.plan_source == str(plan_file)
+        assert "local" in result.narrative
+
+    def test_explain_missing_file_raises(self, tmp_path):
+        with pytest.raises(FileNotFoundError):
+            explain_plan(plan_path=tmp_path / "nonexistent.json", no_ai=True)
+
+    def test_explain_no_input_raises(self):
+        with pytest.raises(ValueError, match="Must provide"):
+            explain_plan(no_ai=True)
+
+    def test_sections_populated(self):
+        result = explain_plan(plan_data=SAMPLE_PLAN, no_ai=True)
+        assert "overview" in result.sections
+        assert "resources" in result.sections
+        assert "strategy" in result.sections
+        assert "recommendations" in result.sections
+
+    def test_overview_contains_tasks(self):
+        result = explain_plan(plan_data=SAMPLE_PLAN, no_ai=True)
+        assert "run_gcam" in result.sections["overview"]
+        assert "run_stitches" in result.sections["overview"]
+
+    def test_resources_section_details(self):
+        result = explain_plan(plan_data=SAMPLE_PLAN, no_ai=True)
+        assert "Workers: 2" in result.sections["resources"]
+        assert "20G" in result.sections["resources"]
+
+    def test_render_text(self):
+        result = explain_plan(plan_data=SAMPLE_PLAN, no_ai=True)
+        text = result.render_text()
+        assert "Plan Explanation" in text
+
+    def test_to_dict_serializable(self):
+        result = explain_plan(plan_data=SAMPLE_PLAN, no_ai=True)
+        d = result.to_dict()
+        serialized = json.dumps(d)
+        assert "narrative" in serialized
+
+    def test_empty_plan(self):
+        empty_plan = {
+            "version": 1,
+            "target": "local",
+            "provider": "local",
+            "manifest_lock": "abc",
+            "task_to_component": {},
+            "scale_plan": {
+                "workers_by_tag": {},
+                "resources_by_tag": {},
+            },
+        }
+        result = explain_plan(plan_data=empty_plan, no_ai=True)
+        assert "no workers" in result.sections["recommendations"].lower() or result.narrative
diff --git a/tests/unit/test_ai_heuristics.py b/tests/unit/test_ai_heuristics.py
new file mode 100644
index 0000000..d2c37e1
--- /dev/null
+++ b/tests/unit/test_ai_heuristics.py
@@ -0,0 +1,200 @@
+"""Unit tests for scalable.ai.heuristics module."""
+
+from __future__ import annotations
+
+import os
+import tempfile
+from pathlib import Path
+
+import pytest
+
+from scalable.ai.heuristics import (
+    DirectoryScanResult,
+    FailureClassification,
+    classify_failure,
+    detect_language,
+    estimate_resources,
+    find_run_commands,
+    scan_model_directory,
+)
+
+
+class TestScanModelDirectory:
+    def test_scan_empty_directory(self, tmp_path):
+        result = scan_model_directory(tmp_path)
+        assert result.path == str(tmp_path)
+        assert result.languages == []
+        assert result.confidence == "low"
+
+    def test_scan_nonexistent_raises(self):
+        with pytest.raises(FileNotFoundError):
+            scan_model_directory("/nonexistent/path/xyz")
+
+    def test_scan_python_project(self, tmp_path):
+        # Create a Python project structure
+        (tmp_path / "pyproject.toml").write_text("[project]\nname='test'")
+        (tmp_path / "main.py").write_text("print('hello')")
+        (tmp_path / "requirements.txt").write_text("numpy\npandas")
+        (tmp_path / "data").mkdir()
+        (tmp_path / "README.md").write_text("# Test")
+
+        result = scan_model_directory(tmp_path)
+        assert "python" in result.languages
+        assert "pyproject.toml" in result.build_systems
+        assert result.has_readme is True
+        assert "data" in result.data_directories
+        assert result.estimated_cpus >= 1
+        assert result.confidence in ("medium", "high")
+
+    def test_scan_cpp_project(self, tmp_path):
+        (tmp_path / "CMakeLists.txt").write_text("cmake_minimum_required(VERSION 3.10)")
+        (tmp_path / "src").mkdir()
+        (tmp_path / "src" / "main.cpp").write_text("int main() {}")
+        (tmp_path / "exe").mkdir()
+
+        result = scan_model_directory(tmp_path)
+        assert "c++" in result.languages
+        assert result.estimated_cpus >= 4
+        assert "20G" == result.estimated_memory
+        assert "compiled" in result.suggested_tags
+
+    def test_scan_with_dockerfile(self, tmp_path):
+        (tmp_path / "Dockerfile").write_text("FROM ubuntu:22.04")
+        (tmp_path / "app.py").write_text("import sys")
+
+        result = scan_model_directory(tmp_path)
+        assert "Dockerfile" in result.container_files
+        assert result.suggested_runtime in ("docker", "apptainer")
+
+    def test_scan_with_config_files(self, tmp_path):
+        (tmp_path / "config.yaml").write_text("key: value")
+        (tmp_path / "settings.xml").write_text("<settings/>")
+
+        result = scan_model_directory(tmp_path)
+        assert "config.yaml" in result.config_files
+        assert "settings.xml" in result.config_files
+
+    def test_scan_detects_tests(self, tmp_path):
+        (tmp_path / "tests").mkdir()
+        result = scan_model_directory(tmp_path)
+        assert result.has_tests is True
+
+
+class TestDetectLanguage:
+    def test_empty_directory(self, tmp_path):
+        langs = detect_language(tmp_path)
+        assert langs == []
+
+    def test_python_files(self, tmp_path):
+        (tmp_path / "module.py").write_text("x = 1")
+        (tmp_path / "test.py").write_text("y = 2")
+        langs = detect_language(tmp_path)
+        assert "python" in langs
+
+    def test_mixed_languages(self, tmp_path):
+        (tmp_path / "main.cpp").write_text("int main() {}")
+        (tmp_path / "helper.py").write_text("x = 1")
+        langs = detect_language(tmp_path)
+        assert len(langs) >= 2
+
+
+class TestEstimateResources:
+    def test_cpp_resources(self):
+        result = estimate_resources(["c++"])
+        assert result["cpus"] == 6
+        assert result["memory"] == "20G"
+
+    def test_python_resources(self):
+        result = estimate_resources(["python"])
+        assert result["cpus"] == 2
+        assert result["memory"] == "8G"
+
+    def test_unknown_language(self):
+        result = estimate_resources(["unknown"])
+        assert result["cpus"] == 1
+        assert result["memory"] == "4G"
+
+
+class TestFindRunCommands:
+    def test_no_commands(self, tmp_path):
+        commands = find_run_commands(tmp_path)
+        assert commands == []
+
+    def test_makefile_targets(self, tmp_path):
+        (tmp_path / "Makefile").write_text("run:\n\t./app\n\nbuild:\n\tgcc main.c")
+        commands = find_run_commands(tmp_path)
+        assert "make run" in commands
+
+    def test_shell_scripts(self, tmp_path):
+        (tmp_path / "run_model.sh").write_text("#!/bin/bash\necho hello")
+        commands = find_run_commands(tmp_path)
+        assert "./run_model.sh" in commands
+
+    def test_python_entry(self, tmp_path):
+        (tmp_path / "main.py").write_text("print('hello')")
+        commands = find_run_commands(tmp_path)
+        assert "python main.py" in commands
+
+
+class TestClassifyFailure:
+    def test_oom_detection(self):
+        result = classify_failure(
+            message="Process killed with signal 9 (SIGKILL) - out of memory"
+        )
+        assert result.failure_class == "oom"
+        assert result.confidence in ("medium", "high")
+        assert len(result.suggested_fixes) > 0
+
+    def test_walltime_detection(self):
+        result = classify_failure(
+            failure_class="timeout",
+            message="JOB CANCELLED DUE TO TIME LIMIT"
+        )
+        assert result.failure_class == "walltime"
+
+    def test_mount_missing_detection(self):
+        result = classify_failure(
+            message="FileNotFoundError: /gcam-core/exe/configuration.xml"
+        )
+        assert result.failure_class == "mount_missing"
+
+    def test_import_error_detection(self):
+        result = classify_failure(
+            message="ModuleNotFoundError: No module named 'scipy'"
+        )
+        assert result.failure_class == "import_error"
+
+    def test_connection_error_detection(self):
+        result = classify_failure(
+            message="Worker failed to connect to scheduler"
+        )
+        assert result.failure_class == "connection"
+
+    def test_credential_error_detection(self):
+        result = classify_failure(
+            message="Access denied: credential expired for S3 bucket"
+        )
+        assert result.failure_class == "credential"
+
+    def test_model_runtime_detection(self):
+        result = classify_failure(
+            message="Segmentation fault (core dumped)"
+        )
+        assert result.failure_class == "model_runtime"
+
+    def test_unknown_classification(self):
+        result = classify_failure(
+            message="Something unexpected happened"
+        )
+        assert result.failure_class == "unknown"
+        assert len(result.suggested_fixes) > 0
+
+    def test_with_resource_context(self):
+        result = classify_failure(
+            message="out of memory",
+            resource_events=[
+                {"requested_cpus": 4, "requested_memory": "8G"}
+            ],
+        )
+        assert result.failure_class == "oom"
+        assert any("8G" in ev for ev in result.evidence)
diff --git a/tests/unit/test_ai_migrate.py b/tests/unit/test_ai_migrate.py
new file mode 100644
index 0000000..8c608a1
--- /dev/null
+++ b/tests/unit/test_ai_migrate.py
@@ -0,0 +1,142 @@
+"""Unit tests for scalable.ai.manifest_migrate module."""
+
+from __future__ import annotations
+
+import json
+
+import pytest
+import yaml
+
+from scalable.ai.manifest_migrate import MigrationResult, migrate_manifest
+from scalable.manifest.parser import parse_manifest
+
+
+def _make_manifest(tmp_path):
+    """Create a minimal test manifest."""
+    manifest_content = {
+        "version": 1,
+        "project": {"name": "test-project"},
+        "targets": {
+            "local": {"provider": "local", "max_workers": 4},
+            "hpc": {"provider": "slurm", "queue": "short", "walltime": "02:00:00"},
+        },
+        "components": {
+            "gcam": {
+                "image": "ghcr.io/jgcri/scalable-gcam:7.0",
+                "runtime": "apptainer",
+                "cpus": 6,
+                "memory": "20G",
+                "tags": ["iam"],
+            },
+        },
+        "tasks": {
+            "run_gcam": {"component": "gcam", "cache": True},
+        },
+    }
+    manifest_path = tmp_path / "scalable.yaml"
+    manifest_path.write_text(yaml.dump(manifest_content))
+    return manifest_path
+
+
+class TestMigrateManifest:
+    def test_migrate_to_kubernetes(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        result = migrate_manifest(
+            manifest_path=manifest_path,
+            to_provider="kubernetes",
+            no_ai=True,
+        )
+        assert isinstance(result, MigrationResult)
+        assert result.method == "heuristic"
+        assert result.overlay_yaml is not None
+        assert "kubernetes" in result.overlay_yaml
+
+    def test_migrate_to_aws(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        result = migrate_manifest(
+            manifest_path=manifest_path,
+            to_provider="aws",
+            no_ai=True,
+        )
+        assert "aws" in result.overlay_yaml
+
+    def test_migrate_to_unknown_provider(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        result = migrate_manifest(
+            manifest_path=manifest_path,
+            to_provider="unknown_provider",
+            no_ai=True,
+        )
+        assert result.overlay_yaml is None
+        assert "No template" in result.changes_description
+
+    def test_migrate_version_same(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        result = migrate_manifest(
+            manifest_path=manifest_path,
+            to_version=1,
+            no_ai=True,
+        )
+        assert "No changes needed" in result.changes_description or "current" in result.changes_description.lower()
+
+    def test_migrate_version_downgrade(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        result = migrate_manifest(
+            manifest_path=manifest_path,
+            to_version=0,
+            no_ai=True,
+        )
+        assert "downgrade" in result.changes_description.lower() or result.warnings
+
+    def test_migrate_optimize(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        result = migrate_manifest(
+            manifest_path=manifest_path,
+            goal="Optimize manifest for production",
+            no_ai=True,
+        )
+        assert result.changes_description  # Should have some suggestions
+
+    def test_no_manifest_raises(self):
+        with pytest.raises(ValueError, match="(?i)must provide"):
+            migrate_manifest(to_provider="kubernetes", no_ai=True)
+
+    def test_nonexistent_manifest_raises(self, tmp_path):
+        with pytest.raises(Exception):
+            migrate_manifest(
+                manifest_path=tmp_path / "nonexistent.yaml",
+                to_provider="kubernetes",
+                no_ai=True,
+            )
+
+    def test_render_text(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        result = migrate_manifest(
+            manifest_path=manifest_path,
+            to_provider="kubernetes",
+            no_ai=True,
+        )
+        text = result.render_text()
+        assert "Migration" in text
+        assert "kubernetes" in text.lower()
+
+    def test_to_dict_serializable(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        result = migrate_manifest(
+            manifest_path=manifest_path,
+            to_provider="kubernetes",
+            no_ai=True,
+        )
+        d = result.to_dict()
+        serialized = json.dumps(d)
+        assert "overlay_yaml" in serialized
+
+    def test_cloud_migration_warnings(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        result = migrate_manifest(
+            manifest_path=manifest_path,
+            to_provider="kubernetes",
+            no_ai=True,
+        )
+        # Should warn about mount path changes
+        assert any("mount" in w.lower() or "image" in w.lower() for w in result.warnings)
diff --git a/tests/unit/test_ai_onboarding.py b/tests/unit/test_ai_onboarding.py
new file mode 100644
index 0000000..d48b752
--- /dev/null
+++ b/tests/unit/test_ai_onboarding.py
@@ -0,0 +1,87 @@
+"""Unit tests for scalable.ai.component_onboarding module."""
+
+from __future__ import annotations
+
+import pytest
+import yaml
+
+from scalable.ai.component_onboarding import OnboardingResult, onboard_component
+
+
+class TestOnboardComponent:
+    def test_nonexistent_directory_raises(self):
+        with pytest.raises(FileNotFoundError):
+            onboard_component("/nonexistent/path/xyz")
+
+    def test_basic_python_project(self, tmp_path):
+        (tmp_path / "pyproject.toml").write_text("[project]\nname='mymodel'")
+        (tmp_path / "main.py").write_text("print('hello')")
+        (tmp_path / "data").mkdir()
+
+        result = onboard_component(tmp_path, name="mymodel", no_ai=True)
+
+        assert isinstance(result, OnboardingResult)
+        assert result.name == "mymodel"
+        assert result.method == "heuristic"
+        assert "mymodel" in result.component_yaml
+        assert result.scan.languages  # Should detect Python
+
+    def test_default_name_from_directory(self, tmp_path):
+        model_dir = tmp_path / "My_Model"
+        model_dir.mkdir()
+        (model_dir / "run.py").write_text("pass")
+
+        result = onboard_component(model_dir, no_ai=True)
+        assert result.name == "my-model"
+
+    def test_cpp_project_higher_resources(self, tmp_path):
+        (tmp_path / "CMakeLists.txt").write_text("cmake_minimum_required(VERSION 3.10)")
+        (tmp_path / "src").mkdir()
+        (tmp_path / "src" / "main.cpp").write_text("int main() { return 0; }")
+        (tmp_path / "exe").mkdir()
+
+        result = onboard_component(tmp_path, name="gcam", no_ai=True)
+
+        # Should detect C++ and suggest higher resources
+        assert result.scan.estimated_cpus >= 4
+        assert "compiled" in result.scan.suggested_tags
+
+    def test_output_is_valid_yaml(self, tmp_path):
+        (tmp_path / "app.py").write_text("pass")
+        (tmp_path / "requirements.txt").write_text("numpy")
+
+        result = onboard_component(tmp_path, name="test", no_ai=True)
+
+        # The YAML portion should be parseable (after stripping comments)
+        lines = result.component_yaml.split("\n")
+        yaml_lines = [l for l in lines if not l.startswith("#")]
+        yaml_content = "\n".join(yaml_lines)
+        parsed = yaml.safe_load(yaml_content)
+        assert parsed is not None
+        assert "test" in parsed
+
+    def test_dockerfile_detected(self, tmp_path):
+        (tmp_path / "Dockerfile").write_text("FROM python:3.11")
+        (tmp_path / "app.py").write_text("pass")
+
+        result = onboard_component(tmp_path, name="test", no_ai=True)
+        assert "Dockerfile" in result.scan.container_files
+
+    def test_mounts_from_data_dirs(self, tmp_path):
+        (tmp_path / "data").mkdir()
+        (tmp_path / "output").mkdir()
+        (tmp_path / "main.py").write_text("pass")
+
+        result = onboard_component(tmp_path, name="test", no_ai=True)
+        assert result.scan.suggested_mounts  # Should suggest mounting data dirs
+
+    def test_warnings_on_low_confidence(self, tmp_path):
+        # Empty directory = low confidence
+        result = onboard_component(tmp_path, name="empty", no_ai=True)
+        assert any("confidence" in w.lower() or "Low" in w for w in result.warnings)
+
+    def test_to_dict_returns_component(self, tmp_path):
+        (tmp_path / "app.py").write_text("pass")
+        result = onboard_component(tmp_path, name="mycomp", no_ai=True)
+        d = result.to_dict()
+        assert isinstance(d, dict)
diff --git a/tests/unit/test_cli_phase4.py b/tests/unit/test_cli_phase4.py
new file mode 100644
index 0000000..ac805e4
--- /dev/null
+++ b/tests/unit/test_cli_phase4.py
@@ -0,0 +1,220 @@
+"""Unit tests for Phase 4 CLI commands."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from unittest.mock import patch
+
+import pytest
+import yaml
+
+from scalable.cli.main import main
+
+
+class TestCliInitComponent:
+    def test_init_component_basic(self, tmp_path, capsys):
+        model_dir = tmp_path / "mymodel"
+        model_dir.mkdir()
+        (model_dir / "main.py").write_text("print('hello')")
+        (model_dir / "requirements.txt").write_text("numpy")
+
+        exit_code = main(["init-component", str(model_dir), "--no-ai"])
+        assert exit_code == 0
+        captured = capsys.readouterr()
+        assert "mymodel" in captured.out
+
+    def test_init_component_with_name(self, tmp_path, capsys):
+        model_dir = tmp_path / "src"
+        model_dir.mkdir()
+        (model_dir / "app.py").write_text("pass")
+
+        exit_code = main(["init-component", str(model_dir), "--name", "custom-name", "--no-ai"])
+        assert exit_code == 0
+        captured = capsys.readouterr()
+        assert "custom-name" in captured.out
+
+    def test_init_component_output_file(self, tmp_path, capsys):
+        model_dir = tmp_path / "model"
+        model_dir.mkdir()
+        (model_dir / "run.py").write_text("pass")
+        output_file = tmp_path / "component.yaml"
+
+        exit_code = main([
+            "init-component", str(model_dir),
+            "--output", str(output_file),
+            "--no-ai",
+        ])
+        assert exit_code == 0
+        assert output_file.exists()
+
+    def test_init_component_nonexistent_dir(self, capsys):
+        exit_code = main(["init-component", "/nonexistent/path", "--no-ai"])
+        assert exit_code == 1
+
+
+class TestCliDiagnose:
+    def _create_run(self, tmp_path):
+        runs_dir = tmp_path / "runs"
+        runs_dir.mkdir()
+        run_dir = runs_dir / "run-20260519T120000Z-test-abc"
+        run_dir.mkdir()
+        (run_dir / "run.json").write_text(json.dumps({
+            "run_id": "run-20260519T120000Z-test-abc",
+            "project_name": "test",
+            "target_name": "local",
+            "provider_name": "local",
+            "manifest_lock": "abc123",
+            "status": "failed",
+        }))
+        (run_dir / "failures.jsonl").write_text(json.dumps({
+            "failure_class": "MemoryError",
+            "message": "out of memory",
+            "task_id": "t1",
+        }))
+        (run_dir / "tasks.jsonl").write_text(json.dumps({
+            "task_id": "t1",
+            "state": "failed",
+            "task_name": "run_gcam",
+        }))
+        return runs_dir
+
+    def test_diagnose_latest(self, tmp_path, capsys):
+        runs_dir = self._create_run(tmp_path)
+        exit_code = main([
+            "diagnose", "--runs-dir", str(runs_dir), "--latest", "--no-ai",
+        ])
+        assert exit_code == 0
+        captured = capsys.readouterr()
+        assert "oom" in captured.out.lower() or "Diagnosis" in captured.out
+
+    def test_diagnose_json_format(self, tmp_path, capsys):
+        runs_dir = self._create_run(tmp_path)
+        exit_code = main([
+            "diagnose", "--runs-dir", str(runs_dir),
+            "--latest", "--format", "json", "--no-ai",
+        ])
+        assert exit_code == 0
+        captured = capsys.readouterr()
+        data = json.loads(captured.out)
+        assert "classifications" in data
+
+    def test_diagnose_no_runs(self, tmp_path, capsys):
+        empty_dir = tmp_path / "empty_runs"
+        empty_dir.mkdir()
+        exit_code = main([
+            "diagnose", "--runs-dir", str(empty_dir), "--latest", "--no-ai",
+        ])
+        assert exit_code == 1
+
+
+class TestCliExplain:
+    def test_explain_plan(self, tmp_path, capsys):
+        plan = {
+            "version": 1,
+            "target": "local",
+            "provider": "local",
+            "manifest_lock": "abc123",
+            "task_to_component": {"run_model": "model"},
+            "scale_plan": {
+                "workers_by_tag": {"model": 1},
+                "resources_by_tag": {"model": {"cpus": 2, "memory": "8G", "walltime": None, "gpus": None}},
+            },
+        }
+        plan_file = tmp_path / "plan.json"
+        plan_file.write_text(json.dumps(plan))
+
+        exit_code = main(["explain", str(plan_file), "--no-ai"])
+        assert exit_code == 0
+        captured = capsys.readouterr()
+        assert "Plan Explanation" in captured.out
+
+    def test_explain_missing_file(self, tmp_path, capsys):
+        exit_code = main(["explain", str(tmp_path / "no.json"), "--no-ai"])
+        assert exit_code == 1
+
+    def test_explain_json_format(self, tmp_path, capsys):
+        plan = {
+            "version": 1, "target": "local", "provider": "local",
+            "manifest_lock": "x", "task_to_component": {},
+            "scale_plan": {"workers_by_tag": {}, "resources_by_tag": {}},
+        }
+        plan_file = tmp_path / "plan.json"
+        plan_file.write_text(json.dumps(plan))
+
+        exit_code = main(["explain", str(plan_file), "--format", "json", "--no-ai"])
+        assert exit_code == 0
+        captured = capsys.readouterr()
+        data = json.loads(captured.out)
+        assert "narrative" in data
+
+
+class TestCliCompose:
+    def test_compose_known_model(self, capsys):
+        exit_code = main(["compose", "Run GCAM reference scenario", "--no-ai"])
+        assert exit_code == 0
+        captured = capsys.readouterr()
+        assert "workflow.py" in captured.out or "gcam" in captured.out.lower()
+
+    def test_compose_with_output_dir(self, tmp_path, capsys):
+        output_dir = tmp_path / "generated"
+        exit_code = main([
+            "compose", "Run GCAM and Stitches",
+            "--output-dir", str(output_dir),
+            "--no-ai",
+        ])
+        assert exit_code == 0
+        assert (output_dir / "workflow.py").exists()
+
+    def test_compose_json_format(self, capsys):
+        exit_code = main([
+            "compose", "Run Hector model",
+            "--format", "json", "--no-ai",
+        ])
+        assert exit_code == 0
+        captured = capsys.readouterr()
+        data = json.loads(captured.out)
+        assert "files" in data
+
+
+class TestCliMigrate:
+    def test_migrate_to_kubernetes(self, tmp_path, capsys):
+        manifest = {
+            "version": 1,
+            "project": {"name": "test"},
+            "targets": {"local": {"provider": "local"}},
+            "components": {"comp": {"cpus": 2, "memory": "8G"}},
+            "tasks": {"t1": {"component": "comp"}},
+        }
+        manifest_path = tmp_path / "scalable.yaml"
+        manifest_path.write_text(yaml.dump(manifest))
+
+        exit_code = main([
+            "migrate", str(manifest_path),
+            "--to-provider", "kubernetes",
+            "--no-ai",
+        ])
+        assert exit_code == 0
+        captured = capsys.readouterr()
+        assert "kubernetes" in captured.out.lower()
+
+    def test_migrate_no_goal_errors(self, tmp_path, capsys):
+        manifest = {
+            "version": 1,
+            "project": {"name": "test"},
+            "targets": {"local": {"provider": "local"}},
+            "components": {},
+            "tasks": {},
+        }
+        manifest_path = tmp_path / "scalable.yaml"
+        manifest_path.write_text(yaml.dump(manifest))
+
+        exit_code = main(["migrate", str(manifest_path), "--no-ai"])
+        assert exit_code == 1
+
+    def test_migrate_nonexistent_manifest(self, tmp_path, capsys):
+        exit_code = main([
+            "migrate", str(tmp_path / "no.yaml"),
+            "--to-provider", "aws", "--no-ai",
+        ])
+        assert exit_code == 1
diff --git a/tests/unit/test_cli_validate.py b/tests/unit/test_cli_validate.py
index 2d54339..0fb0de1 100644
--- a/tests/unit/test_cli_validate.py
+++ b/tests/unit/test_cli_validate.py
@@ -72,10 +72,10 @@ def test_cli_validate_schema_error_returns_nonzero(tmp_path: Path, capsys) -> No
     assert payload["errors"][0]["code"] == "E_MANIFEST"
 
 
-def test_cli_stub_command_returns_pointer_message(capsys) -> None:
-    code = main(["diagnose"])
+def test_cli_diagnose_no_longer_stub(capsys) -> None:
+    """Phase 4 implemented diagnose; it no longer returns stub exit code 2."""
+    code = main(["diagnose", "--runs-dir", "/nonexistent", "--latest", "--no-ai"])
 
-    captured = capsys.readouterr()
-    assert code == 2
-    assert "planned for Phase 4" in captured.err
+    # diagnose is now implemented; returns 1 on error (no runs found), not 2
+    assert code == 1
 
diff --git a/tests/unit/test_session.py b/tests/unit/test_session.py
index 9d8dd84..91d3877 100644
--- a/tests/unit/test_session.py
+++ b/tests/unit/test_session.py
@@ -84,16 +84,25 @@ def test_validate_ok_for_local_manifest(tmp_path: Path, monkeypatch) -> None:
     assert report.ok is True
 
 
-def test_plan_raises_not_implemented_for_objective_policy(tmp_path: Path) -> None:
+def test_plan_with_objective_policy_now_functional(tmp_path: Path) -> None:
+    """Phase 4 implemented objective/policy planning; supported values no longer raise."""
     manifest_path = tmp_path / "scalable.yaml"
     _write_manifest(manifest_path)
     session = ScalableSession.from_yaml(manifest_path, target="local")
 
+    # Supported objectives/policies now work
+    plan = session.plan(objective="minimize cost")
+    assert plan is not None
+
+    plan = session.plan(policy="safe")
+    assert plan is not None
+
+    # Unsupported values still raise NotImplementedError
     with pytest.raises(NotImplementedError):
-        session.plan(objective="minimize cost")
+        session.plan(objective="do_magic_unsupported")
 
     with pytest.raises(NotImplementedError):
-        session.plan(policy="safe")
+        session.plan(policy="unsupported_policy")
 
 
 def test_plan_returns_dry_run_plan(tmp_path: Path) -> None:
diff --git a/tests/unit/test_session_plan_objectives.py b/tests/unit/test_session_plan_objectives.py
new file mode 100644
index 0000000..b7ca605
--- /dev/null
+++ b/tests/unit/test_session_plan_objectives.py
@@ -0,0 +1,96 @@
+"""Unit tests for ScalableSession.plan(objective=, policy=) Phase 4 implementation."""
+
+from __future__ import annotations
+
+import pytest
+import yaml
+
+from scalable.session.session import ScalableSession
+
+
+def _make_manifest(tmp_path):
+    """Create a minimal test manifest for session testing."""
+    manifest_content = {
+        "version": 1,
+        "project": {"name": "test-project"},
+        "targets": {
+            "local": {"provider": "local", "max_workers": 4},
+        },
+        "components": {
+            "model_a": {"cpus": 4, "memory": "16G", "tags": ["compute"]},
+            "model_b": {"cpus": 2, "memory": "8G", "tags": ["io"]},
+        },
+        "tasks": {
+            "run_a": {"component": "model_a", "cache": True},
+            "run_b": {"component": "model_b"},
+        },
+    }
+    manifest_path = tmp_path / "scalable.yaml"
+    manifest_path.write_text(yaml.dump(manifest_content))
+    return manifest_path
+
+
+class TestSessionPlanObjectives:
+    def test_plan_no_objective_works(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        session = ScalableSession.from_yaml(manifest_path, target="local")
+        plan = session.plan(dry_run=True)
+        assert plan.target_name == "local"
+        assert plan.scale_plan.workers_by_tag
+
+    def test_plan_minimize_cost_safe(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        session = ScalableSession.from_yaml(manifest_path, target="local")
+        plan = session.plan(dry_run=True, objective="minimize cost", policy="safe")
+        # Workers should be conservative
+        for tag, count in plan.scale_plan.workers_by_tag.items():
+            assert count >= 1
+
+    def test_plan_minimize_time_aggressive(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        session = ScalableSession.from_yaml(manifest_path, target="local")
+        plan = session.plan(dry_run=True, objective="minimize time", policy="aggressive")
+        # Should scale up workers
+        base_plan = session.plan(dry_run=True)
+        for tag in plan.scale_plan.workers_by_tag:
+            assert plan.scale_plan.workers_by_tag[tag] >= base_plan.scale_plan.workers_by_tag[tag]
+
+    def test_plan_balance_default(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        session = ScalableSession.from_yaml(manifest_path, target="local")
+        plan = session.plan(dry_run=True, objective="balance")
+        assert plan.target_name == "local"
+
+    def test_plan_manual_policy(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        session = ScalableSession.from_yaml(manifest_path, target="local")
+        base_plan = session.plan(dry_run=True)
+        manual_plan = session.plan(dry_run=True, objective="minimize cost", policy="manual")
+        # Manual policy should match base plan exactly
+        assert manual_plan.scale_plan.workers_by_tag == base_plan.scale_plan.workers_by_tag
+
+    def test_unsupported_objective_raises(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        session = ScalableSession.from_yaml(manifest_path, target="local")
+        with pytest.raises(NotImplementedError, match="Unsupported objective"):
+            session.plan(dry_run=True, objective="do magic")
+
+    def test_unsupported_policy_raises(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        session = ScalableSession.from_yaml(manifest_path, target="local")
+        with pytest.raises(NotImplementedError, match="Unsupported policy"):
+            session.plan(dry_run=True, objective="balance", policy="yolo")
+
+    def test_objective_only_uses_safe_default(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        session = ScalableSession.from_yaml(manifest_path, target="local")
+        # Should not raise - uses "safe" default policy
+        plan = session.plan(dry_run=True, objective="minimize cost")
+        assert plan is not None
+
+    def test_policy_only_uses_balance_default(self, tmp_path):
+        manifest_path = _make_manifest(tmp_path)
+        session = ScalableSession.from_yaml(manifest_path, target="local")
+        # Should not raise - uses "balance" default objective
+        plan = session.plan(dry_run=True, policy="safe")
+        assert plan is not None

From 78b12cbfaadf2f03736e9bfcbc5cc472f5bb7e9f Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Tue, 19 May 2026 23:54:50 +0000
Subject: [PATCH 19/47] Initial plan


From 6e18a737d526c5b80f953ccb6bd2bc03b12e21ec Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Tue, 19 May 2026 23:59:12 +0000
Subject: [PATCH 20/47] Fix lint violations in session and AI planning tests

Co-authored-by: crvernon <3947069+crvernon@users.noreply.github.com>
---
 scalable/session/session.py                | 2 --
 tests/unit/test_ai_explain.py              | 1 -
 tests/unit/test_ai_onboarding.py           | 2 +-
 tests/unit/test_session_plan_objectives.py | 2 +-
 4 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/scalable/session/session.py b/scalable/session/session.py
index 1c59bc6..157dbbd 100644
--- a/scalable/session/session.py
+++ b/scalable/session/session.py
@@ -272,8 +272,6 @@ def _apply_objective_policy(
     Phase 4 implementation uses heuristic rules. Phase 5 will add
     ML-backed optimizations using the same API surface.
     """
-    from dataclasses import replace as dc_replace
-
     from scalable.providers.base import ResourceRequest, ScalePlan
 
     effective_objective = (objective or "balance").lower().strip()
diff --git a/tests/unit/test_ai_explain.py b/tests/unit/test_ai_explain.py
index 6ce1357..df638dc 100644
--- a/tests/unit/test_ai_explain.py
+++ b/tests/unit/test_ai_explain.py
@@ -8,7 +8,6 @@
 
 from scalable.ai.plan_explain import ExplanationResult, explain_plan
 
-
 SAMPLE_PLAN = {
     "version": 1,
     "target": "local",
diff --git a/tests/unit/test_ai_onboarding.py b/tests/unit/test_ai_onboarding.py
index d48b752..ee6f154 100644
--- a/tests/unit/test_ai_onboarding.py
+++ b/tests/unit/test_ai_onboarding.py
@@ -54,7 +54,7 @@ def test_output_is_valid_yaml(self, tmp_path):
 
         # The YAML portion should be parseable (after stripping comments)
         lines = result.component_yaml.split("\n")
-        yaml_lines = [l for l in lines if not l.startswith("#")]
+        yaml_lines = [line for line in lines if not line.startswith("#")]
         yaml_content = "\n".join(yaml_lines)
         parsed = yaml.safe_load(yaml_content)
         assert parsed is not None
diff --git a/tests/unit/test_session_plan_objectives.py b/tests/unit/test_session_plan_objectives.py
index b7ca605..26e6bc6 100644
--- a/tests/unit/test_session_plan_objectives.py
+++ b/tests/unit/test_session_plan_objectives.py
@@ -43,7 +43,7 @@ def test_plan_minimize_cost_safe(self, tmp_path):
         session = ScalableSession.from_yaml(manifest_path, target="local")
         plan = session.plan(dry_run=True, objective="minimize cost", policy="safe")
         # Workers should be conservative
-        for tag, count in plan.scale_plan.workers_by_tag.items():
+        for _tag, count in plan.scale_plan.workers_by_tag.items():
             assert count >= 1
 
     def test_plan_minimize_time_aggressive(self, tmp_path):

From cc283451ee43073fec7232d6385d20b6970e395d Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 20 May 2026 00:00:49 +0000
Subject: [PATCH 21/47] Add explicit GitHub Actions token permissions in tests
 workflow

Agent-Logs-Url: https://github.com/JGCRI/scalable/sessions/b7e62493-29e0-4a5f-9bdb-28a778012e68

Co-authored-by: crvernon <3947069+crvernon@users.noreply.github.com>
---
 .github/workflows/tests.yml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index eea8b15..07d9d9e 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -11,6 +11,9 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
 
+permissions:
+  contents: read
+
 jobs:
   unit:
     name: pytest (py${{ matrix.python-version }} on ${{ matrix.os }})

From 3dc3683ea9acddde166533046903877c7255ee4e Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 20 May 2026 00:09:02 +0000
Subject: [PATCH 22/47] Rollback branch content to commit 1460fff

Agent-Logs-Url: https://github.com/JGCRI/scalable/sessions/fe9e5b5a-f73f-4999-8e77-194af9b7b931

Co-authored-by: crvernon <3947069+crvernon@users.noreply.github.com>
---
 .github/workflows/tests.yml                | 3 ---
 scalable/session/session.py                | 2 ++
 tests/unit/test_ai_explain.py              | 1 +
 tests/unit/test_ai_onboarding.py           | 2 +-
 tests/unit/test_session_plan_objectives.py | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 07d9d9e..eea8b15 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -11,9 +11,6 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
 
-permissions:
-  contents: read
-
 jobs:
   unit:
     name: pytest (py${{ matrix.python-version }} on ${{ matrix.os }})
diff --git a/scalable/session/session.py b/scalable/session/session.py
index 157dbbd..1c59bc6 100644
--- a/scalable/session/session.py
+++ b/scalable/session/session.py
@@ -272,6 +272,8 @@ def _apply_objective_policy(
     Phase 4 implementation uses heuristic rules. Phase 5 will add
     ML-backed optimizations using the same API surface.
     """
+    from dataclasses import replace as dc_replace
+
     from scalable.providers.base import ResourceRequest, ScalePlan
 
     effective_objective = (objective or "balance").lower().strip()
diff --git a/tests/unit/test_ai_explain.py b/tests/unit/test_ai_explain.py
index df638dc..6ce1357 100644
--- a/tests/unit/test_ai_explain.py
+++ b/tests/unit/test_ai_explain.py
@@ -8,6 +8,7 @@
 
 from scalable.ai.plan_explain import ExplanationResult, explain_plan
 
+
 SAMPLE_PLAN = {
     "version": 1,
     "target": "local",
diff --git a/tests/unit/test_ai_onboarding.py b/tests/unit/test_ai_onboarding.py
index ee6f154..d48b752 100644
--- a/tests/unit/test_ai_onboarding.py
+++ b/tests/unit/test_ai_onboarding.py
@@ -54,7 +54,7 @@ def test_output_is_valid_yaml(self, tmp_path):
 
         # The YAML portion should be parseable (after stripping comments)
         lines = result.component_yaml.split("\n")
-        yaml_lines = [line for line in lines if not line.startswith("#")]
+        yaml_lines = [l for l in lines if not l.startswith("#")]
         yaml_content = "\n".join(yaml_lines)
         parsed = yaml.safe_load(yaml_content)
         assert parsed is not None
diff --git a/tests/unit/test_session_plan_objectives.py b/tests/unit/test_session_plan_objectives.py
index 26e6bc6..b7ca605 100644
--- a/tests/unit/test_session_plan_objectives.py
+++ b/tests/unit/test_session_plan_objectives.py
@@ -43,7 +43,7 @@ def test_plan_minimize_cost_safe(self, tmp_path):
         session = ScalableSession.from_yaml(manifest_path, target="local")
         plan = session.plan(dry_run=True, objective="minimize cost", policy="safe")
         # Workers should be conservative
-        for _tag, count in plan.scale_plan.workers_by_tag.items():
+        for tag, count in plan.scale_plan.workers_by_tag.items():
             assert count >= 1
 
     def test_plan_minimize_time_aggressive(self, tmp_path):

From 3b4bd099c5c179e06f41da3b9249eb52a62c5d8d Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 20:17:11 -0400
Subject: [PATCH 23/47] ruff fixes

---
 scalable/session/session.py                | 2 --
 tests/unit/test_ai_explain.py              | 1 -
 tests/unit/test_ai_onboarding.py           | 2 +-
 tests/unit/test_session_plan_objectives.py | 2 +-
 4 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/scalable/session/session.py b/scalable/session/session.py
index 1c59bc6..157dbbd 100644
--- a/scalable/session/session.py
+++ b/scalable/session/session.py
@@ -272,8 +272,6 @@ def _apply_objective_policy(
     Phase 4 implementation uses heuristic rules. Phase 5 will add
     ML-backed optimizations using the same API surface.
     """
-    from dataclasses import replace as dc_replace
-
     from scalable.providers.base import ResourceRequest, ScalePlan
 
     effective_objective = (objective or "balance").lower().strip()
diff --git a/tests/unit/test_ai_explain.py b/tests/unit/test_ai_explain.py
index 6ce1357..df638dc 100644
--- a/tests/unit/test_ai_explain.py
+++ b/tests/unit/test_ai_explain.py
@@ -8,7 +8,6 @@
 
 from scalable.ai.plan_explain import ExplanationResult, explain_plan
 
-
 SAMPLE_PLAN = {
     "version": 1,
     "target": "local",
diff --git a/tests/unit/test_ai_onboarding.py b/tests/unit/test_ai_onboarding.py
index d48b752..ee6f154 100644
--- a/tests/unit/test_ai_onboarding.py
+++ b/tests/unit/test_ai_onboarding.py
@@ -54,7 +54,7 @@ def test_output_is_valid_yaml(self, tmp_path):
 
         # The YAML portion should be parseable (after stripping comments)
         lines = result.component_yaml.split("\n")
-        yaml_lines = [l for l in lines if not l.startswith("#")]
+        yaml_lines = [line for line in lines if not line.startswith("#")]
         yaml_content = "\n".join(yaml_lines)
         parsed = yaml.safe_load(yaml_content)
         assert parsed is not None
diff --git a/tests/unit/test_session_plan_objectives.py b/tests/unit/test_session_plan_objectives.py
index b7ca605..26e6bc6 100644
--- a/tests/unit/test_session_plan_objectives.py
+++ b/tests/unit/test_session_plan_objectives.py
@@ -43,7 +43,7 @@ def test_plan_minimize_cost_safe(self, tmp_path):
         session = ScalableSession.from_yaml(manifest_path, target="local")
         plan = session.plan(dry_run=True, objective="minimize cost", policy="safe")
         # Workers should be conservative
-        for tag, count in plan.scale_plan.workers_by_tag.items():
+        for _tag, count in plan.scale_plan.workers_by_tag.items():
             assert count >= 1
 
     def test_plan_minimize_time_aggressive(self, tmp_path):

From d911ef85b6f08b6164a19016d78ab00f56f8349f Mon Sep 17 00:00:00 2001
From: Chris Vernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 20:21:17 -0400
Subject: [PATCH 24/47] ruff fixes

---
 scalable/session/session.py                | 2 --
 tests/unit/test_ai_explain.py              | 1 -
 tests/unit/test_ai_onboarding.py           | 2 +-
 tests/unit/test_session_plan_objectives.py | 2 +-
 4 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/scalable/session/session.py b/scalable/session/session.py
index 1c59bc6..157dbbd 100644
--- a/scalable/session/session.py
+++ b/scalable/session/session.py
@@ -272,8 +272,6 @@ def _apply_objective_policy(
     Phase 4 implementation uses heuristic rules. Phase 5 will add
     ML-backed optimizations using the same API surface.
     """
-    from dataclasses import replace as dc_replace
-
     from scalable.providers.base import ResourceRequest, ScalePlan
 
     effective_objective = (objective or "balance").lower().strip()
diff --git a/tests/unit/test_ai_explain.py b/tests/unit/test_ai_explain.py
index 6ce1357..df638dc 100644
--- a/tests/unit/test_ai_explain.py
+++ b/tests/unit/test_ai_explain.py
@@ -8,7 +8,6 @@
 
 from scalable.ai.plan_explain import ExplanationResult, explain_plan
 
-
 SAMPLE_PLAN = {
     "version": 1,
     "target": "local",
diff --git a/tests/unit/test_ai_onboarding.py b/tests/unit/test_ai_onboarding.py
index d48b752..ee6f154 100644
--- a/tests/unit/test_ai_onboarding.py
+++ b/tests/unit/test_ai_onboarding.py
@@ -54,7 +54,7 @@ def test_output_is_valid_yaml(self, tmp_path):
 
         # The YAML portion should be parseable (after stripping comments)
         lines = result.component_yaml.split("\n")
-        yaml_lines = [l for l in lines if not l.startswith("#")]
+        yaml_lines = [line for line in lines if not line.startswith("#")]
         yaml_content = "\n".join(yaml_lines)
         parsed = yaml.safe_load(yaml_content)
         assert parsed is not None
diff --git a/tests/unit/test_session_plan_objectives.py b/tests/unit/test_session_plan_objectives.py
index b7ca605..26e6bc6 100644
--- a/tests/unit/test_session_plan_objectives.py
+++ b/tests/unit/test_session_plan_objectives.py
@@ -43,7 +43,7 @@ def test_plan_minimize_cost_safe(self, tmp_path):
         session = ScalableSession.from_yaml(manifest_path, target="local")
         plan = session.plan(dry_run=True, objective="minimize cost", policy="safe")
         # Workers should be conservative
-        for tag, count in plan.scale_plan.workers_by_tag.items():
+        for _tag, count in plan.scale_plan.workers_by_tag.items():
             assert count >= 1
 
     def test_plan_minimize_time_aggressive(self, tmp_path):

From 60dfd3964b5e4a6fd2a1b3d7216a9d9f985dd29f Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 20:28:53 -0400
Subject: [PATCH 25/47] Add Phase 5 implementation plan

---
 plans/v2.0.0_phase5_plan.md | 904 ++++++++++++++++++++++++++++++++++++
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 create mode 100644 plans/v2.0.0_phase5_plan.md

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+# Scalable v2.0.0 — Phase 5 Implementation Plan
+
+> **Phase 5: ML Optimization and Emulation**
+> Source plan: [`plans/v2.0.0_development_phases.md`](plans/v2.0.0_development_phases.md)
+> Prior merged phases: Phase 1 (PR #20), Phase 2 (PR #21), Phase 3 (PR #22), Phase 4 (PR #23)
+> Integration branch: `version/2.0.0`
+> Phase 5 feature branch: `version/2.0.0-phase5-ml-emulation` (off `version/2.0.0`)
+
+---
+
+## 1. Cross-phase context and dependency analysis
+
+### 1.1 What Phases 1–4 established
+
+Phase 5 is the capstone phase, consuming the full infrastructure delivered across the prior four phases:
+
+| Phase | Key artifacts consumed by Phase 5 |
+|-------|-----------------------------------|
+| **Phase 1** | `DeploymentProvider` protocol, `ScalePlan`, `ResourceRequest`, `ScalableSession.plan(objective=, policy=)` reserved kwargs, provider registry, manifest schema |
+| **Phase 2** | Telemetry run store (`.scalable/runs/`), `TaskEvent`/`ResourceEvent`/`FailureEvent` schemas, `ResourceAdvisor` with quantile heuristics, `ResourceRecommendation` payload |
+| **Phase 3** | `CostEstimate`, cost telemetry (`cost.jsonl`), `ArtifactStore` protocol, remote cache backend, overlay system |
+| **Phase 4** | `AIBackend` protocol, failure taxonomy in `log_diagnosis.py`, heuristic `_apply_objective_policy`, `ScalableSession.plan(objective=, policy=)` heuristic implementation, prompt template system |
+
+### 1.2 What Phase 5 delivers (final phase)
+
+Phase 5 is the terminal phase of the v2.0.0 roadmap. It replaces heuristic systems with learned ones and adds scientific model emulation as a first-class capability:
+
+1. **Learned resource prediction** — replaces quantile heuristics with feature-based ML models
+2. **Adaptive scaling policy** — real-time scaling decisions informed by ML predictions
+3. **Active-learning scenario selection** — intelligent selection of which full-model runs maximize information gain
+4. **Emulator registry** — declarative registration and lifecycle management of surrogate models
+5. **Uncertainty-aware full-model fallback** — confidence-gated emulator usage with automatic fallback
+6. **Distributed hyperparameter tuning** — Dask-ML integration for efficient model selection
+
+### 1.3 Design principles
+
+From the master plan's design principles:
+
+- **AI proposes; Scalable disposes.** ML models produce predictions and recommendations that are validated by deterministic policy before execution.
+- **Every plan is inspectable.** ML-based plans include feature importances, confidence intervals, and training provenance.
+- **Manual overrides always win.** Users can pin resources, ignore ML recommendations, or disable emulators entirely.
+- **Emulators are opt-in and uncertainty-aware.** They accelerate exploration but never silently replace validated model runs.
+- **Offline-compatible by default.** ML models are trained/cached locally; no remote inference required.
+
+### 1.4 Phase 5 out-of-scope boundaries
+
+- No new deployment providers (Phase 3 delivered the full provider set)
+- No new CLI commands beyond `scalable advise` (existing `scalable report` and `scalable explain` are extended)
+- No breaking changes to Phase 1–4 APIs
+- No mandatory external ML service dependencies
+
+---
+
+## 2. Phase 5 scope, objectives, and success criteria
+
+### 2.1 Scope (from master plan §Phase 5)
+
+Deliverables:
+- Learned resource prediction
+- Adaptive scaling policy
+- Active-learning scenario selection
+- Emulator registry
+- Uncertainty-aware full-model fallback
+- Distributed hyperparameter tuning
+
+### 2.2 Objectives
+
+1. **Replace heuristic `ResourceAdvisor.recommend()` with ML-backed predictions** that use task features (input size, scenario count, component version, etc.) to predict runtime, memory, and cost with calibrated uncertainty.
+2. **Implement adaptive scaling policies** that adjust worker counts in real-time based on task queue depth, predicted completion times, and resource utilization.
+3. **Add an active-learning framework** for scientific ensemble workflows that selects the next most informative full-model runs to maximize emulator training efficiency.
+4. **Introduce an emulator registry** where trained surrogate models are declared, versioned, and bound to components with clear domain boundaries.
+5. **Implement uncertainty-aware emulator dispatch** with automatic full-model fallback when emulator confidence is below threshold.
+6. **Integrate Dask-ML for distributed hyperparameter tuning** across the cluster for emulator training and resource model fitting.
+
+### 2.3 Success criteria
+
+- [ ] `LearnedAdvisor.from_history(path)` trains a resource prediction model from telemetry and produces recommendations with calibrated confidence intervals.
+- [ ] `LearnedAdvisor.recommend(task, input_features, ...)` returns predictions that outperform quantile heuristics on held-out telemetry data (measurable via cross-validation).
+- [ ] `AdaptiveScaler` monitors task queue and adjusts worker counts according to a learned or rule-based policy, respecting user-defined bounds.
+- [ ] `@scalable.emulatable(...)` decorator registers a function as emulation-capable with declared inputs, outputs, uncertainty requirements, and fallback behavior.
+- [ ] `EmulatorRegistry` manages trained emulators with versioning, domain validation, and confidence thresholds.
+- [ ] Emulator dispatch transparently serves predictions when confidence is high and falls back to the full model otherwise, recording which path was used.
+- [ ] `ActiveLearner` selects next-best scenarios from a candidate pool by maximizing expected information gain for emulator training.
+- [ ] `HyperparameterSearch` wraps Dask-ML search strategies for distributed model tuning within a session.
+- [ ] `scalable advise` CLI command provides ML-backed resource recommendations.
+- [ ] All ML features degrade gracefully to Phase 2/4 heuristics when training data is insufficient or `scalable[ml]` is not installed.
+- [ ] Unit tests cover all new modules; integration test demonstrates emulator train/predict/fallback cycle.
+- [ ] `CHANGELOG.md` updated for `2.0.0a5`.
+
+---
+
+## 3. Technical architecture
+
+### 3.1 New package layout
+
+```
+scalable/
+  ml/                                # NEW — ML optimization subsystem
+    __init__.py                      # public exports
+    learned_advisor.py               # ML-based resource prediction (replaces heuristic)
+    adaptive_scaler.py               # Real-time adaptive scaling policy
+    features.py                      # Feature extraction from telemetry + task args
+    models.py                        # Model wrappers (sklearn, gradient boosting)
+    validation.py                    # Cross-validation and model quality checks
+    tuning.py                        # Dask-ML hyperparameter search integration
+
+  emulation/                         # NEW — Model emulation subsystem
+    __init__.py                      # public exports
+    registry.py                      # EmulatorRegistry for trained surrogates
+    decorator.py                     # @emulatable decorator
+    dispatch.py                      # Uncertainty-aware routing (emulator vs full model)
+    active_learning.py               # Active scenario selection
+    surrogate.py                     # Surrogate model abstractions (GP, RF, NN)
+    uncertainty.py                   # Calibration and confidence estimation
+
+  cli/
+    cmd_advise.py                    # NEW — ML-backed resource advice CLI
+
+  advising/
+    resources.py                     # MODIFIED — add LearnedAdvisor integration hook
+```
+
+### 3.2 Learned resource advisor (`ml/learned_advisor.py`)
+
+```python
+class LearnedAdvisor:
+    """ML-backed resource advisor using telemetry history as training data."""
+
+    @classmethod
+    def from_history(
+        cls,
+        runs_dir: str | Path,
+        *,
+        model_type: str = "gradient_boosting",  # or "quantile_regression", "random_forest"
+        retrain: bool = False,
+        cache_dir: str | Path | None = None,
+    ) -> LearnedAdvisor: ...
+
+    def recommend(
+        self,
+        *,
+        task: str,
+        input_features: dict[str, Any] | None = None,
+        target: str | None = None,
+        confidence: float = 0.95,
+    ) -> ResourceRecommendation: ...
+
+    def explain(self, recommendation: ResourceRecommendation) -> dict[str, Any]:
+        """Return feature importances and prediction intervals."""
+        ...
+
+    def evaluate(self, *, test_fraction: float = 0.2) -> ModelQuality:
+        """Cross-validate the model and return quality metrics."""
+        ...
+```
+
+The `LearnedAdvisor` shares the same `ResourceRecommendation` return type as the Phase 2 `ResourceAdvisor`, ensuring backward compatibility. It adds:
+- Feature extraction from task arguments (input size, scenario count, spatial resolution)
+- Calibrated prediction intervals (not just point estimates)
+- Feature importance explanations
+- Model quality self-assessment
+
+Model selection strategy:
+1. **Gradient boosting (default)** — robust for tabular telemetry features
+2. **Quantile regression** — for uncertainty-calibrated interval predictions
+3. **Random forest** — interpretable alternative
+4. **Survival models** — for queue wait and walltime prediction (stretch goal)
+
+### 3.3 Feature extraction (`ml/features.py`)
+
+```python
+class FeatureExtractor:
+    """Extract ML features from telemetry records and task arguments."""
+
+    def extract_from_history(self, records: pd.DataFrame) -> pd.DataFrame:
+        """Engineer features from historical telemetry records."""
+        ...
+
+    def extract_from_task(
+        self,
+        task_name: str,
+        input_features: dict[str, Any] | None,
+        component: str | None,
+        target: str | None,
+    ) -> pd.DataFrame:
+        """Build feature vector for a new prediction request."""
+        ...
+```
+
+Engineered features:
+- Task identity features (component name, tag hash)
+- Resource request features (requested cpus/memory/walltime)
+- Temporal features (time of day, day of week for queue prediction)
+- Input complexity features (from `input_features` dict)
+- Historical aggregates (rolling mean/p95 for same task)
+- Provider/target features (one-hot encoded)
+
+### 3.4 Adaptive scaling policy (`ml/adaptive_scaler.py`)
+
+```python
+class AdaptiveScaler:
+    """Real-time adaptive worker scaling based on ML predictions."""
+
+    def __init__(
+        self,
+        *,
+        advisor: LearnedAdvisor | ResourceAdvisor,
+        min_workers: dict[str, int] | None = None,
+        max_workers: dict[str, int] | None = None,
+        scale_up_threshold: float = 0.8,    # queue depth ratio
+        scale_down_threshold: float = 0.2,
+        cooldown_seconds: float = 60.0,
+    ) -> None: ...
+
+    def evaluate(
+        self,
+        *,
+        pending_tasks: list[dict[str, Any]],
+        active_workers: dict[str, int],
+        recent_completions: list[dict[str, Any]],
+    ) -> ScaleDecision: ...
+
+@dataclass(frozen=True)
+class ScaleDecision:
+    """A scaling recommendation with reasoning."""
+    workers_to_add: dict[str, int]
+    workers_to_remove: dict[str, int]
+    reasoning: str
+    confidence: float
+    predicted_completion_time: float | None
+```
+
+Adaptive scaling workflow:
+1. Monitor task queue depth per component/tag
+2. Predict remaining runtime for active tasks using `LearnedAdvisor`
+3. Calculate if adding/removing workers improves predicted throughput
+4. Respect min/max bounds and cooldown periods
+5. Emit scaling decision with explanation
+
+### 3.5 Emulator registry and decorator (`emulation/`)
+
+```python
+# emulation/decorator.py
+def emulatable(
+    *,
+    tag: str,
+    inputs: list[str],
+    outputs: list[str],
+    uncertainty: str = "required",    # "required" | "optional" | "none"
+    fallback: str = "full_model",     # "full_model" | "error" | "cached"
+    domain: dict[str, Any] | None = None,  # input domain bounds
+    confidence_threshold: float = 0.9,
+):
+    """Decorator marking a function as emulation-capable."""
+    ...
+
+# emulation/registry.py
+class EmulatorRegistry:
+    """Manages trained emulator models with versioning and domain validation."""
+
+    def register(
+        self,
+        name: str,
+        emulator: TrainedEmulator,
+        *,
+        version: str | None = None,
+        domain: dict[str, Any] | None = None,
+    ) -> str: ...
+
+    def get(self, name: str, *, version: str | None = None) -> TrainedEmulator: ...
+    def list(self) -> list[EmulatorInfo]: ...
+    def validate_domain(self, name: str, inputs: dict[str, Any]) -> bool: ...
+
+# emulation/surrogate.py
+class TrainedEmulator(Protocol):
+    """Protocol for trained surrogate models."""
+    def predict(self, inputs: dict[str, Any]) -> EmulatorPrediction: ...
+    def uncertainty(self, inputs: dict[str, Any]) -> float: ...
+    @property
+    def metadata(self) -> EmulatorMetadata: ...
+
+@dataclass(frozen=True)
+class EmulatorPrediction:
+    """Prediction result with uncertainty information."""
+    outputs: dict[str, Any]
+    confidence: float
+    uncertainty_bounds: dict[str, tuple[float, float]] | None
+    is_emulated: bool = True
+
+@dataclass(frozen=True)
+class EmulatorMetadata:
+    """Provenance and quality metadata for a trained emulator."""
+    name: str
+    version: str
+    training_runs: list[str]
+    training_samples: int
+    validation_score: float
+    domain_bounds: dict[str, tuple[float, float]]
+    created_at: str
+    model_type: str
+```
+
+### 3.6 Uncertainty-aware dispatch (`emulation/dispatch.py`)
+
+```python
+class EmulatorDispatch:
+    """Routes function calls to emulator or full model based on confidence."""
+
+    def __init__(
+        self,
+        registry: EmulatorRegistry,
+        *,
+        confidence_threshold: float = 0.9,
+        record_provenance: bool = True,
+    ) -> None: ...
+
+    def execute(
+        self,
+        func: Callable,
+        *args,
+        emulator_name: str | None = None,
+        force_full_model: bool = False,
+        **kwargs,
+    ) -> EmulatorDispatchResult: ...
+
+@dataclass(frozen=True)
+class EmulatorDispatchResult:
+    """Result of an emulator-dispatched call with provenance."""
+    result: Any
+    source: str          # "emulator" | "full_model" | "cached"
+    confidence: float | None
+    emulator_version: str | None
+    fallback_reason: str | None
+```
+
+Dispatch logic:
+1. Check if input is within declared emulator domain
+2. If in domain, query emulator for prediction + uncertainty
+3. If confidence ≥ threshold, return emulated result with provenance
+4. If confidence < threshold, execute full model and record as training data
+5. If no emulator registered, always execute full model
+6. Record dispatch decision in telemetry (new `EmulationEvent`)
+
+### 3.7 Active learning (`emulation/active_learning.py`)
+
+```python
+class ActiveLearner:
+    """Select next-best scenarios to maximize emulator training efficiency."""
+
+    def __init__(
+        self,
+        emulator: TrainedEmulator,
+        *,
+        acquisition: str = "expected_improvement",  # or "uncertainty", "random"
+        batch_size: int = 1,
+    ) -> None: ...
+
+    def suggest(
+        self,
+        candidates: pd.DataFrame,
+        *,
+        n_suggestions: int = 1,
+    ) -> pd.DataFrame:
+        """Select the most informative candidates for full-model evaluation."""
+        ...
+
+    def update(self, new_observations: pd.DataFrame) -> None:
+        """Incorporate new full-model results into acquisition state."""
+        ...
+```
+
+Acquisition strategies:
+- **Expected improvement** — maximize expected reduction in emulator uncertainty
+- **Maximum uncertainty** — sample where emulator is least confident
+- **Random baseline** — for comparison and diversity
+
+### 3.8 Distributed tuning (`ml/tuning.py`)
+
+```python
+class HyperparameterSearch:
+    """Distributed hyperparameter tuning via Dask-ML."""
+
+    def __init__(
+        self,
+        estimator: Any,        # sklearn-compatible estimator
+        param_space: dict[str, Any],
+        *,
+        strategy: str = "hyperband",  # or "successive_halving", "random"
+        n_iter: int = 50,
+        scoring: str | None = None,
+    ) -> None: ...
+
+    def fit(
+        self,
+        X: pd.DataFrame | Any,
+        y: pd.Series | Any,
+        *,
+        client: ScalableClient | None = None,
+    ) -> TuningResult: ...
+
+@dataclass(frozen=True)
+class TuningResult:
+    """Result of a hyperparameter search."""
+    best_params: dict[str, Any]
+    best_score: float
+    all_results: pd.DataFrame
+    best_estimator: Any
+    n_iterations: int
+    wall_time_s: float
+```
+
+### 3.9 Telemetry extensions
+
+New event type for Phase 5:
+
+```python
+@dataclass(frozen=True)
+class EmulationEvent:
+    """Emulation dispatch event record (Phase 5)."""
+    run_id: str
+    task_name: str
+    component: str | None
+    emulator_name: str | None
+    source: str            # "emulator" | "full_model" | "cached"
+    confidence: float | None
+    fallback_reason: str | None
+    timestamp: str
+    domain_valid: bool
+    event_type: str = "emulation"
+    schema_version: int = SCHEMA_VERSION
+```
+
+### 3.10 Settings extensions
+
+| Setting | Env var | Default | Purpose |
+|---------|---------|---------|---------|
+| `ml_model_cache_dir` | `SCALABLE_ML_CACHE_DIR` | `.scalable/models` | Cached trained ML models |
+| `emulator_registry_dir` | `SCALABLE_EMULATOR_DIR` | `.scalable/emulators` | Trained emulator storage |
+| `ml_enabled` | `SCALABLE_ML` | `1` | Enable/disable ML features |
+| `emulation_enabled` | `SCALABLE_EMULATION` | `0` | Enable/disable emulation (opt-in) |
+| `emulation_confidence_threshold` | `SCALABLE_EMULATION_CONFIDENCE` | `0.9` | Default confidence threshold |
+
+---
+
+## 4. Ordered work breakdown
+
+1. **WU-1 — Branch and package scaffolding**
+   - Create `version/2.0.0-phase5-ml-emulation` from `version/2.0.0`
+   - Create `scalable/ml/` and `scalable/emulation/` package scaffolds
+   - Bump version to `2.0.0a5`
+   - Add `ml` optional extra to `pyproject.toml`
+
+2. **WU-2 — Feature extraction engine**
+   - Implement `scalable/ml/features.py`
+   - Extract engineered features from telemetry DataFrames
+   - Build feature vectors from task arguments
+   - Unit tests for feature engineering
+
+3. **WU-3 — ML model wrappers**
+   - Implement `scalable/ml/models.py`
+   - Gradient boosting, quantile regression, random forest wrappers
+   - Sklearn-compatible interface with `fit`/`predict`/`predict_intervals`
+   - Model serialization/deserialization
+   - Unit tests
+
+4. **WU-4 — Model validation and quality**
+   - Implement `scalable/ml/validation.py`
+   - Cross-validation framework
+   - `ModelQuality` dataclass with metrics (MAE, coverage, calibration)
+   - Comparison against heuristic baseline
+   - Unit tests
+
+5. **WU-5 — Learned resource advisor**
+   - Implement `scalable/ml/learned_advisor.py`
+   - `LearnedAdvisor.from_history()` trains from telemetry
+   - `recommend()` produces `ResourceRecommendation` with ML predictions
+   - `explain()` returns feature importances
+   - Model caching for fast re-load
+   - Unit tests
+
+6. **WU-6 — Adaptive scaling policy**
+   - Implement `scalable/ml/adaptive_scaler.py`
+   - `AdaptiveScaler` with configurable thresholds and bounds
+   - `ScaleDecision` with reasoning
+   - Integration with `LearnedAdvisor` for completion time prediction
+   - Unit tests
+
+7. **WU-7 — Hyperparameter tuning integration**
+   - Implement `scalable/ml/tuning.py`
+   - Dask-ML wrapper for distributed search
+   - Support hyperband, successive halving, random strategies
+   - `TuningResult` payload
+   - Unit tests (mocked Dask client)
+
+8. **WU-8 — Surrogate model abstractions**
+   - Implement `scalable/emulation/surrogate.py`
+   - `TrainedEmulator` protocol
+   - `GaussianProcessEmulator`, `GradientBoostingEmulator`, `RandomForestEmulator`
+   - `EmulatorPrediction` and `EmulatorMetadata` dataclasses
+   - Unit tests
+
+9. **WU-9 — Emulator registry**
+   - Implement `scalable/emulation/registry.py`
+   - `EmulatorRegistry` with versioning and persistence
+   - Domain validation
+   - Serialization to/from `.scalable/emulators/`
+   - Unit tests
+
+10. **WU-10 — `@emulatable` decorator**
+    - Implement `scalable/emulation/decorator.py`
+    - Decorator that registers functions with emulation metadata
+    - Domain bounds declaration
+    - Confidence threshold configuration
+    - Unit tests
+
+11. **WU-11 — Uncertainty-aware dispatch**
+    - Implement `scalable/emulation/dispatch.py`
+    - `EmulatorDispatch` routing logic
+    - Confidence-gated fallback to full model
+    - Provenance recording
+    - Unit tests
+
+12. **WU-12 — Uncertainty calibration**
+    - Implement `scalable/emulation/uncertainty.py`
+    - Confidence calibration utilities
+    - Coverage checking for prediction intervals
+    - Domain boundary detection
+    - Unit tests
+
+13. **WU-13 — Active learning**
+    - Implement `scalable/emulation/active_learning.py`
+    - `ActiveLearner` with acquisition strategies
+    - Expected improvement, maximum uncertainty, random
+    - Batch suggestion support
+    - Unit tests
+
+14. **WU-14 — Telemetry extensions**
+    - Add `EmulationEvent` to `scalable/telemetry/events.py`
+    - Add `emulation.jsonl` stream to `TelemetryStore`
+    - Add emulation summary to `scalable report`
+    - Unit tests
+
+15. **WU-15 — Settings and configuration**
+    - Add Phase 5 settings to `scalable/common.py`
+    - ML/emulation env var support
+    - Configuration validation
+    - Unit tests
+
+16. **WU-16 — CLI `scalable advise` command**
+    - Implement `scalable/cli/cmd_advise.py`
+    - ML-backed resource recommendations from CLI
+    - Flags: `--task`, `--target`, `--runs-dir`, `--model-type`, `--format`
+    - Graceful degradation to heuristic when ML unavailable
+    - Unit tests
+
+17. **WU-17 — Integration with `ResourceAdvisor` and session**
+    - Add `LearnedAdvisor` as opt-in replacement in `scalable/advising/resources.py`
+    - Wire into `ScalableSession.plan(objective=, policy=)` when ML enabled
+    - Maintain backward compatibility with Phase 2 heuristic path
+    - Integration tests
+
+18. **WU-18 — Public API surface updates**
+    - Export ML/emulation types from `scalable/__init__.py`
+    - Update `__all__` with new exports
+    - Graceful import when `scalable[ml]` not installed
+    - Unit tests for import paths
+
+19. **WU-19 — Test suite**
+    - Full unit test coverage for all new modules
+    - Integration test: emulator train → predict → fallback cycle
+    - Regression tests for Phase 1–4 behavior
+    - Cross-validation test for LearnedAdvisor quality
+
+20. **WU-20 — Documentation and changelog**
+    - New docs: `docs/ml_optimization.rst`, `docs/emulation.rst`
+    - Update `README.md` with ML/emulation examples
+    - Update `docs/index.rst` navigation
+    - Add `2.0.0a5` changelog entry
+
+21. **WU-21 — Phase 5 PR**
+    - Open PR from `version/2.0.0-phase5-ml-emulation` to `version/2.0.0`
+    - Validate all success criteria
+    - Merge after review
+
+---
+
+## 5. Files to create, modify, and remove
+
+### 5.1 Files to create
+
+**ML package:**
+- `scalable/ml/__init__.py`
+- `scalable/ml/learned_advisor.py`
+- `scalable/ml/adaptive_scaler.py`
+- `scalable/ml/features.py`
+- `scalable/ml/models.py`
+- `scalable/ml/validation.py`
+- `scalable/ml/tuning.py`
+
+**Emulation package:**
+- `scalable/emulation/__init__.py`
+- `scalable/emulation/registry.py`
+- `scalable/emulation/decorator.py`
+- `scalable/emulation/dispatch.py`
+- `scalable/emulation/active_learning.py`
+- `scalable/emulation/surrogate.py`
+- `scalable/emulation/uncertainty.py`
+
+**CLI:**
+- `scalable/cli/cmd_advise.py`
+
+**Tests:**
+- `tests/unit/test_ml_features.py`
+- `tests/unit/test_ml_models.py`
+- `tests/unit/test_ml_validation.py`
+- `tests/unit/test_ml_learned_advisor.py`
+- `tests/unit/test_ml_adaptive_scaler.py`
+- `tests/unit/test_ml_tuning.py`
+- `tests/unit/test_emulation_surrogate.py`
+- `tests/unit/test_emulation_registry.py`
+- `tests/unit/test_emulation_decorator.py`
+- `tests/unit/test_emulation_dispatch.py`
+- `tests/unit/test_emulation_uncertainty.py`
+- `tests/unit/test_emulation_active_learning.py`
+- `tests/unit/test_cli_advise.py`
+- `tests/unit/test_telemetry_emulation.py`
+- `tests/integration/test_emulator_lifecycle.py`
+
+**Docs:**
+- `docs/ml_optimization.rst`
+- `docs/emulation.rst`
+
+**Plan:**
+- `plans/v2.0.0_phase5_plan.md` (this document)
+
+### 5.2 Files to modify
+
+- [`pyproject.toml`](pyproject.toml) — bump version to `2.0.0a5`; add `ml` optional extra with `scikit-learn`, `dask-ml`
+- [`scalable/__init__.py`](scalable/__init__.py) — add Phase 5 ML/emulation exports (gated)
+- [`scalable/common.py`](scalable/common.py) — add Phase 5 settings (ML/emulation env vars)
+- [`scalable/advising/__init__.py`](scalable/advising/__init__.py) — export `LearnedAdvisor`
+- [`scalable/advising/resources.py`](scalable/advising/resources.py) — add `LearnedAdvisor` integration hook
+- [`scalable/telemetry/events.py`](scalable/telemetry/events.py) — add `EmulationEvent`
+- [`scalable/telemetry/store.py`](scalable/telemetry/store.py) — add `emulation.jsonl` stream
+- [`scalable/telemetry/collectors.py`](scalable/telemetry/collectors.py) — add emulation summary
+- [`scalable/session/session.py`](scalable/session/session.py) — integrate `LearnedAdvisor` into planning
+- [`scalable/cli/main.py`](scalable/cli/main.py) — add `advise` command
+- [`README.md`](README.md) — add ML/emulation examples
+- [`CHANGELOG.md`](CHANGELOG.md) — `2.0.0a5` entry
+- [`docs/index.rst`](docs/index.rst) — include new pages
+
+### 5.3 Files to remove
+
+- None. Phase 5 is strictly additive.
+
+---
+
+## 6. New dependencies, configuration, and migrations
+
+### 6.1 Dependencies
+
+**New optional extra `[project.optional-dependencies] ml`:**
+```toml
+ml = [
+    "scikit-learn >= 1.3",
+    "dask-ml >= 2023.3.24",
+]
+```
+
+Design rationale:
+- `scikit-learn` — core ML algorithms (gradient boosting, random forest, quantile regression, Gaussian processes)
+- `dask-ml` — distributed hyperparameter search, incremental learning, scalable model selection
+
+The ML features degrade to Phase 2 heuristics when `scalable[ml]` is not installed. All ML imports are lazy and gated behind `try/except ImportError`.
+
+### 6.2 Configuration additions
+
+| Setting | Env var | Default | Purpose |
+|---------|---------|---------|---------|
+| `ml_model_cache_dir` | `SCALABLE_ML_CACHE_DIR` | `.scalable/models` | Cached trained ML models |
+| `emulator_registry_dir` | `SCALABLE_EMULATOR_DIR` | `.scalable/emulators` | Trained emulator storage |
+| `ml_enabled` | `SCALABLE_ML` | `1` | Enable/disable ML features |
+| `emulation_enabled` | `SCALABLE_EMULATION` | `0` | Enable emulation (off by default) |
+| `emulation_confidence_threshold` | `SCALABLE_EMULATION_CONFIDENCE` | `0.9` | Confidence threshold for emulator dispatch |
+
+### 6.3 Migrations
+
+- No data migration required.
+- `ResourceAdvisor` from Phase 2 remains fully functional; `LearnedAdvisor` is an opt-in upgrade.
+- `scalable advise` is a new CLI command (no existing stub to replace).
+- Emulation features are entirely opt-in (`SCALABLE_EMULATION=1`).
+- Existing `ScalableSession.plan(objective=, policy=)` gains ML-backed behavior when trained models are available, but falls back to Phase 4 heuristics otherwise.
+
+---
+
+## 7. Testing strategy
+
+| Layer | Coverage focus |
+|-------|----------------|
+| **Unit** | Feature extraction, model wrappers (fit/predict contract), validation metrics, advisor recommendations, emulator protocol, registry CRUD, decorator metadata, dispatch routing, active learning acquisition, tuning result parsing, CLI argument handling, settings validation |
+| **Integration** | End-to-end emulator lifecycle: register → train → predict → fallback; LearnedAdvisor train from synthetic telemetry and recommend; `scalable advise` CLI with fixture history |
+| **Regression** | Phase 1–4 behavior unchanged; Phase 2 `ResourceAdvisor` still works identically; existing tests pass |
+| **Quality** | Cross-validation showing LearnedAdvisor outperforms heuristic on synthetic workload data |
+| **Mock** | Dask-ML search mocked for CI (no real distributed cluster required); sklearn estimators used directly in unit tests |
+| **Static** | `mypy` and `ruff` clean over new `scalable/ml/` and `scalable/emulation/` modules |
+
+Key test scenarios:
+- `LearnedAdvisor` with sufficient history → better-than-heuristic predictions
+- `LearnedAdvisor` with insufficient history → graceful fallback to `ResourceAdvisor`
+- `AdaptiveScaler` with high queue depth → scale-up decision
+- `AdaptiveScaler` with idle workers → scale-down decision respecting cooldown
+- `@emulatable` decorated function → metadata accessible via registry
+- Emulator dispatch with high confidence → emulated result
+- Emulator dispatch with low confidence → full model execution + provenance
+- Emulator dispatch outside domain → immediate fallback
+- Active learner → selects high-uncertainty candidates
+- All features graceful when `scalable[ml]` not installed
+
+---
+
+## 8. Documentation updates
+
+- **New:** [`docs/ml_optimization.rst`](docs/ml_optimization.rst) — full guide covering:
+  - `LearnedAdvisor` usage and training
+  - Feature extraction configuration
+  - Adaptive scaling policies
+  - Hyperparameter tuning integration
+  - Model quality assessment
+  - Graceful degradation behavior
+- **New:** [`docs/emulation.rst`](docs/emulation.rst) — full guide covering:
+  - `@emulatable` decorator usage
+  - Emulator registry management
+  - Uncertainty-aware dispatch
+  - Active learning for scenario selection
+  - Supported surrogate model types
+  - Domain bounds and validation
+  - Provenance and reproducibility
+- **Updated:** [`README.md`](README.md) — ML advisor and emulation examples
+- **Updated:** [`docs/index.rst`](docs/index.rst) — include new pages in toctree
+- **Updated:** [`docs/advising.rst`](docs/advising.rst) — cross-link to ML optimization
+- **Updated:** [`CHANGELOG.md`](CHANGELOG.md) — `2.0.0a5` entry
+
+---
+
+## 9. Risks, open questions, and assumptions
+
+### 9.1 Risks
+
+| Risk | Likelihood | Mitigation |
+|------|------------|------------|
+| Insufficient telemetry data for meaningful ML training | High (early adoption) | Graceful fallback to heuristics; require minimum sample count before ML activates |
+| Overfitting on small telemetry histories | Medium | Conservative model selection; cross-validation mandatory before deployment; wide confidence intervals when data is sparse |
+| Scikit-learn version incompatibility with serialized models | Medium | Pin minimum sklearn version; include model metadata with sklearn version; retrain on version mismatch |
+| Emulator confidence miscalibration | Medium | Require held-out validation before registry registration; coverage checks at prediction time; conservative default threshold (0.9) |
+| Dask-ML API instability | Low | Pin minimum version; wrap in thin abstraction layer; fallback to sequential search if Dask-ML unavailable |
+| Performance overhead from ML inference in hot paths | Low | Cache predictions; batch feature extraction; lazy model loading |
+| Complex interaction between adaptive scaler and provider scaling | Medium | Respect provider min/max bounds; implement cooldown; require explicit opt-in for adaptive scaling |
+
+### 9.2 Open questions
+
+1. **Minimum sample count for ML activation.** Proposal: require ≥ 10 completed tasks for the same task/component before activating ML predictions. Below that, fall back to quantile heuristics.
+2. **Model retraining frequency.** Proposal: retrain on-demand via `LearnedAdvisor.from_history(retrain=True)` or when model cache is stale (configurable staleness window). No automatic background retraining in Phase 5.
+3. **Emulator storage format.** Proposal: joblib serialization for sklearn models stored under `.scalable/emulators/<name>/<version>/model.joblib` + `metadata.json`.
+4. **Should adaptive scaling be auto-activated?** Proposal: No — require explicit `AdaptiveScaler` instantiation or `SCALABLE_ADAPTIVE_SCALING=1` env var. Phase 5 is opt-in only.
+5. **Gaussian Process scalability.** For large emulator training sets (>1000 samples), GP becomes expensive. Proposal: auto-switch to sparse GP or gradient boosting when training set exceeds threshold.
+6. **Should `scalable advise` be a new command or enhance `scalable report`?** Proposal: New dedicated command for clarity, with `scalable report` continuing to show historical summaries and `scalable advise` providing forward-looking recommendations.
+
+### 9.3 Assumptions
+
+- Phase 1–4 code on `version/2.0.0` (with Phase 4 merged) is the implementation baseline.
+- Python 3.11+ remains the runtime target.
+- scikit-learn 1.3+ provides all needed model implementations (GradientBoostingRegressor, QuantileRegressor, RandomForestRegressor, GaussianProcessRegressor).
+- No live Dask cluster or Slurm allocation required for Phase 5 CI; all ML tests use synthetic data and mocked distributed execution.
+- Users accept that ML predictions may be less accurate than manual tuning early in adoption; the system improves with more history.
+
+---
+
+## 10. Phase 5 as the capstone — what it completes
+
+Phase 5 completes the v2.0.0 vision of Scalable as a "portable scientific-model execution control plane" with optional ML services:
+
+| Master plan capability | Phase 5 deliverable |
+|------------------------|---------------------|
+| "Learned resource prediction" (§5) | `LearnedAdvisor` with gradient boosting + calibrated intervals |
+| "Adaptive correction" (§5.4) | `AdaptiveScaler` with ML-predicted completion times |
+| "Feature-based regression" (§5.2) | `FeatureExtractor` + engineered features from telemetry |
+| "Uncertainty-aware planning" (§5.3) | Calibrated prediction intervals + confidence-based decisions |
+| "Active-learning scenario selection" (§9) | `ActiveLearner` with acquisition strategies |
+| "Emulator registry" (§9) | `EmulatorRegistry` with versioning and domain validation |
+| "Uncertainty-aware full-model fallback" (§9) | `EmulatorDispatch` with confidence gating |
+| "Distributed hyperparameter tuning" (§9) | `HyperparameterSearch` via Dask-ML |
+| "Emulators opt-in, uncertainty-aware, labeled" (§9 guardrail) | Default off; provenance recorded; results labeled |
+
+After Phase 5 merges, the `version/2.0.0` branch contains the complete v2.0.0 feature set and can proceed toward release candidate status.
+
+---
+
+## 11. Branching and PR workflow
+
+```text
+origin/version/2.0.0  ──────────────────────────────────────────►  long-lived integration
+        │
+        └── version/2.0.0-phase5-ml-emulation  ── PR ────────────┘  phase 5 work (final)
+```
+
+- Develop all Phase 5 commits on `version/2.0.0-phase5-ml-emulation`.
+- Open a dedicated PR targeting `version/2.0.0`.
+- After merge, `version/2.0.0` contains the complete 2.0.0 feature set.
+- Next step after Phase 5: stabilization, release candidates, final merge to `main`.
+
+---
+
+## 12. Phase 5 architecture diagram
+
+```mermaid
+flowchart TB
+    subgraph ML_Optimization
+        FE[FeatureExtractor]
+        MW[Model Wrappers sklearn]
+        LA[LearnedAdvisor]
+        AS[AdaptiveScaler]
+        VL[ModelValidation]
+        HT[HyperparameterSearch Dask-ML]
+    end
+
+    subgraph Emulation
+        DEC[@emulatable decorator]
+        REG[EmulatorRegistry]
+        SUR[Surrogate Models GP RF NN]
+        DSP[EmulatorDispatch]
+        UNC[Uncertainty Calibration]
+        ACT[ActiveLearner]
+    end
+
+    subgraph Phase_1_4_Foundation
+        TS[Telemetry Store tasks.jsonl]
+        RA[ResourceAdvisor heuristic]
+        SS[ScalableSession.plan]
+        PV[Providers scale]
+        CE[CostEstimate]
+        AB[AIBackend]
+    end
+
+    subgraph Outputs
+        RR[ResourceRecommendation]
+        SD[ScaleDecision]
+        EP[EmulatorPrediction]
+        AL[ActiveLearning suggestions]
+        TR[TuningResult]
+    end
+
+    TS --> FE
+    FE --> MW
+    MW --> LA
+    LA --> RR
+    LA --> AS
+    AS --> SD
+    AS --> PV
+
+    RA -.->|fallback| LA
+    SS --> LA
+    CE --> AS
+
+    DEC --> REG
+    REG --> DSP
+    SUR --> DSP
+    UNC --> DSP
+    DSP --> EP
+    ACT --> AL
+    SUR --> ACT
+
+    MW --> HT
+    HT --> TR
+
+    AB -.->|optional enhancement| LA
+```
+
+---
+
+## 13. Phase 5 execution checklist
+
+1. Create branch `version/2.0.0-phase5-ml-emulation` from `version/2.0.0`.
+2. Scaffold `scalable/ml/` and `scalable/emulation/` packages.
+3. Add `ml` optional extra to `pyproject.toml`; bump version to `2.0.0a5`.
+4. Implement feature extraction engine.
+5. Implement ML model wrappers with sklearn.
+6. Implement model validation and quality framework.
+7. Implement `LearnedAdvisor` with training and prediction.
+8. Implement `AdaptiveScaler` with configurable policies.
+9. Implement distributed hyperparameter tuning integration.
+10. Implement surrogate model abstractions.
+11. Implement emulator registry with versioning.
+12. Implement `@emulatable` decorator.
+13. Implement uncertainty-aware dispatch.
+14. Implement uncertainty calibration utilities.
+15. Implement active learning with acquisition strategies.
+16. Add telemetry extensions (`EmulationEvent`, `emulation.jsonl`).
+17. Add Phase 5 settings to `common.py`.
+18. Implement `scalable advise` CLI command.
+19. Wire `LearnedAdvisor` into session planning and advising exports.
+20. Update public API exports.
+21. Add comprehensive unit + integration tests.
+22. Add documentation and update changelog.
+23. Open Phase 5 PR and validate all success criteria.

From 2efbe9d18e4b3559ee8e9194695464595f040d12 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 20:42:42 -0400
Subject: [PATCH 26/47] Phase 5: ML optimization and emulation

- Add scalable.ml package: LearnedAdvisor, AdaptiveScaler, FeatureExtractor,
  ResourceModel, HyperparameterSearch, cross_validate_advisor
- Add scalable.emulation package: @emulatable decorator, EmulatorRegistry,
  EmulatorDispatch, ActiveLearner, GradientBoostingEmulator,
  RandomForestEmulator, uncertainty calibration
- Add scalable advise CLI command with ML-backed recommendations
- Add EmulationEvent to telemetry events
- Add Phase 5 settings (ML cache, emulator registry, enable flags)
- Add [ml] optional dependency extra (scikit-learn, dask-ml, joblib)
- Bump version to 2.0.0a5
- 75 new unit tests, 431 total passing
---
 CHANGELOG.md                            |  65 ++++
 pyproject.toml                          |   7 +-
 scalable/__init__.py                    |  29 ++
 scalable/cli/cmd_advise.py              | 174 ++++++++++
 scalable/cli/main.py                    |  15 +
 scalable/common.py                      |  16 +
 scalable/emulation/__init__.py          |  41 +++
 scalable/emulation/active_learning.py   | 192 +++++++++++
 scalable/emulation/decorator.py         | 157 +++++++++
 scalable/emulation/dispatch.py          | 245 +++++++++++++
 scalable/emulation/registry.py          | 335 ++++++++++++++++++
 scalable/emulation/surrogate.py         | 280 +++++++++++++++
 scalable/emulation/uncertainty.py       | 193 +++++++++++
 scalable/ml/__init__.py                 |  34 ++
 scalable/ml/adaptive_scaler.py          | 243 +++++++++++++
 scalable/ml/features.py                 | 197 +++++++++++
 scalable/ml/learned_advisor.py          | 415 ++++++++++++++++++++++
 scalable/ml/models.py                   | 338 ++++++++++++++++++
 scalable/ml/tuning.py                   | 201 +++++++++++
 scalable/ml/validation.py               | 134 ++++++++
 scalable/telemetry/events.py            |  28 ++
 tests/unit/test_emulation.py            | 440 ++++++++++++++++++++++++
 tests/unit/test_ml_adaptive_scaler.py   | 147 ++++++++
 tests/unit/test_ml_features.py          | 105 ++++++
 tests/unit/test_ml_models.py            | 101 ++++++
 tests/unit/test_phase5_telemetry_cli.py |  79 +++++
 26 files changed, 4210 insertions(+), 1 deletion(-)
 create mode 100644 scalable/cli/cmd_advise.py
 create mode 100644 scalable/emulation/__init__.py
 create mode 100644 scalable/emulation/active_learning.py
 create mode 100644 scalable/emulation/decorator.py
 create mode 100644 scalable/emulation/dispatch.py
 create mode 100644 scalable/emulation/registry.py
 create mode 100644 scalable/emulation/surrogate.py
 create mode 100644 scalable/emulation/uncertainty.py
 create mode 100644 scalable/ml/__init__.py
 create mode 100644 scalable/ml/adaptive_scaler.py
 create mode 100644 scalable/ml/features.py
 create mode 100644 scalable/ml/learned_advisor.py
 create mode 100644 scalable/ml/models.py
 create mode 100644 scalable/ml/tuning.py
 create mode 100644 scalable/ml/validation.py
 create mode 100644 tests/unit/test_emulation.py
 create mode 100644 tests/unit/test_ml_adaptive_scaler.py
 create mode 100644 tests/unit/test_ml_features.py
 create mode 100644 tests/unit/test_ml_models.py
 create mode 100644 tests/unit/test_phase5_telemetry_cli.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 265273f..5cc496c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,71 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [2.0.0a5] — Phase 5: ML Optimization and Emulation
+
+### Added
+
+- **ML optimization subsystem** (`scalable.ml`) with learned resource prediction
+  and adaptive scaling:
+  - `LearnedAdvisor` — ML-backed resource recommendations using gradient
+    boosting, random forest, or quantile regression trained on telemetry history
+  - `AdaptiveScaler` — real-time adaptive worker scaling with configurable
+    thresholds, min/max bounds, and cooldown periods
+  - `FeatureExtractor` — telemetry feature engineering with rolling aggregates,
+    task identity hashing, and user-provided input features
+  - `ResourceModel` — unified sklearn model wrapper with fit/predict/intervals,
+    model serialization, and percentile fallback when sklearn unavailable
+  - `HyperparameterSearch` — Dask-ML distributed tuning integration
+    (hyperband, successive halving, random) with sklearn fallback
+  - `cross_validate_advisor` — model quality assessment framework
+- **Model emulation subsystem** (`scalable.emulation`) with uncertainty-aware
+  surrogate model dispatch:
+  - `@emulatable` decorator marking functions as emulation-capable with
+    declared inputs, outputs, domain bounds, and confidence thresholds
+  - `EmulatorRegistry` — versioned emulator management with filesystem
+    persistence, domain validation, and joblib serialization
+  - `EmulatorDispatch` — confidence-gated routing between emulator and
+    full model with provenance recording
+  - `ActiveLearner` — intelligent scenario selection using expected
+    improvement, maximum uncertainty, or random acquisition strategies
+  - `GradientBoostingEmulator` and `RandomForestEmulator` surrogate
+    model implementations with tree-based uncertainty estimation
+  - `calibrate_emulator` — uncertainty calibration assessment with
+    coverage and sharpness metrics
+- **`scalable advise` CLI command** for ML-backed resource recommendations:
+  - Supports `--task`, `--target`, `--model-type`, `--confidence`, `--format`
+  - Graceful degradation to Phase 2 heuristic advisor when ML unavailable
+  - Text and JSON output formats
+- **`EmulationEvent` telemetry** for tracking emulator dispatch decisions
+  including source, confidence, fallback reason, and domain validity.
+- **Settings extensions** (`scalable.common.Settings`):
+  - `ml_model_cache_dir` (`SCALABLE_ML_CACHE_DIR`)
+  - `emulator_registry_dir` (`SCALABLE_EMULATOR_DIR`)
+  - `ml_enabled` (`SCALABLE_ML`)
+  - `emulation_enabled` (`SCALABLE_EMULATION`)
+  - `emulation_confidence_threshold` (`SCALABLE_EMULATION_CONFIDENCE`)
+- **`[project.optional-dependencies] ml`** extra with `scikit-learn >= 1.3`,
+  `dask-ml >= 2023.3.24`, and `joblib >= 1.3`.
+- **Public API**: `LearnedAdvisor`, `AdaptiveScaler`, `HyperparameterSearch`,
+  `EmulatorRegistry`, `EmulatorDispatch`, `ActiveLearner`, `emulatable`
+  exported from `scalable.__init__` with optional-dep guards.
+
+### Changed
+
+- Bumped version to `2.0.0a5`.
+- CLI main dispatcher now supports both `handler` and `func` argument
+  patterns for command registration.
+- `scalable advise` added as implemented CLI command.
+
+### Tests
+
+- 75 new unit tests for ML features, models, adaptive scaler, emulation
+  decorator, registry, dispatch, uncertainty calibration, active learning,
+  telemetry events, settings, and CLI.
+- 431 total unit tests passing (no regressions from Phases 1–4).
+
+---
+
 ## [2.0.0a4] — Phase 4: AI Assistant Features
 
 ### Added
diff --git a/pyproject.toml b/pyproject.toml
index a65f287..73ce484 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "scalable"
-version = "2.0.0a4"
+version = "2.0.0a5"
 description = "Assist with running models on job queing systems like Slurm"
 authors = [
     { name = "Shashank Lamba" },
@@ -72,6 +72,11 @@ kubernetes = [
     "dask-kubernetes >= 2024.1.0",
     "kubernetes >= 27.0",
 ]
+ml = [
+    "scikit-learn >= 1.3",
+    "dask-ml >= 2023.3.24",
+    "joblib >= 1.3",
+]
 
 [project.urls]
 "Github" = "https://github.com/JGCRI/scalable/tree/master/scalable"
diff --git a/scalable/__init__.py b/scalable/__init__.py
index d3993ff..aa2cae2 100755
--- a/scalable/__init__.py
+++ b/scalable/__init__.py
@@ -80,6 +80,27 @@
     migrate_manifest = None  # type: ignore[assignment,misc]
     onboard_component = None  # type: ignore[assignment,misc]
 
+# Phase 5: ML optimization and emulation exports (optional deps)
+try:
+    from .ml import AdaptiveScaler, HyperparameterSearch, LearnedAdvisor
+except ImportError:  # pragma: no cover
+    LearnedAdvisor = None  # type: ignore[assignment,misc]
+    AdaptiveScaler = None  # type: ignore[assignment,misc]
+    HyperparameterSearch = None  # type: ignore[assignment,misc]
+
+try:
+    from .emulation import (
+        ActiveLearner,
+        EmulatorDispatch,
+        EmulatorRegistry,
+        emulatable,
+    )
+except ImportError:  # pragma: no cover
+    ActiveLearner = None  # type: ignore[assignment,misc]
+    EmulatorDispatch = None  # type: ignore[assignment,misc]
+    EmulatorRegistry = None  # type: ignore[assignment,misc]
+    emulatable = None  # type: ignore[assignment,misc]
+
 try:
     __version__ = _pkg_version("scalable")
 except PackageNotFoundError:  # pragma: no cover - source checkout w/o install
@@ -97,6 +118,7 @@
     "GCPProvider",
     "JobQueueCluster",
     "KubernetesProvider",
+    "LearnedAdvisor",
     "LocalArtifactStore",
     "LocalProvider",
     "MigrationResult",
@@ -113,9 +135,16 @@
     "build_artifact_store",
     "compose_workflow",
     "diagnose_run",
+    "emulatable",
     "explain_plan",
     "get_worker",
     "migrate_manifest",
     "onboard_component",
     "settings",
+    # Phase 5 ML/emulation
+    "ActiveLearner",
+    "AdaptiveScaler",
+    "EmulatorDispatch",
+    "EmulatorRegistry",
+    "HyperparameterSearch",
 ]
diff --git a/scalable/cli/cmd_advise.py b/scalable/cli/cmd_advise.py
new file mode 100644
index 0000000..f1910a3
--- /dev/null
+++ b/scalable/cli/cmd_advise.py
@@ -0,0 +1,174 @@
+"""CLI command: ``scalable advise`` — ML-backed resource recommendations."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from typing import Any
+
+
+def register_advise_parser(subparsers: Any) -> None:
+    """Register the ``advise`` subcommand."""
+    parser = subparsers.add_parser(
+        "advise",
+        help="Get ML-backed resource recommendations for a task",
+        description=(
+            "Analyze telemetry history and provide ML-backed resource "
+            "recommendations. Falls back to heuristic quantiles when "
+            "insufficient data or scalable[ml] is not installed."
+        ),
+    )
+    parser.add_argument(
+        "--task",
+        required=True,
+        help="Task name to get recommendations for",
+    )
+    parser.add_argument(
+        "--target",
+        default=None,
+        help="Deployment target to scope recommendations",
+    )
+    parser.add_argument(
+        "--runs-dir",
+        default=None,
+        help="Path to runs directory (default: .scalable/runs)",
+    )
+    parser.add_argument(
+        "--model-type",
+        default="gradient_boosting",
+        choices=["gradient_boosting", "random_forest", "quantile_regression"],
+        help="ML model type for predictions (default: gradient_boosting)",
+    )
+    parser.add_argument(
+        "--confidence",
+        type=float,
+        default=0.95,
+        help="Confidence level for recommendations (default: 0.95)",
+    )
+    parser.add_argument(
+        "--format",
+        choices=["text", "json"],
+        default="text",
+        dest="output_format",
+        help="Output format (default: text)",
+    )
+    parser.add_argument(
+        "--output",
+        default=None,
+        help="Output file path (default: stdout)",
+    )
+    parser.set_defaults(func=_run_advise)
+
+
+def _run_advise(args: argparse.Namespace) -> int:
+    """Execute the advise command."""
+    from scalable.common import settings
+
+    runs_dir = args.runs_dir or settings.runs_dir
+
+    # Try ML advisor first, fall back to heuristic
+    recommendation = None
+    method = "heuristic"
+
+    try:
+        from scalable.ml.learned_advisor import LearnedAdvisor
+
+        advisor = LearnedAdvisor.from_history(
+            runs_dir,
+            model_type=args.model_type,
+        )
+        recommendation = advisor.recommend(
+            task=args.task,
+            target=args.target,
+            confidence=args.confidence,
+        )
+        method = recommendation.evidence.get("method", "ml")
+    except (ImportError, Exception):
+        # Fall back to Phase 2 heuristic advisor
+        try:
+            from scalable.advising.resources import ResourceAdvisor
+
+            advisor_h = ResourceAdvisor.from_history(runs_dir)
+            recommendation = advisor_h.recommend(
+                task=args.task,
+                target=args.target,
+                confidence=args.confidence,
+            )
+            method = "heuristic"
+        except Exception as e:
+            sys.stderr.write(f"Error: Could not load run history: {e}\n")
+            return 1
+
+    if recommendation is None:
+        sys.stderr.write("Error: No recommendation could be generated\n")
+        return 1
+
+    # Format output
+    if args.output_format == "json":
+        output = json.dumps(
+            {
+                "task": recommendation.task,
+                "target": recommendation.target,
+                "confidence": recommendation.confidence,
+                "method": method,
+                "workers": recommendation.workers,
+                "resources": recommendation.resources,
+                "evidence": recommendation.evidence,
+            },
+            indent=2,
+        )
+    else:
+        output = _format_text(recommendation, method)
+
+    if args.output:
+        with open(args.output, "w") as f:
+            f.write(output + "\n")
+    else:
+        sys.stdout.write(output + "\n")
+
+    return 0
+
+
+def _format_text(recommendation: Any, method: str) -> str:
+    """Format recommendation as human-readable text."""
+    lines = [
+        f"Resource Recommendation for: {recommendation.task}",
+        f"{'=' * 50}",
+        f"Method: {method}",
+        f"Confidence: {recommendation.confidence:.2f}",
+        f"Target: {recommendation.target or 'any'}",
+        "",
+        "Workers:",
+    ]
+
+    for tag, count in recommendation.workers.items():
+        lines.append(f"  {tag}: {count}")
+
+    lines.append("")
+    lines.append("Resources:")
+    for tag, res in recommendation.resources.items():
+        lines.append(f"  {tag}:")
+        lines.append(f"    CPUs: {res.get('cpus', 'N/A')}")
+        lines.append(f"    Memory: {res.get('memory', 'N/A')}")
+        lines.append(f"    Walltime: {res.get('walltime', 'N/A')}")
+
+    if recommendation.evidence:
+        lines.append("")
+        lines.append("Evidence:")
+        records = recommendation.evidence.get("records", 0)
+        lines.append(f"  Historical records: {records}")
+        if "predicted_duration_s" in recommendation.evidence:
+            dur = recommendation.evidence["predicted_duration_s"]
+            lines.append(f"  Predicted duration: {dur:.1f}s")
+        if "feature_importances" in recommendation.evidence:
+            importances = recommendation.evidence["feature_importances"]
+            if importances:
+                lines.append("  Top features:")
+                sorted_features = sorted(
+                    importances.items(), key=lambda x: x[1], reverse=True
+                )[:5]
+                for feat, imp in sorted_features:
+                    lines.append(f"    {feat}: {imp:.3f}")
+
+    return "\n".join(lines)
diff --git a/scalable/cli/main.py b/scalable/cli/main.py
index 56a462e..a895bb3 100644
--- a/scalable/cli/main.py
+++ b/scalable/cli/main.py
@@ -11,6 +11,7 @@
 * ``scalable explain``
 * ``scalable compose``
 * ``scalable migrate``
+* ``scalable advise``
 
 """
 
@@ -408,6 +409,11 @@ def _build_parser() -> argparse.ArgumentParser:
     )
     migrate_parser.set_defaults(handler=_handle_migrate)
 
+    # --- advise (Phase 5) ---
+    from .cmd_advise import register_advise_parser
+
+    register_advise_parser(subparsers)
+
     # --- stubs for future phases ---
     for command, phase in _STUB_COMMANDS.items():
         stub_parser = subparsers.add_parser(command, help=f"Reserved command (planned for {phase})")
@@ -416,6 +422,12 @@ def _build_parser() -> argparse.ArgumentParser:
     return parser
 
 
+def _handle_advise(args: argparse.Namespace) -> int:
+    from .cmd_advise import _run_advise
+
+    return _run_advise(args)
+
+
 def main(argv: list[str] | None = None) -> int:
     """Run the ``scalable`` CLI and return a process-compatible exit code."""
     parser = _build_parser()
@@ -426,6 +438,9 @@ def main(argv: list[str] | None = None) -> int:
         return int(exc.code)
 
     handler = getattr(args, "handler", None)
+    # Also check for Phase 5 "func" pattern
+    if handler is None:
+        handler = getattr(args, "func", None)
     if handler is None:
         parser.print_help(sys.stderr)
         return 2
diff --git a/scalable/common.py b/scalable/common.py
index 5f8209b..bcbe2ad 100755
--- a/scalable/common.py
+++ b/scalable/common.py
@@ -96,6 +96,22 @@ class Settings:
     ai_endpoint: str | None = field(
         default_factory=lambda: os.environ.get("SCALABLE_AI_ENDPOINT")
     )
+    # Phase 5 ML/Emulation additions
+    ml_model_cache_dir: str = field(
+        default_factory=lambda: os.environ.get("SCALABLE_ML_CACHE_DIR", ".scalable/models")
+    )
+    emulator_registry_dir: str = field(
+        default_factory=lambda: os.environ.get("SCALABLE_EMULATOR_DIR", ".scalable/emulators")
+    )
+    ml_enabled: bool = field(
+        default_factory=lambda: bool(int(os.environ.get("SCALABLE_ML", "1")))
+    )
+    emulation_enabled: bool = field(
+        default_factory=lambda: bool(int(os.environ.get("SCALABLE_EMULATION", "0")))
+    )
+    emulation_confidence_threshold: float = field(
+        default_factory=lambda: float(os.environ.get("SCALABLE_EMULATION_CONFIDENCE", "0.9"))
+    )
 
 
 #: Process-wide settings singleton. Mutating attributes on this instance
diff --git a/scalable/emulation/__init__.py b/scalable/emulation/__init__.py
new file mode 100644
index 0000000..90f3ceb
--- /dev/null
+++ b/scalable/emulation/__init__.py
@@ -0,0 +1,41 @@
+"""Model emulation subsystem for Scalable (Phase 5).
+
+This package provides scientific model emulation capabilities including:
+
+* :class:`EmulatorRegistry` — manage trained surrogate models
+* :func:`emulatable` — decorator marking functions as emulation-capable
+* :class:`EmulatorDispatch` — uncertainty-aware routing
+* :class:`ActiveLearner` — intelligent scenario selection
+* Surrogate model abstractions (GP, RF, gradient boosting)
+"""
+
+from __future__ import annotations
+
+from .active_learning import ActiveLearner
+from .decorator import emulatable
+from .dispatch import EmulatorDispatch, EmulatorDispatchResult
+from .registry import EmulatorInfo, EmulatorRegistry
+from .surrogate import (
+    EmulatorMetadata,
+    EmulatorPrediction,
+    GradientBoostingEmulator,
+    RandomForestEmulator,
+    TrainedEmulator,
+)
+from .uncertainty import CalibrationResult, calibrate_emulator
+
+__all__ = [
+    "ActiveLearner",
+    "CalibrationResult",
+    "EmulatorDispatch",
+    "EmulatorDispatchResult",
+    "EmulatorInfo",
+    "EmulatorMetadata",
+    "EmulatorPrediction",
+    "EmulatorRegistry",
+    "GradientBoostingEmulator",
+    "RandomForestEmulator",
+    "TrainedEmulator",
+    "calibrate_emulator",
+    "emulatable",
+]
diff --git a/scalable/emulation/active_learning.py b/scalable/emulation/active_learning.py
new file mode 100644
index 0000000..9d33c10
--- /dev/null
+++ b/scalable/emulation/active_learning.py
@@ -0,0 +1,192 @@
+"""Active learning for intelligent scenario selection."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+from scalable.emulation.surrogate import TrainedEmulator
+
+
+class ActiveLearner:
+    """Select next-best scenarios to maximize emulator training efficiency.
+
+    Uses acquisition functions to identify which candidate scenarios
+    would be most informative for improving the emulator, reducing the
+    number of expensive full-model runs needed.
+
+    Parameters
+    ----------
+    emulator
+        A trained emulator to query for uncertainty estimates.
+    acquisition
+        Acquisition strategy: ``"expected_improvement"``,
+        ``"uncertainty"``, or ``"random"``.
+    batch_size
+        Default number of suggestions per call.
+    random_state
+        Random state for reproducibility.
+    """
+
+    def __init__(
+        self,
+        emulator: TrainedEmulator,
+        *,
+        acquisition: str = "expected_improvement",
+        batch_size: int = 1,
+        random_state: int = 42,
+    ) -> None:
+        valid_strategies = {"expected_improvement", "uncertainty", "random"}
+        if acquisition not in valid_strategies:
+            raise ValueError(
+                f"acquisition must be one of {sorted(valid_strategies)}, got {acquisition!r}"
+            )
+
+        self._emulator = emulator
+        self._acquisition = acquisition
+        self._batch_size = batch_size
+        self._rng = np.random.default_rng(random_state)
+        self._observations: list[dict[str, Any]] = []
+
+    @property
+    def acquisition_strategy(self) -> str:
+        """Current acquisition strategy."""
+        return self._acquisition
+
+    @property
+    def n_observations(self) -> int:
+        """Number of observations incorporated."""
+        return len(self._observations)
+
+    def suggest(
+        self,
+        candidates: pd.DataFrame,
+        *,
+        n_suggestions: int | None = None,
+    ) -> pd.DataFrame:
+        """Select the most informative candidates for full-model evaluation.
+
+        Parameters
+        ----------
+        candidates
+            DataFrame of candidate scenarios. Each row represents a
+            candidate input configuration. Column names should match
+            the emulator's input names.
+        n_suggestions
+            Number of candidates to select. Defaults to ``batch_size``.
+
+        Returns
+        -------
+        pd.DataFrame
+            Selected candidates (subset of input DataFrame).
+        """
+        n = n_suggestions or self._batch_size
+        n = min(n, len(candidates))
+
+        if n <= 0 or candidates.empty:
+            return candidates.iloc[:0]
+
+        if self._acquisition == "random":
+            return self._suggest_random(candidates, n)
+        elif self._acquisition == "uncertainty":
+            return self._suggest_max_uncertainty(candidates, n)
+        else:  # expected_improvement
+            return self._suggest_expected_improvement(candidates, n)
+
+    def _suggest_random(self, candidates: pd.DataFrame, n: int) -> pd.DataFrame:
+        """Random baseline selection."""
+        indices = self._rng.choice(len(candidates), size=n, replace=False)
+        return candidates.iloc[indices].reset_index(drop=True)
+
+    def _suggest_max_uncertainty(self, candidates: pd.DataFrame, n: int) -> pd.DataFrame:
+        """Select candidates where emulator is least confident."""
+        uncertainties = []
+        for _, row in candidates.iterrows():
+            inputs = row.to_dict()
+            unc = self._emulator.uncertainty(inputs)
+            uncertainties.append(unc)
+
+        uncertainties_arr = np.array(uncertainties)
+        # Select top-n highest uncertainty candidates
+        top_indices = np.argsort(uncertainties_arr)[-n:][::-1]
+        return candidates.iloc[top_indices].reset_index(drop=True)
+
+    def _suggest_expected_improvement(
+        self, candidates: pd.DataFrame, n: int
+    ) -> pd.DataFrame:
+        """Select candidates maximizing expected information gain.
+
+        Uses uncertainty as a proxy for expected improvement when the
+        full acquisition function requires more sophisticated modeling.
+        Also incorporates diversity by penalizing candidates too similar
+        to existing observations.
+        """
+        scores: list[float] = []
+
+        for _, row in candidates.iterrows():
+            inputs = row.to_dict()
+
+            # Uncertainty component
+            unc = self._emulator.uncertainty(inputs)
+
+            # Diversity component (distance from existing observations)
+            diversity = self._compute_diversity(inputs)
+
+            # Combined score: uncertainty * diversity bonus
+            score = unc * (1.0 + 0.3 * diversity)
+            scores.append(score)
+
+        scores_arr = np.array(scores)
+        top_indices = np.argsort(scores_arr)[-n:][::-1]
+        return candidates.iloc[top_indices].reset_index(drop=True)
+
+    def _compute_diversity(self, inputs: dict[str, Any]) -> float:
+        """Compute diversity score based on distance from observations."""
+        if not self._observations:
+            return 1.0  # Maximum diversity when no observations
+
+        # Simple Euclidean-like distance in normalized space
+        input_vec = np.array([
+            float(v) for v in inputs.values() if isinstance(v, (int, float))
+        ])
+
+        if len(input_vec) == 0:
+            return 1.0
+
+        min_dist = float("inf")
+        for obs in self._observations:
+            obs_vec = np.array([
+                float(obs.get(k, 0))
+                for k in inputs.keys()
+                if isinstance(inputs.get(k), (int, float))
+            ])
+            if len(obs_vec) == len(input_vec):
+                dist = float(np.linalg.norm(input_vec - obs_vec))
+                min_dist = min(min_dist, dist)
+
+        if min_dist == float("inf"):
+            return 1.0
+
+        # Normalize to [0, 1] using a sigmoid-like transform
+        return float(1.0 - np.exp(-min_dist / (np.linalg.norm(input_vec) + 1e-10)))
+
+    def update(self, new_observations: pd.DataFrame) -> None:
+        """Incorporate new full-model results into acquisition state.
+
+        Parameters
+        ----------
+        new_observations
+            DataFrame of new observations. Each row should contain
+            the input values that were evaluated with the full model.
+        """
+        for _, row in new_observations.iterrows():
+            self._observations.append(row.to_dict())
+
+    def reset(self) -> None:
+        """Clear all observations and reset state."""
+        self._observations.clear()
+
+
+__all__ = ["ActiveLearner"]
diff --git a/scalable/emulation/decorator.py b/scalable/emulation/decorator.py
new file mode 100644
index 0000000..b8168c0
--- /dev/null
+++ b/scalable/emulation/decorator.py
@@ -0,0 +1,157 @@
+"""@emulatable decorator for marking functions as emulation-capable."""
+
+from __future__ import annotations
+
+import functools
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import Any
+
+# Module-level registry of emulatable functions
+_EMULATABLE_REGISTRY: dict[str, EmulationSpec] = {}
+
+
+@dataclass(frozen=True)
+class EmulationSpec:
+    """Specification for an emulatable function."""
+
+    function_name: str
+    tag: str
+    inputs: list[str]
+    outputs: list[str]
+    uncertainty: str  # "required" | "optional" | "none"
+    fallback: str  # "full_model" | "error" | "cached"
+    domain: dict[str, tuple[float, float]]
+    confidence_threshold: float
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "function_name": self.function_name,
+            "tag": self.tag,
+            "inputs": self.inputs,
+            "outputs": self.outputs,
+            "uncertainty": self.uncertainty,
+            "fallback": self.fallback,
+            "domain": {k: list(v) for k, v in self.domain.items()},
+            "confidence_threshold": self.confidence_threshold,
+        }
+
+
+def emulatable(
+    *,
+    tag: str,
+    inputs: list[str],
+    outputs: list[str],
+    uncertainty: str = "required",
+    fallback: str = "full_model",
+    domain: dict[str, tuple[float, float]] | None = None,
+    confidence_threshold: float = 0.9,
+) -> Callable:
+    """Decorator marking a function as emulation-capable.
+
+    When a trained emulator is available and its confidence exceeds the
+    threshold, the function call can be routed to the emulator instead of
+    the full model. Provenance is recorded for every dispatch decision.
+
+    Parameters
+    ----------
+    tag
+        Component tag for worker routing.
+    inputs
+        List of input parameter names the emulator expects.
+    outputs
+        List of output names the emulator produces.
+    uncertainty
+        Uncertainty requirement: ``"required"`` means emulator must provide
+        calibrated uncertainty bounds; ``"optional"`` allows point estimates;
+        ``"none"`` skips uncertainty checks.
+    fallback
+        Fallback strategy when emulator is unavailable or confidence is low:
+        ``"full_model"`` runs the original function; ``"error"`` raises;
+        ``"cached"`` attempts cache lookup.
+    domain
+        Optional domain bounds for input validation. Dict mapping input
+        names to (min, max) tuples.
+    confidence_threshold
+        Minimum confidence for emulator predictions to be accepted.
+
+    Examples
+    --------
+    >>> @emulatable(
+    ...     tag="gcam",
+    ...     inputs=["carbon_price", "population", "gdp"],
+    ...     outputs=["emissions", "energy_price"],
+    ...     uncertainty="required",
+    ...     fallback="full_model",
+    ...     confidence_threshold=0.9,
+    ... )
+    ... def run_gcam_scenario(params):
+    ...     ...
+    """
+    valid_uncertainty = {"required", "optional", "none"}
+    valid_fallback = {"full_model", "error", "cached"}
+
+    if uncertainty not in valid_uncertainty:
+        raise ValueError(
+            f"uncertainty must be one of {sorted(valid_uncertainty)}, got {uncertainty!r}"
+        )
+    if fallback not in valid_fallback:
+        raise ValueError(
+            f"fallback must be one of {sorted(valid_fallback)}, got {fallback!r}"
+        )
+    if not 0.0 <= confidence_threshold <= 1.0:
+        raise ValueError(
+            f"confidence_threshold must be between 0.0 and 1.0, got {confidence_threshold}"
+        )
+
+    def decorator(func: Callable) -> Callable:
+        spec = EmulationSpec(
+            function_name=func.__qualname__,
+            tag=tag,
+            inputs=inputs,
+            outputs=outputs,
+            uncertainty=uncertainty,
+            fallback=fallback,
+            domain=domain or {},
+            confidence_threshold=confidence_threshold,
+        )
+
+        # Register in module-level registry
+        _EMULATABLE_REGISTRY[func.__qualname__] = spec
+
+        @functools.wraps(func)
+        def wrapper(*args: Any, **kwargs: Any) -> Any:
+            # The actual dispatch logic is handled by EmulatorDispatch
+            # when invoked through a session. Direct calls run the full model.
+            return func(*args, **kwargs)
+
+        # Attach spec as metadata
+        wrapper._emulation_spec = spec  # type: ignore[attr-defined]
+        wrapper._original_func = func  # type: ignore[attr-defined]
+        return wrapper
+
+    return decorator
+
+
+def get_emulation_spec(func: Callable) -> EmulationSpec | None:
+    """Retrieve the emulation spec for a decorated function, if any."""
+    return getattr(func, "_emulation_spec", None)
+
+
+def get_original_function(func: Callable) -> Callable:
+    """Get the original unwrapped function from an @emulatable-decorated callable."""
+    return getattr(func, "_original_func", func)
+
+
+def list_emulatable_functions() -> dict[str, EmulationSpec]:
+    """Return all registered emulatable function specs."""
+    return dict(_EMULATABLE_REGISTRY)
+
+
+__all__ = [
+    "EmulationSpec",
+    "emulatable",
+    "get_emulation_spec",
+    "get_original_function",
+    "list_emulatable_functions",
+]
diff --git a/scalable/emulation/dispatch.py b/scalable/emulation/dispatch.py
new file mode 100644
index 0000000..1eb4886
--- /dev/null
+++ b/scalable/emulation/dispatch.py
@@ -0,0 +1,245 @@
+"""Uncertainty-aware emulator dispatch routing."""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import Any
+
+from scalable.emulation.decorator import get_emulation_spec, get_original_function
+from scalable.emulation.registry import EmulatorRegistry
+
+
+@dataclass(frozen=True)
+class EmulatorDispatchResult:
+    """Result of an emulator-dispatched call with provenance."""
+
+    result: Any
+    source: str  # "emulator" | "full_model" | "cached"
+    confidence: float | None
+    emulator_version: str | None
+    fallback_reason: str | None
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "source": self.source,
+            "confidence": self.confidence,
+            "emulator_version": self.emulator_version,
+            "fallback_reason": self.fallback_reason,
+        }
+
+
+class EmulatorDispatch:
+    """Routes function calls to emulator or full model based on confidence.
+
+    The dispatch decision follows this logic:
+
+    1. Check if the function has an ``@emulatable`` spec
+    2. Check if an emulator is registered for the function
+    3. Validate that inputs are within the emulator's domain
+    4. Query emulator for prediction + uncertainty
+    5. If confidence ≥ threshold, return emulated result
+    6. Otherwise, execute full model (or apply fallback strategy)
+
+    Parameters
+    ----------
+    registry
+        The :class:`EmulatorRegistry` containing trained emulators.
+    confidence_threshold
+        Global confidence threshold override (function-level threshold
+        takes precedence if specified).
+    record_provenance
+        If ``True``, dispatch decisions are recorded for telemetry.
+    """
+
+    def __init__(
+        self,
+        registry: EmulatorRegistry,
+        *,
+        confidence_threshold: float = 0.9,
+        record_provenance: bool = True,
+    ) -> None:
+        self._registry = registry
+        self._confidence_threshold = confidence_threshold
+        self._record_provenance = record_provenance
+        self._dispatch_log: list[EmulatorDispatchResult] = []
+
+    @property
+    def dispatch_log(self) -> list[EmulatorDispatchResult]:
+        """Log of all dispatch decisions made."""
+        return list(self._dispatch_log)
+
+    def execute(
+        self,
+        func: Callable,
+        *args: Any,
+        emulator_name: str | None = None,
+        force_full_model: bool = False,
+        **kwargs: Any,
+    ) -> EmulatorDispatchResult:
+        """Execute a function through the emulator dispatch pipeline.
+
+        Parameters
+        ----------
+        func
+            The function to execute (may be ``@emulatable``-decorated).
+        *args
+            Positional arguments to pass to the function.
+        emulator_name
+            Explicit emulator name. If ``None``, derives from function spec.
+        force_full_model
+            If ``True``, skip emulation and run the full model directly.
+        **kwargs
+            Keyword arguments to pass to the function.
+
+        Returns
+        -------
+        EmulatorDispatchResult
+            The result with provenance information.
+        """
+        spec = get_emulation_spec(func)
+        original_func = get_original_function(func)
+
+        # If forced full model or no emulation spec, run directly
+        if force_full_model or spec is None:
+            result = original_func(*args, **kwargs)
+            dispatch_result = EmulatorDispatchResult(
+                result=result,
+                source="full_model",
+                confidence=None,
+                emulator_version=None,
+                fallback_reason="forced" if force_full_model else "no_emulation_spec",
+            )
+            self._record(dispatch_result)
+            return dispatch_result
+
+        # Determine emulator name
+        emu_name = emulator_name or spec.function_name
+        threshold = spec.confidence_threshold or self._confidence_threshold
+
+        # Try to get emulator from registry
+        try:
+            emulator = self._registry.get(emu_name)
+        except KeyError:
+            return self._fallback(
+                original_func, args, kwargs, spec, reason="emulator_not_registered"
+            )
+
+        # Extract inputs for emulator from kwargs
+        inputs = self._extract_inputs(spec.inputs, args, kwargs)
+
+        # Validate domain
+        if spec.domain:
+            if not self._validate_domain(inputs, spec.domain):
+                return self._fallback(
+                    original_func, args, kwargs, spec, reason="outside_domain"
+                )
+
+        # Query emulator
+        prediction = emulator.predict(inputs)
+
+        # Check confidence
+        if prediction.confidence < threshold:
+            return self._fallback(
+                original_func,
+                args,
+                kwargs,
+                spec,
+                reason=f"low_confidence ({prediction.confidence:.3f} < {threshold})",
+            )
+
+        # Check uncertainty requirement
+        if spec.uncertainty == "required" and prediction.uncertainty_bounds is None:
+            return self._fallback(
+                original_func,
+                args,
+                kwargs,
+                spec,
+                reason="uncertainty_required_but_not_provided",
+            )
+
+        # Accept emulated result
+        dispatch_result = EmulatorDispatchResult(
+            result=prediction.outputs,
+            source="emulator",
+            confidence=prediction.confidence,
+            emulator_version=emulator.metadata.version,
+            fallback_reason=None,
+        )
+        self._record(dispatch_result)
+        return dispatch_result
+
+    def _fallback(
+        self,
+        func: Callable,
+        args: tuple,
+        kwargs: dict,
+        spec: Any,
+        *,
+        reason: str,
+    ) -> EmulatorDispatchResult:
+        """Execute fallback strategy based on spec."""
+        if spec.fallback == "error":
+            raise RuntimeError(
+                f"Emulation failed for {spec.function_name}: {reason}. "
+                f"Fallback strategy is 'error'."
+            )
+        elif spec.fallback == "cached":
+            # For now, fall through to full model (cache integration TBD)
+            pass
+
+        # Default: full_model
+        result = func(*args, **kwargs)
+        dispatch_result = EmulatorDispatchResult(
+            result=result,
+            source="full_model",
+            confidence=None,
+            emulator_version=None,
+            fallback_reason=reason,
+        )
+        self._record(dispatch_result)
+        return dispatch_result
+
+    def _extract_inputs(
+        self,
+        input_names: list[str],
+        args: tuple,
+        kwargs: dict,
+    ) -> dict[str, Any]:
+        """Extract named inputs from function arguments."""
+        inputs: dict[str, Any] = {}
+
+        # First try kwargs
+        for name in input_names:
+            if name in kwargs:
+                inputs[name] = kwargs[name]
+
+        # If first arg is a dict (common pattern), try extracting from it
+        if args and isinstance(args[0], dict):
+            for name in input_names:
+                if name not in inputs and name in args[0]:
+                    inputs[name] = args[0][name]
+
+        return inputs
+
+    def _validate_domain(
+        self,
+        inputs: dict[str, Any],
+        domain: dict[str, tuple[float, float]],
+    ) -> bool:
+        """Check if inputs are within declared domain bounds."""
+        for key, (lower, upper) in domain.items():
+            if key in inputs:
+                value = inputs[key]
+                if isinstance(value, (int, float)):
+                    if value < lower or value > upper:
+                        return False
+        return True
+
+    def _record(self, result: EmulatorDispatchResult) -> None:
+        """Record dispatch decision for telemetry."""
+        if self._record_provenance:
+            self._dispatch_log.append(result)
+
+
+__all__ = ["EmulatorDispatch", "EmulatorDispatchResult"]
diff --git a/scalable/emulation/registry.py b/scalable/emulation/registry.py
new file mode 100644
index 0000000..4747a99
--- /dev/null
+++ b/scalable/emulation/registry.py
@@ -0,0 +1,335 @@
+"""Emulator registry for managing trained surrogate models."""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from scalable.emulation.surrogate import TrainedEmulator
+
+
+@dataclass(frozen=True)
+class EmulatorInfo:
+    """Summary information about a registered emulator."""
+
+    name: str
+    version: str
+    model_type: str
+    training_samples: int
+    validation_score: float
+    input_names: list[str]
+    output_names: list[str]
+    domain_bounds: dict[str, tuple[float, float]]
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "name": self.name,
+            "version": self.version,
+            "model_type": self.model_type,
+            "training_samples": self.training_samples,
+            "validation_score": self.validation_score,
+            "input_names": self.input_names,
+            "output_names": self.output_names,
+            "domain_bounds": self.domain_bounds,
+        }
+
+
+class EmulatorRegistry:
+    """Manages trained emulator models with versioning and domain validation.
+
+    Emulators are stored in a directory structure:
+
+    .. code-block:: text
+
+        <registry_dir>/
+          <name>/
+            <version>/
+              metadata.json
+              model.joblib (or other serialization)
+
+    Parameters
+    ----------
+    registry_dir
+        Path to the emulator registry directory.
+    """
+
+    def __init__(self, registry_dir: str | Path) -> None:
+        self._registry_dir = Path(registry_dir)
+        self._emulators: dict[str, dict[str, TrainedEmulator]] = {}
+        self._load_metadata()
+
+    def _load_metadata(self) -> None:
+        """Discover registered emulators from the filesystem."""
+        if not self._registry_dir.exists():
+            return
+        for name_dir in self._registry_dir.iterdir():
+            if not name_dir.is_dir():
+                continue
+            name = name_dir.name
+            for version_dir in name_dir.iterdir():
+                if not version_dir.is_dir():
+                    continue
+                meta_file = version_dir / "metadata.json"
+                if meta_file.exists():
+                    if name not in self._emulators:
+                        self._emulators[name] = {}
+                    # Metadata loaded lazily; actual model loaded on get()
+
+    def register(
+        self,
+        name: str,
+        emulator: TrainedEmulator,
+        *,
+        version: str | None = None,
+        domain: dict[str, tuple[float, float]] | None = None,
+    ) -> str:
+        """Register a trained emulator in the registry.
+
+        Parameters
+        ----------
+        name
+            Logical name for the emulator (e.g., "gcam_emissions").
+        emulator
+            A trained emulator implementing the :class:`TrainedEmulator` protocol.
+        version
+            Version string. If ``None``, auto-increments from latest.
+        domain
+            Optional domain bounds override (defaults to emulator metadata).
+
+        Returns
+        -------
+        str
+            The version string assigned to this registration.
+        """
+        if version is None:
+            existing_versions = list(self._emulators.get(name, {}).keys())
+            if existing_versions:
+                # Simple integer versioning
+                max_v = max(int(v) for v in existing_versions if v.isdigit())
+                version = str(max_v + 1)
+            else:
+                version = "1"
+
+        # Store in memory
+        if name not in self._emulators:
+            self._emulators[name] = {}
+        self._emulators[name][version] = emulator
+
+        # Persist metadata
+        meta = emulator.metadata
+        version_dir = self._registry_dir / name / version
+        version_dir.mkdir(parents=True, exist_ok=True)
+
+        meta_dict = meta.to_dict()
+        if domain:
+            meta_dict["domain_bounds"] = {
+                k: list(v) for k, v in domain.items()
+            }
+
+        (version_dir / "metadata.json").write_text(
+            json.dumps(meta_dict, indent=2, default=str)
+        )
+
+        # Try to persist the model itself
+        try:
+            import joblib
+
+            joblib.dump(emulator, version_dir / "model.joblib")
+        except (ImportError, Exception):
+            pass  # Model not serializable or joblib unavailable
+
+        return version
+
+    def get(
+        self,
+        name: str,
+        *,
+        version: str | None = None,
+    ) -> TrainedEmulator:
+        """Retrieve a registered emulator by name and optional version.
+
+        Parameters
+        ----------
+        name
+            Emulator name.
+        version
+            Specific version to retrieve. If ``None``, returns latest.
+
+        Returns
+        -------
+        TrainedEmulator
+            The registered emulator instance.
+
+        Raises
+        ------
+        KeyError
+            If the emulator or version is not found.
+        """
+        if name not in self._emulators or not self._emulators[name]:
+            # Try loading from disk
+            self._load_emulator_from_disk(name, version)
+
+        if name not in self._emulators or not self._emulators[name]:
+            raise KeyError(f"Emulator {name!r} not found in registry")
+
+        versions = self._emulators[name]
+        if version is not None:
+            if version not in versions:
+                raise KeyError(f"Emulator {name!r} version {version!r} not found")
+            return versions[version]
+
+        # Return latest version
+        latest = max(versions.keys(), key=lambda v: int(v) if v.isdigit() else 0)
+        return versions[latest]
+
+    def _load_emulator_from_disk(self, name: str, version: str | None) -> None:
+        """Attempt to load an emulator from disk."""
+        name_dir = self._registry_dir / name
+        if not name_dir.exists():
+            return
+
+        for version_dir in name_dir.iterdir():
+            if not version_dir.is_dir():
+                continue
+            if version is not None and version_dir.name != version:
+                continue
+
+            model_path = version_dir / "model.joblib"
+            if model_path.exists():
+                try:
+                    import joblib
+
+                    emulator = joblib.load(model_path)
+                    if name not in self._emulators:
+                        self._emulators[name] = {}
+                    self._emulators[name][version_dir.name] = emulator
+                except (ImportError, Exception):
+                    pass
+
+    def list(self) -> list[EmulatorInfo]:
+        """List all registered emulators with summary info."""
+        results: list[EmulatorInfo] = []
+
+        # Check in-memory emulators
+        for _name, versions in self._emulators.items():
+            for _ver, emulator in versions.items():
+                meta = emulator.metadata
+                results.append(
+                    EmulatorInfo(
+                        name=meta.name,
+                        version=meta.version,
+                        model_type=meta.model_type,
+                        training_samples=meta.training_samples,
+                        validation_score=meta.validation_score,
+                        input_names=meta.input_names,
+                        output_names=meta.output_names,
+                        domain_bounds=meta.domain_bounds,
+                    )
+                )
+
+        # Check filesystem for any not in memory
+        if self._registry_dir.exists():
+            for name_dir in self._registry_dir.iterdir():
+                if not name_dir.is_dir():
+                    continue
+                name = name_dir.name
+                for version_dir in name_dir.iterdir():
+                    if not version_dir.is_dir():
+                        continue
+                    ver = version_dir.name
+                    # Skip if already listed from memory
+                    if name in self._emulators and ver in self._emulators[name]:
+                        continue
+                    meta_file = version_dir / "metadata.json"
+                    if meta_file.exists():
+                        try:
+                            meta_dict = json.loads(meta_file.read_text())
+                            domain = {}
+                            for k, v in meta_dict.get("domain_bounds", {}).items():
+                                if isinstance(v, (list, tuple)) and len(v) == 2:
+                                    domain[k] = (float(v[0]), float(v[1]))
+                            results.append(
+                                EmulatorInfo(
+                                    name=meta_dict.get("name", name),
+                                    version=meta_dict.get("version", ver),
+                                    model_type=meta_dict.get("model_type", "unknown"),
+                                    training_samples=meta_dict.get("training_samples", 0),
+                                    validation_score=meta_dict.get("validation_score", 0.0),
+                                    input_names=meta_dict.get("input_names", []),
+                                    output_names=meta_dict.get("output_names", []),
+                                    domain_bounds=domain,
+                                )
+                            )
+                        except (json.JSONDecodeError, Exception):
+                            pass
+
+        return results
+
+    def validate_domain(self, name: str, inputs: dict[str, Any]) -> bool:
+        """Check if inputs fall within the emulator's declared domain bounds.
+
+        Parameters
+        ----------
+        name
+            Emulator name.
+        inputs
+            Input values to validate.
+
+        Returns
+        -------
+        bool
+            ``True`` if all inputs are within declared bounds (or no bounds declared).
+        """
+        try:
+            emulator = self.get(name)
+        except KeyError:
+            return False
+
+        domain = emulator.metadata.domain_bounds
+        if not domain:
+            return True  # No bounds declared — assume valid
+
+        for key, (lower, upper) in domain.items():
+            if key in inputs:
+                value = inputs[key]
+                if isinstance(value, (int, float)):
+                    if value < lower or value > upper:
+                        return False
+        return True
+
+    def remove(self, name: str, *, version: str | None = None) -> None:
+        """Remove an emulator from the registry.
+
+        Parameters
+        ----------
+        name
+            Emulator name to remove.
+        version
+            If specified, remove only this version. Otherwise remove all versions.
+        """
+        if name in self._emulators:
+            if version is not None:
+                self._emulators[name].pop(version, None)
+                if not self._emulators[name]:
+                    del self._emulators[name]
+            else:
+                del self._emulators[name]
+
+        # Clean up filesystem
+        if version is not None:
+            version_dir = self._registry_dir / name / version
+            if version_dir.exists():
+                import shutil
+
+                shutil.rmtree(version_dir)
+        else:
+            name_dir = self._registry_dir / name
+            if name_dir.exists():
+                import shutil
+
+                shutil.rmtree(name_dir)
+
+
+__all__ = ["EmulatorInfo", "EmulatorRegistry"]
diff --git a/scalable/emulation/surrogate.py b/scalable/emulation/surrogate.py
new file mode 100644
index 0000000..8d73ac6
--- /dev/null
+++ b/scalable/emulation/surrogate.py
@@ -0,0 +1,280 @@
+"""Surrogate model abstractions for scientific model emulation."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any, Protocol
+
+import numpy as np
+
+
+@dataclass(frozen=True)
+class EmulatorPrediction:
+    """Prediction result with uncertainty information."""
+
+    outputs: dict[str, Any]
+    confidence: float
+    uncertainty_bounds: dict[str, tuple[float, float]] | None = None
+    is_emulated: bool = True
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "outputs": self.outputs,
+            "confidence": self.confidence,
+            "uncertainty_bounds": self.uncertainty_bounds,
+            "is_emulated": self.is_emulated,
+        }
+
+
+@dataclass(frozen=True)
+class EmulatorMetadata:
+    """Provenance and quality metadata for a trained emulator."""
+
+    name: str
+    version: str
+    training_runs: list[str]
+    training_samples: int
+    validation_score: float
+    domain_bounds: dict[str, tuple[float, float]]
+    created_at: str
+    model_type: str
+    output_names: list[str] = field(default_factory=list)
+    input_names: list[str] = field(default_factory=list)
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "name": self.name,
+            "version": self.version,
+            "training_runs": self.training_runs,
+            "training_samples": self.training_samples,
+            "validation_score": self.validation_score,
+            "domain_bounds": self.domain_bounds,
+            "created_at": self.created_at,
+            "model_type": self.model_type,
+            "output_names": self.output_names,
+            "input_names": self.input_names,
+        }
+
+
+class TrainedEmulator(Protocol):
+    """Protocol for trained surrogate models."""
+
+    def predict(self, inputs: dict[str, Any]) -> EmulatorPrediction:
+        """Produce predictions with uncertainty for the given inputs."""
+        ...
+
+    def uncertainty(self, inputs: dict[str, Any]) -> float:
+        """Return scalar uncertainty estimate for the given inputs."""
+        ...
+
+    @property
+    def metadata(self) -> EmulatorMetadata:
+        """Return emulator provenance and quality metadata."""
+        ...
+
+
+class GradientBoostingEmulator:
+    """Gradient boosting-based surrogate model.
+
+    Uses sklearn's GradientBoostingRegressor for tabular scenario features.
+    Uncertainty is estimated from ensemble variance.
+    """
+
+    def __init__(
+        self,
+        *,
+        metadata: EmulatorMetadata,
+        models: dict[str, Any] | None = None,
+    ) -> None:
+        self._metadata = metadata
+        self._models: dict[str, Any] = models or {}
+        self._input_names = list(metadata.input_names)
+        self._output_names = list(metadata.output_names)
+
+    @property
+    def metadata(self) -> EmulatorMetadata:
+        return self._metadata
+
+    def train(
+        self,
+        X: Any,
+        y: dict[str, Any],
+        *,
+        n_estimators: int = 100,
+        max_depth: int = 5,
+        random_state: int = 42,
+    ) -> None:
+        """Train the emulator on input/output data.
+
+        Parameters
+        ----------
+        X
+            Input features (numpy array or DataFrame).
+        y
+            Dict mapping output name to target arrays.
+        """
+        try:
+            from sklearn.ensemble import GradientBoostingRegressor
+
+            for output_name, y_vals in y.items():
+                model = GradientBoostingRegressor(
+                    n_estimators=n_estimators,
+                    max_depth=max_depth,
+                    random_state=random_state,
+                )
+                model.fit(X, y_vals)
+                self._models[output_name] = model
+        except ImportError:
+            pass
+
+    def predict(self, inputs: dict[str, Any]) -> EmulatorPrediction:
+        """Predict outputs with uncertainty estimation."""
+        if not self._models:
+            return EmulatorPrediction(
+                outputs={},
+                confidence=0.0,
+                uncertainty_bounds=None,
+                is_emulated=True,
+            )
+
+        # Build input vector
+        X = np.array([[inputs.get(name, 0) for name in self._input_names]])
+
+        outputs: dict[str, Any] = {}
+        bounds: dict[str, tuple[float, float]] = {}
+        confidences: list[float] = []
+
+        for output_name, model in self._models.items():
+            pred = float(model.predict(X)[0])
+            outputs[output_name] = pred
+
+            # Estimate uncertainty from staged predictions variance
+            if hasattr(model, "estimators_"):
+                staged_preds = np.array(
+                    [est[0].predict(X)[0] for est in model.estimators_]
+                )
+                std = float(np.std(staged_preds))
+                lower = pred - 2 * std
+                upper = pred + 2 * std
+                bounds[output_name] = (lower, upper)
+                # Confidence inversely proportional to relative uncertainty
+                rel_uncertainty = std / (abs(pred) + 1e-10)
+                conf = max(0.0, min(1.0, 1.0 - rel_uncertainty))
+                confidences.append(conf)
+            else:
+                bounds[output_name] = (pred * 0.8, pred * 1.2)
+                confidences.append(0.7)
+
+        overall_confidence = float(np.mean(confidences)) if confidences else 0.5
+
+        return EmulatorPrediction(
+            outputs=outputs,
+            confidence=overall_confidence,
+            uncertainty_bounds=bounds,
+            is_emulated=True,
+        )
+
+    def uncertainty(self, inputs: dict[str, Any]) -> float:
+        """Return scalar uncertainty (1 - confidence)."""
+        pred = self.predict(inputs)
+        return 1.0 - pred.confidence
+
+
+class RandomForestEmulator:
+    """Random forest-based surrogate model.
+
+    Uses sklearn's RandomForestRegressor. Uncertainty estimated from
+    individual tree prediction variance.
+    """
+
+    def __init__(
+        self,
+        *,
+        metadata: EmulatorMetadata,
+        models: dict[str, Any] | None = None,
+    ) -> None:
+        self._metadata = metadata
+        self._models: dict[str, Any] = models or {}
+        self._input_names = list(metadata.input_names)
+        self._output_names = list(metadata.output_names)
+
+    @property
+    def metadata(self) -> EmulatorMetadata:
+        return self._metadata
+
+    def train(
+        self,
+        X: Any,
+        y: dict[str, Any],
+        *,
+        n_estimators: int = 100,
+        max_depth: int = 10,
+        random_state: int = 42,
+    ) -> None:
+        """Train the emulator on input/output data."""
+        try:
+            from sklearn.ensemble import RandomForestRegressor
+
+            for output_name, y_vals in y.items():
+                model = RandomForestRegressor(
+                    n_estimators=n_estimators,
+                    max_depth=max_depth,
+                    random_state=random_state,
+                    n_jobs=-1,
+                )
+                model.fit(X, y_vals)
+                self._models[output_name] = model
+        except ImportError:
+            pass
+
+    def predict(self, inputs: dict[str, Any]) -> EmulatorPrediction:
+        """Predict outputs with tree-based uncertainty estimation."""
+        if not self._models:
+            return EmulatorPrediction(
+                outputs={},
+                confidence=0.0,
+                uncertainty_bounds=None,
+                is_emulated=True,
+            )
+
+        X = np.array([[inputs.get(name, 0) for name in self._input_names]])
+
+        outputs: dict[str, Any] = {}
+        bounds: dict[str, tuple[float, float]] = {}
+        confidences: list[float] = []
+
+        for output_name, model in self._models.items():
+            # Get individual tree predictions
+            tree_preds = np.array([t.predict(X)[0] for t in model.estimators_])
+            pred = float(np.mean(tree_preds))
+            std = float(np.std(tree_preds))
+
+            outputs[output_name] = pred
+            bounds[output_name] = (pred - 2 * std, pred + 2 * std)
+
+            rel_uncertainty = std / (abs(pred) + 1e-10)
+            conf = max(0.0, min(1.0, 1.0 - rel_uncertainty))
+            confidences.append(conf)
+
+        overall_confidence = float(np.mean(confidences)) if confidences else 0.5
+
+        return EmulatorPrediction(
+            outputs=outputs,
+            confidence=overall_confidence,
+            uncertainty_bounds=bounds,
+            is_emulated=True,
+        )
+
+    def uncertainty(self, inputs: dict[str, Any]) -> float:
+        """Return scalar uncertainty (1 - confidence)."""
+        pred = self.predict(inputs)
+        return 1.0 - pred.confidence
+
+
+__all__ = [
+    "EmulatorMetadata",
+    "EmulatorPrediction",
+    "GradientBoostingEmulator",
+    "RandomForestEmulator",
+    "TrainedEmulator",
+]
diff --git a/scalable/emulation/uncertainty.py b/scalable/emulation/uncertainty.py
new file mode 100644
index 0000000..c7aa791
--- /dev/null
+++ b/scalable/emulation/uncertainty.py
@@ -0,0 +1,193 @@
+"""Uncertainty calibration utilities for emulators."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+import numpy as np
+
+
+@dataclass(frozen=True)
+class CalibrationResult:
+    """Result of emulator uncertainty calibration assessment."""
+
+    coverage_90: float  # Fraction of true values within 90% interval
+    coverage_95: float  # Fraction of true values within 95% interval
+    mean_interval_width: float
+    sharpness: float  # Average interval width relative to prediction magnitude
+    n_samples: int
+    is_calibrated: bool  # True if coverage is within acceptable range
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "coverage_90": self.coverage_90,
+            "coverage_95": self.coverage_95,
+            "mean_interval_width": self.mean_interval_width,
+            "sharpness": self.sharpness,
+            "n_samples": self.n_samples,
+            "is_calibrated": self.is_calibrated,
+        }
+
+
+def calibrate_emulator(
+    predictions: list[dict[str, Any]],
+    actuals: list[dict[str, Any]],
+    *,
+    output_name: str,
+    tolerance: float = 0.1,
+) -> CalibrationResult:
+    """Assess calibration of emulator uncertainty estimates.
+
+    Checks whether the stated confidence intervals actually contain
+    the true values at the stated rate.
+
+    Parameters
+    ----------
+    predictions
+        List of emulator prediction dicts (from ``EmulatorPrediction.to_dict()``).
+        Each should have ``outputs`` and ``uncertainty_bounds``.
+    actuals
+        List of actual output dicts from full-model runs.
+        Each should have the ``output_name`` key with the true value.
+    output_name
+        Which output variable to assess calibration for.
+    tolerance
+        Acceptable deviation from nominal coverage (e.g., 0.1 means
+        90% coverage for a 95% interval is acceptable).
+
+    Returns
+    -------
+    CalibrationResult
+        Calibration assessment with coverage and sharpness metrics.
+    """
+    if not predictions or not actuals or len(predictions) != len(actuals):
+        return CalibrationResult(
+            coverage_90=0.0,
+            coverage_95=0.0,
+            mean_interval_width=0.0,
+            sharpness=0.0,
+            n_samples=0,
+            is_calibrated=False,
+        )
+
+    in_90: list[bool] = []
+    in_95: list[bool] = []
+    widths: list[float] = []
+    relative_widths: list[float] = []
+
+    for pred, actual in zip(predictions, actuals, strict=False):
+        true_value = actual.get(output_name)
+        if true_value is None:
+            continue
+
+        true_value = float(true_value)
+        outputs = pred.get("outputs", {})
+        bounds = pred.get("uncertainty_bounds", {})
+
+        if output_name not in bounds:
+            continue
+
+        bound = bounds[output_name]
+        if not isinstance(bound, (list, tuple)) or len(bound) != 2:
+            continue
+
+        lower, upper = float(bound[0]), float(bound[1])
+        width = upper - lower
+        widths.append(width)
+
+        pred_value = float(outputs.get(output_name, 0))
+        mag = abs(pred_value) + 1e-10
+        relative_widths.append(width / mag)
+
+        # 95% interval check (using full bounds)
+        in_95.append(lower <= true_value <= upper)
+
+        # 90% interval (shrink bounds by ~5% on each side)
+        shrink = width * 0.05
+        in_90.append((lower + shrink) <= true_value <= (upper - shrink))
+
+    n = len(in_95)
+    if n == 0:
+        return CalibrationResult(
+            coverage_90=0.0,
+            coverage_95=0.0,
+            mean_interval_width=0.0,
+            sharpness=0.0,
+            n_samples=0,
+            is_calibrated=False,
+        )
+
+    coverage_90 = float(np.mean(in_90))
+    coverage_95 = float(np.mean(in_95))
+    mean_width = float(np.mean(widths))
+    sharpness = float(np.mean(relative_widths))
+
+    # Check if calibration is acceptable
+    # For 95% intervals, coverage should be at least 95% - tolerance
+    is_calibrated = coverage_95 >= (0.95 - tolerance)
+
+    return CalibrationResult(
+        coverage_90=coverage_90,
+        coverage_95=coverage_95,
+        mean_interval_width=mean_width,
+        sharpness=sharpness,
+        n_samples=n,
+        is_calibrated=is_calibrated,
+    )
+
+
+def compute_confidence_from_uncertainty(
+    uncertainty: float,
+    *,
+    max_uncertainty: float = 1.0,
+) -> float:
+    """Convert a scalar uncertainty value to a confidence score.
+
+    Parameters
+    ----------
+    uncertainty
+        Raw uncertainty value (higher = less confident).
+    max_uncertainty
+        Maximum expected uncertainty for normalization.
+
+    Returns
+    -------
+    float
+        Confidence in [0, 1] range (higher = more confident).
+    """
+    if uncertainty <= 0:
+        return 1.0
+    if uncertainty >= max_uncertainty:
+        return 0.0
+    return 1.0 - (uncertainty / max_uncertainty)
+
+
+def is_in_domain(
+    inputs: dict[str, Any],
+    domain_bounds: dict[str, tuple[float, float]],
+) -> bool:
+    """Check if all inputs are within declared domain bounds.
+
+    Parameters
+    ----------
+    inputs
+        Input values to check.
+    domain_bounds
+        Dict mapping input names to (min, max) tuples.
+
+    Returns
+    -------
+    bool
+        ``True`` if all specified inputs are within bounds.
+    """
+    for key, (lower, upper) in domain_bounds.items():
+        if key in inputs:
+            value = inputs[key]
+            if isinstance(value, (int, float)):
+                if value < lower or value > upper:
+                    return False
+    return True
+
+
+__all__ = ["CalibrationResult", "calibrate_emulator", "compute_confidence_from_uncertainty", "is_in_domain"]
diff --git a/scalable/ml/__init__.py b/scalable/ml/__init__.py
new file mode 100644
index 0000000..4d6e28d
--- /dev/null
+++ b/scalable/ml/__init__.py
@@ -0,0 +1,34 @@
+"""ML optimization subsystem for Scalable (Phase 5).
+
+This package provides machine-learning-backed resource prediction, adaptive
+scaling, and distributed hyperparameter tuning. All features degrade gracefully
+to Phase 2 heuristics when ``scalable[ml]`` is not installed.
+
+Key components:
+
+* :class:`LearnedAdvisor` — ML-based resource recommendations
+* :class:`AdaptiveScaler` — real-time adaptive worker scaling
+* :class:`HyperparameterSearch` — Dask-ML distributed tuning
+* :class:`FeatureExtractor` — telemetry feature engineering
+"""
+
+from __future__ import annotations
+
+from .adaptive_scaler import AdaptiveScaler, ScaleDecision
+from .features import FeatureExtractor
+from .learned_advisor import LearnedAdvisor
+from .models import ModelQuality, PredictionResult
+from .tuning import HyperparameterSearch, TuningResult
+from .validation import cross_validate_advisor
+
+__all__ = [
+    "AdaptiveScaler",
+    "FeatureExtractor",
+    "HyperparameterSearch",
+    "LearnedAdvisor",
+    "ModelQuality",
+    "PredictionResult",
+    "ScaleDecision",
+    "TuningResult",
+    "cross_validate_advisor",
+]
diff --git a/scalable/ml/adaptive_scaler.py b/scalable/ml/adaptive_scaler.py
new file mode 100644
index 0000000..9e30dd9
--- /dev/null
+++ b/scalable/ml/adaptive_scaler.py
@@ -0,0 +1,243 @@
+"""Real-time adaptive worker scaling based on ML predictions."""
+
+from __future__ import annotations
+
+import time
+from dataclasses import dataclass, field
+from typing import Any
+
+
+@dataclass(frozen=True)
+class ScaleDecision:
+    """A scaling recommendation with reasoning."""
+
+    workers_to_add: dict[str, int]
+    workers_to_remove: dict[str, int]
+    reasoning: str
+    confidence: float
+    predicted_completion_time: float | None = None
+    timestamp: float = field(default_factory=time.time)
+
+    @property
+    def has_changes(self) -> bool:
+        """Whether this decision suggests any scaling changes."""
+        return bool(self.workers_to_add or self.workers_to_remove)
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "workers_to_add": dict(self.workers_to_add),
+            "workers_to_remove": dict(self.workers_to_remove),
+            "reasoning": self.reasoning,
+            "confidence": self.confidence,
+            "predicted_completion_time": self.predicted_completion_time,
+            "timestamp": self.timestamp,
+        }
+
+
+class AdaptiveScaler:
+    """Real-time adaptive worker scaling based on ML predictions.
+
+    Monitors task queue depth and active worker utilization to recommend
+    scaling actions. Respects user-defined min/max bounds and cooldown
+    periods to prevent thrashing.
+
+    Parameters
+    ----------
+    advisor
+        A :class:`~scalable.ml.learned_advisor.LearnedAdvisor` or
+        :class:`~scalable.advising.resources.ResourceAdvisor` instance
+        for predicting task durations.
+    min_workers
+        Minimum worker count per tag (floor).
+    max_workers
+        Maximum worker count per tag (ceiling).
+    scale_up_threshold
+        Queue depth ratio that triggers scale-up (0.0–1.0).
+    scale_down_threshold
+        Queue depth ratio that triggers scale-down (0.0–1.0).
+    cooldown_seconds
+        Minimum time between scaling decisions.
+    """
+
+    def __init__(
+        self,
+        *,
+        advisor: Any = None,
+        min_workers: dict[str, int] | None = None,
+        max_workers: dict[str, int] | None = None,
+        scale_up_threshold: float = 0.8,
+        scale_down_threshold: float = 0.2,
+        cooldown_seconds: float = 60.0,
+    ) -> None:
+        self._advisor = advisor
+        self._min_workers = min_workers or {}
+        self._max_workers = max_workers or {}
+        self._scale_up_threshold = scale_up_threshold
+        self._scale_down_threshold = scale_down_threshold
+        self._cooldown_seconds = cooldown_seconds
+        self._last_decision_time: float = 0.0
+        self._decision_history: list[ScaleDecision] = []
+
+    @property
+    def decision_history(self) -> list[ScaleDecision]:
+        """List of all scaling decisions made."""
+        return list(self._decision_history)
+
+    def evaluate(
+        self,
+        *,
+        pending_tasks: list[dict[str, Any]],
+        active_workers: dict[str, int],
+        recent_completions: list[dict[str, Any]] | None = None,
+    ) -> ScaleDecision:
+        """Evaluate current state and recommend scaling actions.
+
+        Parameters
+        ----------
+        pending_tasks
+            List of pending task metadata dicts. Each should have at least
+            ``tag`` or ``component`` key.
+        active_workers
+            Current worker count per tag/component.
+        recent_completions
+            Recently completed task metadata for throughput estimation.
+
+        Returns
+        -------
+        ScaleDecision
+            Recommended scaling action with reasoning.
+        """
+        now = time.time()
+
+        # Check cooldown
+        if now - self._last_decision_time < self._cooldown_seconds:
+            return ScaleDecision(
+                workers_to_add={},
+                workers_to_remove={},
+                reasoning="Cooldown period active",
+                confidence=1.0,
+                predicted_completion_time=None,
+            )
+
+        # Group pending tasks by tag/component
+        pending_by_tag: dict[str, int] = {}
+        for task in pending_tasks:
+            tag = task.get("tag") or task.get("component") or "default"
+            pending_by_tag[tag] = pending_by_tag.get(tag, 0) + 1
+
+        workers_to_add: dict[str, int] = {}
+        workers_to_remove: dict[str, int] = {}
+        reasons: list[str] = []
+
+        for tag, pending_count in pending_by_tag.items():
+            current_workers = active_workers.get(tag, 0)
+            max_allowed = self._max_workers.get(tag, current_workers + 10)
+            min_allowed = self._min_workers.get(tag, 0)
+
+            if current_workers == 0:
+                # No workers — always scale up if there's pending work
+                to_add = min(pending_count, max_allowed)
+                if to_add > 0:
+                    workers_to_add[tag] = to_add
+                    reasons.append(f"{tag}: no workers, adding {to_add} for {pending_count} pending")
+                continue
+
+            # Calculate queue ratio (pending per worker)
+            queue_ratio = pending_count / max(current_workers, 1)
+
+            if queue_ratio > self._scale_up_threshold:
+                # Scale up: add workers proportional to excess queue
+                desired = min(
+                    int(pending_count / self._scale_up_threshold),
+                    max_allowed,
+                )
+                to_add = max(0, desired - current_workers)
+                if to_add > 0:
+                    workers_to_add[tag] = to_add
+                    reasons.append(
+                        f"{tag}: queue ratio {queue_ratio:.2f} > {self._scale_up_threshold}, "
+                        f"adding {to_add} workers"
+                    )
+
+            elif queue_ratio < self._scale_down_threshold and current_workers > min_allowed:
+                # Scale down: remove excess workers
+                desired = max(
+                    int(pending_count / self._scale_up_threshold) + 1,
+                    min_allowed,
+                )
+                to_remove = max(0, current_workers - desired)
+                if to_remove > 0:
+                    workers_to_remove[tag] = to_remove
+                    reasons.append(
+                        f"{tag}: queue ratio {queue_ratio:.2f} < {self._scale_down_threshold}, "
+                        f"removing {to_remove} workers"
+                    )
+
+        # Check for tags with workers but no pending tasks
+        for tag, count in active_workers.items():
+            if tag not in pending_by_tag and count > self._min_workers.get(tag, 0):
+                excess = count - self._min_workers.get(tag, 0)
+                if excess > 0:
+                    workers_to_remove[tag] = excess
+                    reasons.append(f"{tag}: no pending tasks, removing {excess} idle workers")
+
+        # Estimate completion time
+        predicted_completion = self._estimate_completion_time(
+            pending_by_tag, active_workers, workers_to_add, recent_completions
+        )
+
+        reasoning = "; ".join(reasons) if reasons else "No scaling changes needed"
+        confidence = 0.9 if self._advisor is not None else 0.7
+
+        decision = ScaleDecision(
+            workers_to_add=workers_to_add,
+            workers_to_remove=workers_to_remove,
+            reasoning=reasoning,
+            confidence=confidence,
+            predicted_completion_time=predicted_completion,
+        )
+
+        self._last_decision_time = now
+        self._decision_history.append(decision)
+        return decision
+
+    def _estimate_completion_time(
+        self,
+        pending_by_tag: dict[str, int],
+        active_workers: dict[str, int],
+        workers_to_add: dict[str, int],
+        recent_completions: list[dict[str, Any]] | None,
+    ) -> float | None:
+        """Estimate time to complete all pending tasks."""
+        if not pending_by_tag:
+            return 0.0
+
+        if not recent_completions:
+            return None
+
+        # Simple throughput-based estimation
+        total_pending = sum(pending_by_tag.values())
+        total_workers = sum(active_workers.values()) + sum(workers_to_add.values())
+
+        if total_workers == 0:
+            return None
+
+        # Estimate average task duration from recent completions
+        durations = [
+            c.get("duration_s", 60)
+            for c in recent_completions
+            if c.get("duration_s") is not None
+        ]
+        if not durations:
+            return None
+
+        avg_duration = sum(durations) / len(durations)
+        estimated_time = (total_pending / total_workers) * avg_duration
+        return estimated_time
+
+    def reset_cooldown(self) -> None:
+        """Reset the cooldown timer (for testing or manual override)."""
+        self._last_decision_time = 0.0
+
+
+__all__ = ["AdaptiveScaler", "ScaleDecision"]
diff --git a/scalable/ml/features.py b/scalable/ml/features.py
new file mode 100644
index 0000000..4dd8a31
--- /dev/null
+++ b/scalable/ml/features.py
@@ -0,0 +1,197 @@
+"""Feature extraction from telemetry records and task arguments."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+
+@dataclass
+class FeatureExtractor:
+    """Extract ML features from telemetry records and task arguments.
+
+    Engineered features include:
+    - Task identity (component name hash, tag hash)
+    - Resource request features (requested cpus/memory/walltime)
+    - Temporal features (hour of day, day of week)
+    - Historical aggregates (rolling mean/p95 for same task)
+    - Input complexity features (from user-provided input_features dict)
+    """
+
+    #: Minimum rows per task group for rolling aggregates
+    min_group_size: int = 3
+
+    #: Known numeric input feature names (auto-discovered if not set)
+    known_input_features: list[str] = field(default_factory=list)
+
+    def extract_from_history(self, records: pd.DataFrame) -> pd.DataFrame:
+        """Engineer features from historical telemetry records.
+
+        Parameters
+        ----------
+        records
+            DataFrame from :class:`~scalable.advising.resources.ResourceAdvisor`
+            internal format: columns include ``task_name``, ``component``,
+            ``duration_s``, ``requested_cpus``, ``requested_memory_bytes``, etc.
+
+        Returns
+        -------
+        pd.DataFrame
+            Feature matrix suitable for ML model training with target columns
+            preserved (``duration_s``, ``requested_memory_bytes``).
+        """
+        if records.empty:
+            return pd.DataFrame()
+
+        df = records.copy()
+
+        # Task identity features (hashed for model consumption)
+        df["task_name_hash"] = df["task_name"].apply(
+            lambda x: hash(str(x)) % 10000 if pd.notna(x) else 0
+        )
+        df["component_hash"] = df["component"].apply(
+            lambda x: hash(str(x)) % 10000 if pd.notna(x) else 0
+        )
+
+        # Numeric resource features
+        df["requested_cpus_num"] = pd.to_numeric(
+            df.get("requested_cpus"), errors="coerce"
+        ).fillna(1)
+        df["requested_memory_num"] = pd.to_numeric(
+            df.get("requested_memory_bytes"), errors="coerce"
+        ).fillna(0)
+        df["requested_workers_num"] = pd.to_numeric(
+            df.get("requested_workers"), errors="coerce"
+        ).fillna(1)
+
+        # Duration target (kept for training, not used as input feature)
+        df["duration_num"] = pd.to_numeric(
+            df.get("duration_s"), errors="coerce"
+        )
+
+        # Historical rolling aggregates per task_name
+        df = df.sort_index()
+        grouped = df.groupby("task_name", sort=False)
+        df["hist_mean_duration"] = grouped["duration_num"].transform(
+            lambda s: s.expanding(min_periods=1).mean().shift(1)
+        )
+        df["hist_p95_duration"] = grouped["duration_num"].transform(
+            lambda s: s.expanding(min_periods=1).quantile(0.95).shift(1)
+        )
+        df["hist_mean_memory"] = grouped["requested_memory_num"].transform(
+            lambda s: s.expanding(min_periods=1).mean().shift(1)
+        )
+        df["hist_count"] = grouped["duration_num"].transform(
+            lambda s: s.expanding(min_periods=1).count().shift(1)
+        )
+
+        # Fill NaN from rolling aggregates with global means
+        for col in ["hist_mean_duration", "hist_p95_duration", "hist_mean_memory", "hist_count"]:
+            df[col] = df[col].fillna(0)
+
+        feature_cols = [
+            "task_name_hash",
+            "component_hash",
+            "requested_cpus_num",
+            "requested_memory_num",
+            "requested_workers_num",
+            "hist_mean_duration",
+            "hist_p95_duration",
+            "hist_mean_memory",
+            "hist_count",
+        ]
+
+        # Keep targets for training
+        target_cols = ["duration_num", "requested_memory_num"]
+        available_targets = [c for c in target_cols if c in df.columns]
+
+        return df[feature_cols + available_targets].copy()
+
+    def extract_from_task(
+        self,
+        task_name: str,
+        input_features: dict[str, Any] | None,
+        component: str | None,
+        target: str | None,
+        *,
+        history_stats: dict[str, Any] | None = None,
+    ) -> pd.DataFrame:
+        """Build feature vector for a new prediction request.
+
+        Parameters
+        ----------
+        task_name
+            Name of the task to predict for.
+        input_features
+            User-provided features (scenario count, input size, etc.).
+        component
+            Component name associated with the task.
+        target
+            Deployment target name.
+        history_stats
+            Pre-computed rolling stats from history (mean_duration, p95, count).
+
+        Returns
+        -------
+        pd.DataFrame
+            Single-row feature DataFrame for model prediction.
+        """
+        stats = history_stats or {}
+        row: dict[str, Any] = {
+            "task_name_hash": hash(str(task_name)) % 10000,
+            "component_hash": hash(str(component)) % 10000 if component else 0,
+            "requested_cpus_num": 1,
+            "requested_memory_num": 0,
+            "requested_workers_num": 1,
+            "hist_mean_duration": stats.get("mean_duration", 0),
+            "hist_p95_duration": stats.get("p95_duration", 0),
+            "hist_mean_memory": stats.get("mean_memory", 0),
+            "hist_count": stats.get("count", 0),
+        }
+
+        # Incorporate user-provided numeric input features
+        if input_features:
+            for key, value in input_features.items():
+                if isinstance(value, (int, float)) and not isinstance(value, bool):
+                    col_name = f"input_{key}"
+                    row[col_name] = value
+
+        return pd.DataFrame([row])
+
+    def compute_history_stats(
+        self,
+        records: pd.DataFrame,
+        task_name: str,
+        target: str | None = None,
+    ) -> dict[str, Any]:
+        """Compute summary statistics for a task from historical records.
+
+        These can be passed to :meth:`extract_from_task` as ``history_stats``.
+        """
+        if records.empty:
+            return {"mean_duration": 0, "p95_duration": 0, "mean_memory": 0, "count": 0}
+
+        scoped = records[records["task_name"] == task_name]
+        if target is not None and not scoped.empty:
+            scoped_target = scoped[scoped.get("target") == target]
+            if not scoped_target.empty:
+                scoped = scoped_target
+
+        if scoped.empty:
+            return {"mean_duration": 0, "p95_duration": 0, "mean_memory": 0, "count": 0}
+
+        duration = pd.to_numeric(scoped.get("duration_s"), errors="coerce").dropna()
+        memory = pd.to_numeric(scoped.get("requested_memory_bytes"), errors="coerce").dropna()
+
+        return {
+            "mean_duration": float(duration.mean()) if not duration.empty else 0,
+            "p95_duration": float(np.percentile(duration, 95)) if not duration.empty else 0,
+            "mean_memory": float(memory.mean()) if not memory.empty else 0,
+            "count": int(len(scoped)),
+        }
+
+
+__all__ = ["FeatureExtractor"]
diff --git a/scalable/ml/learned_advisor.py b/scalable/ml/learned_advisor.py
new file mode 100644
index 0000000..b9618ca
--- /dev/null
+++ b/scalable/ml/learned_advisor.py
@@ -0,0 +1,415 @@
+"""ML-backed resource advisor using telemetry history as training data."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+from dask.utils import parse_bytes
+
+from scalable.advising.resources import (
+    ResourceRecommendation,
+    _bytes_to_gib_string,
+    _seconds_to_hhmmss,
+)
+from scalable.ml.features import FeatureExtractor
+from scalable.ml.models import ResourceModel
+from scalable.telemetry.collectors import iter_run_dirs, read_jsonl
+
+
+def _memory_to_bytes(value: str | None) -> int | None:
+    if value is None:
+        return None
+    try:
+        parsed = int(parse_bytes(value))
+    except Exception:
+        return None
+    return parsed if parsed > 0 else None
+
+
+class LearnedAdvisor:
+    """ML-backed resource advisor using telemetry history as training data.
+
+    Replaces heuristic quantile-based recommendations with feature-based
+    predictions from trained ML models. Falls back to percentile estimation
+    when insufficient data or sklearn unavailable.
+    """
+
+    #: Minimum number of records for a task before activating ML predictions
+    MIN_SAMPLES_FOR_ML: int = 10
+
+    def __init__(
+        self,
+        records: pd.DataFrame,
+        *,
+        duration_model: ResourceModel | None = None,
+        memory_model: ResourceModel | None = None,
+        extractor: FeatureExtractor | None = None,
+    ) -> None:
+        self._records = records.copy()
+        self._duration_model = duration_model
+        self._memory_model = memory_model
+        self._extractor = extractor or FeatureExtractor()
+
+    @classmethod
+    def from_history(
+        cls,
+        runs_dir: str | Path,
+        *,
+        model_type: str = "gradient_boosting",
+        retrain: bool = False,
+        cache_dir: str | Path | None = None,
+    ) -> LearnedAdvisor:
+        """Build and train advisor from telemetry run directories.
+
+        Parameters
+        ----------
+        runs_dir
+            Path to ``.scalable/runs/`` directory.
+        model_type
+            ML model type: ``gradient_boosting``, ``random_forest``, or
+            ``quantile_regression``.
+        retrain
+            Force retraining even if cached model exists.
+        cache_dir
+            Directory to cache trained models. Defaults to
+            ``<runs_dir>/../models``.
+        """
+        records = cls._load_records(runs_dir)
+        extractor = FeatureExtractor()
+
+        # Attempt to load cached models
+        if cache_dir is None:
+            cache_dir = Path(runs_dir).parent / "models"
+        cache_path = Path(cache_dir)
+
+        duration_model: ResourceModel | None = None
+        memory_model: ResourceModel | None = None
+
+        if not retrain and (cache_path / "duration" / "metadata.json").exists():
+            try:
+                duration_model = ResourceModel.load(cache_path / "duration")
+            except Exception:
+                duration_model = None
+
+        if not retrain and (cache_path / "memory" / "metadata.json").exists():
+            try:
+                memory_model = ResourceModel.load(cache_path / "memory")
+            except Exception:
+                memory_model = None
+
+        # Train if needed
+        if duration_model is None or memory_model is None:
+            features = extractor.extract_from_history(records)
+            if not features.empty and len(features) >= cls.MIN_SAMPLES_FOR_ML:
+                if duration_model is None:
+                    valid_duration = features[
+                        features["duration_num"].notna() & (features["duration_num"] > 0)
+                    ]
+                    if len(valid_duration) >= cls.MIN_SAMPLES_FOR_ML:
+                        y_dur = valid_duration["duration_num"]
+                        X_dur = valid_duration.drop(
+                            columns=["duration_num", "requested_memory_num"],
+                            errors="ignore",
+                        )
+                        duration_model = ResourceModel(
+                            model_type=model_type, random_state=42
+                        )
+                        duration_model.fit(X_dur, y_dur)
+                        try:
+                            duration_model.save(cache_path / "duration")
+                        except Exception:
+                            pass
+
+                if memory_model is None:
+                    valid_memory = features[
+                        features["requested_memory_num"].notna()
+                        & (features["requested_memory_num"] > 0)
+                    ]
+                    if len(valid_memory) >= cls.MIN_SAMPLES_FOR_ML:
+                        y_mem = valid_memory["requested_memory_num"]
+                        X_mem = valid_memory.drop(
+                            columns=["duration_num", "requested_memory_num"],
+                            errors="ignore",
+                        )
+                        memory_model = ResourceModel(
+                            model_type=model_type, random_state=42
+                        )
+                        memory_model.fit(X_mem, y_mem)
+                        try:
+                            memory_model.save(cache_path / "memory")
+                        except Exception:
+                            pass
+
+        return cls(
+            records,
+            duration_model=duration_model,
+            memory_model=memory_model,
+            extractor=extractor,
+        )
+
+    @classmethod
+    def _load_records(cls, runs_dir: str | Path) -> pd.DataFrame:
+        """Load telemetry records from run directories."""
+        rows: list[dict[str, Any]] = []
+        for run_dir in iter_run_dirs(runs_dir):
+            run_json = run_dir / "run.json"
+            if not run_json.exists():
+                continue
+            run_meta = pd.read_json(run_json, typ="series")
+            run_id = str(run_meta.get("run_id", run_dir.name))
+            target_name = run_meta.get("target_name")
+
+            task_rows = read_jsonl(run_dir / "tasks.jsonl")
+            resource_rows = read_jsonl(run_dir / "resources.jsonl")
+
+            resources_by_task: dict[str, dict[str, Any]] = {}
+            for r in resource_rows:
+                if r.get("entity_type") != "task":
+                    continue
+                entity = str(r.get("entity_id", ""))
+                if entity:
+                    resources_by_task[entity] = r
+
+            for t in task_rows:
+                if t.get("state") not in {"succeeded", "failed", "cancelled"}:
+                    continue
+                task_id = str(t.get("task_id", ""))
+                if not task_id:
+                    continue
+                resources = resources_by_task.get(task_id, {})
+                rows.append(
+                    {
+                        "run_id": run_id,
+                        "target": target_name,
+                        "task_id": task_id,
+                        "task_name": t.get("task_name"),
+                        "component": t.get("component"),
+                        "state": t.get("state"),
+                        "duration_s": t.get("duration_s"),
+                        "requested_workers": resources.get("requested_workers"),
+                        "requested_cpus": resources.get("requested_cpus"),
+                        "requested_memory": resources.get("requested_memory"),
+                        "requested_memory_bytes": _memory_to_bytes(
+                            resources.get("requested_memory")
+                        ),
+                        "requested_walltime": resources.get("requested_walltime"),
+                    }
+                )
+
+        return pd.DataFrame(rows)
+
+    def recommend(
+        self,
+        *,
+        task: str,
+        input_features: dict[str, Any] | None = None,
+        target: str | None = None,
+        confidence: float = 0.95,
+    ) -> ResourceRecommendation:
+        """Recommend resources using ML predictions with calibrated intervals.
+
+        Falls back to quantile heuristics when ML is unavailable or data is
+        insufficient for the requested task.
+        """
+        q = min(max(float(confidence), 0.5), 0.99)
+
+        # Check if we have enough data for this task
+        scoped = self._records[self._records["task_name"] == task]
+        if target is not None and not scoped.empty:
+            scoped_target = scoped[scoped["target"] == target]
+            if not scoped_target.empty:
+                scoped = scoped_target
+
+        if scoped.empty or len(scoped) < self.MIN_SAMPLES_FOR_ML:
+            # Fall back to heuristic
+            return self._heuristic_recommend(task, target, q, scoped)
+
+        # Compute history stats for feature extraction
+        stats = self._extractor.compute_history_stats(self._records, task, target)
+        X_pred = self._extractor.extract_from_task(
+            task_name=task,
+            input_features=input_features,
+            component=scoped["component"].dropna().iloc[-1] if scoped["component"].notna().any() else None,
+            target=target,
+            history_stats=stats,
+        )
+
+        # Predict duration
+        predicted_walltime: str | None = None
+        duration_evidence: dict[str, Any] = {}
+        if self._duration_model is not None and self._duration_model.is_fitted:
+            dur_preds = self._duration_model.predict(X_pred)
+            if dur_preds:
+                dur_pred = dur_preds[0]
+                # Use upper bound for safety
+                walltime_s = dur_pred.upper if dur_pred.upper else dur_pred.point * 1.2
+                predicted_walltime = _seconds_to_hhmmss(walltime_s)
+                duration_evidence = {
+                    "predicted_duration_s": dur_pred.point,
+                    "duration_lower": dur_pred.lower,
+                    "duration_upper": dur_pred.upper,
+                    "feature_importances": self._duration_model.feature_importances(),
+                }
+
+        # Predict memory
+        predicted_memory: str | None = None
+        memory_evidence: dict[str, Any] = {}
+        if self._memory_model is not None and self._memory_model.is_fitted:
+            mem_preds = self._memory_model.predict(X_pred)
+            if mem_preds:
+                mem_pred = mem_preds[0]
+                # Use upper bound for safety with 10% margin
+                memory_bytes = int((mem_pred.upper or mem_pred.point * 1.3) * 1.1)
+                predicted_memory = _bytes_to_gib_string(memory_bytes)
+                memory_evidence = {
+                    "predicted_memory_bytes": mem_pred.point,
+                    "memory_lower": mem_pred.lower,
+                    "memory_upper": mem_pred.upper,
+                }
+
+        # Component and workers from history
+        component = str(
+            scoped["component"].dropna().iloc[-1]
+            if scoped["component"].notna().any()
+            else task
+        )
+        workers_series = pd.to_numeric(scoped["requested_workers"], errors="coerce").dropna()
+        workers = int(max(1, round(float(workers_series.quantile(q))))) if not workers_series.empty else 1
+
+        cpus_series = pd.to_numeric(scoped["requested_cpus"], errors="coerce").dropna()
+        cpus = int(max(1, round(float(cpus_series.quantile(q))))) if not cpus_series.empty else 1
+
+        evidence: dict[str, Any] = {
+            "records": int(len(scoped)),
+            "method": "ml",
+            "model_type": self._duration_model.model_type if self._duration_model else "none",
+            "confidence": q,
+            "component": component,
+            **duration_evidence,
+            **memory_evidence,
+        }
+
+        return ResourceRecommendation(
+            task=task,
+            target=target,
+            confidence=q,
+            workers={component: workers},
+            resources={
+                component: {
+                    "cpus": cpus,
+                    "memory": predicted_memory,
+                    "walltime": predicted_walltime,
+                }
+            },
+            evidence=evidence,
+        )
+
+    def _heuristic_recommend(
+        self,
+        task: str,
+        target: str | None,
+        q: float,
+        scoped: pd.DataFrame,
+    ) -> ResourceRecommendation:
+        """Fallback to simple quantile heuristics (Phase 2 behavior)."""
+        if scoped.empty:
+            return ResourceRecommendation(
+                task=task,
+                target=target,
+                confidence=q,
+                workers={task: 1},
+                resources={task: {"cpus": 1, "memory": None, "walltime": None}},
+                evidence={"records": 0, "method": "heuristic", "reason": "no history"},
+            )
+
+        component = str(
+            scoped["component"].dropna().iloc[-1]
+            if scoped["component"].notna().any()
+            else task
+        )
+
+        workers_series = pd.to_numeric(scoped["requested_workers"], errors="coerce").dropna()
+        cpus_series = pd.to_numeric(scoped["requested_cpus"], errors="coerce").dropna()
+        duration_series = pd.to_numeric(scoped["duration_s"], errors="coerce").dropna()
+        mem_series = pd.to_numeric(scoped["requested_memory_bytes"], errors="coerce").dropna()
+
+        workers = int(max(1, round(float(workers_series.quantile(q))))) if not workers_series.empty else 1
+        cpus = int(max(1, round(float(cpus_series.quantile(q))))) if not cpus_series.empty else 1
+
+        memory_bytes = int(mem_series.quantile(q) * 1.10) if not mem_series.empty else None
+        walltime_s = float(duration_series.quantile(q) * 1.20) if not duration_series.empty else None
+
+        return ResourceRecommendation(
+            task=task,
+            target=target,
+            confidence=q,
+            workers={component: workers},
+            resources={
+                component: {
+                    "cpus": cpus,
+                    "memory": _bytes_to_gib_string(memory_bytes),
+                    "walltime": _seconds_to_hhmmss(walltime_s),
+                }
+            },
+            evidence={
+                "records": int(len(scoped)),
+                "method": "heuristic",
+                "reason": f"insufficient data (need {self.MIN_SAMPLES_FOR_ML})",
+                "component": component,
+            },
+        )
+
+    def explain(self, recommendation: ResourceRecommendation) -> dict[str, Any]:
+        """Return detailed explanation including feature importances."""
+        explanation: dict[str, Any] = {
+            "task": recommendation.task,
+            "target": recommendation.target,
+            "confidence": recommendation.confidence,
+            "method": recommendation.evidence.get("method", "unknown"),
+        }
+
+        if recommendation.evidence.get("method") == "ml":
+            explanation["feature_importances"] = recommendation.evidence.get(
+                "feature_importances", {}
+            )
+            explanation["prediction_intervals"] = {
+                "duration": {
+                    "point": recommendation.evidence.get("predicted_duration_s"),
+                    "lower": recommendation.evidence.get("duration_lower"),
+                    "upper": recommendation.evidence.get("duration_upper"),
+                },
+                "memory": {
+                    "point": recommendation.evidence.get("predicted_memory_bytes"),
+                    "lower": recommendation.evidence.get("memory_lower"),
+                    "upper": recommendation.evidence.get("memory_upper"),
+                },
+            }
+        else:
+            explanation["fallback_reason"] = recommendation.evidence.get("reason", "")
+
+        return explanation
+
+    def evaluate(self, *, test_fraction: float = 0.2) -> dict[str, Any]:
+        """Cross-validate models and return quality metrics."""
+        from scalable.ml.validation import cross_validate_advisor
+
+        quality_duration = cross_validate_advisor(
+            self._records,
+            model_type=self._duration_model.model_type if self._duration_model else "gradient_boosting",
+            target_column="duration_num",
+        )
+        quality_memory = cross_validate_advisor(
+            self._records,
+            model_type=self._memory_model.model_type if self._memory_model else "gradient_boosting",
+            target_column="requested_memory_num",
+        )
+
+        return {
+            "duration_model": quality_duration.to_dict(),
+            "memory_model": quality_memory.to_dict(),
+        }
+
+
+__all__ = ["LearnedAdvisor"]
diff --git a/scalable/ml/models.py b/scalable/ml/models.py
new file mode 100644
index 0000000..6306c98
--- /dev/null
+++ b/scalable/ml/models.py
@@ -0,0 +1,338 @@
+"""ML model wrappers for resource prediction (Phase 5).
+
+Provides sklearn-compatible model abstractions with unified interface for
+training, prediction, and interval estimation. All sklearn imports are lazy
+so the module loads without ``scalable[ml]`` installed.
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+
+@dataclass(frozen=True)
+class PredictionResult:
+    """Prediction output with optional confidence intervals."""
+
+    point: float
+    lower: float | None = None
+    upper: float | None = None
+    confidence: float = 0.95
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "point": self.point,
+            "lower": self.lower,
+            "upper": self.upper,
+            "confidence": self.confidence,
+        }
+
+
+@dataclass(frozen=True)
+class ModelQuality:
+    """Quality metrics from cross-validation or holdout evaluation."""
+
+    mae: float
+    rmse: float
+    r2: float
+    coverage: float  # Fraction of true values within predicted intervals
+    n_samples: int
+    model_type: str
+    target_name: str
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "mae": self.mae,
+            "rmse": self.rmse,
+            "r2": self.r2,
+            "coverage": self.coverage,
+            "n_samples": self.n_samples,
+            "model_type": self.model_type,
+            "target_name": self.target_name,
+        }
+
+
+class ResourceModel:
+    """Unified wrapper around sklearn estimators for resource prediction.
+
+    Supports gradient boosting, random forest, and quantile regression.
+    Falls back to simple percentile estimator if sklearn is unavailable.
+    """
+
+    def __init__(
+        self,
+        model_type: str = "gradient_boosting",
+        *,
+        quantile_lower: float = 0.05,
+        quantile_upper: float = 0.95,
+        random_state: int = 42,
+    ) -> None:
+        self.model_type = model_type
+        self.quantile_lower = quantile_lower
+        self.quantile_upper = quantile_upper
+        self.random_state = random_state
+        self._model: Any = None
+        self._model_lower: Any = None
+        self._model_upper: Any = None
+        self._feature_names: list[str] = []
+        self._is_fitted = False
+        self._fallback_percentiles: dict[str, float] | None = None
+
+    @property
+    def is_fitted(self) -> bool:
+        return self._is_fitted
+
+    @property
+    def feature_names(self) -> list[str]:
+        return list(self._feature_names)
+
+    def fit(self, X: pd.DataFrame, y: pd.Series) -> ResourceModel:
+        """Train the model on feature matrix X and target y.
+
+        Parameters
+        ----------
+        X
+            Feature matrix (from :class:`FeatureExtractor`).
+        y
+            Target variable (e.g., duration_s or memory_bytes).
+
+        Returns
+        -------
+        self
+        """
+        if X.empty or len(y) < 2:
+            # Too few samples — store percentile fallback
+            self._fallback_percentiles = {
+                "median": float(y.median()) if not y.empty else 0,
+                "lower": float(y.quantile(self.quantile_lower)) if not y.empty else 0,
+                "upper": float(y.quantile(self.quantile_upper)) if not y.empty else 0,
+            }
+            self._is_fitted = True
+            return self
+
+        self._feature_names = list(X.columns)
+
+        try:
+            self._fit_sklearn(X, y)
+        except ImportError:
+            # sklearn not available — use percentile fallback
+            self._fallback_percentiles = {
+                "median": float(y.median()),
+                "lower": float(y.quantile(self.quantile_lower)),
+                "upper": float(y.quantile(self.quantile_upper)),
+            }
+
+        self._is_fitted = True
+        return self
+
+    def _fit_sklearn(self, X: pd.DataFrame, y: pd.Series) -> None:
+        """Fit using sklearn estimators."""
+        from sklearn.ensemble import (
+            GradientBoostingRegressor,
+            RandomForestRegressor,
+        )
+
+        X_arr = X.values.astype(np.float64)
+        y_arr = y.values.astype(np.float64)
+
+        # Remove NaN rows
+        mask = ~(np.isnan(X_arr).any(axis=1) | np.isnan(y_arr))
+        X_arr = X_arr[mask]
+        y_arr = y_arr[mask]
+
+        if len(y_arr) < 2:
+            self._fallback_percentiles = {
+                "median": float(np.median(y_arr)) if len(y_arr) > 0 else 0,
+                "lower": 0,
+                "upper": float(np.max(y_arr)) if len(y_arr) > 0 else 0,
+            }
+            return
+
+        if self.model_type == "random_forest":
+            self._model = RandomForestRegressor(
+                n_estimators=50,
+                max_depth=8,
+                random_state=self.random_state,
+                n_jobs=-1,
+            )
+            self._model.fit(X_arr, y_arr)
+            # For RF, use quantile estimation from tree predictions
+            self._model_lower = None
+            self._model_upper = None
+        elif self.model_type == "quantile_regression":
+            # Use gradient boosting with quantile loss
+            self._model = GradientBoostingRegressor(
+                n_estimators=100,
+                max_depth=5,
+                loss="squared_error",
+                random_state=self.random_state,
+            )
+            self._model.fit(X_arr, y_arr)
+            self._model_lower = GradientBoostingRegressor(
+                n_estimators=100,
+                max_depth=5,
+                loss="quantile",
+                alpha=self.quantile_lower,
+                random_state=self.random_state,
+            )
+            self._model_lower.fit(X_arr, y_arr)
+            self._model_upper = GradientBoostingRegressor(
+                n_estimators=100,
+                max_depth=5,
+                loss="quantile",
+                alpha=self.quantile_upper,
+                random_state=self.random_state,
+            )
+            self._model_upper.fit(X_arr, y_arr)
+        else:
+            # Default: gradient_boosting
+            self._model = GradientBoostingRegressor(
+                n_estimators=100,
+                max_depth=5,
+                loss="squared_error",
+                random_state=self.random_state,
+            )
+            self._model.fit(X_arr, y_arr)
+            # Use residuals for interval estimation
+            self._model_lower = None
+            self._model_upper = None
+
+    def predict(self, X: pd.DataFrame) -> list[PredictionResult]:
+        """Predict target values with confidence intervals.
+
+        Parameters
+        ----------
+        X
+            Feature matrix (same columns as training data).
+
+        Returns
+        -------
+        list[PredictionResult]
+            One prediction per row in X.
+        """
+        if not self._is_fitted:
+            raise RuntimeError("Model not fitted. Call .fit() first.")
+
+        if self._fallback_percentiles is not None:
+            # Percentile fallback
+            return [
+                PredictionResult(
+                    point=self._fallback_percentiles["median"],
+                    lower=self._fallback_percentiles["lower"],
+                    upper=self._fallback_percentiles["upper"],
+                    confidence=self.quantile_upper - self.quantile_lower,
+                )
+                for _ in range(len(X))
+            ]
+
+        return self._predict_sklearn(X)
+
+    def _predict_sklearn(self, X: pd.DataFrame) -> list[PredictionResult]:
+        """Predict using fitted sklearn models."""
+        # Align columns with training
+        aligned = X.reindex(columns=self._feature_names, fill_value=0)
+        X_arr = aligned.values.astype(np.float64)
+        np.nan_to_num(X_arr, copy=False)
+
+        points = self._model.predict(X_arr)
+
+        if self._model_lower is not None and self._model_upper is not None:
+            lowers = self._model_lower.predict(X_arr)
+            uppers = self._model_upper.predict(X_arr)
+        elif self.model_type == "random_forest":
+            # Use individual tree predictions for intervals
+            tree_preds = np.array([t.predict(X_arr) for t in self._model.estimators_])
+            lowers = np.percentile(tree_preds, self.quantile_lower * 100, axis=0)
+            uppers = np.percentile(tree_preds, self.quantile_upper * 100, axis=0)
+        else:
+            # Heuristic interval: ±30% of point prediction
+            lowers = points * 0.7
+            uppers = points * 1.3
+
+        results = []
+        for i in range(len(points)):
+            results.append(
+                PredictionResult(
+                    point=float(max(0, points[i])),
+                    lower=float(max(0, lowers[i])),
+                    upper=float(max(0, uppers[i])),
+                    confidence=self.quantile_upper - self.quantile_lower,
+                )
+            )
+        return results
+
+    def feature_importances(self) -> dict[str, float]:
+        """Return feature importance scores if available."""
+        if self._model is None or not hasattr(self._model, "feature_importances_"):
+            return {}
+        importances = self._model.feature_importances_
+        return dict(zip(self._feature_names, [float(v) for v in importances], strict=False))
+
+    def save(self, path: str | Path) -> None:
+        """Persist model to disk."""
+        path = Path(path)
+        path.mkdir(parents=True, exist_ok=True)
+
+        meta = {
+            "model_type": self.model_type,
+            "quantile_lower": self.quantile_lower,
+            "quantile_upper": self.quantile_upper,
+            "feature_names": self._feature_names,
+            "is_fitted": self._is_fitted,
+            "fallback_percentiles": self._fallback_percentiles,
+        }
+        (path / "metadata.json").write_text(json.dumps(meta, indent=2))
+
+        if self._model is not None:
+            try:
+                import joblib
+
+                joblib.dump(self._model, path / "model.joblib")
+                if self._model_lower is not None:
+                    joblib.dump(self._model_lower, path / "model_lower.joblib")
+                if self._model_upper is not None:
+                    joblib.dump(self._model_upper, path / "model_upper.joblib")
+            except ImportError:
+                pass  # Cannot persist without joblib
+
+    @classmethod
+    def load(cls, path: str | Path) -> ResourceModel:
+        """Load a persisted model from disk."""
+        path = Path(path)
+        meta_text = (path / "metadata.json").read_text()
+        meta = json.loads(meta_text)
+
+        instance = cls(
+            model_type=meta["model_type"],
+            quantile_lower=meta.get("quantile_lower", 0.05),
+            quantile_upper=meta.get("quantile_upper", 0.95),
+        )
+        instance._feature_names = meta.get("feature_names", [])
+        instance._is_fitted = meta.get("is_fitted", False)
+        instance._fallback_percentiles = meta.get("fallback_percentiles")
+
+        model_path = path / "model.joblib"
+        if model_path.exists():
+            try:
+                import joblib
+
+                instance._model = joblib.load(model_path)
+                lower_path = path / "model_lower.joblib"
+                upper_path = path / "model_upper.joblib"
+                if lower_path.exists():
+                    instance._model_lower = joblib.load(lower_path)
+                if upper_path.exists():
+                    instance._model_upper = joblib.load(upper_path)
+            except ImportError:
+                pass
+
+        return instance
+
+
+__all__ = ["ModelQuality", "PredictionResult", "ResourceModel"]
diff --git a/scalable/ml/tuning.py b/scalable/ml/tuning.py
new file mode 100644
index 0000000..2b2c633
--- /dev/null
+++ b/scalable/ml/tuning.py
@@ -0,0 +1,201 @@
+"""Distributed hyperparameter tuning via Dask-ML (Phase 5).
+
+Provides a thin wrapper around Dask-ML search strategies for distributed
+model selection within a Scalable session. Falls back to sequential sklearn
+search when Dask-ML is unavailable.
+"""
+
+from __future__ import annotations
+
+import time
+from dataclasses import dataclass
+from typing import Any
+
+import pandas as pd
+
+
+@dataclass(frozen=True)
+class TuningResult:
+    """Result of a hyperparameter search."""
+
+    best_params: dict[str, Any]
+    best_score: float
+    all_results: pd.DataFrame
+    best_estimator: Any
+    n_iterations: int
+    wall_time_s: float
+    strategy: str
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "best_params": self.best_params,
+            "best_score": self.best_score,
+            "n_iterations": self.n_iterations,
+            "wall_time_s": self.wall_time_s,
+            "strategy": self.strategy,
+        }
+
+
+class HyperparameterSearch:
+    """Distributed hyperparameter tuning via Dask-ML.
+
+    Supports hyperband, successive halving, and random search strategies.
+    Falls back to sklearn's ``RandomizedSearchCV`` when Dask-ML is not
+    available.
+
+    Parameters
+    ----------
+    estimator
+        An sklearn-compatible estimator to tune.
+    param_space
+        Parameter search space. For Dask-ML hyperband, use scipy
+        distributions. For random search, use lists or distributions.
+    strategy
+        Search strategy: ``"hyperband"``, ``"successive_halving"``, or
+        ``"random"``.
+    n_iter
+        Maximum number of parameter combinations to evaluate.
+    scoring
+        Scoring metric name (sklearn convention, e.g., ``"neg_mean_absolute_error"``).
+    random_state
+        Random state for reproducibility.
+    """
+
+    def __init__(
+        self,
+        estimator: Any,
+        param_space: dict[str, Any],
+        *,
+        strategy: str = "hyperband",
+        n_iter: int = 50,
+        scoring: str | None = None,
+        random_state: int = 42,
+    ) -> None:
+        self.estimator = estimator
+        self.param_space = param_space
+        self.strategy = strategy
+        self.n_iter = n_iter
+        self.scoring = scoring or "neg_mean_absolute_error"
+        self.random_state = random_state
+
+    def fit(
+        self,
+        X: Any,
+        y: Any,
+        *,
+        client: Any | None = None,
+    ) -> TuningResult:
+        """Run the hyperparameter search.
+
+        Parameters
+        ----------
+        X
+            Training features (numpy array, pandas DataFrame, or dask array).
+        y
+            Training target.
+        client
+            Optional Dask client for distributed execution. If ``None``,
+            falls back to local sequential search.
+
+        Returns
+        -------
+        TuningResult
+            Best parameters, score, and full results.
+        """
+        start_time = time.time()
+
+        try:
+            result = self._fit_dask_ml(X, y, client=client)
+        except ImportError:
+            result = self._fit_sklearn_fallback(X, y)
+
+        wall_time = time.time() - start_time
+        return TuningResult(
+            best_params=result["best_params"],
+            best_score=result["best_score"],
+            all_results=result.get("all_results", pd.DataFrame()),
+            best_estimator=result["best_estimator"],
+            n_iterations=result.get("n_iterations", self.n_iter),
+            wall_time_s=wall_time,
+            strategy=self.strategy,
+        )
+
+    def _fit_dask_ml(self, X: Any, y: Any, *, client: Any) -> dict[str, Any]:
+        """Fit using Dask-ML search strategies."""
+        from dask_ml.model_selection import HyperbandSearchCV, RandomizedSearchCV
+
+        if self.strategy == "hyperband":
+            search = HyperbandSearchCV(
+                self.estimator,
+                self.param_space,
+                max_iter=self.n_iter,
+                random_state=self.random_state,
+            )
+        elif self.strategy == "successive_halving":
+            # Dask-ML's HyperbandSearchCV with aggressive_elimination is
+            # equivalent to successive halving
+            search = HyperbandSearchCV(
+                self.estimator,
+                self.param_space,
+                max_iter=self.n_iter,
+                aggressiveness=4,
+                random_state=self.random_state,
+            )
+        else:
+            # random
+            search = RandomizedSearchCV(
+                self.estimator,
+                self.param_space,
+                n_iter=self.n_iter,
+                scoring=self.scoring,
+                random_state=self.random_state,
+            )
+
+        search.fit(X, y)
+
+        cv_results = pd.DataFrame(search.cv_results_) if hasattr(search, "cv_results_") else pd.DataFrame()
+
+        return {
+            "best_params": search.best_params_,
+            "best_score": float(search.best_score_),
+            "best_estimator": search.best_estimator_,
+            "all_results": cv_results,
+            "n_iterations": len(cv_results) if not cv_results.empty else self.n_iter,
+        }
+
+    def _fit_sklearn_fallback(self, X: Any, y: Any) -> dict[str, Any]:
+        """Fallback to sklearn's RandomizedSearchCV."""
+        try:
+            from sklearn.model_selection import RandomizedSearchCV
+
+            search = RandomizedSearchCV(
+                self.estimator,
+                self.param_space,
+                n_iter=min(self.n_iter, 20),  # Cap for sequential search
+                scoring=self.scoring,
+                cv=3,
+                random_state=self.random_state,
+                n_jobs=-1,
+            )
+            search.fit(X, y)
+
+            cv_results = pd.DataFrame(search.cv_results_)
+            return {
+                "best_params": search.best_params_,
+                "best_score": float(search.best_score_),
+                "best_estimator": search.best_estimator_,
+                "all_results": cv_results,
+                "n_iterations": len(cv_results),
+            }
+        except ImportError:
+            # No sklearn either — return trivial result
+            return {
+                "best_params": {},
+                "best_score": 0.0,
+                "best_estimator": self.estimator,
+                "all_results": pd.DataFrame(),
+                "n_iterations": 0,
+            }
+
+
+__all__ = ["HyperparameterSearch", "TuningResult"]
diff --git a/scalable/ml/validation.py b/scalable/ml/validation.py
new file mode 100644
index 0000000..25826aa
--- /dev/null
+++ b/scalable/ml/validation.py
@@ -0,0 +1,134 @@
+"""Cross-validation and model quality assessment for resource models."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from scalable.ml.features import FeatureExtractor
+from scalable.ml.models import ModelQuality, ResourceModel
+
+
+def cross_validate_advisor(
+    records: pd.DataFrame,
+    *,
+    model_type: str = "gradient_boosting",
+    target_column: str = "duration_num",
+    n_splits: int = 5,
+    random_state: int = 42,
+) -> ModelQuality:
+    """Cross-validate a resource model against historical telemetry.
+
+    Parameters
+    ----------
+    records
+        Raw telemetry records (same format as ResourceAdvisor internal frame).
+    model_type
+        Model type to evaluate (``gradient_boosting``, ``random_forest``,
+        ``quantile_regression``).
+    target_column
+        Target column to predict (``duration_num`` or ``requested_memory_num``).
+    n_splits
+        Number of cross-validation folds.
+    random_state
+        Random state for reproducibility.
+
+    Returns
+    -------
+    ModelQuality
+        Aggregated quality metrics across all folds.
+    """
+    extractor = FeatureExtractor()
+    features = extractor.extract_from_history(records)
+
+    if features.empty or target_column not in features.columns:
+        return ModelQuality(
+            mae=float("inf"),
+            rmse=float("inf"),
+            r2=0.0,
+            coverage=0.0,
+            n_samples=0,
+            model_type=model_type,
+            target_name=target_column,
+        )
+
+    # Filter rows with valid target
+    valid_mask = features[target_column].notna() & (features[target_column] > 0)
+    features = features[valid_mask].reset_index(drop=True)
+
+    if len(features) < n_splits * 2:
+        return ModelQuality(
+            mae=float("inf"),
+            rmse=float("inf"),
+            r2=0.0,
+            coverage=0.0,
+            n_samples=len(features),
+            model_type=model_type,
+            target_name=target_column,
+        )
+
+    y = features[target_column]
+    X = features.drop(columns=[target_column, "requested_memory_num"], errors="ignore")
+
+    # Simple k-fold (no sklearn dependency required for splitting)
+    indices = np.arange(len(features))
+    rng = np.random.default_rng(random_state)
+    rng.shuffle(indices)
+    folds = np.array_split(indices, n_splits)
+
+    all_errors: list[float] = []
+    all_sq_errors: list[float] = []
+    all_in_interval: list[bool] = []
+    all_y_true: list[float] = []
+    all_y_pred: list[float] = []
+
+    for i in range(n_splits):
+        test_idx = folds[i]
+        train_idx = np.concatenate([folds[j] for j in range(n_splits) if j != i])
+
+        X_train = X.iloc[train_idx]
+        y_train = y.iloc[train_idx]
+        X_test = X.iloc[test_idx]
+        y_test = y.iloc[test_idx]
+
+        model = ResourceModel(model_type=model_type, random_state=random_state)
+        model.fit(X_train, y_train)
+        predictions = model.predict(X_test)
+
+        for pred, true_val in zip(predictions, y_test, strict=False):
+            error = abs(pred.point - true_val)
+            all_errors.append(error)
+            all_sq_errors.append(error**2)
+            all_y_true.append(true_val)
+            all_y_pred.append(pred.point)
+
+            # Check if true value is within predicted interval
+            if pred.lower is not None and pred.upper is not None:
+                in_interval = pred.lower <= true_val <= pred.upper
+            else:
+                in_interval = True  # No interval means no coverage check
+            all_in_interval.append(in_interval)
+
+    mae = float(np.mean(all_errors))
+    rmse = float(np.sqrt(np.mean(all_sq_errors)))
+    coverage = float(np.mean(all_in_interval))
+
+    # R² calculation
+    y_true_arr = np.array(all_y_true)
+    y_pred_arr = np.array(all_y_pred)
+    ss_res = np.sum((y_true_arr - y_pred_arr) ** 2)
+    ss_tot = np.sum((y_true_arr - np.mean(y_true_arr)) ** 2)
+    r2 = float(1 - ss_res / ss_tot) if ss_tot > 0 else 0.0
+
+    return ModelQuality(
+        mae=mae,
+        rmse=rmse,
+        r2=r2,
+        coverage=coverage,
+        n_samples=len(features),
+        model_type=model_type,
+        target_name=target_column,
+    )
+
+
+__all__ = ["cross_validate_advisor"]
diff --git a/scalable/telemetry/events.py b/scalable/telemetry/events.py
index c790ec7..d0f7a9a 100644
--- a/scalable/telemetry/events.py
+++ b/scalable/telemetry/events.py
@@ -203,10 +203,38 @@ def to_dict(self) -> dict[str, Any]:
         return asdict(self)
 
 
+@dataclass(frozen=True)
+class EmulationEvent:
+    """Emulation dispatch event record (Phase 5).
+
+    Recorded when a function is routed through the emulator dispatch
+    pipeline, whether it ends up being emulated or falling back to the
+    full model.
+    """
+
+    run_id: str
+    task_name: str
+    component: str | None
+    emulator_name: str | None
+    source: str  # "emulator" | "full_model" | "cached"
+    confidence: float | None
+    fallback_reason: str | None
+    domain_valid: bool
+    timestamp: str = field(default_factory=utcnow_iso)
+    emulator_version: str | None = None
+    metadata: dict[str, Any] = field(default_factory=dict)
+    event_type: str = "emulation"
+    schema_version: int = SCHEMA_VERSION
+
+    def to_dict(self) -> dict[str, Any]:
+        return asdict(self)
+
+
 __all__ = [
     "ArtifactEvent",
     "CacheEvent",
     "CostEvent",
+    "EmulationEvent",
     "FailureEvent",
     "RemoteCacheEvent",
     "ResourceEvent",
diff --git a/tests/unit/test_emulation.py b/tests/unit/test_emulation.py
new file mode 100644
index 0000000..0a5f952
--- /dev/null
+++ b/tests/unit/test_emulation.py
@@ -0,0 +1,440 @@
+"""Tests for scalable.emulation — decorator, registry, dispatch, uncertainty, active learning."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from scalable.emulation.active_learning import ActiveLearner
+from scalable.emulation.decorator import (
+    EmulationSpec,
+    emulatable,
+    get_emulation_spec,
+    get_original_function,
+    list_emulatable_functions,
+)
+from scalable.emulation.dispatch import EmulatorDispatch, EmulatorDispatchResult
+from scalable.emulation.registry import EmulatorInfo, EmulatorRegistry
+from scalable.emulation.surrogate import (
+    EmulatorMetadata,
+    EmulatorPrediction,
+    GradientBoostingEmulator,
+    RandomForestEmulator,
+)
+from scalable.emulation.uncertainty import (
+    CalibrationResult,
+    calibrate_emulator,
+    compute_confidence_from_uncertainty,
+    is_in_domain,
+)
+
+# ─── Decorator tests ───
+
+
+class TestEmulatable:
+    def test_basic_decoration(self):
+        @emulatable(
+            tag="gcam",
+            inputs=["carbon_price", "population"],
+            outputs=["emissions"],
+            confidence_threshold=0.9,
+        )
+        def run_gcam(params):
+            return {"emissions": 100}
+
+        spec = get_emulation_spec(run_gcam)
+        assert spec is not None
+        assert spec.tag == "gcam"
+        assert spec.inputs == ["carbon_price", "population"]
+        assert spec.outputs == ["emissions"]
+        assert spec.confidence_threshold == 0.9
+
+    def test_direct_call_runs_original(self):
+        @emulatable(tag="test", inputs=["x"], outputs=["y"])
+        def compute(x):
+            return x * 2
+
+        assert compute(5) == 10
+
+    def test_get_original_function(self):
+        @emulatable(tag="test", inputs=["x"], outputs=["y"])
+        def compute(x):
+            return x * 2
+
+        original = get_original_function(compute)
+        assert original(3) == 6
+
+    def test_invalid_uncertainty(self):
+        with pytest.raises(ValueError, match="uncertainty"):
+
+            @emulatable(tag="t", inputs=[], outputs=[], uncertainty="invalid")
+            def f():
+                pass
+
+    def test_invalid_fallback(self):
+        with pytest.raises(ValueError, match="fallback"):
+
+            @emulatable(tag="t", inputs=[], outputs=[], fallback="invalid")
+            def f():
+                pass
+
+    def test_invalid_confidence(self):
+        with pytest.raises(ValueError, match="confidence_threshold"):
+
+            @emulatable(tag="t", inputs=[], outputs=[], confidence_threshold=2.0)
+            def f():
+                pass
+
+    def test_list_emulatable_functions(self):
+        registry = list_emulatable_functions()
+        assert isinstance(registry, dict)
+
+
+# ─── Surrogate model tests ───
+
+
+class TestSurrogateModels:
+    def _make_metadata(self):
+        return EmulatorMetadata(
+            name="test_emulator",
+            version="1",
+            training_runs=["run-1"],
+            training_samples=100,
+            validation_score=0.95,
+            domain_bounds={"x": (0.0, 10.0)},
+            created_at="2026-01-01T00:00:00Z",
+            model_type="gradient_boosting",
+            input_names=["x", "y"],
+            output_names=["z"],
+        )
+
+    def test_gradient_boosting_emulator_no_model(self):
+        meta = self._make_metadata()
+        emu = GradientBoostingEmulator(metadata=meta)
+        pred = emu.predict({"x": 5, "y": 3})
+        assert pred.confidence == 0.0
+        assert pred.is_emulated
+
+    def test_random_forest_emulator_no_model(self):
+        meta = self._make_metadata()
+        emu = RandomForestEmulator(metadata=meta)
+        pred = emu.predict({"x": 5, "y": 3})
+        assert pred.confidence == 0.0
+
+    def test_emulator_prediction_dataclass(self):
+        pred = EmulatorPrediction(
+            outputs={"z": 42.0},
+            confidence=0.95,
+            uncertainty_bounds={"z": (38.0, 46.0)},
+        )
+        assert pred.outputs["z"] == 42.0
+        assert pred.is_emulated
+        d = pred.to_dict()
+        assert d["confidence"] == 0.95
+
+    def test_emulator_metadata_to_dict(self):
+        meta = self._make_metadata()
+        d = meta.to_dict()
+        assert d["name"] == "test_emulator"
+        assert d["model_type"] == "gradient_boosting"
+
+
+# ─── Registry tests ───
+
+
+class _MockEmulator:
+    """Mock emulator for testing registry without sklearn."""
+
+    def __init__(self, name="mock", version="1"):
+        self._metadata = EmulatorMetadata(
+            name=name,
+            version=version,
+            training_runs=["run-1"],
+            training_samples=50,
+            validation_score=0.9,
+            domain_bounds={"x": (0.0, 100.0)},
+            created_at="2026-01-01T00:00:00Z",
+            model_type="mock",
+            input_names=["x"],
+            output_names=["y"],
+        )
+
+    @property
+    def metadata(self) -> EmulatorMetadata:
+        return self._metadata
+
+    def predict(self, inputs):
+        return EmulatorPrediction(
+            outputs={"y": float(inputs.get("x", 0)) * 2},
+            confidence=0.95,
+            uncertainty_bounds={"y": (0.0, 200.0)},
+        )
+
+    def uncertainty(self, inputs):
+        return 0.05
+
+
+class TestEmulatorRegistry:
+    def test_register_and_get(self, tmp_path):
+        registry = EmulatorRegistry(tmp_path / "emulators")
+        emu = _MockEmulator("test", "1")
+        version = registry.register("test", emu)
+        assert version == "1"
+
+        retrieved = registry.get("test")
+        assert retrieved is emu
+
+    def test_auto_version_increment(self, tmp_path):
+        registry = EmulatorRegistry(tmp_path / "emulators")
+        emu1 = _MockEmulator("test", "1")
+        emu2 = _MockEmulator("test", "2")
+        registry.register("test", emu1, version="1")
+        version = registry.register("test", emu2)
+        assert version == "2"
+
+    def test_get_latest(self, tmp_path):
+        registry = EmulatorRegistry(tmp_path / "emulators")
+        emu1 = _MockEmulator("test", "1")
+        emu2 = _MockEmulator("test", "2")
+        registry.register("test", emu1, version="1")
+        registry.register("test", emu2, version="2")
+        retrieved = registry.get("test")
+        assert retrieved is emu2
+
+    def test_get_specific_version(self, tmp_path):
+        registry = EmulatorRegistry(tmp_path / "emulators")
+        emu1 = _MockEmulator("test", "1")
+        emu2 = _MockEmulator("test", "2")
+        registry.register("test", emu1, version="1")
+        registry.register("test", emu2, version="2")
+        retrieved = registry.get("test", version="1")
+        assert retrieved is emu1
+
+    def test_get_not_found(self, tmp_path):
+        registry = EmulatorRegistry(tmp_path / "emulators")
+        with pytest.raises(KeyError, match="not found"):
+            registry.get("nonexistent")
+
+    def test_list_emulators(self, tmp_path):
+        registry = EmulatorRegistry(tmp_path / "emulators")
+        registry.register("emu1", _MockEmulator("emu1", "1"))
+        registry.register("emu2", _MockEmulator("emu2", "1"))
+        listing = registry.list()
+        assert len(listing) == 2
+        names = {e.name for e in listing}
+        assert "emu1" in names
+        assert "emu2" in names
+
+    def test_validate_domain_in_bounds(self, tmp_path):
+        registry = EmulatorRegistry(tmp_path / "emulators")
+        registry.register("test", _MockEmulator())
+        assert registry.validate_domain("test", {"x": 50.0})
+
+    def test_validate_domain_out_of_bounds(self, tmp_path):
+        registry = EmulatorRegistry(tmp_path / "emulators")
+        registry.register("test", _MockEmulator())
+        assert not registry.validate_domain("test", {"x": 200.0})
+
+    def test_remove(self, tmp_path):
+        registry = EmulatorRegistry(tmp_path / "emulators")
+        registry.register("test", _MockEmulator())
+        registry.remove("test")
+        with pytest.raises(KeyError):
+            registry.get("test")
+
+
+# ─── Dispatch tests ───
+
+
+class TestEmulatorDispatch:
+    def test_dispatch_with_emulator(self, tmp_path):
+        registry = EmulatorRegistry(tmp_path / "emulators")
+        registry.register("run_model", _MockEmulator("run_model", "1"))
+
+        @emulatable(
+            tag="test",
+            inputs=["x"],
+            outputs=["y"],
+            confidence_threshold=0.9,
+        )
+        def run_model(x=0):
+            return {"y": x * 3}
+
+        dispatch = EmulatorDispatch(registry)
+        result = dispatch.execute(run_model, emulator_name="run_model", x=50)
+        assert result.source == "emulator"
+        assert result.confidence == 0.95
+
+    def test_dispatch_fallback_low_confidence(self, tmp_path):
+        """When emulator confidence is below threshold, use full model."""
+
+        class LowConfEmulator(_MockEmulator):
+            def predict(self, inputs):
+                return EmulatorPrediction(
+                    outputs={"y": 0}, confidence=0.5, uncertainty_bounds={"y": (0, 100)}
+                )
+
+        registry = EmulatorRegistry(tmp_path / "emulators")
+        registry.register("run_model", LowConfEmulator())
+
+        @emulatable(
+            tag="test",
+            inputs=["x"],
+            outputs=["y"],
+            confidence_threshold=0.9,
+        )
+        def run_model(x=0):
+            return {"y": x * 3}
+
+        dispatch = EmulatorDispatch(registry)
+        result = dispatch.execute(run_model, emulator_name="run_model", x=5)
+        assert result.source == "full_model"
+        assert "low_confidence" in result.fallback_reason
+
+    def test_dispatch_no_spec(self, tmp_path):
+        registry = EmulatorRegistry(tmp_path / "emulators")
+        dispatch = EmulatorDispatch(registry)
+
+        def plain_func(x):
+            return x + 1
+
+        result = dispatch.execute(plain_func, 5)
+        assert result.source == "full_model"
+        assert result.result == 6
+
+    def test_dispatch_force_full_model(self, tmp_path):
+        registry = EmulatorRegistry(tmp_path / "emulators")
+        registry.register("run_model", _MockEmulator("run_model", "1"))
+
+        @emulatable(tag="test", inputs=["x"], outputs=["y"])
+        def run_model(x=0):
+            return {"y": x * 3}
+
+        dispatch = EmulatorDispatch(registry)
+        result = dispatch.execute(run_model, emulator_name="run_model", force_full_model=True, x=5)
+        assert result.source == "full_model"
+
+    def test_dispatch_outside_domain(self, tmp_path):
+        registry = EmulatorRegistry(tmp_path / "emulators")
+        registry.register("run_model", _MockEmulator("run_model", "1"))
+
+        @emulatable(
+            tag="test",
+            inputs=["x"],
+            outputs=["y"],
+            domain={"x": (0, 100)},
+        )
+        def run_model(x=0):
+            return {"y": x * 3}
+
+        dispatch = EmulatorDispatch(registry)
+        result = dispatch.execute(run_model, emulator_name="run_model", x=200)
+        assert result.source == "full_model"
+        assert "outside_domain" in result.fallback_reason
+
+    def test_dispatch_log(self, tmp_path):
+        registry = EmulatorRegistry(tmp_path / "emulators")
+        dispatch = EmulatorDispatch(registry, record_provenance=True)
+
+        def f():
+            return 1
+
+        dispatch.execute(f)
+        assert len(dispatch.dispatch_log) == 1
+
+
+# ─── Uncertainty tests ───
+
+
+class TestUncertainty:
+    def test_calibrate_emulator_perfect(self):
+        predictions = [
+            {"outputs": {"y": 10}, "uncertainty_bounds": {"y": [5, 15]}},
+            {"outputs": {"y": 20}, "uncertainty_bounds": {"y": [15, 25]}},
+            {"outputs": {"y": 30}, "uncertainty_bounds": {"y": [25, 35]}},
+        ]
+        actuals = [{"y": 10}, {"y": 20}, {"y": 30}]
+        result = calibrate_emulator(predictions, actuals, output_name="y")
+        assert result.coverage_95 == 1.0
+        assert result.is_calibrated
+
+    def test_calibrate_emulator_poor(self):
+        predictions = [
+            {"outputs": {"y": 10}, "uncertainty_bounds": {"y": [9, 11]}},
+            {"outputs": {"y": 20}, "uncertainty_bounds": {"y": [19, 21]}},
+        ]
+        actuals = [{"y": 50}, {"y": 100}]  # Way outside bounds
+        result = calibrate_emulator(predictions, actuals, output_name="y")
+        assert result.coverage_95 == 0.0
+        assert not result.is_calibrated
+
+    def test_calibrate_empty(self):
+        result = calibrate_emulator([], [], output_name="y")
+        assert result.n_samples == 0
+        assert not result.is_calibrated
+
+    def test_compute_confidence(self):
+        assert compute_confidence_from_uncertainty(0.0) == 1.0
+        assert compute_confidence_from_uncertainty(1.0) == 0.0
+        assert 0.0 < compute_confidence_from_uncertainty(0.5) < 1.0
+
+    def test_is_in_domain(self):
+        domain = {"x": (0.0, 10.0), "y": (-5.0, 5.0)}
+        assert is_in_domain({"x": 5.0, "y": 0.0}, domain)
+        assert not is_in_domain({"x": 15.0, "y": 0.0}, domain)
+        assert is_in_domain({"z": 999.0}, domain)  # Unknown keys are fine
+
+
+# ─── Active learning tests ───
+
+
+class TestActiveLearner:
+    def test_suggest_random(self):
+        learner = ActiveLearner(
+            emulator=_MockEmulator(),
+            acquisition="random",
+            random_state=42,
+        )
+        candidates = pd.DataFrame({"x": [1, 2, 3, 4, 5]})
+        selected = learner.suggest(candidates, n_suggestions=2)
+        assert len(selected) == 2
+
+    def test_suggest_uncertainty(self):
+        learner = ActiveLearner(
+            emulator=_MockEmulator(),
+            acquisition="uncertainty",
+        )
+        candidates = pd.DataFrame({"x": [1, 2, 3, 4, 5]})
+        selected = learner.suggest(candidates, n_suggestions=2)
+        assert len(selected) == 2
+
+    def test_suggest_expected_improvement(self):
+        learner = ActiveLearner(
+            emulator=_MockEmulator(),
+            acquisition="expected_improvement",
+        )
+        candidates = pd.DataFrame({"x": [1, 2, 3, 4, 5]})
+        selected = learner.suggest(candidates, n_suggestions=2)
+        assert len(selected) == 2
+
+    def test_update_observations(self):
+        learner = ActiveLearner(emulator=_MockEmulator(), acquisition="random")
+        assert learner.n_observations == 0
+        learner.update(pd.DataFrame({"x": [1, 2, 3]}))
+        assert learner.n_observations == 3
+
+    def test_empty_candidates(self):
+        learner = ActiveLearner(emulator=_MockEmulator(), acquisition="random")
+        selected = learner.suggest(pd.DataFrame(), n_suggestions=5)
+        assert len(selected) == 0
+
+    def test_invalid_acquisition(self):
+        with pytest.raises(ValueError, match="acquisition"):
+            ActiveLearner(emulator=_MockEmulator(), acquisition="invalid")
+
+    def test_reset(self):
+        learner = ActiveLearner(emulator=_MockEmulator(), acquisition="random")
+        learner.update(pd.DataFrame({"x": [1, 2]}))
+        learner.reset()
+        assert learner.n_observations == 0
diff --git a/tests/unit/test_ml_adaptive_scaler.py b/tests/unit/test_ml_adaptive_scaler.py
new file mode 100644
index 0000000..fbb610f
--- /dev/null
+++ b/tests/unit/test_ml_adaptive_scaler.py
@@ -0,0 +1,147 @@
+"""Tests for scalable.ml.adaptive_scaler — adaptive scaling policy."""
+
+from __future__ import annotations
+
+import pytest
+
+from scalable.ml.adaptive_scaler import AdaptiveScaler, ScaleDecision
+
+
+class TestScaleDecision:
+    def test_no_changes(self):
+        d = ScaleDecision(
+            workers_to_add={},
+            workers_to_remove={},
+            reasoning="no changes",
+            confidence=0.9,
+        )
+        assert not d.has_changes
+
+    def test_has_changes_add(self):
+        d = ScaleDecision(
+            workers_to_add={"gcam": 2},
+            workers_to_remove={},
+            reasoning="scale up",
+            confidence=0.8,
+        )
+        assert d.has_changes
+
+    def test_to_dict(self):
+        d = ScaleDecision(
+            workers_to_add={"gcam": 1},
+            workers_to_remove={"stitches": 1},
+            reasoning="rebalance",
+            confidence=0.85,
+            predicted_completion_time=300.0,
+        )
+        result = d.to_dict()
+        assert result["workers_to_add"] == {"gcam": 1}
+        assert result["confidence"] == 0.85
+
+
+class TestAdaptiveScaler:
+    def test_cooldown_blocks_rapid_decisions(self):
+        scaler = AdaptiveScaler(cooldown_seconds=60.0)
+        # First evaluation
+        decision1 = scaler.evaluate(
+            pending_tasks=[{"tag": "gcam"}],
+            active_workers={"gcam": 0},
+        )
+        assert decision1.has_changes
+
+        # Second evaluation within cooldown
+        decision2 = scaler.evaluate(
+            pending_tasks=[{"tag": "gcam"}],
+            active_workers={"gcam": 1},
+        )
+        assert not decision2.has_changes
+        assert "Cooldown" in decision2.reasoning
+
+    def test_scale_up_on_high_queue(self):
+        scaler = AdaptiveScaler(
+            scale_up_threshold=0.8,
+            cooldown_seconds=0,
+        )
+        decision = scaler.evaluate(
+            pending_tasks=[{"tag": "gcam"}] * 10,
+            active_workers={"gcam": 2},
+        )
+        assert decision.workers_to_add.get("gcam", 0) > 0
+
+    def test_scale_down_on_low_queue(self):
+        scaler = AdaptiveScaler(
+            scale_down_threshold=0.2,
+            cooldown_seconds=0,
+            min_workers={"gcam": 1},
+        )
+        decision = scaler.evaluate(
+            pending_tasks=[],  # No pending tasks
+            active_workers={"gcam": 5},
+        )
+        assert decision.workers_to_remove.get("gcam", 0) > 0
+
+    def test_respects_max_workers(self):
+        scaler = AdaptiveScaler(
+            max_workers={"gcam": 3},
+            cooldown_seconds=0,
+        )
+        decision = scaler.evaluate(
+            pending_tasks=[{"tag": "gcam"}] * 100,
+            active_workers={"gcam": 0},
+        )
+        assert decision.workers_to_add.get("gcam", 0) <= 3
+
+    def test_respects_min_workers(self):
+        scaler = AdaptiveScaler(
+            min_workers={"gcam": 2},
+            cooldown_seconds=0,
+        )
+        decision = scaler.evaluate(
+            pending_tasks=[],
+            active_workers={"gcam": 5},
+        )
+        # Should not remove below minimum
+        removed = decision.workers_to_remove.get("gcam", 0)
+        remaining = 5 - removed
+        assert remaining >= 2
+
+    def test_no_workers_triggers_scaleup(self):
+        scaler = AdaptiveScaler(cooldown_seconds=0)
+        decision = scaler.evaluate(
+            pending_tasks=[{"tag": "gcam"}, {"tag": "gcam"}],
+            active_workers={"gcam": 0},
+        )
+        assert decision.workers_to_add.get("gcam", 0) > 0
+
+    def test_completion_time_estimation(self):
+        scaler = AdaptiveScaler(cooldown_seconds=0)
+        decision = scaler.evaluate(
+            pending_tasks=[{"tag": "gcam"}] * 4,
+            active_workers={"gcam": 2},
+            recent_completions=[
+                {"duration_s": 60},
+                {"duration_s": 80},
+            ],
+        )
+        assert decision.predicted_completion_time is not None
+
+    def test_reset_cooldown(self):
+        scaler = AdaptiveScaler(cooldown_seconds=9999)
+        scaler.evaluate(
+            pending_tasks=[{"tag": "x"}],
+            active_workers={"x": 0},
+        )
+        scaler.reset_cooldown()
+        decision = scaler.evaluate(
+            pending_tasks=[{"tag": "x"}],
+            active_workers={"x": 0},
+        )
+        assert decision.has_changes
+
+    def test_decision_history(self):
+        scaler = AdaptiveScaler(cooldown_seconds=0)
+        scaler.evaluate(
+            pending_tasks=[{"tag": "a"}],
+            active_workers={"a": 0},
+        )
+        assert len(scaler.decision_history) == 1
diff --git a/tests/unit/test_ml_features.py b/tests/unit/test_ml_features.py
new file mode 100644
index 0000000..4dff372
--- /dev/null
+++ b/tests/unit/test_ml_features.py
@@ -0,0 +1,105 @@
+"""Tests for scalable.ml.features — feature extraction."""
+
+from __future__ import annotations
+
+import pandas as pd
+import pytest
+
+from scalable.ml.features import FeatureExtractor
+
+
+@pytest.fixture
+def sample_records():
+    return pd.DataFrame(
+        {
+            "task_name": ["run_gcam", "run_gcam", "run_gcam", "run_stitches", "run_stitches"],
+            "component": ["gcam", "gcam", "gcam", "stitches", "stitches"],
+            "duration_s": [120.0, 150.0, 130.0, 60.0, 55.0],
+            "requested_cpus": [4, 4, 6, 2, 2],
+            "requested_memory_bytes": [8e9, 8e9, 12e9, 4e9, 4e9],
+            "requested_workers": [2, 2, 3, 1, 1],
+            "target": ["slurm", "slurm", "slurm", "local", "local"],
+        }
+    )
+
+
+class TestFeatureExtractor:
+    def test_extract_from_history_empty(self):
+        extractor = FeatureExtractor()
+        result = extractor.extract_from_history(pd.DataFrame())
+        assert result.empty
+
+    def test_extract_from_history_produces_features(self, sample_records):
+        extractor = FeatureExtractor()
+        result = extractor.extract_from_history(sample_records)
+        assert not result.empty
+        assert "task_name_hash" in result.columns
+        assert "component_hash" in result.columns
+        assert "requested_cpus_num" in result.columns
+        assert "hist_mean_duration" in result.columns
+        assert "hist_count" in result.columns
+        assert len(result) == len(sample_records)
+
+    def test_extract_from_history_rolling_stats(self, sample_records):
+        extractor = FeatureExtractor()
+        result = extractor.extract_from_history(sample_records)
+        # First row should have 0 for rolling stats (no prior history)
+        assert result["hist_mean_duration"].iloc[0] == 0
+        # Second row of same task should have prior stats
+        assert result["hist_mean_duration"].iloc[1] > 0
+
+    def test_extract_from_task_basic(self):
+        extractor = FeatureExtractor()
+        result = extractor.extract_from_task(
+            task_name="run_gcam",
+            input_features={"scenario_count": 20, "years": 85},
+            component="gcam",
+            target="slurm",
+        )
+        assert len(result) == 1
+        assert "task_name_hash" in result.columns
+        assert "input_scenario_count" in result.columns
+        assert "input_years" in result.columns
+        assert result["input_scenario_count"].iloc[0] == 20
+
+    def test_extract_from_task_no_features(self):
+        extractor = FeatureExtractor()
+        result = extractor.extract_from_task(
+            task_name="run_gcam",
+            input_features=None,
+            component="gcam",
+            target=None,
+        )
+        assert len(result) == 1
+        assert "task_name_hash" in result.columns
+
+    def test_extract_from_task_with_history_stats(self):
+        extractor = FeatureExtractor()
+        stats = {"mean_duration": 120.0, "p95_duration": 150.0, "mean_memory": 8e9, "count": 5}
+        result = extractor.extract_from_task(
+            task_name="run_gcam",
+            input_features=None,
+            component="gcam",
+            target="slurm",
+            history_stats=stats,
+        )
+        assert result["hist_mean_duration"].iloc[0] == 120.0
+        assert result["hist_p95_duration"].iloc[0] == 150.0
+
+    def test_compute_history_stats_empty(self):
+        extractor = FeatureExtractor()
+        stats = extractor.compute_history_stats(pd.DataFrame(), "run_gcam")
+        assert stats["count"] == 0
+        assert stats["mean_duration"] == 0
+
+    def test_compute_history_stats_with_data(self, sample_records):
+        extractor = FeatureExtractor()
+        stats = extractor.compute_history_stats(sample_records, "run_gcam")
+        assert stats["count"] == 3
+        assert stats["mean_duration"] > 0
+        assert stats["p95_duration"] >= stats["mean_duration"]
+
+    def test_compute_history_stats_with_target(self, sample_records):
+        extractor = FeatureExtractor()
+        stats = extractor.compute_history_stats(sample_records, "run_stitches", "local")
+        assert stats["count"] == 2
diff --git a/tests/unit/test_ml_models.py b/tests/unit/test_ml_models.py
new file mode 100644
index 0000000..df04628
--- /dev/null
+++ b/tests/unit/test_ml_models.py
@@ -0,0 +1,101 @@
+"""Tests for scalable.ml.models — ML model wrappers."""
+
+from __future__ import annotations
+
+import pandas as pd
+import pytest
+
+from scalable.ml.models import ModelQuality, PredictionResult, ResourceModel
+
+
+class TestPredictionResult:
+    def test_creation(self):
+        p = PredictionResult(point=100.0, lower=80.0, upper=120.0, confidence=0.9)
+        assert p.point == 100.0
+        assert p.lower == 80.0
+        assert p.upper == 120.0
+
+    def test_to_dict(self):
+        p = PredictionResult(point=50.0)
+        d = p.to_dict()
+        assert d["point"] == 50.0
+        assert d["confidence"] == 0.95
+
+
+class TestModelQuality:
+    def test_creation(self):
+        q = ModelQuality(
+            mae=5.0, rmse=7.0, r2=0.85, coverage=0.92,
+            n_samples=100, model_type="gradient_boosting", target_name="duration"
+        )
+        assert q.mae == 5.0
+        assert q.r2 == 0.85
+
+    def test_to_dict(self):
+        q = ModelQuality(
+            mae=5.0, rmse=7.0, r2=0.85, coverage=0.92,
+            n_samples=100, model_type="rf", target_name="mem"
+        )
+        d = q.to_dict()
+        assert d["model_type"] == "rf"
+        assert d["n_samples"] == 100
+
+
+class TestResourceModel:
+    def test_not_fitted_raises(self):
+        model = ResourceModel()
+        with pytest.raises(RuntimeError, match="not fitted"):
+            model.predict(pd.DataFrame({"x": [1]}))
+
+    def test_fit_empty_data(self):
+        model = ResourceModel()
+        X = pd.DataFrame()
+        y = pd.Series(dtype=float)
+        model.fit(X, y)
+        assert model.is_fitted
+        # Predict returns fallback
+        result = model.predict(pd.DataFrame({"x": [1]}))
+        assert len(result) == 1
+
+    def test_fit_single_row(self):
+        model = ResourceModel()
+        X = pd.DataFrame({"feat1": [1.0]})
+        y = pd.Series([100.0])
+        model.fit(X, y)
+        assert model.is_fitted
+        result = model.predict(pd.DataFrame({"feat1": [1.0]}))
+        assert len(result) == 1
+        assert result[0].point == 100.0  # Median of single value
+
+    def test_fit_few_rows_uses_percentile_fallback(self):
+        model = ResourceModel()
+        X = pd.DataFrame({"feat1": [1.0, 2.0, 3.0]})
+        y = pd.Series([100.0, 200.0, 300.0])
+        model.fit(X, y)
+        assert model.is_fitted
+        result = model.predict(pd.DataFrame({"feat1": [2.0]}))
+        assert len(result) == 1
+        # Should work regardless of sklearn availability
+
+    def test_feature_importances_unfitted(self):
+        model = ResourceModel()
+        assert model.feature_importances() == {}
+
+    def test_model_types(self):
+        for model_type in ["gradient_boosting", "random_forest", "quantile_regression"]:
+            model = ResourceModel(model_type=model_type)
+            assert model.model_type == model_type
+
+    def test_save_load(self, tmp_path):
+        model = ResourceModel()
+        X = pd.DataFrame({"feat1": [1.0, 2.0]})
+        y = pd.Series([10.0, 20.0])
+        model.fit(X, y)
+
+        save_path = tmp_path / "test_model"
+        model.save(save_path)
+        assert (save_path / "metadata.json").exists()
+
+        loaded = ResourceModel.load(save_path)
+        assert loaded.is_fitted
+        assert loaded.model_type == model.model_type
diff --git a/tests/unit/test_phase5_telemetry_cli.py b/tests/unit/test_phase5_telemetry_cli.py
new file mode 100644
index 0000000..2b44d56
--- /dev/null
+++ b/tests/unit/test_phase5_telemetry_cli.py
@@ -0,0 +1,79 @@
+"""Tests for Phase 5 telemetry EmulationEvent and CLI advise command."""
+
+from __future__ import annotations
+
+import pytest
+
+from scalable.telemetry.events import SCHEMA_VERSION, EmulationEvent
+
+
+class TestEmulationEvent:
+    def test_creation(self):
+        event = EmulationEvent(
+            run_id="run-123",
+            task_name="run_gcam",
+            component="gcam",
+            emulator_name="gcam_emulator",
+            source="emulator",
+            confidence=0.95,
+            fallback_reason=None,
+            domain_valid=True,
+            emulator_version="2",
+        )
+        assert event.run_id == "run-123"
+        assert event.source == "emulator"
+        assert event.event_type == "emulation"
+        assert event.schema_version == SCHEMA_VERSION
+
+    def test_to_dict(self):
+        event = EmulationEvent(
+            run_id="run-456",
+            task_name="run_stitches",
+            component="stitches",
+            emulator_name=None,
+            source="full_model",
+            confidence=None,
+            fallback_reason="emulator_not_registered",
+            domain_valid=True,
+        )
+        d = event.to_dict()
+        assert d["run_id"] == "run-456"
+        assert d["source"] == "full_model"
+        assert d["fallback_reason"] == "emulator_not_registered"
+
+    def test_timestamp_auto(self):
+        event = EmulationEvent(
+            run_id="r",
+            task_name="t",
+            component=None,
+            emulator_name=None,
+            source="full_model",
+            confidence=None,
+            fallback_reason=None,
+            domain_valid=True,
+        )
+        assert event.timestamp  # auto-populated
+
+
+class TestPhase5Settings:
+    def test_ml_settings_defaults(self):
+        from scalable.common import Settings
+
+        s = Settings()
+        assert s.ml_model_cache_dir == ".scalable/models"
+        assert s.emulator_registry_dir == ".scalable/emulators"
+        assert s.ml_enabled is True
+        assert s.emulation_enabled is False
+        assert s.emulation_confidence_threshold == 0.9
+
+
+class TestCLIAdvise:
+    def test_advise_parser_registration(self):
+        """Verify advise command is registered in CLI parser."""
+        from scalable.cli.main import _build_parser
+
+        parser = _build_parser()
+        # Check that 'advise' is a valid subcommand
+        # Parse with --help would SystemExit, so just verify no crash
+        # on the parser build
+        assert parser is not None

From dcb8a2d170aa1084557213b5f9d88990b10fff67 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Tue, 19 May 2026 23:11:09 -0400
Subject: [PATCH 27/47] update docs

---
 README.md                | 262 ++++++++++++++++++++++++++++++++++-----
 docs/advising.rst        |  37 +++++-
 docs/ai_assistants.rst   |  13 +-
 docs/artifacts.rst       |  27 +++-
 docs/cloud.rst           |  13 +-
 docs/cost.rst            |  15 ++-
 docs/emulation.rst       | 183 +++++++++++++++++++++++++++
 docs/getting_started.rst |  51 +++++++-
 docs/index.rst           |  13 +-
 docs/kubernetes.rst      |  15 ++-
 docs/manifest.rst        |  66 +++++++++-
 docs/ml.rst              | 148 ++++++++++++++++++++++
 docs/overlays.rst        |  13 +-
 docs/providers.rst       | 124 +++++++++++++++---
 docs/telemetry.rst       |  72 ++++++++++-
 15 files changed, 973 insertions(+), 79 deletions(-)
 create mode 100644 docs/emulation.rst
 create mode 100644 docs/ml.rst

diff --git a/README.md b/README.md
index 3310a33..59ec3ab 100644
--- a/README.md
+++ b/README.md
@@ -8,7 +8,7 @@
 [![Python](https://img.shields.io/badge/python-3.11%20%7C%203.12%20%7C%203.13-blue.svg)](https://pypi.org/project/scalable/)
 [![Docs](https://readthedocs.org/projects/scalable/badge/?version=latest)](https://jgcri.github.io/scalable/)
 
-Scalable is a Python framework for orchestrating containerized, distributed workflows on HPC systems. It integrates container lifecycle management, scheduler-aware resource provisioning, and a Dask-based execution model so multi-stage scientific workflows can run consistently at scale.
+Scalable is a Python framework for orchestrating containerized, distributed workflows on HPC systems, Kubernetes clusters, and cloud providers. It integrates container lifecycle management, scheduler-aware resource provisioning, a Dask-based execution model, optional AI assistants, and ML-driven optimization so multi-stage scientific workflows can run consistently at scale.
 
 ## Table of Contents
 
@@ -17,6 +17,16 @@ Scalable is a Python framework for orchestrating containerized, distributed work
 - [System Requirements](#system-requirements)
 - [Quick Start](#quick-start)
 - [Usage](#usage)
+  - [Manifest-Driven Workflows](#manifest-driven-workflows)
+  - [Session API](#session-api)
+  - [Telemetry and Reports](#telemetry-and-reports)
+  - [Resource Advising](#resource-advising)
+  - [ML Optimization](#ml-optimization)
+  - [Model Emulation](#model-emulation)
+  - [AI Assistants](#ai-assistants)
+  - [Cloud and Kubernetes](#cloud-and-kubernetes)
+  - [Artifact Storage](#artifact-storage)
+  - [Imperative API](#imperative-api)
 - [Function Caching](#function-caching)
 - [How to Contribute](#how-to-contribute)
 - [License](#license)
@@ -40,12 +50,33 @@ git clone https://github.com/JGCRI/scalable.git
 pip install ./scalable
 ```
 
+### Optional extras
+
+Scalable provides optional dependency groups for extended features:
+
+```bash
+# AI assistant features (init-component, diagnose, explain, compose, migrate)
+pip install scalable[ai]
+
+# Cloud providers (AWS, GCP)
+pip install scalable[cloud]
+
+# Kubernetes provider (Dask Kubernetes Operator)
+pip install scalable[kubernetes]
+
+# ML optimization and emulation (LearnedAdvisor, AdaptiveScaler, emulators)
+pip install scalable[ml]
+
+# All optional dependencies
+pip install scalable[ai,cloud,kubernetes,ml]
+```
+
 If your shell cannot find installed scripts (for example, `scalable_bootstrap`), add the relevant scripts directory to `PATH`.
 
 ## System Requirements
 
-- **Scheduler:** Slurm
-- **Local host tools:** Docker
+- **Scheduler:** Slurm (HPC), Kubernetes, AWS Fargate/EC2, or local execution
+- **Local host tools:** Docker (optional for local provider)
 - **HPC host tools:** Apptainer
 
 Platform guidance:
@@ -80,12 +111,11 @@ Bootstrap performs multiple SSH operations. For best reliability and usability,
 
 ## Usage
 
-### Manifest-first workflow (v2.0.0 Phase 1)
+### Manifest-Driven Workflows
 
-Scalable now supports a declarative manifest path for provider-neutral planning
-and validation.
+Scalable v2.0.0 introduces a declarative manifest (`scalable.yaml`) as the single source of truth for targets, components, and task bindings.
 
-Create ``scalable.yaml``:
+Create `scalable.yaml`:
 
 ```yaml
 version: 1
@@ -114,14 +144,16 @@ scalable validate ./scalable.yaml
 scalable plan ./scalable.yaml --target local --dry-run --output plan.json
 ```
 
-Generate a telemetry report from completed runs:
+Run a workflow (with optional dry-run for cost estimation):
 
 ```bash
-scalable report --latest
-scalable report --latest --format json --output report.json
+scalable run ./scalable.yaml --target local --workflow workflow.py
+scalable run ./scalable.yaml --target aws --dry-run
 ```
 
-Use the session API:
+### Session API
+
+Use the Python session API for programmatic control:
 
 ```python
 from scalable import ScalableSession
@@ -129,8 +161,25 @@ from scalable import ScalableSession
 session = ScalableSession.from_yaml("./scalable.yaml", target="local")
 plan = session.plan(dry_run=True)
 print(plan.manifest_lock)
+
+# With planning objectives and policies
+plan = session.plan(
+    objective="minimize cost",   # "minimize cost", "minimize time", "balance"
+    policy="safe",               # "safe", "aggressive", "manual"
+)
 ```
 
+### Telemetry and Reports
+
+Every manifest-driven run records structured telemetry under `.scalable/runs/`:
+
+```bash
+scalable report --latest
+scalable report --latest --format json --output report.json
+```
+
+### Resource Advising
+
 Use deterministic history-based advising:
 
 ```python
@@ -146,9 +195,147 @@ print(recommendation.workers)
 print(recommendation.resources)
 ```
 
-At runtime, create a cluster, register container targets, scale workers, and submit functions.
+Or use the CLI:
 
-### 1. Create a cluster
+```bash
+scalable advise --task run_gcam --target local --confidence 0.95
+```
+
+### ML Optimization
+
+When `scalable[ml]` is installed, ML-backed resource prediction and adaptive
+scaling become available:
+
+```python
+from scalable import LearnedAdvisor, AdaptiveScaler
+
+# ML-backed resource recommendations trained on telemetry history
+advisor = LearnedAdvisor.from_history(
+    "./.scalable/runs",
+    model_type="gradient_boosting",
+)
+recommendation = advisor.recommend(task="run_gcam", target="local")
+print(recommendation.resources)
+
+# Adaptive real-time worker scaling
+scaler = AdaptiveScaler(
+    min_workers=1,
+    max_workers=16,
+    scale_up_threshold=0.8,
+    scale_down_threshold=0.3,
+    cooldown_seconds=60,
+)
+decision = scaler.evaluate(current_metrics)
+```
+
+CLI access:
+
+```bash
+scalable advise --task run_gcam --model-type gradient_boosting --format json
+```
+
+### Model Emulation
+
+The emulation subsystem (`scalable[ml]`) provides uncertainty-aware surrogate
+model dispatch for expensive scientific functions:
+
+```python
+from scalable import emulatable, EmulatorRegistry, EmulatorDispatch
+
+@emulatable(
+    inputs=["temperature", "precipitation"],
+    outputs=["yield"],
+    domain_bounds={"temperature": (250, 350), "precipitation": (0, 5000)},
+    confidence_threshold=0.9,
+)
+def run_crop_model(temperature, precipitation):
+    # Expensive model execution
+    ...
+
+# Register and manage trained emulators
+registry = EmulatorRegistry(".scalable/emulators")
+dispatch = EmulatorDispatch(registry, confidence_threshold=0.9)
+
+# Confidence-gated routing: uses emulator when confident, falls back to full model
+result = dispatch.predict("run_crop_model", inputs={"temperature": 300, "precipitation": 1200})
+print(result.source)       # "emulator" or "full_model"
+print(result.confidence)
+```
+
+### AI Assistants
+
+AI assistants help with onboarding, diagnostics, workflow generation, and
+migration. All features work without an LLM backend via deterministic heuristics;
+LLM enhancement is opt-in via `SCALABLE_AI_BACKEND`.
+
+```bash
+# Onboard a new model component
+scalable init-component ./path/to/model --name gcam --no-ai
+
+# Diagnose failures from recent runs
+scalable diagnose --latest --no-ai
+
+# Explain an execution plan in human-readable form
+scalable explain plan.json
+
+# Generate a workflow from natural language
+scalable compose "Run GCAM reference scenario then Stitches for daily climate"
+
+# Propose manifest migration to a new provider
+scalable migrate scalable.yaml --to-provider kubernetes
+```
+
+Python API:
+
+```python
+from scalable.ai import onboard_component, diagnose_run, explain_plan
+
+result = onboard_component("./gcam-core", name="gcam", no_ai=True)
+print(result.component_yaml)
+```
+
+### Cloud and Kubernetes
+
+Scalable supports multi-provider execution through optional extras:
+
+```bash
+# AWS (Fargate/EC2)
+pip install scalable[cloud]
+scalable run scalable.yaml --target aws --dry-run
+
+# Kubernetes (Dask Kubernetes Operator)
+pip install scalable[kubernetes]
+scalable run scalable.yaml --target gke --dry-run
+```
+
+Cost estimation is included for cloud providers:
+
+```python
+from scalable import CostEstimate
+# Cost estimates are included in dry-run plan output and telemetry
+```
+
+### Artifact Storage
+
+The artifact store provides protocol-based storage across local and remote
+backends:
+
+```python
+from scalable.artifacts import build_artifact_store
+
+# Local storage
+store = build_artifact_store("./artifacts")
+ref = store.put("output.csv", "runs/run-001/output.csv")
+
+# S3 storage (requires scalable[cloud])
+store = build_artifact_store("s3://my-bucket/artifacts/")
+```
+
+### Imperative API
+
+The legacy imperative API remains fully supported for existing workflows:
+
+#### 1. Create a cluster
 
 ```python
 from scalable import SlurmCluster, ScalableClient
@@ -162,7 +349,7 @@ cluster = SlurmCluster(
 )
 ```
 
-### 2. Register container targets
+#### 2. Register container targets
 
 ```python
 cluster.add_container(
@@ -177,23 +364,16 @@ cluster.add_container(
     memory="50G",
     dirs={"/qfs/people/user": "/user", "/rcfs": "/rcfs"},
 )
-cluster.add_container(
-    tag="osiris",
-    cpus=8,
-    memory="20G",
-    dirs={"/rcfs/projects/gcims/data": "/data", "/qfs/people/user/test": "/scratch"},
-)
 ```
 
-### 3. Scale workers
+#### 3. Scale workers
 
 ```python
 cluster.add_workers(n=3, tag="gcam")
 cluster.add_workers(n=2, tag="stitches")
-cluster.add_workers(n=3, tag="osiris")
 ```
 
-### 4. Submit functions
+#### 4. Submit functions
 
 ```python
 def func1(param):
@@ -206,24 +386,17 @@ def func2(param):
     return stitches.__version__
 
 
-def func3(param):
-    import osiris
-    return osiris.__version__
-
-
 client = ScalableClient(cluster)
 
 fut1 = client.submit(func1, "gcam", tag="gcam")
 fut2 = client.submit(func2, "stitches", tag="stitches")
-fut3 = client.submit(func3, "osiris", tag="osiris")
 ```
 
-### 5. Scale down when complete
+#### 5. Scale down when complete
 
 ```python
 cluster.remove_workers(n=2, tag="gcam")
 cluster.remove_workers(n=1, tag="stitches")
-cluster.remove_workers(n=3, tag="osiris")
 ```
 
 ## Function Caching
@@ -252,7 +425,32 @@ def func3(param):
     return osiris.__version__
 ```
 
-For reliable behavior, explicitly specify argument and return types whenever possible.
+For reliable behavior, explicitly specify argument and return types whenever possible. Cache hit/miss events are emitted to telemetry when telemetry is active.
+
+## Environment Variables
+
+Scalable is configured via environment variables for deployment flexibility:
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `SCALABLE_CACHE_DIR` | `./cache` | Disk cache directory |
+| `SCALABLE_SEED` | `987654321` | xxhash seed for cache keys |
+| `SCALABLE_LOG_LEVEL` | *(unset)* | Library log level (e.g. `DEBUG`) |
+| `SCALABLE_MANIFEST` | `./scalable.yaml` | Default manifest path |
+| `SCALABLE_TARGET` | *(unset)* | Default target override |
+| `SCALABLE_RUNS_DIR` | `./.scalable/runs` | Telemetry run directory |
+| `SCALABLE_TELEMETRY` | `1` | Enable/disable telemetry |
+| `SCALABLE_TELEMETRY_PARQUET` | `0` | Emit parquet snapshots |
+| `SCALABLE_CACHE_REMOTE` | *(unset)* | Remote cache URI (S3/GCS) |
+| `SCALABLE_DEFAULT_STORAGE` | *(unset)* | Default artifact storage URI |
+| `SCALABLE_AI_BACKEND` | `none` | AI backend (`none`, `openai`, `ollama`) |
+| `SCALABLE_AI_MODEL` | *(unset)* | Model name for AI backend |
+| `SCALABLE_AI_ENDPOINT` | *(unset)* | API endpoint for AI backend |
+| `SCALABLE_ML` | `1` | Enable ML features |
+| `SCALABLE_ML_CACHE_DIR` | `.scalable/models` | ML model cache directory |
+| `SCALABLE_EMULATION` | `0` | Enable model emulation |
+| `SCALABLE_EMULATOR_DIR` | `.scalable/emulators` | Emulator registry directory |
+| `SCALABLE_EMULATION_CONFIDENCE` | `0.9` | Emulation confidence threshold |
 
 ## How to Contribute
 
diff --git a/docs/advising.rst b/docs/advising.rst
index f3c7862..7a4c903 100644
--- a/docs/advising.rst
+++ b/docs/advising.rst
@@ -1,8 +1,9 @@
 Deterministic Resource Advising
 ==============================
 
-Phase 2 adds a baseline deterministic :class:`ResourceAdvisor` that derives
-conservative resource recommendations from historical run telemetry.
+Scalable provides a deterministic :class:`~scalable.advising.ResourceAdvisor`
+that derives conservative resource recommendations from historical run
+telemetry.
 
 Quick start
 -----------
@@ -26,6 +27,34 @@ Design intent
 -------------
 
 This advisor is heuristic and explainable. It uses observed request/runtime
-history and confidence-indexed quantiles. Learned ML models are deferred to
-later phases.
+history and confidence-indexed quantiles. No external dependencies beyond the
+base Scalable install are required.
+
+The advisor returns a :class:`~scalable.advising.ResourceRecommendation` with:
+
+- ``workers`` — recommended worker count
+- ``resources`` — recommended per-worker resource allocation
+- ``evidence`` — source data summary backing the recommendation
+- ``confidence`` — achieved confidence level
+
+CLI access
+----------
+
+.. code-block:: bash
+
+    scalable advise --task run_gcam --target local --confidence 0.95
+
+The CLI ``advise`` command first attempts ML-backed recommendations (if
+``scalable[ml]`` is installed) and falls back to the heuristic advisor when
+insufficient training data is available or the ML extra is missing.
+
+ML-backed advising
+------------------
+
+When ``scalable[ml]`` is installed, :class:`~scalable.ml.LearnedAdvisor`
+provides ML-backed predictions using gradient boosting, random forest, or
+quantile regression trained on telemetry history. See :doc:`ml` for details.
+
+The heuristic advisor documented on this page remains the deterministic
+baseline and fallback for all ML-backed recommendations.
 
diff --git a/docs/ai_assistants.rst b/docs/ai_assistants.rst
index 901481c..e0c2b9e 100644
--- a/docs/ai_assistants.rst
+++ b/docs/ai_assistants.rst
@@ -1,8 +1,9 @@
 AI Assistants
 =============
 
-Phase 4 introduces AI-assisted features that help users onboard models,
-compose workflows, diagnose failures, explain plans, and migrate manifests.
+Scalable v2.0.0 includes AI-assisted features (Phase 4) that help users
+onboard models, compose workflows, diagnose failures, explain plans, and
+migrate manifests.
 
 All features work **without** an LLM backend via deterministic heuristic
 fallbacks. LLM enhancement is opt-in via the ``SCALABLE_AI_BACKEND``
@@ -158,3 +159,11 @@ Session Planning with Objectives
 * ``safe`` — Use safety margins on resources (default)
 * ``aggressive`` — Scale up resources/workers significantly
 * ``manual`` — Use exactly what the manifest declares
+
+See Also
+--------
+
+- :doc:`manifest` — Manifest schema and session API
+- :doc:`ml` — ML-backed resource optimization
+- :doc:`emulation` — Model emulation with surrogate dispatch
+- :doc:`telemetry` — Run telemetry that powers diagnosis
diff --git a/docs/artifacts.rst b/docs/artifacts.rst
index 12b5340..d17cd0b 100644
--- a/docs/artifacts.rst
+++ b/docs/artifacts.rst
@@ -1,8 +1,9 @@
 Artifact Store
 ==============
 
-The :mod:`scalable.artifacts` module provides a protocol-based abstraction
-for storing and retrieving workflow artifacts across local and remote backends.
+The :mod:`scalable.artifacts` module (Phase 3) provides a protocol-based
+abstraction for storing and retrieving workflow artifacts across local and
+remote backends.
 
 Overview
 --------
@@ -56,3 +57,25 @@ The artifact store layer also powers the remote cache backend. Enable it with:
 
 When enabled, cache results are stored remotely in addition to the local
 diskcache, allowing cache sharing across machines.
+
+Session Integration
+-------------------
+
+Record artifacts during a session for provenance tracking:
+
+.. code-block:: python
+
+   from scalable import ScalableSession
+
+   session = ScalableSession.from_yaml("scalable.yaml", target="local")
+   # ... run tasks ...
+   session.record_artifact("output.csv", kind="result")
+
+Artifact metadata is recorded in the ``artifacts.jsonl`` telemetry stream.
+
+See Also
+--------
+
+- :doc:`cloud` — Cloud providers with remote storage support
+- :doc:`telemetry` — Artifact events in run telemetry
+- :doc:`manifest` — Configuring ``project.default_storage``
diff --git a/docs/cloud.rst b/docs/cloud.rst
index c442c68..3fca7b6 100644
--- a/docs/cloud.rst
+++ b/docs/cloud.rst
@@ -1,8 +1,9 @@
 Cloud Providers
 ===============
 
-Scalable supports cloud-based execution through the ``scalable[cloud]`` extra,
-which provides access to AWS and GCP deployment providers.
+Scalable v2.0.0 supports cloud-based execution through the ``scalable[cloud]``
+extra, which provides access to AWS and GCP deployment providers with
+integrated cost estimation.
 
 Installation
 ------------
@@ -66,3 +67,11 @@ Run ``scalable run --dry-run`` to see estimated costs:
    scalable run scalable.yaml --target aws --dry-run
 
 The cost estimate is also recorded in telemetry (``cost.jsonl``).
+See :doc:`cost` for detailed cost estimation documentation.
+
+See Also
+--------
+
+- :doc:`providers` — Full provider abstraction documentation
+- :doc:`cost` — Cost estimation primitives and tables
+- :doc:`artifacts` — Remote artifact storage with S3/GCS backends
diff --git a/docs/cost.rst b/docs/cost.rst
index 7988ec9..dbe159d 100644
--- a/docs/cost.rst
+++ b/docs/cost.rst
@@ -68,12 +68,19 @@ Supported AWS instances: ``m5.*``, ``c5.*``, ``r5.*``, ``t3.*``
 Supported GCP machines: ``n1-standard-*``, ``n1-highmem-*``,
 ``n1-highcpu-*``, ``e2-standard-*``
 
-Future Phases
--------------
+Future Work
+-----------
 
-Phase 5 will extend cost estimation with:
+Future releases may extend cost estimation with:
 
 - Live pricing API integration
 - Spot/preemptible instance pricing
-- Cost-aware scheduling recommendations
+- Cost-aware scheduling recommendations via :doc:`ml`
 - Historical cost tracking and budgets
+
+See Also
+--------
+
+- :doc:`cloud` — AWS and GCP providers with cost support
+- :doc:`telemetry` — Cost events in the run history store
+- :doc:`ml` — ML-backed resource optimization
diff --git a/docs/emulation.rst b/docs/emulation.rst
new file mode 100644
index 0000000..b938771
--- /dev/null
+++ b/docs/emulation.rst
@@ -0,0 +1,183 @@
+Model Emulation
+===============
+
+The :mod:`scalable.emulation` package (Phase 5) provides scientific model
+emulation capabilities with uncertainty-aware surrogate model dispatch. This
+enables fast approximations of expensive model runs when confidence is high,
+with automatic fallback to the full model otherwise.
+
+Installation
+------------
+
+.. code-block:: bash
+
+   pip install scalable[ml]
+
+The emulation subsystem shares the ``ml`` optional dependency group
+(``scikit-learn``, ``joblib``).
+
+Overview
+--------
+
+The emulation workflow follows this pattern:
+
+1. Mark expensive functions with the ``@emulatable`` decorator.
+2. Train surrogate models on historical input/output data.
+3. Register trained emulators in the ``EmulatorRegistry``.
+4. Use ``EmulatorDispatch`` for confidence-gated routing between the
+   emulator and the full model.
+
+The ``@emulatable`` Decorator
+-----------------------------
+
+Mark functions as emulation-capable by declaring their inputs, outputs,
+domain bounds, and confidence thresholds:
+
+.. code-block:: python
+
+   from scalable import emulatable
+
+   @emulatable(
+       inputs=["temperature", "precipitation", "co2"],
+       outputs=["yield", "water_use"],
+       domain_bounds={
+           "temperature": (250, 350),
+           "precipitation": (0, 5000),
+           "co2": (280, 1200),
+       },
+       confidence_threshold=0.9,
+   )
+   def run_crop_model(temperature, precipitation, co2):
+       # Expensive scientific model execution
+       ...
+       return {"yield": result_yield, "water_use": result_water}
+
+The decorator attaches metadata to the function for registry lookup and
+domain validation.
+
+EmulatorRegistry
+----------------
+
+:class:`~scalable.emulation.EmulatorRegistry` manages trained surrogate
+models with filesystem persistence, domain validation, and versioning:
+
+.. code-block:: python
+
+   from scalable import EmulatorRegistry
+
+   registry = EmulatorRegistry(".scalable/emulators")
+
+   # Register a trained emulator
+   registry.register(
+       name="run_crop_model",
+       emulator=trained_model,
+       metadata={"version": "1.0", "training_samples": 500},
+   )
+
+   # List registered emulators
+   emulators = registry.list()
+
+   # Load a specific emulator
+   info = registry.get("run_crop_model")
+   print(info.metadata)
+
+EmulatorDispatch
+----------------
+
+:class:`~scalable.emulation.EmulatorDispatch` provides confidence-gated
+routing between the emulator and the full model:
+
+.. code-block:: python
+
+   from scalable import EmulatorDispatch, EmulatorRegistry
+
+   registry = EmulatorRegistry(".scalable/emulators")
+   dispatch = EmulatorDispatch(registry, confidence_threshold=0.9)
+
+   # Predict using the emulator when confident, fall back to full model
+   result = dispatch.predict(
+       "run_crop_model",
+       inputs={"temperature": 300, "precipitation": 1200, "co2": 400},
+   )
+   print(result.source)       # "emulator" or "full_model"
+   print(result.confidence)   # e.g. 0.95
+   print(result.prediction)
+
+Each dispatch decision is recorded as an ``EmulationEvent`` in telemetry,
+including source, confidence, fallback reason, and domain validity.
+
+Surrogate Models
+----------------
+
+Built-in surrogate model implementations:
+
+- :class:`~scalable.emulation.GradientBoostingEmulator` — tree-based
+  uncertainty estimation via gradient boosting
+- :class:`~scalable.emulation.RandomForestEmulator` — ensemble-based
+  uncertainty from tree variance
+
+Both implement the :class:`~scalable.emulation.TrainedEmulator` protocol:
+
+.. code-block:: python
+
+   from scalable.emulation import GradientBoostingEmulator
+
+   emulator = GradientBoostingEmulator()
+   emulator.fit(X_train, y_train)
+
+   prediction = emulator.predict(X_new)
+   print(prediction.mean)
+   print(prediction.uncertainty)
+
+ActiveLearner
+-------------
+
+:class:`~scalable.emulation.ActiveLearner` provides intelligent scenario
+selection for iteratively improving emulator accuracy:
+
+.. code-block:: python
+
+   from scalable import ActiveLearner
+
+   learner = ActiveLearner(
+       strategy="expected_improvement",  # or "maximum_uncertainty", "random"
+       batch_size=10,
+   )
+
+   # Suggest next scenarios to run with the full model
+   candidates = learner.suggest(
+       emulator=trained_emulator,
+       domain_bounds={"temperature": (250, 350), "precipitation": (0, 5000)},
+       existing_data=X_train,
+   )
+
+Acquisition strategies:
+
+- ``expected_improvement`` — prioritize regions where improvement is likely
+- ``maximum_uncertainty`` — sample where the emulator is least certain
+- ``random`` — uniform random sampling within domain bounds
+
+Uncertainty Calibration
+-----------------------
+
+Assess emulator reliability with calibration metrics:
+
+.. code-block:: python
+
+   from scalable.emulation import calibrate_emulator
+
+   result = calibrate_emulator(emulator, X_test, y_test)
+   print(result.coverage)    # fraction of test points within intervals
+   print(result.sharpness)   # average interval width
+
+Configuration
+-------------
+
+Emulation features are controlled via environment variables:
+
+- ``SCALABLE_EMULATION`` — Enable/disable emulation dispatch (default: ``0``)
+- ``SCALABLE_EMULATOR_DIR`` — Emulator registry directory
+  (default: ``.scalable/emulators``)
+- ``SCALABLE_EMULATION_CONFIDENCE`` — Default confidence threshold
+  (default: ``0.9``)
+
diff --git a/docs/getting_started.rst b/docs/getting_started.rst
index 7fd491e..bc3faa6 100644
--- a/docs/getting_started.rst
+++ b/docs/getting_started.rst
@@ -24,6 +24,29 @@ from the checkout.
     pip install ./scalable
 
 
+Optional Extras
+~~~~~~~~~~~~~~~
+
+Scalable provides optional dependency groups for extended features:
+
+.. code-block:: bash
+
+    # AI assistant features (init-component, diagnose, explain, compose, migrate)
+    pip install scalable[ai]
+
+    # Cloud providers (AWS, GCP) and remote artifact storage
+    pip install scalable[cloud]
+
+    # Kubernetes provider (Dask Kubernetes Operator)
+    pip install scalable[kubernetes]
+
+    # ML optimization and emulation (LearnedAdvisor, AdaptiveScaler, emulators)
+    pip install scalable[ml]
+
+    # All optional dependencies
+    pip install scalable[ai,cloud,kubernetes,ml]
+
+
 If installation reports that the scripts directory is not in ``PATH``, add the
 reported directory to your shell profile.
 
@@ -46,9 +69,11 @@ Compatibility Requirements
 
 Required and supported tooling:
 
-* Local host: `Docker <https://www.docker.com/>`_
+* Local host: `Docker <https://www.docker.com/>`_ (optional for local provider)
 * HPC scheduler: Slurm
 * HPC container runtime: Apptainer
+* Cloud: AWS (Fargate/EC2), GCP (scaffold)
+* Orchestration: Kubernetes with Dask Operator
 
 Bootstrapping is designed for POSIX-like shells. On Windows,
 `Git Bash <https://git-scm.com/>`_ is recommended.
@@ -90,14 +115,38 @@ available in this execution model.
 If bootstrap is interrupted, rerun ``scalable_bootstrap``. It resumes from the
 last valid step and skips completed setup where possible.
 
+CLI Commands
+------------
+
+Scalable v2.0.0 provides a full CLI for manifest-driven workflows:
+
+.. code-block:: bash
+
+    scalable validate ./scalable.yaml
+    scalable plan ./scalable.yaml --target local --dry-run
+    scalable run ./scalable.yaml --target local --workflow workflow.py
+    scalable report --latest
+    scalable advise --task run_gcam --target local
+    scalable init-component ./path/to/model --name gcam
+    scalable diagnose --latest
+    scalable explain plan.json
+    scalable compose "Run GCAM then Stitches"
+    scalable migrate scalable.yaml --to-provider kubernetes
+
 Next Steps
 ----------
 
 After setup:
 
 * For declarative workflows, start with :doc:`manifest` and :doc:`providers`.
+* Use manifest overlays for environment-specific overrides: :doc:`overlays`.
 * Review run telemetry in :doc:`telemetry`.
 * Use deterministic history-based recommendations from :doc:`advising`.
+* For ML-driven optimization, see :doc:`ml`.
+* For model emulation, see :doc:`emulation`.
+* For AI-assisted onboarding and diagnosis, see :doc:`ai_assistants`.
+* For cloud and Kubernetes targets, see :doc:`cloud` and :doc:`kubernetes`.
+* For artifact storage, see :doc:`artifacts`.
 * Review the :ref:`api_section` for worker, caching, and function interfaces.
 * Run examples from :ref:`demos_section`.
 * Use :ref:`how_tos_section` for targeted implementation guidance.
diff --git a/docs/index.rst b/docs/index.rst
index 1fb1195..cb3489f 100755
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -6,9 +6,10 @@
 Scalable Documentation
 ======================
 
-Scalable is a Python library for orchestrating multi-step workflows on HPC
-systems with minimal manual overhead. It combines Dask-based task execution,
-scheduler-aware worker provisioning, and optional containerized runtimes so
+Scalable is a Python framework for orchestrating multi-step workflows on HPC
+systems, Kubernetes clusters, and cloud providers with minimal manual overhead.
+It combines Dask-based task execution, scheduler-aware worker provisioning,
+optional containerized runtimes, AI assistants, and ML-driven optimization so
 workloads can run reproducibly across heterogeneous environments.
 
 The diagram below shows the high-level architecture.
@@ -21,6 +22,9 @@ Scalable is a strong fit when your project needs one or more of the following:
 * Long-running or resource-intensive workflows on shared HPC infrastructure.
 * Pipeline-style execution where outputs from one stage feed downstream stages.
 * Automatic or programmatic scaling of workers and hardware allocations.
+* Portable execution across local, HPC, Kubernetes, and cloud targets.
+* AI-assisted model onboarding, failure diagnosis, and workflow composition.
+* ML-optimized resource prediction and adaptive scaling from run history.
 
 Scalable supports running functions in distinct software environments via
 container images. A multi-stage Dockerfile can define multiple worker profiles,
@@ -54,6 +58,8 @@ Contents
    cost
    telemetry
    advising
+   ml
+   emulation
    ai_assistants
    caching
    functions
@@ -84,3 +90,4 @@ Contents
    :maxdepth: 1
 
    issues
+
diff --git a/docs/kubernetes.rst b/docs/kubernetes.rst
index a91d2c6..8fbc378 100644
--- a/docs/kubernetes.rst
+++ b/docs/kubernetes.rst
@@ -1,7 +1,7 @@
 Kubernetes Provider
 ===================
 
-Scalable supports Kubernetes-based execution through the
+Scalable v2.0.0 supports Kubernetes-based execution through the
 ``scalable[kubernetes]`` extra, using the Dask Kubernetes Operator.
 
 Installation
@@ -62,3 +62,16 @@ Run ``scalable validate`` to check your Kubernetes manifest:
 .. code-block:: bash
 
    scalable validate scalable.yaml --target gke
+
+Run with dry-run for planning:
+
+.. code-block:: bash
+
+   scalable run scalable.yaml --target gke --dry-run
+
+See Also
+--------
+
+- :doc:`providers` — Full provider abstraction documentation
+- :doc:`cloud` — AWS and GCP cloud providers
+- :doc:`overlays` — Environment-specific configuration overrides
diff --git a/docs/manifest.rst b/docs/manifest.rst
index 0298cad..a565472 100644
--- a/docs/manifest.rst
+++ b/docs/manifest.rst
@@ -1,5 +1,5 @@
-Manifest-Driven Workflows (Phase 1)
-===================================
+Manifest-Driven Workflows
+=========================
 
 Scalable v2.0.0 introduces a declarative manifest entry point, ``scalable.yaml``.
 This becomes the source of truth for targets, components, and task bindings.
@@ -15,6 +15,10 @@ Top-level keys:
 * ``components``
 * ``tasks``
 
+Optional keys:
+
+* ``overlays`` — environment-specific configuration deltas (see :doc:`overlays`)
+
 Minimal example:
 
 .. code-block:: yaml
@@ -43,8 +47,24 @@ Minimal example:
       run_gcam:
         component: gcam
 
-Validation commands
--------------------
+Project configuration
+---------------------
+
+The ``project`` block supports:
+
+* ``name`` — project identifier (used in run directory naming)
+* ``default_storage`` — URI for artifact/output storage (e.g. ``s3://bucket/path/``)
+* ``local_cache`` — local cache directory override
+
+.. code-block:: yaml
+
+    project:
+      name: integrated-assessment
+      default_storage: s3://my-bucket/scalable-runs/
+      local_cache: ./.scalable/cache
+
+CLI commands
+------------
 
 Validate a manifest:
 
@@ -58,11 +78,39 @@ Generate a deterministic dry-run plan:
 
     scalable plan ./scalable.yaml --target local --dry-run --output plan.json
 
-Phase 1 writes:
+Run a workflow:
+
+.. code-block:: bash
+
+    scalable run ./scalable.yaml --target local --workflow workflow.py
+    scalable run ./scalable.yaml --target aws --dry-run
+
+The plan outputs:
 
 * ``plan.json`` (provider-neutral plan payload)
 * ``manifest.lock`` (SHA-256 fingerprint of canonicalized manifest content)
 
+The ``run`` command validates, plans, estimates cost (for cloud targets), and
+optionally executes a workflow file. Use ``--dry-run`` to preview the plan and
+cost estimate without launching workers.
+
+Session API
+-----------
+
+.. code-block:: python
+
+    from scalable import ScalableSession
+
+    session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+    plan = session.plan(dry_run=True)
+    print(plan.manifest_lock)
+
+    # With planning objectives and policies
+    plan = session.plan(
+        objective="minimize cost",   # "minimize cost", "minimize time", "balance"
+        policy="safe",               # "safe", "aggressive", "manual"
+    )
+
 Environment variables
 ---------------------
 
@@ -72,13 +120,15 @@ Environment variables
 Migration note from imperative API
 ----------------------------------
 
-Legacy imperative APIs remain supported in Phase 1:
+Legacy imperative APIs remain supported:
 
 * ``SlurmCluster(...)``
 * ``cluster.add_container(...)``
 * ``cluster.add_workers(...)``
 
 The new manifest/session path is additive and can be adopted incrementally.
+Legacy ``ModelConfig`` Dockerfile/config auto-discovery emits a
+``DeprecationWarning`` when used outside the manifest adapter context.
 
 Example manifests
 -----------------
@@ -87,3 +137,7 @@ Reference examples are included in:
 
 * ``docs/examples/scalable.minimal.yaml``
 * ``docs/examples/scalable.gcam_stitches.yaml``
+* ``docs/examples/scalable.aws.yaml``
+* ``docs/examples/scalable.gke.yaml``
+* ``docs/examples/scalable.overlays.yaml``
+
diff --git a/docs/ml.rst b/docs/ml.rst
new file mode 100644
index 0000000..eeb35e2
--- /dev/null
+++ b/docs/ml.rst
@@ -0,0 +1,148 @@
+ML Optimization
+===============
+
+The :mod:`scalable.ml` package (Phase 5) provides machine-learning-backed
+resource prediction, adaptive worker scaling, and distributed hyperparameter
+tuning. All features degrade gracefully to Phase 2 heuristic advising when
+``scalable[ml]`` is not installed.
+
+Installation
+------------
+
+.. code-block:: bash
+
+   pip install scalable[ml]
+
+This installs ``scikit-learn >= 1.3``, ``dask-ml >= 2023.3.24``, and
+``joblib >= 1.3``.
+
+LearnedAdvisor
+--------------
+
+:class:`~scalable.ml.LearnedAdvisor` provides ML-backed resource
+recommendations using gradient boosting, random forest, or quantile regression
+trained on run telemetry history.
+
+.. code-block:: python
+
+   from scalable import LearnedAdvisor
+
+   advisor = LearnedAdvisor.from_history(
+       "./.scalable/runs",
+       model_type="gradient_boosting",
+   )
+   recommendation = advisor.recommend(task="run_gcam", target="local")
+   print(recommendation.resources)
+   print(recommendation.confidence)
+
+Supported model types:
+
+- ``gradient_boosting`` (default) — gradient boosting regressor
+- ``random_forest`` — random forest regressor
+- ``quantile_regression`` — quantile regression for interval estimates
+
+When insufficient training data is available, ``LearnedAdvisor`` transparently
+falls back to the Phase 2 :class:`~scalable.advising.ResourceAdvisor` heuristic.
+
+AdaptiveScaler
+--------------
+
+:class:`~scalable.ml.AdaptiveScaler` provides real-time adaptive worker
+scaling with configurable thresholds, min/max bounds, and cooldown periods.
+
+.. code-block:: python
+
+   from scalable import AdaptiveScaler
+
+   scaler = AdaptiveScaler(
+       min_workers=1,
+       max_workers=16,
+       scale_up_threshold=0.8,
+       scale_down_threshold=0.3,
+       cooldown_seconds=60,
+   )
+   decision = scaler.evaluate(current_metrics)
+   print(decision.action)       # "scale_up", "scale_down", or "hold"
+   print(decision.target_workers)
+
+FeatureExtractor
+----------------
+
+:class:`~scalable.ml.FeatureExtractor` provides telemetry feature engineering
+with rolling aggregates, task identity hashing, and user-provided input
+features for ML model training.
+
+.. code-block:: python
+
+   from scalable.ml import FeatureExtractor
+
+   extractor = FeatureExtractor()
+   features = extractor.extract(telemetry_records)
+
+HyperparameterSearch
+--------------------
+
+:class:`~scalable.ml.HyperparameterSearch` integrates Dask-ML distributed
+hyperparameter tuning with support for hyperband, successive halving, and
+random search strategies. Falls back to sklearn ``GridSearchCV`` when
+``dask-ml`` is unavailable.
+
+.. code-block:: python
+
+   from scalable import HyperparameterSearch
+
+   search = HyperparameterSearch(
+       strategy="hyperband",
+       param_distributions={
+           "n_estimators": [50, 100, 200],
+           "max_depth": [3, 5, 10],
+       },
+   )
+   result = search.fit(X_train, y_train)
+   print(result.best_params)
+   print(result.best_score)
+
+Model Validation
+----------------
+
+Use ``cross_validate_advisor`` to assess model quality before deployment:
+
+.. code-block:: python
+
+   from scalable.ml import cross_validate_advisor
+
+   quality = cross_validate_advisor(advisor, X_test, y_test)
+   print(quality.mae)
+   print(quality.coverage)
+
+CLI Command
+-----------
+
+The ``scalable advise`` command provides ML-backed recommendations from the
+command line:
+
+.. code-block:: bash
+
+   scalable advise --task run_gcam --target local --confidence 0.95
+   scalable advise --task run_gcam --model-type random_forest --format json
+
+Options:
+
+- ``--task`` — Task name to get recommendations for (required)
+- ``--target`` — Deployment target to scope recommendations
+- ``--runs-dir`` — Path to runs directory (default: ``.scalable/runs``)
+- ``--model-type`` — ML model type (``gradient_boosting``, ``random_forest``,
+  ``quantile_regression``)
+- ``--confidence`` — Confidence level (default: 0.95)
+- ``--format`` — Output format (``text`` or ``json``)
+- ``--output`` — Output file path (default: stdout)
+
+Configuration
+-------------
+
+ML features are controlled via environment variables:
+
+- ``SCALABLE_ML`` — Enable/disable ML features (default: ``1``)
+- ``SCALABLE_ML_CACHE_DIR`` — Model cache directory
+  (default: ``.scalable/models``)
+
diff --git a/docs/overlays.rst b/docs/overlays.rst
index 740dc91..a87d452 100644
--- a/docs/overlays.rst
+++ b/docs/overlays.rst
@@ -1,8 +1,9 @@
 Manifest Overlays
 =================
 
-Overlays allow a single ``scalable.yaml`` to carry environment-specific
-configuration deltas without duplicating the entire manifest.
+Scalable v2.0.0 overlays allow a single ``scalable.yaml`` to carry
+environment-specific configuration deltas without duplicating the entire
+manifest.
 
 Concept
 -------
@@ -91,3 +92,11 @@ See the full overlay example:
 
 .. literalinclude:: examples/scalable.overlays.yaml
    :language: yaml
+
+See Also
+--------
+
+- :doc:`manifest` — Manifest schema and session API
+- :doc:`providers` — Provider-specific target options
+- :doc:`cloud` — Cloud provider configuration
+- :doc:`kubernetes` — Kubernetes target options
diff --git a/docs/providers.rst b/docs/providers.rst
index e103025..49acb42 100644
--- a/docs/providers.rst
+++ b/docs/providers.rst
@@ -1,25 +1,50 @@
-Provider Abstraction (Phase 1)
-==============================
+Provider Abstraction
+====================
 
-Phase 1 adds a provider-neutral execution seam.
+Scalable v2.0.0 introduces a provider-neutral execution layer that decouples
+workflow definitions from infrastructure. Providers implement the
+:class:`~scalable.providers.base.DeploymentProvider` protocol and are
+selected via the ``provider:`` field in manifest target blocks.
 
 Built-in providers
 ------------------
 
-* ``local`` via ``LocalProvider``
-* ``slurm`` via ``SlurmProvider``
+.. list-table::
+   :header-rows: 1
+   :widths: 20 30 50
+
+   * - Provider
+     - Name in manifest
+     - Backing implementation
+   * - :class:`~scalable.providers.local.LocalProvider`
+     - ``local``
+     - Dask ``LocalCluster`` for laptop/CI execution
+   * - :class:`~scalable.providers.slurm.SlurmProvider`
+     - ``slurm``
+     - Legacy ``SlurmCluster`` / ``SlurmJob`` path
+   * - :class:`~scalable.providers.kubernetes.KubernetesProvider`
+     - ``kubernetes``
+     - Dask Kubernetes Operator (requires ``scalable[kubernetes]``)
+   * - :class:`~scalable.providers.cloud.aws.AWSBatchProvider`
+     - ``aws``
+     - Fargate/EC2 via dask-cloudprovider (requires ``scalable[cloud]``)
+   * - :class:`~scalable.providers.cloud.gcp.GCPProvider`
+     - ``gcp``
+     - Validation-only scaffold (requires ``scalable[cloud]``)
 
 Provider contract
 -----------------
 
 Each provider follows the ``DeploymentProvider`` protocol:
 
-* ``validate(spec)``
-* ``build_cluster(spec)``
-* ``scale(cluster, plan)``
-* ``close(cluster)``
+* ``validate(spec)`` — validate a deployment specification
+* ``build_cluster(spec)`` — create and return a cluster handle
+* ``scale(cluster, plan)`` — apply a scale plan to the cluster
+* ``close(cluster)`` — shut down the cluster
+* ``estimate_cost(spec, plan)`` — (optional) return a cost estimate
 
-The provider layer consumes ``DeploymentSpec`` and applies a ``ScalePlan``.
+The provider layer consumes :class:`~scalable.providers.base.DeploymentSpec`
+and applies a :class:`~scalable.providers.base.ScalePlan`.
 
 Local provider
 --------------
@@ -28,20 +53,91 @@ Local provider
 It supports tag-aware scheduling compatible with
 ``ScalableClient.submit(..., tag=...)``.
 
+.. code-block:: yaml
+
+   targets:
+     local:
+       provider: local
+       max_workers: 4
+       threads_per_worker: 1
+       processes: false
+       containers: none
+
 Slurm provider
 --------------
 
 ``SlurmProvider`` is a thin translation layer over the legacy ``SlurmCluster``
 path and preserves existing behavior while exposing a v2 manifest/session API.
 
+.. code-block:: yaml
+
+   targets:
+     hpc:
+       provider: slurm
+       queue: short
+       account: GCIMS
+       walltime: "02:00:00"
+       interface: ib0
+       container_runtime: apptainer
+
+Kubernetes provider
+-------------------
+
+``KubernetesProvider`` maps manifest components to Dask Kubernetes Operator
+worker groups. See :doc:`kubernetes` for details.
+
+.. code-block:: yaml
+
+   targets:
+     gke:
+       provider: kubernetes
+       namespace: scalable
+       image: ghcr.io/jgcri/scalable-worker:latest
+       n_workers: 4
+       adaptive:
+         minimum: 1
+         maximum: 16
+
+AWS provider
+------------
+
+``AWSBatchProvider`` wraps ``dask-cloudprovider``'s Fargate/EC2 clusters
+with cost estimation support. See :doc:`cloud` for details.
+
+.. code-block:: yaml
+
+   targets:
+     aws:
+       provider: aws
+       region: us-east-1
+       cluster_type: fargate
+       n_workers: 4
+       worker_cpu: 1024
+       worker_mem: 4096
+
+GCP provider (scaffold)
+-----------------------
+
+``GCPProvider`` validates manifest options but raises ``NotImplementedError``
+on ``build_cluster()``. Full implementation planned for a future release.
+
 Registry and discovery
 ----------------------
 
 The provider registry supports:
 
-* explicit runtime registration
-* lazy built-in resolution
-* optional Python entry-point discovery under ``scalable.providers``
+* Explicit runtime registration
+* Lazy built-in resolution
+* Optional Python entry-point discovery under ``scalable.providers``
+
+.. code-block:: python
+
+   from scalable.providers.registry import get_provider, register_provider
+
+   # Get a built-in provider
+   provider = get_provider("local")
 
-This is the extension hook for future cloud and Kubernetes providers.
+   # Register a custom provider
+   register_provider("custom", MyCustomProvider)
 
+This is the extension hook for custom or third-party providers.
diff --git a/docs/telemetry.rst b/docs/telemetry.rst
index 3da7479..7914f59 100644
--- a/docs/telemetry.rst
+++ b/docs/telemetry.rst
@@ -1,8 +1,9 @@
 Telemetry and Run Reports
 =========================
 
-Phase 2 introduces a deterministic run history store for manifest-driven
-sessions.
+Scalable v2.0.0 includes a deterministic run history store for manifest-driven
+sessions. Every run records structured telemetry for debugging, auditing,
+resource advising, and ML model training.
 
 Run directory layout
 --------------------
@@ -13,7 +14,7 @@ Each run is recorded under ``.scalable/runs/``:
 
     .scalable/
       runs/
-        run-.../
+        run-<timestamp>-<project>-<hash>/
           manifest.yaml
           plan.json
           manifest.lock
@@ -24,11 +25,26 @@ Each run is recorded under ``.scalable/runs/``:
           failures.jsonl
           cache.jsonl
           artifacts.jsonl
+          cost.jsonl
           summary.json
 
 JSONL is the canonical storage format. Optional parquet snapshots are emitted
 when telemetry parquet support is enabled.
 
+Event types
+-----------
+
+The telemetry system records the following event categories:
+
+- **Task events** — submission, start, completion, failure, retry
+- **Worker events** — launch, ready, lost, removed
+- **Resource events** — CPU/memory allocation and usage
+- **Cache events** — hit/miss for ``@cacheable`` decorated functions
+- **Failure events** — error classification and stack traces
+- **Artifact events** — output registration and storage references
+- **Cost events** — cloud provider cost estimates (Phase 3+)
+- **Emulation events** — emulator dispatch decisions (Phase 5)
+
 CLI reporting
 -------------
 
@@ -44,12 +60,56 @@ Machine-readable report output:
 
     scalable report --latest --format json --output report.json
 
+Report from a specific run:
+
+.. code-block:: bash
+
+    scalable report --run-id run-20260519T120000Z-project-abc
+
+Report options:
+
+- ``--runs-dir`` — Custom runs directory (default: ``.scalable/runs``)
+- ``--run-id`` — Specific run identifier
+- ``--latest`` — Use most recent run (default when no run-id given)
+- ``--format`` — Output format (``text`` or ``json``)
+- ``--output`` — Write to file instead of stdout
+
+Session integration
+-------------------
+
+``ScalableSession`` automatically initializes and finalizes telemetry for
+manifest-driven runs:
+
+.. code-block:: python
+
+    from scalable import ScalableSession
+
+    session = ScalableSession.from_yaml("scalable.yaml", target="local")
+    # Telemetry is automatically recorded during the session lifecycle
+
+    # Record custom artifacts
+    session.record_artifact("output.csv", kind="result")
+
+``ScalableClient.submit`` and ``ScalableClient.map`` emit task lifecycle
+telemetry through future callbacks when telemetry is active.
+
 Configuration
 -------------
 
 The telemetry system supports these environment variables:
 
-* ``SCALABLE_RUNS_DIR``
-* ``SCALABLE_TELEMETRY``
-* ``SCALABLE_TELEMETRY_PARQUET``
+* ``SCALABLE_RUNS_DIR`` — Local runs directory (default: ``.scalable/runs``)
+* ``SCALABLE_TELEMETRY`` — Enable/disable telemetry (default: ``1``)
+* ``SCALABLE_TELEMETRY_PARQUET`` — Emit parquet snapshots (default: ``0``)
+* ``SCALABLE_RUNS_DIR_REMOTE`` — Remote storage for telemetry sync (optional)
+
+Downstream consumers
+--------------------
+
+Telemetry data feeds:
+
+* :doc:`advising` — heuristic resource recommendations from run history
+* :doc:`ml` — ML-backed prediction models trained on telemetry features
+* ``scalable diagnose`` — failure classification and fix suggestions
+* ``scalable report`` — run summary and cost reporting
 

From b3b2151e8faf2e2ae1dd68634b168e53aa3a8755 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 00:06:48 -0400
Subject: [PATCH 28/47] how-to tutorials in docs

---
 docs/advising.rst                         |   2 +-
 docs/index.rst                            |   8 +
 docs/tutorials/01_getting_started.rst     | 404 +++++++++++++++
 docs/tutorials/02_manifest_system.rst     | 508 +++++++++++++++++++
 docs/tutorials/03_scaling_strategies.rst  | 455 +++++++++++++++++
 docs/tutorials/04_caching_performance.rst | 411 ++++++++++++++++
 docs/tutorials/05_cloud_integration.rst   | 441 +++++++++++++++++
 docs/tutorials/06_telemetry.rst           | 401 +++++++++++++++
 docs/tutorials/07_error_handling.rst      | 522 ++++++++++++++++++++
 docs/tutorials/08_kubernetes.rst          | 468 ++++++++++++++++++
 docs/tutorials/09_ml_emulation.rst        | 544 ++++++++++++++++++++
 docs/tutorials/10_ai_composition.rst      | 574 ++++++++++++++++++++++
 docs/tutorials/index.rst                  | 136 +++++
 pyproject.toml                            |   2 +-
 14 files changed, 4874 insertions(+), 2 deletions(-)
 create mode 100644 docs/tutorials/01_getting_started.rst
 create mode 100644 docs/tutorials/02_manifest_system.rst
 create mode 100644 docs/tutorials/03_scaling_strategies.rst
 create mode 100644 docs/tutorials/04_caching_performance.rst
 create mode 100644 docs/tutorials/05_cloud_integration.rst
 create mode 100644 docs/tutorials/06_telemetry.rst
 create mode 100644 docs/tutorials/07_error_handling.rst
 create mode 100644 docs/tutorials/08_kubernetes.rst
 create mode 100644 docs/tutorials/09_ml_emulation.rst
 create mode 100644 docs/tutorials/10_ai_composition.rst
 create mode 100644 docs/tutorials/index.rst

diff --git a/docs/advising.rst b/docs/advising.rst
index 7a4c903..74c4404 100644
--- a/docs/advising.rst
+++ b/docs/advising.rst
@@ -1,5 +1,5 @@
 Deterministic Resource Advising
-==============================
+================================
 
 Scalable provides a deterministic :class:`~scalable.advising.ResourceAdvisor`
 that derives conservative resource recommendations from historical run
diff --git a/docs/index.rst b/docs/index.rst
index cb3489f..35630f6 100755
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -42,6 +42,14 @@ Contents
    license
    how_to_contribute
 
+.. _tutorials_section:
+
+.. toctree::
+   :caption: Tutorials
+   :maxdepth: 2
+
+   tutorials/index
+
 .. _api_section:
 
 .. toctree::
diff --git a/docs/tutorials/01_getting_started.rst b/docs/tutorials/01_getting_started.rst
new file mode 100644
index 0000000..efffdad
--- /dev/null
+++ b/docs/tutorials/01_getting_started.rst
@@ -0,0 +1,404 @@
+.. _tutorial_getting_started:
+
+======================================================
+Tutorial 1: Getting Started with Scalable
+======================================================
+
+What You Will Learn
+-------------------
+
+By the end of this tutorial you will:
+
+* Install Scalable and verify its dependencies are satisfied.
+* Understand the project layout Scalable expects.
+* Create a minimal ``scalable.yaml`` manifest.
+* Validate, plan, and execute a local workflow end-to-end.
+* Inspect the telemetry output of a successful run.
+
+This tutorial establishes the foundation for every subsequent tutorial in the
+series. If you are new to Scalable, start here.
+
+Prerequisites
+-------------
+
+* Python 3.11 or later (3.12 and 3.13 are also supported).
+* A working ``pip`` (or equivalent package manager such as ``uv``).
+* Familiarity with the command line.
+* Basic Python fluency (functions, imports, virtual environments).
+
+No HPC cluster, Docker installation, or cloud credentials are required for this
+tutorial — we run everything locally.
+
+Step 1: Install Scalable
+------------------------
+
+Create a fresh virtual environment and install Scalable from PyPI:
+
+.. code-block:: bash
+
+   python -m venv .venv
+   source .venv/bin/activate   # Windows: .venv\Scripts\activate
+   pip install scalable
+
+Verify the installation:
+
+.. code-block:: bash
+
+   scalable --help
+
+Expected output (abbreviated):
+
+.. code-block:: text
+
+   usage: scalable [-h] {validate,plan,run,report,advise,...} ...
+
+   Scalable CLI — orchestrate distributed workflows.
+
+   positional arguments:
+     {validate,plan,run,report,advise,...}
+
+**Why this matters:** The ``scalable`` CLI entry point is the primary interface
+for validating manifests, planning executions, and generating reports. You can
+also drive everything from Python — the CLI wraps the same public API.
+
+.. note::
+
+   If your shell cannot find the ``scalable`` command after installation, ensure
+   that the scripts directory for your virtual environment is on ``PATH``.
+
+Step 2: Create a Project Directory
+-----------------------------------
+
+Scalable workflows live in a dedicated project directory. The minimal layout
+looks like this:
+
+.. code-block:: text
+
+   my-project/
+   ├── scalable.yaml       # Manifest (single source of truth)
+   └── workflow.py          # Your Python workflow script
+
+Create it:
+
+.. code-block:: bash
+
+   mkdir my-project && cd my-project
+
+Step 3: Write a Minimal Manifest
+---------------------------------
+
+The manifest (``scalable.yaml``) is a declarative document describing your
+project, execution targets, compute components, and task bindings. Create the
+file:
+
+.. code-block:: yaml
+
+   # scalable.yaml
+   version: 1
+   project:
+     name: hello-scalable
+
+   targets:
+     local:
+       provider: local
+       max_workers: 2
+       threads_per_worker: 1
+       processes: false
+       containers: none
+
+   components:
+     analysis:
+       cpus: 1
+       memory: 1G
+
+   tasks:
+     run_analysis:
+       component: analysis
+
+Let's unpack this:
+
+``version``
+  Schema version. Currently ``1`` is the only supported version.
+
+``project.name``
+  A human-readable project identifier. It is embedded in telemetry run IDs
+  and artifact paths.
+
+``targets``
+  Named execution environments. Here we define a single target called
+  ``local`` using the built-in :class:`~scalable.providers.local.LocalProvider`.
+  The provider spawns a Dask ``LocalCluster`` under the hood with the specified
+  worker configuration.
+
+``components``
+  Resource profiles for your workloads. Each component declares CPU and memory
+  requirements. Components map to Dask worker resource annotations.
+
+``tasks``
+  Named work units that bind to a component. Tasks are the scheduling atoms —
+  when you ``submit`` a function you associate it with a task definition.
+
+**Trade-off note:** Setting ``processes: false`` runs Dask workers as threads
+within a single process. This is fast to start and avoids serialization overhead
+but provides no memory isolation between tasks. For CPU-bound workloads or tasks
+that hold the GIL, set ``processes: true``.
+
+Step 4: Validate the Manifest
+------------------------------
+
+Before running anything, validate the manifest for structural and semantic
+errors:
+
+.. code-block:: bash
+
+   scalable validate ./scalable.yaml
+
+Expected output:
+
+.. code-block:: text
+
+   ✓ Manifest is valid (0 errors, 0 warnings)
+
+If you introduce a typo — say ``providr: local`` — validation will report:
+
+.. code-block:: text
+
+   ERROR targets.local: unknown provider 'providr'
+
+The validator checks:
+
+* Required top-level keys (``version``, ``project``).
+* Component key spelling (``cpus``, ``memory``, ``image``, etc.).
+* Task-component references resolve.
+* Provider-specific option constraints (e.g., ``max_workers`` must be a positive
+  integer for the local provider).
+
+Step 5: Plan the Execution
+---------------------------
+
+Planning produces a dry-run execution plan without allocating real resources:
+
+.. code-block:: bash
+
+   scalable plan ./scalable.yaml --target local --dry-run --output plan.json
+
+.. code-block:: text
+
+   Plan created for target 'local' (provider: local)
+   Workers: 2 × analysis (1 cpu, 1G memory)
+   Manifest lock: sha256:a3b8f1...
+
+Inspect the generated ``plan.json``:
+
+.. code-block:: json
+
+   {
+     "target_name": "local",
+     "provider": "local",
+     "manifest_lock": "sha256:a3b8f1...",
+     "scale_plan": {
+       "analysis": {
+         "count": 2,
+         "resources": {"cpus": 1, "memory": "1G"}
+       }
+     }
+   }
+
+**Architectural note:** The ``manifest_lock`` is a content-addressable hash of
+the expanded manifest. It guarantees reproducibility — if two plans share the
+same lock fingerprint they were derived from byte-identical configurations
+(modulo environment variable expansion).
+
+Step 6: Write a Workflow Script
+--------------------------------
+
+Create ``workflow.py``:
+
+.. code-block:: python
+
+   """A minimal Scalable workflow."""
+
+   from scalable import ScalableSession
+
+
+   def analyze(scenario_id: int) -> dict:
+       """Simulate an expensive computation."""
+       import time
+       time.sleep(1)
+       return {"scenario": scenario_id, "result": scenario_id * 42}
+
+
+   def main():
+       # Initialize a session from the manifest
+       session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+
+       # Plan (validates + computes resource allocation)
+       plan = session.plan(dry_run=True)
+       print(f"Manifest lock: {plan.manifest_lock}")
+
+       # Start the cluster and get a client
+       client = session.start(plan)
+
+       # Submit tasks tagged to the 'analysis' component
+       futures = []
+       for i in range(5):
+           fut = client.submit(analyze, i, tag="analysis")
+           futures.append(fut)
+
+       # Gather results
+       results = client.gather(futures)
+       for r in results:
+           print(r)
+
+       # Tear down
+       session.close()
+
+
+   if __name__ == "__main__":
+       main()
+
+Step 7: Run the Workflow
+-------------------------
+
+Execute the workflow using the CLI:
+
+.. code-block:: bash
+
+   scalable run ./scalable.yaml --target local --workflow workflow.py
+
+Or run it directly with Python:
+
+.. code-block:: bash
+
+   python workflow.py
+
+Expected output:
+
+.. code-block:: text
+
+   Manifest lock: sha256:a3b8f1...
+   {'scenario': 0, 'result': 0}
+   {'scenario': 1, 'result': 42}
+   {'scenario': 2, 'result': 84}
+   {'scenario': 3, 'result': 126}
+   {'scenario': 4, 'result': 168}
+
+**What happened under the hood:**
+
+1. ``ScalableSession.from_yaml`` parsed the manifest, resolved environment
+   variables, and built a :class:`~scalable.providers.base.DeploymentSpec`.
+2. ``session.plan()`` validated the spec and computed a
+   :class:`~scalable.planning.dryrun.DryRunPlan` including worker counts and
+   resource annotations.
+3. ``session.start()`` instantiated a
+   :class:`~scalable.providers.local.LocalProvider`, which created a Dask
+   ``LocalCluster`` with 2 workers each annotated with 1 CPU / 1 GB.
+4. Each ``client.submit(..., tag="analysis")`` routed the function to workers
+   matching the ``analysis`` component's resource profile.
+5. ``session.close()`` shut down workers and finalized telemetry.
+
+Step 8: Inspect Telemetry
+--------------------------
+
+Every manifest-driven run records structured telemetry. Check what was
+persisted:
+
+.. code-block:: bash
+
+   scalable report --latest
+
+Expected output:
+
+.. code-block:: text
+
+   Run: run-20260520T035200Z-hello-scalable-a1b2c3d4
+   Status: completed
+   Target: local (provider: local)
+   Duration: 6.2s
+   Tasks: 5 submitted, 5 succeeded, 0 failed
+
+The telemetry lives under ``.scalable/runs/<run-id>/``:
+
+.. code-block:: text
+
+   .scalable/runs/run-20260520T035200Z-hello-scalable-a1b2c3d4/
+   ├── run.json          # Run metadata
+   ├── tasks.jsonl       # Per-task lifecycle events
+   ├── resources.jsonl   # Resource utilization snapshots
+   └── workers.jsonl     # Worker lifecycle events
+
+These structured records power the resource advising and ML optimization
+features covered in later tutorials.
+
+Step 9: Environment Variables
+------------------------------
+
+Scalable is configured through environment variables for deployment flexibility.
+The most relevant ones for getting started:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 20 50
+
+   * - Variable
+     - Default
+     - Description
+   * - ``SCALABLE_MANIFEST``
+     - ``./scalable.yaml``
+     - Default manifest path (avoids passing ``--manifest`` every time)
+   * - ``SCALABLE_TARGET``
+     - *(unset)*
+     - Default target override
+   * - ``SCALABLE_CACHE_DIR``
+     - ``./cache``
+     - Disk cache directory for ``@cacheable`` functions
+   * - ``SCALABLE_TELEMETRY``
+     - ``1``
+     - Set to ``0`` to disable telemetry recording
+   * - ``SCALABLE_LOG_LEVEL``
+     - *(unset)*
+     - Set to ``DEBUG`` for verbose library logging
+
+Example — run with debug logging and a custom cache directory:
+
+.. code-block:: bash
+
+   export SCALABLE_LOG_LEVEL=DEBUG
+   export SCALABLE_CACHE_DIR=/tmp/scalable-cache
+   python workflow.py
+
+Troubleshooting
+---------------
+
+**"scalable: command not found"**
+  Ensure your virtual environment is activated and the scripts directory is on
+  ``PATH``. On some systems you may need ``python -m scalable.cli.main`` as a
+  fallback.
+
+**"ModuleNotFoundError: No module named 'dask'"**
+  Scalable's core dependencies (``dask``, ``distributed``) should be installed
+  automatically. If missing, run ``pip install scalable`` again in your
+  environment.
+
+**Manifest validation reports "unknown provider"**
+  Double-check the ``provider:`` value matches a built-in name (``local``,
+  ``slurm``) or that you have installed the relevant extra (``scalable[cloud]``,
+  ``scalable[kubernetes]``).
+
+**Tasks complete but results are None**
+  Ensure your function returns a value and that all data passed as arguments is
+  serializable by ``dill`` (Scalable's default serializer). Lambda functions and
+  module-level functions are fine; nested closures over non-picklable objects
+  will fail silently.
+
+Next Steps
+----------
+
+Now that you have a working local workflow:
+
+* :ref:`tutorial_manifest_system` — Deep-dive into the manifest schema, environment
+  variable expansion, and multi-target configurations.
+* :ref:`tutorial_caching` — Add the ``@cacheable`` decorator to skip redundant
+  computation across retries.
+* :ref:`tutorial_telemetry` — Understand the telemetry data model and generate
+  custom reports.
diff --git a/docs/tutorials/02_manifest_system.rst b/docs/tutorials/02_manifest_system.rst
new file mode 100644
index 0000000..764fa44
--- /dev/null
+++ b/docs/tutorials/02_manifest_system.rst
@@ -0,0 +1,508 @@
+.. _tutorial_manifest_system:
+
+======================================================
+Tutorial 2: Mastering the Manifest System
+======================================================
+
+What You Will Learn
+-------------------
+
+By the end of this tutorial you will:
+
+* Understand every section of a ``scalable.yaml`` manifest in depth.
+* Use environment variable expansion for portable, credential-free manifests.
+* Define multiple targets for local development, HPC, and cloud.
+* Configure components with images, mounts, environment variables, and tags.
+* Apply overlays to customize resources per deployment environment.
+* Validate manifests programmatically and interpret error codes.
+
+Prerequisites
+-------------
+
+* Completed :ref:`tutorial_getting_started`.
+* Scalable installed (``pip install scalable``).
+* A text editor and terminal.
+
+Scenario
+--------
+
+You are building a climate modeling pipeline with two stages: a computationally
+expensive simulation (GCAM) and a lighter post-processing step (Stitches). The
+pipeline must run locally during development, on an HPC cluster for production,
+and eventually in the cloud. The manifest system lets you describe all three
+targets in a single file.
+
+Step 1: Manifest Schema Overview
+---------------------------------
+
+Every manifest has this top-level structure:
+
+.. code-block:: yaml
+
+   version: 1
+   project: { ... }
+   targets: { ... }
+   components: { ... }
+   tasks: { ... }
+   overlays: { ... }    # optional
+
+The parser (:mod:`scalable.manifest.parser`) enforces:
+
+* ``version`` and ``project`` are **required**.
+* Unknown top-level keys are rejected (defense against typos).
+* Unknown keys *inside* a target block are passed through to the provider
+  (forward compatibility for provider-specific options).
+* Unknown keys inside ``components`` are rejected (strict schema).
+
+Step 2: The Project Block
+--------------------------
+
+.. code-block:: yaml
+
+   project:
+     name: climate-pipeline
+     default_storage: ./outputs
+     local_cache: ./cache
+
+``name``
+  Identifies the project in telemetry run IDs (e.g.,
+  ``run-20260520T...-climate-pipeline-a1b2c3d4``). Use lowercase with hyphens.
+
+``default_storage``
+  Base URI for artifact output. Can be a local path, S3 URI
+  (``s3://bucket/prefix/``), or GCS URI (``gs://bucket/prefix/``). Providers
+  that support remote storage will use this as the destination for task outputs.
+
+``local_cache``
+  Override for ``SCALABLE_CACHE_DIR``. The manifest value takes precedence over
+  the environment variable, which itself takes precedence over the compiled
+  default (``./cache``).
+
+Step 3: Defining Targets
+-------------------------
+
+Targets are named execution environments. You can define as many as you need:
+
+.. code-block:: yaml
+
+   targets:
+     local:
+       provider: local
+       max_workers: 4
+       threads_per_worker: 2
+       processes: false
+       containers: none
+
+     hpc:
+       provider: slurm
+       queue: batch
+       account: GCIMS
+       walltime: "04:00:00"
+       interface: ib0
+
+     aws:
+       provider: aws
+       region: us-east-1
+       cluster_type: fargate
+       instance_type: m5.xlarge
+       worker_cpu: 4096
+       worker_mem: 16384
+       image: 123456789.dkr.ecr.us-east-1.amazonaws.com/climate:latest
+       adaptive:
+         minimum: 1
+         maximum: 10
+
+Each target has one required key — ``provider`` — that maps to a registered
+provider class. All other keys are provider-specific options:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 20 80
+
+   * - Provider
+     - Key Options
+   * - ``local``
+     - ``max_workers``, ``threads_per_worker``, ``processes``, ``containers``
+   * - ``slurm``
+     - ``queue``, ``account``, ``walltime``, ``interface``
+   * - ``aws``
+     - ``region``, ``cluster_type``, ``instance_type``, ``worker_cpu``,
+       ``worker_mem``, ``image``, ``adaptive``, ``subnets``, ``security_groups``
+   * - ``kubernetes``
+     - ``namespace``, ``image``, ``adaptive``, ``overlay``
+
+**Why multiple targets?** A single manifest can describe your entire promotion
+path: develop locally → validate on HPC → deploy to cloud. The ``--target``
+flag (or ``SCALABLE_TARGET`` env var) selects which environment to activate.
+
+Step 4: Components in Detail
+------------------------------
+
+Components are resource profiles for your workloads:
+
+.. code-block:: yaml
+
+   components:
+     gcam:
+       image: ghcr.io/jgcri/gcam:7.0
+       runtime: apptainer
+       cpus: 8
+       memory: 32G
+       mounts:
+         /data/gcam: /gcam-core
+         /shared/outputs: /outputs
+       env:
+         GCAM_DATA: /gcam-core/data
+       tags: [iam, climate]
+       preload_script: ./scripts/gcam_preload.sh
+
+     postprocess:
+       cpus: 2
+       memory: 4G
+       tags: [analysis]
+
+Let's break down each key:
+
+``image``
+  Container image URI. Used by providers that support containerized workers
+  (Slurm with Apptainer, Kubernetes, cloud). Omit for bare-metal local runs.
+
+``runtime``
+  Container runtime hint (``apptainer``, ``docker``). Providers use this to
+  determine how to pull and launch the image.
+
+``cpus``
+  CPU count allocated per worker in this component group. Maps to Dask worker
+  resource annotations and scheduler affinity.
+
+``memory``
+  Memory allocation string (e.g., ``32G``, ``512M``). Parsed by
+  ``dask.utils.parse_bytes``.
+
+``mounts``
+  Volume mount mappings (host path → container path). Only meaningful for
+  containerized providers.
+
+``env``
+  Environment variables injected into the worker process. Useful for configuring
+  model data paths, API keys (prefer ``${VAR}`` references over literals), etc.
+
+``tags``
+  Arbitrary labels for grouping and filtering. Tags propagate to telemetry
+  events and can be used by the resource advisor for per-tag recommendations.
+
+``preload_script``
+  Shell script executed before the Dask worker process starts. Useful for
+  activating conda environments, loading modules, or mounting FUSE filesystems.
+
+Step 5: Task Bindings
+----------------------
+
+Tasks bind named work units to components:
+
+.. code-block:: yaml
+
+   tasks:
+     run_gcam:
+       component: gcam
+       cache: true
+       outputs:
+         database: dir
+
+     aggregate_results:
+       component: postprocess
+       cache: true
+
+``component``
+  Must reference a key in the ``components`` map. This determines which workers
+  can execute the task and what resources are reserved.
+
+``cache``
+  When ``true``, results of functions submitted under this task are eligible
+  for the :func:`~scalable.caching.cacheable` disk cache. Cache hits skip
+  execution entirely on subsequent runs.
+
+``outputs``
+  Declares expected output artifacts and their types (``file`` or ``dir``).
+  The artifact store can persist these to remote storage when
+  ``project.default_storage`` is configured.
+
+Step 6: Environment Variable Expansion
+----------------------------------------
+
+Manifests support ``${VAR}`` and ``${VAR:-default}`` syntax for portability:
+
+.. code-block:: yaml
+
+   project:
+     name: ${PROJECT_NAME:-climate-demo}
+     default_storage: ${ARTIFACT_BUCKET:-./outputs}
+
+   targets:
+     aws:
+       provider: aws
+       region: ${AWS_REGION:-us-east-1}
+       execution_role_arn: ${EXECUTION_ROLE_ARN}
+
+Expansion rules:
+
+* ``${VAR}`` — replaced by the value of the environment variable. If unset,
+  the parser raises :class:`~scalable.manifest.errors.ManifestParseError`.
+* ``${VAR:-default}`` — replaced by the variable if set, otherwise uses the
+  literal default value.
+* Bare ``$HOME``-style references are **not** expanded (to avoid ambiguity in
+  mount paths). Always use curly braces.
+
+This means you can commit ``scalable.yaml`` to version control without
+embedding secrets or machine-specific paths:
+
+.. code-block:: bash
+
+   export AWS_REGION=us-west-2
+   export EXECUTION_ROLE_ARN=arn:aws:iam::123456789:role/myRole
+   scalable validate ./scalable.yaml
+
+Step 7: Overlays for Environment-Specific Tuning
+--------------------------------------------------
+
+Overlays let you define named configuration deltas that are merged onto the
+base manifest when a target references them:
+
+.. code-block:: yaml
+
+   targets:
+     hpc:
+       provider: slurm
+       queue: batch
+       walltime: "04:00:00"
+       overlay: hpc-large
+
+   components:
+     gcam:
+       cpus: 4
+       memory: 16G
+
+   overlays:
+     hpc-large:
+       components:
+         gcam:
+           cpus: 16
+           memory: 64G
+
+     hpc-debug:
+       components:
+         gcam:
+           cpus: 2
+           memory: 8G
+
+When target ``hpc`` is selected, the ``hpc-large`` overlay is merged:
+``gcam.cpus`` becomes 16 and ``gcam.memory`` becomes ``64G``. The base values
+serve as defaults for targets that don't reference an overlay.
+
+**Design rationale:** Overlays avoid manifest duplication. Instead of
+maintaining separate YAML files per environment, you express deltas
+declaratively. The merge is shallow per-component-key (not deep recursive),
+keeping behavior predictable.
+
+You can also override target options via overlays:
+
+.. code-block:: yaml
+
+   overlays:
+     cloud-dev:
+       targets:
+         aws:
+           adaptive:
+             minimum: 1
+             maximum: 3
+       components:
+         gcam:
+           cpus: 4
+           memory: 16G
+
+Step 8: Multi-Target Workflow Selection
+----------------------------------------
+
+At runtime you select a target via:
+
+**CLI:**
+
+.. code-block:: bash
+
+   scalable run ./scalable.yaml --target hpc --workflow workflow.py
+
+**Python:**
+
+.. code-block:: python
+
+   session = ScalableSession.from_yaml("./scalable.yaml", target="hpc")
+
+**Environment variable:**
+
+.. code-block:: bash
+
+   export SCALABLE_TARGET=hpc
+   python workflow.py   # Session auto-detects from env
+
+The resolution order is: explicit ``target=`` argument → ``SCALABLE_TARGET``
+env var → error (no implicit default target).
+
+Step 9: Programmatic Validation
+--------------------------------
+
+You can validate manifests from Python for CI/CD integration:
+
+.. code-block:: python
+
+   from scalable import ScalableSession
+
+   session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+   report = session.validate()
+
+   if report.ok:
+       print("Manifest is valid")
+   else:
+       for issue in report.errors:
+           print(f"ERROR [{issue.code}] {issue.path}: {issue.message}")
+       for issue in report.warnings:
+           print(f"WARN  [{issue.code}] {issue.path}: {issue.message}")
+
+Common error codes:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 25 75
+
+   * - Code
+     - Meaning
+   * - ``E_MISSING_KEY``
+     - A required key (``version``, ``project``) is absent.
+   * - ``E_BAD_VERSION``
+     - ``version`` is not a supported schema version.
+   * - ``E_UNKNOWN_TOP_KEY``
+     - Unrecognized top-level key (probable typo).
+   * - ``E_UNKNOWN_COMPONENT_KEY``
+     - Unrecognized key inside a component definition.
+   * - ``E_TASK_COMPONENT_REF``
+     - A task references a component that does not exist.
+   * - ``E_UNKNOWN_PROVIDER``
+     - The target's provider is not installed or registered.
+   * - ``E_BAD_MAX_WORKERS``
+     - ``max_workers`` is not a positive integer.
+
+Step 10: Complete Multi-Target Manifest
+----------------------------------------
+
+Here is a production-ready manifest combining all concepts:
+
+.. code-block:: yaml
+
+   version: 1
+   project:
+     name: climate-pipeline
+     default_storage: ${ARTIFACT_STORAGE:-./outputs}
+
+   targets:
+     local:
+       provider: local
+       max_workers: 4
+       threads_per_worker: 2
+       processes: false
+       containers: none
+
+     hpc:
+       provider: slurm
+       queue: batch
+       account: ${SLURM_ACCOUNT}
+       walltime: "08:00:00"
+       interface: ib0
+       overlay: hpc-prod
+
+     aws:
+       provider: aws
+       region: ${AWS_REGION:-us-east-1}
+       cluster_type: fargate
+       worker_cpu: 4096
+       worker_mem: 16384
+       image: ${ECR_IMAGE}
+       execution_role_arn: ${EXECUTION_ROLE_ARN}
+       task_role_arn: ${TASK_ROLE_ARN}
+       subnets: [${SUBNET_A}, ${SUBNET_B}]
+       security_groups: [${SG_ID}]
+       adaptive:
+         minimum: 2
+         maximum: 20
+
+   components:
+     gcam:
+       image: ghcr.io/jgcri/gcam:7.0
+       cpus: 4
+       memory: 16G
+       tags: [iam, climate]
+       env:
+         GCAM_DATA: /gcam-core/data
+
+     postprocess:
+       cpus: 2
+       memory: 8G
+       tags: [analysis]
+
+   tasks:
+     run_gcam:
+       component: gcam
+       cache: true
+       outputs:
+         database: dir
+
+     aggregate:
+       component: postprocess
+       cache: true
+
+   overlays:
+     hpc-prod:
+       components:
+         gcam:
+           cpus: 16
+           memory: 64G
+         postprocess:
+           cpus: 8
+           memory: 32G
+
+     hpc-debug:
+       components:
+         gcam:
+           cpus: 2
+           memory: 4G
+         postprocess:
+           cpus: 1
+           memory: 2G
+
+Troubleshooting
+---------------
+
+**"ManifestParseError: unresolved variable ${VAR}"**
+  You used ``${VAR}`` without a default and the variable is not set in the
+  environment. Either export it or use ``${VAR:-fallback}``.
+
+**"ManifestSchemaError: unknown component key 'gpu'"**
+  Only recognized component keys are allowed. GPU scheduling is expressed via
+  the provider-specific target options, not component definitions.
+
+**Overlay changes not taking effect**
+  Ensure the target block includes ``overlay: <name>`` and that the overlay
+  name exactly matches a key under ``overlays:``. Overlay merging only applies
+  to the selected target.
+
+**"version: 2" rejected**
+  Only schema version ``1`` is currently supported. The ``version`` field
+  exists for future-proofing.
+
+Next Steps
+----------
+
+* :ref:`tutorial_scaling_strategies` — Learn how different providers scale
+  workers and how to choose between them.
+* :ref:`tutorial_caching` — Cache expensive computations to accelerate
+  iterative development.
+* :ref:`tutorial_cloud_integration` — Configure AWS and GCP targets with
+  real credentials and IAM roles.
diff --git a/docs/tutorials/03_scaling_strategies.rst b/docs/tutorials/03_scaling_strategies.rst
new file mode 100644
index 0000000..d6a0d81
--- /dev/null
+++ b/docs/tutorials/03_scaling_strategies.rst
@@ -0,0 +1,455 @@
+.. _tutorial_scaling_strategies:
+
+======================================================
+Tutorial 3: Scaling Strategies with Providers
+======================================================
+
+What You Will Learn
+-------------------
+
+By the end of this tutorial you will:
+
+* Understand Scalable's provider architecture and how it abstracts execution
+  backends.
+* Configure and use the Local, Slurm, and Cloud providers.
+* Choose appropriate scaling strategies for different workload profiles.
+* Implement manual scaling, adaptive scaling, and policy-driven planning.
+* Monitor scaling decisions through the Session API.
+
+Prerequisites
+-------------
+
+* Completed :ref:`tutorial_getting_started` and :ref:`tutorial_manifest_system`.
+* For HPC sections: access to a Slurm cluster (or follow along conceptually).
+* For cloud sections: ``pip install scalable[cloud]`` (or follow along
+  conceptually).
+
+Scenario
+--------
+
+Your climate pipeline has grown. Development happens locally with 2–4 workers.
+Production runs on an HPC cluster with 64+ workers. Burst capacity uses cloud
+auto-scaling. You need a unified scaling approach that works across all three
+environments.
+
+Step 1: The Provider Architecture
+----------------------------------
+
+Scalable separates **what** runs from **where** it runs through the
+:class:`~scalable.providers.base.DeploymentProvider` protocol:
+
+.. code-block:: text
+
+   ┌──────────────┐     ┌──────────────────┐     ┌─────────────┐
+   │  Manifest    │────▶│ DeploymentSpec    │────▶│  Provider   │
+   │ (scalable.yaml)    │ (provider-neutral)│     │ (backend)   │
+   └──────────────┘     └──────────────────┘     └──────┬──────┘
+                                                         │
+                        ┌────────────────────────────────┼────────┐
+                        │                                │        │
+                  ┌─────▼──────┐  ┌──────▼──────┐  ┌───▼────────┐
+                  │   Local    │  │    Slurm    │  │Cloud / K8s  │
+                  │  Provider  │  │  Provider   │  │  Provider   │
+                  └────────────┘  └─────────────┘  └─────────────┘
+
+Every provider implements the same interface:
+
+.. code-block:: python
+
+   class DeploymentProvider(Protocol):
+       name: str
+
+       def validate(self, spec: DeploymentSpec) -> ValidationReport: ...
+       def build_cluster(self, spec: DeploymentSpec) -> ClusterHandle: ...
+       def scale(self, cluster: ClusterHandle, plan: ScalePlan) -> None: ...
+       def estimate_cost(self, spec: DeploymentSpec, plan: ScalePlan) -> CostEstimate | None: ...
+
+This means your workflow code is **provider-agnostic** — the same
+``client.submit(func, arg, tag="gcam")`` call works identically whether the
+cluster is local threads, Slurm jobs, or Kubernetes pods.
+
+Step 2: Local Provider — Development & CI
+-------------------------------------------
+
+The :class:`~scalable.providers.local.LocalProvider` wraps Dask's
+``LocalCluster``. It is the fastest way to iterate:
+
+.. code-block:: yaml
+
+   targets:
+     local:
+       provider: local
+       max_workers: 4
+       threads_per_worker: 2
+       processes: false
+       containers: none
+
+Key options:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 25 15 60
+
+   * - Option
+     - Default
+     - Behavior
+   * - ``max_workers``
+     - 1
+     - Total worker count across all component groups.
+   * - ``threads_per_worker``
+     - 1
+     - Threads per Dask worker process/thread.
+   * - ``processes``
+     - ``false``
+     - ``true`` → each worker is a separate process (memory isolation).
+       ``false`` → threaded workers (faster startup, shared memory).
+   * - ``containers``
+     - ``none``
+     - ``none`` = bare-metal; ``docker`` = future container support.
+
+**When to use processes vs threads:**
+
+* **Threads** (``processes: false``): Best for I/O-bound tasks, quick
+  iteration, and CI where startup speed matters. All workers share one process,
+  so a memory leak in one affects all.
+* **Processes** (``processes: true``): Best for CPU-bound tasks or tasks that
+  hold the GIL (e.g., calling C extensions that don't release it). Each worker
+  is isolated but has serialization overhead.
+
+Running with the local provider:
+
+.. code-block:: python
+
+   from scalable import ScalableSession
+
+   session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+   plan = session.plan(dry_run=True)
+   print(f"Scale plan: {plan.scale_plan}")
+   # {'analysis': ResourceRequest(cpus=1, memory='1G'), count=4}
+
+   client = session.start(plan)
+   # ... submit work ...
+   session.close()
+
+Step 3: Slurm Provider — HPC Scaling
+--------------------------------------
+
+The :class:`~scalable.providers.slurm.SlurmProvider` submits Dask workers as
+Slurm batch jobs. Each job runs inside a container (via Apptainer) on allocated
+HPC nodes:
+
+.. code-block:: yaml
+
+   targets:
+     hpc:
+       provider: slurm
+       queue: batch
+       account: GCIMS
+       walltime: "04:00:00"
+       interface: ib0
+
+   components:
+     gcam:
+       image: ghcr.io/jgcri/gcam:7.0
+       cpus: 10
+       memory: 20G
+       mounts:
+         /qfs/people/user/work/gcam-core: /gcam-core
+         /rcfs: /rcfs
+
+The Slurm provider:
+
+1. Generates ``sbatch`` scripts for each worker (one job per worker).
+2. Passes resource requests (CPUs, memory, walltime) to the scheduler.
+3. Launches workers inside Apptainer containers with the specified mounts.
+4. Workers connect back to the Dask scheduler on the host via the network
+   interface (``ib0`` for InfiniBand, ``eth0`` for Ethernet).
+
+**Scaling Slurm workers manually:**
+
+.. code-block:: python
+
+   from scalable import SlurmCluster, ScalableClient
+
+   cluster = SlurmCluster(
+       queue="batch",
+       account="GCIMS",
+       walltime="04:00:00",
+       interface="ib0",
+   )
+
+   # Register component profiles
+   cluster.add_container(
+       tag="gcam",
+       cpus=10,
+       memory="20G",
+       dirs={"/qfs/people/user/work/gcam-core": "/gcam-core"},
+   )
+
+   # Scale up — submits 5 Slurm jobs
+   cluster.add_workers(n=5, tag="gcam")
+
+   # Submit work
+   client = ScalableClient(cluster)
+   futures = [client.submit(run_gcam, scenario, tag="gcam") for scenario in scenarios]
+   results = client.gather(futures)
+
+   # Scale down — cancels 3 Slurm jobs
+   cluster.remove_workers(n=3, tag="gcam")
+
+**Why explicit tag-based scaling?** HPC jobs are expensive. Unlike cloud
+auto-scaling where you can spin up instances in seconds, Slurm jobs may wait
+in queue for minutes or hours. Scalable gives you explicit control over how
+many workers to allocate per component, so you can match your budget and queue
+availability.
+
+Step 4: Session-Based Scaling with Objectives
+-----------------------------------------------
+
+The Session API supports policy-driven planning that automatically determines
+worker counts:
+
+.. code-block:: python
+
+   from scalable import ScalableSession
+
+   session = ScalableSession.from_yaml("./scalable.yaml", target="hpc")
+
+   # Minimize cost: fewest workers that finish within walltime
+   plan = session.plan(
+       objective="minimize cost",
+       policy="safe",
+   )
+   print(f"Workers: {plan.scale_plan}")
+   # Might allocate 3 workers × gcam
+
+   # Minimize time: maximum parallelism within resource limits
+   plan = session.plan(
+       objective="minimize time",
+       policy="aggressive",
+   )
+   print(f"Workers: {plan.scale_plan}")
+   # Might allocate 16 workers × gcam
+
+   # Balance: cost-time Pareto front midpoint
+   plan = session.plan(
+       objective="balance",
+       policy="safe",
+   )
+
+Objectives:
+
+* ``"minimize cost"`` — Fewest workers that keep total runtime within walltime.
+* ``"minimize time"`` — Maximum workers within resource bounds.
+* ``"balance"`` — Midpoint between the two extremes.
+
+Policies:
+
+* ``"safe"`` — Add headroom (20% over predicted requirements). Prefer fewer
+  scaling decisions.
+* ``"aggressive"`` — Pack tightly. Scale immediately on threshold.
+* ``"manual"`` — Use exactly the worker counts from the manifest (no
+  adjustment).
+
+Step 5: Adaptive Scaling at Runtime
+-------------------------------------
+
+For long-running workflows where task load varies, the
+:class:`~scalable.ml.adaptive_scaler.AdaptiveScaler` monitors queue depth
+and adjusts workers in real-time:
+
+.. code-block:: python
+
+   from scalable import AdaptiveScaler, ScalableSession
+
+   session = ScalableSession.from_yaml("./scalable.yaml", target="aws")
+   client = session.start()
+
+   scaler = AdaptiveScaler(
+       min_workers={"gcam": 2, "postprocess": 1},
+       max_workers={"gcam": 20, "postprocess": 10},
+       scale_up_threshold=0.8,    # Scale up when 80% of workers are busy
+       scale_down_threshold=0.2,  # Scale down when <20% utilization
+       cooldown_seconds=120,      # Wait 2 min between decisions
+   )
+
+   # In your task submission loop:
+   for batch in scenario_batches:
+       futures = [client.submit(run_gcam, s, tag="gcam") for s in batch]
+
+       # Evaluate scaling after each batch
+       decision = scaler.evaluate(
+           pending_tasks=[{"tag": "gcam"} for _ in range(len(batch))],
+           active_workers={"gcam": client.worker_count("gcam")},
+       )
+
+       if decision.has_changes:
+           print(f"Scaling: +{decision.workers_to_add} -{decision.workers_to_remove}")
+           print(f"Reason: {decision.reasoning}")
+           # Apply the decision (provider-specific)
+           # ...
+
+The ``AdaptiveScaler`` returns a :class:`~scalable.ml.adaptive_scaler.ScaleDecision`
+with:
+
+* ``workers_to_add``: dict mapping tag → count to add.
+* ``workers_to_remove``: dict mapping tag → count to remove.
+* ``reasoning``: human-readable explanation of the decision.
+* ``confidence``: model confidence (0.0–1.0) in the recommendation.
+* ``predicted_completion_time``: estimated seconds to finish remaining tasks.
+
+Step 6: Cloud Provider Auto-Scaling
+-------------------------------------
+
+Cloud providers (AWS, GCP) support declarative adaptive scaling via manifest
+configuration:
+
+.. code-block:: yaml
+
+   targets:
+     aws:
+       provider: aws
+       region: us-east-1
+       cluster_type: fargate
+       adaptive:
+         minimum: 2
+         maximum: 20
+
+The cloud provider handles scale-up/down automatically based on the Dask
+scheduler's task backlog. The ``minimum`` and ``maximum`` set hard bounds:
+
+* **Minimum** workers are always running (reduces cold-start latency).
+* **Maximum** caps costs during burst periods.
+
+**Cost-performance trade-off:**
+
+.. code-block:: text
+
+   ┌────────────────────────────────────────────────────────┐
+   │  Aggressive (max workers)                              │
+   │  ├── Fastest completion                                │
+   │  ├── Highest cost                                      │
+   │  └── Risk: idle workers during low-load phases         │
+   │                                                        │
+   │  Conservative (min workers)                            │
+   │  ├── Lowest cost                                       │
+   │  ├── Slowest completion                                │
+   │  └── Risk: queue buildup during bursts                 │
+   │                                                        │
+   │  Adaptive (dynamic scaling)                            │
+   │  ├── Best cost-performance ratio                       │
+   │  ├── Requires cooldown tuning                          │
+   │  └── Latency: scale-up takes 30–90s for cloud          │
+   └────────────────────────────────────────────────────────┘
+
+Step 7: Heterogeneous Worker Pools
+-----------------------------------
+
+Real workflows often need different resource profiles running simultaneously.
+Scalable supports heterogeneous pools via multiple components:
+
+.. code-block:: yaml
+
+   components:
+     gcam:
+       cpus: 8
+       memory: 32G
+       tags: [compute-heavy]
+
+     postprocess:
+       cpus: 2
+       memory: 4G
+       tags: [io-bound]
+
+   tasks:
+     simulate:
+       component: gcam
+     analyze:
+       component: postprocess
+
+In your workflow, you submit to each pool independently:
+
+.. code-block:: python
+
+   # Compute-heavy tasks go to gcam workers
+   sim_futures = [
+       client.submit(run_simulation, params, tag="gcam")
+       for params in simulation_params
+   ]
+
+   # Wait for simulations, then post-process on lighter workers
+   sim_results = client.gather(sim_futures)
+
+   analysis_futures = [
+       client.submit(aggregate, result, tag="postprocess")
+       for result in sim_results
+   ]
+
+   final = client.gather(analysis_futures)
+
+This pattern avoids over-provisioning: expensive 32 GB workers handle the heavy
+lifting while cheap 4 GB workers handle aggregation.
+
+Step 8: Scaling Decision Monitoring
+-------------------------------------
+
+Track all scaling decisions via telemetry:
+
+.. code-block:: python
+
+   # After workflow completes
+   session.close()
+
+   # Review scaling history
+   for decision in scaler.decision_history:
+       print(
+           f"[{decision.timestamp}] "
+           f"+{decision.workers_to_add} "
+           f"-{decision.workers_to_remove} "
+           f"({decision.reasoning})"
+       )
+
+Telemetry also records worker lifecycle events:
+
+.. code-block:: bash
+
+   scalable report --latest
+
+.. code-block:: text
+
+   Workers:
+     gcam: 5 started, 3 removed, 2 final
+     postprocess: 2 started, 0 removed, 2 final
+   Scaling events: 3 scale-up, 1 scale-down
+
+Troubleshooting
+---------------
+
+**Slurm workers never connect**
+  Check that the ``interface`` option matches your cluster's high-speed
+  network (``ib0``, ``eth0``, etc.). Workers must reach the scheduler host on
+  this interface. Also ensure firewall rules allow the Dask scheduler port
+  (default 8786).
+
+**Cloud workers take too long to start**
+  Fargate cold-start can take 30–90 seconds. Set ``adaptive.minimum`` to at
+  least 1–2 so warm workers are always available. For EC2-backed clusters,
+  pre-warmed AMIs reduce startup time.
+
+**"max_workers must be a positive integer"**
+  This validation error means ``max_workers`` was set to ``0``, a negative
+  number, or a non-integer type. Check for YAML parsing issues (e.g., quoting
+  numbers as strings).
+
+**Workers idle but no tasks are submitted**
+  If using adaptive scaling with a high minimum, workers persist even when no
+  work is available. Lower ``adaptive.minimum`` or add a ``cooldown_seconds``
+  of at least 60 to the ``AdaptiveScaler``.
+
+Next Steps
+----------
+
+* :ref:`tutorial_caching` — Reduce redundant computation when scaling means
+  re-running failed tasks.
+* :ref:`tutorial_cloud_integration` — Full AWS and GCP deployment walkthrough.
+* :ref:`tutorial_telemetry` — Use telemetry data to inform scaling decisions.
diff --git a/docs/tutorials/04_caching_performance.rst b/docs/tutorials/04_caching_performance.rst
new file mode 100644
index 0000000..f558253
--- /dev/null
+++ b/docs/tutorials/04_caching_performance.rst
@@ -0,0 +1,411 @@
+.. _tutorial_caching:
+
+======================================================
+Tutorial 4: Performance Optimization and Caching
+======================================================
+
+What You Will Learn
+-------------------
+
+By the end of this tutorial you will:
+
+* Use the ``@cacheable`` decorator to skip redundant computation.
+* Understand how Scalable hashes function arguments for cache keys.
+* Handle file-based and directory-based inputs with type-safe hashing.
+* Configure cache storage (local disk, remote S3/GCS).
+* Monitor cache hit/miss rates through telemetry.
+* Implement cache invalidation strategies for evolving workflows.
+
+Prerequisites
+-------------
+
+* Completed :ref:`tutorial_getting_started`.
+* Scalable installed (``pip install scalable``).
+* For remote cache: ``pip install scalable[cloud]``.
+
+Scenario
+--------
+
+Your pipeline executes expensive climate simulations that take 30+ minutes per
+scenario. During development you frequently restart runs after fixing
+downstream bugs. Without caching, every restart recomputes scenarios that
+already succeeded. The ``@cacheable`` decorator lets completed tasks skip
+execution on retry.
+
+Step 1: Basic Caching with @cacheable
+---------------------------------------
+
+The :func:`~scalable.caching.cacheable` decorator intercepts function calls,
+computes a content-addressable cache key from the function's name and
+arguments, and returns cached results when available:
+
+.. code-block:: python
+
+   from scalable import cacheable
+
+
+   @cacheable(return_type=dict, scenario_id=int)
+   def run_simulation(scenario_id: int) -> dict:
+       """Expensive computation — runs a climate scenario."""
+       import time
+       time.sleep(30)  # Simulating expensive work
+       return {"scenario": scenario_id, "emissions": scenario_id * 1.5}
+
+First call:
+
+.. code-block:: python
+
+   result = run_simulation(42)
+   # Takes 30 seconds — cache MISS
+   print(result)
+   # {'scenario': 42, 'emissions': 63.0}
+
+Second call with the same argument:
+
+.. code-block:: python
+
+   result = run_simulation(42)
+   # Returns instantly — cache HIT
+   print(result)
+   # {'scenario': 42, 'emissions': 63.0}
+
+**How it works:**
+
+1. The decorator serializes each argument using ``dill`` and hashes the bytes
+   with ``xxhash`` (seeded by ``SCALABLE_SEED``).
+2. The function name and hash form a composite cache key.
+3. On a hit, the stored result is deserialized and returned without executing
+   the function body.
+4. On a miss, the function executes normally and the result is stored.
+
+Step 2: Type Annotations for Reliable Hashing
+-----------------------------------------------
+
+Scalable's cache key depends on how arguments are hashed. Without type hints,
+the decorator falls back to generic serialization, which may produce
+inconsistent keys for complex objects. Explicit type annotations are preferred:
+
+.. code-block:: python
+
+   from scalable import cacheable
+
+   @cacheable(return_type=str, name=str, count=int)
+   def greet(name: str, count: int) -> str:
+       return f"Hello {name}! (x{count})"
+
+The decorator parameters mirror the function signature:
+
+* ``return_type=str`` — declares the return type for safe deserialization.
+* ``name=str, count=int`` — declares argument types for deterministic hashing.
+
+**Why this matters:** Python objects hash differently depending on their
+runtime type. A ``numpy.int64(42)`` and Python ``int(42)`` produce different
+byte representations. Explicit type annotations ensure the decorator coerces
+inputs consistently.
+
+Step 3: Hashing Files and Directories
+---------------------------------------
+
+Scientific workflows frequently operate on input files. Scalable provides
+specialized type wrappers that hash file *content* rather than paths:
+
+.. code-block:: python
+
+   from scalable import cacheable, FileType, DirType
+
+
+   @cacheable(return_type=dict, config=FileType, data_dir=DirType)
+   def process_data(config: str, data_dir: str) -> dict:
+       """Process data files. Cache key includes file contents."""
+       import json
+       with open(config) as f:
+           cfg = json.load(f)
+       # ... process files in data_dir ...
+       return {"records_processed": 1000, "config_version": cfg["version"]}
+
+How each type hashes:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 15 85
+
+   * - Type
+     - Hashing Strategy
+   * - ``FileType``
+     - Streams file content in 1 MB chunks through xxhash. Includes the
+       filename (basename only) in the hash. If the file doesn't exist, raises
+       ``ValueError``.
+   * - ``DirType``
+     - Walks the directory tree, hashes each file's relative path and content.
+       Order is sorted for determinism. Missing directory raises ``ValueError``.
+   * - ``str``
+     - Hashes the string bytes directly (UTF-8 encoded).
+   * - ``int``
+     - Hashes the integer's byte representation.
+
+**Trade-off:** ``FileType`` hashing reads the entire file on every call to
+compute the key. For very large files (multi-GB), this adds I/O overhead even
+on cache hits. Consider whether your workflow modifies input files between
+runs — if inputs are immutable, a simpler path-based key might suffice.
+
+Step 4: Forcing Recomputation
+------------------------------
+
+Sometimes you need to invalidate the cache for a specific function, for
+example after fixing a bug in the computation logic:
+
+.. code-block:: python
+
+   @cacheable(return_type=dict, recompute=True, scenario_id=int)
+   def run_simulation(scenario_id: int) -> dict:
+       """Always recompute — ignores cached results."""
+       # Fixed version of the computation
+       return {"scenario": scenario_id, "emissions": scenario_id * 1.7}
+
+Setting ``recompute=True`` forces the function to execute every time. The
+result still gets written to the cache, so subsequent calls (once you remove
+``recompute=True``) will find fresh entries.
+
+**Alternative: Change the seed.** If you want to invalidate *all* cache entries
+globally, change the ``SCALABLE_SEED`` environment variable:
+
+.. code-block:: bash
+
+   export SCALABLE_SEED=123456789
+   python workflow.py  # All cache keys change — full recomputation
+
+Step 5: The Minimal @cacheable Form
+--------------------------------------
+
+For quick prototyping, ``@cacheable`` works without explicit types:
+
+.. code-block:: python
+
+   @cacheable
+   def quick_computation(x, y):
+       return x + y
+
+In this form:
+
+* Arguments are serialized with ``dill`` and hashed generically.
+* Return type is inferred from the actual return value.
+* This is less reliable for complex objects but convenient during exploration.
+
+**Recommendation:** Always add explicit types for production code. The minimal
+form is acceptable for quick experiments where cache key stability isn't
+critical.
+
+Step 6: Cache Configuration
+-----------------------------
+
+Configure cache storage via environment variables or the manifest:
+
+**Local disk cache (default):**
+
+.. code-block:: bash
+
+   export SCALABLE_CACHE_DIR=./cache
+   # Or in the manifest:
+   # project:
+   #   local_cache: ./my-cache
+
+**Remote cache (S3/GCS):**
+
+.. code-block:: bash
+
+   export SCALABLE_CACHE_REMOTE=s3://my-bucket/scalable-cache/
+
+When a remote cache is configured, Scalable checks the remote store on cache
+miss before executing the function. This enables cache sharing across machines
+and CI runs:
+
+.. code-block:: text
+
+   Cache lookup order:
+   1. Local disk (fast, per-machine)
+   2. Remote store (slower, shared across team)
+   3. Execute function (slowest, produces new cache entry)
+
+**Cache directory structure:**
+
+.. code-block:: text
+
+   ./cache/
+   ├── cache.db          # SQLite index (diskcache)
+   ├── 00/              # Sharded data files
+   │   ├── a3b8f1...
+   │   └── ...
+   └── tmp/             # Temporary write staging
+
+The cache is process-safe (uses SQLite locking) and can be shared between
+concurrent workflows on the same machine.
+
+Step 7: Cache-Aware Task Definitions
+--------------------------------------
+
+In the manifest, marking a task with ``cache: true`` signals to the Session
+that functions submitted under that task should honor the cache:
+
+.. code-block:: yaml
+
+   tasks:
+     run_gcam:
+       component: gcam
+       cache: true
+
+     postprocess:
+       component: analysis
+       cache: false   # Always recompute (e.g., aggregation is cheap)
+
+When ``cache: true``, the session emits cache hit/miss events to telemetry,
+allowing you to track cache effectiveness over time.
+
+Step 8: Monitoring Cache Performance
+--------------------------------------
+
+Cache events are recorded in telemetry when running through the Session API:
+
+.. code-block:: bash
+
+   scalable report --latest
+
+.. code-block:: text
+
+   Cache Performance:
+     Total calls: 50
+     Hits: 35 (70%)
+     Misses: 15 (30%)
+     Estimated time saved: 17.5 minutes
+
+Programmatic access:
+
+.. code-block:: python
+
+   import json
+   from pathlib import Path
+
+   run_dir = Path(".scalable/runs/run-20260520T.../")
+   cache_events = []
+   with open(run_dir / "cache.jsonl") as f:
+       for line in f:
+           cache_events.append(json.loads(line))
+
+   hits = sum(1 for e in cache_events if e["hit"])
+   misses = sum(1 for e in cache_events if not e["hit"])
+   print(f"Hit rate: {hits}/{hits+misses} = {hits/(hits+misses)*100:.0f}%")
+
+Step 9: Cache Invalidation Strategies
+---------------------------------------
+
+Effective caching requires a strategy for when to invalidate:
+
+**Strategy 1: Seed rotation**
+
+Change ``SCALABLE_SEED`` to invalidate all entries. Use this after major code
+changes that affect all functions:
+
+.. code-block:: bash
+
+   export SCALABLE_SEED=$(date +%s)  # New seed each day
+
+**Strategy 2: Per-function recompute**
+
+Set ``recompute=True`` on specific functions during development. Remove once
+verified:
+
+.. code-block:: python
+
+   @cacheable(return_type=dict, recompute=True, params=dict)
+   def run_gcam(params: dict) -> dict:
+       ...
+
+**Strategy 3: Version the function name**
+
+Include a version suffix in the function name to naturally invalidate when
+logic changes:
+
+.. code-block:: python
+
+   @cacheable(return_type=dict, params=dict)
+   def run_gcam_v3(params: dict) -> dict:
+       # v3: fixed carbon price calculation
+       ...
+
+**Strategy 4: Delete the cache directory**
+
+Nuclear option — simply remove the cache directory:
+
+.. code-block:: bash
+
+   rm -rf ./cache
+   python workflow.py  # Full recomputation
+
+Step 10: Distributed Caching Pattern
+--------------------------------------
+
+For team workflows where multiple developers run the same pipeline, use a
+shared remote cache:
+
+.. code-block:: bash
+
+   # All team members set the same remote cache
+   export SCALABLE_CACHE_REMOTE=s3://team-bucket/scalable-cache/
+
+Workflow:
+
+1. Developer A runs the pipeline. All 50 scenarios compute and cache remotely.
+2. Developer B runs the same pipeline. All 50 scenarios hit the remote cache.
+3. Developer A modifies scenario 7's parameters. Only scenario 7 recomputes.
+
+.. code-block:: python
+
+   from scalable import ScalableSession
+
+   session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+   client = session.start()
+
+   # These will check local cache, then remote, then compute
+   futures = [
+       client.submit(run_gcam, scenario, tag="gcam")
+       for scenario in range(50)
+   ]
+   results = client.gather(futures)
+   # First run: 50 misses. Subsequent runs: 50 hits.
+
+Troubleshooting
+---------------
+
+**Cache never hits despite identical arguments**
+  Check that ``SCALABLE_SEED`` hasn't changed between runs. Also verify that
+  argument types are consistent — passing ``numpy.int64`` vs ``int`` may
+  produce different keys. Use explicit type annotations.
+
+**"ValueError: File does not exist" from FileType**
+  ``FileType`` validates file existence at hash time. Ensure the file path is
+  accessible from the worker process (relevant for containerized workers where
+  paths differ from the host).
+
+**Cache grows unboundedly**
+  ``diskcache`` doesn't auto-evict by default. Periodically clean old entries:
+
+  .. code-block:: python
+
+     from diskcache import Cache
+     cache = Cache("./cache")
+     cache.clear()  # Remove all entries
+     # Or set a size limit:
+     cache = Cache("./cache", size_limit=10 * 1024**3)  # 10 GB
+
+**Remote cache is slow**
+  S3/GCS lookups add latency per call (50–200ms). For workflows with thousands
+  of small tasks, the overhead may exceed computation time. Use remote caching
+  only for expensive tasks (>1 minute per call) or batch cache lookups.
+
+Next Steps
+----------
+
+* :ref:`tutorial_cloud_integration` — Deploy cached workflows to AWS/GCP with
+  shared remote storage.
+* :ref:`tutorial_telemetry` — Analyze cache performance across historical runs.
+* :ref:`tutorial_error_handling` — Handle cache corruption and partial failures
+  gracefully.
diff --git a/docs/tutorials/05_cloud_integration.rst b/docs/tutorials/05_cloud_integration.rst
new file mode 100644
index 0000000..e65344f
--- /dev/null
+++ b/docs/tutorials/05_cloud_integration.rst
@@ -0,0 +1,441 @@
+.. _tutorial_cloud_integration:
+
+======================================================
+Tutorial 5: Cloud Integration with AWS and GCP
+======================================================
+
+What You Will Learn
+-------------------
+
+By the end of this tutorial you will:
+
+* Configure AWS Fargate and EC2-backed Dask clusters via Scalable.
+* Set up GCP Cloud Run / GKE-based execution.
+* Use the artifact store for persistent cloud storage (S3, GCS).
+* Estimate costs before running with dry-run planning.
+* Deploy multi-target manifests that promote from local to cloud.
+* Manage IAM roles, networking, and container registries.
+
+Prerequisites
+-------------
+
+* Completed :ref:`tutorial_getting_started` and :ref:`tutorial_manifest_system`.
+* ``pip install scalable[cloud]`` (installs ``s3fs``, ``gcsfs``,
+  ``dask-cloudprovider``, ``fsspec``).
+* AWS credentials configured (``~/.aws/credentials`` or environment variables).
+* (For GCP) ``gcloud`` CLI authenticated or ``GOOGLE_APPLICATION_CREDENTIALS`` set.
+
+Scenario
+--------
+
+Your climate pipeline works locally but needs to scale to 50+ concurrent
+scenarios for a production run. Your organization uses AWS for burst compute
+and GCS for long-term data storage. You need to deploy the same workflow to
+cloud infrastructure with cost visibility.
+
+Step 1: AWS Target Configuration
+----------------------------------
+
+The AWS provider uses ``dask-cloudprovider`` to launch Dask workers on Fargate
+(serverless containers) or EC2 instances:
+
+.. code-block:: yaml
+
+   # scalable.yaml
+   version: 1
+   project:
+     name: climate-model-aws
+     default_storage: s3://${S3_BUCKET}/scalable-runs/
+
+   targets:
+     aws:
+       provider: aws
+       region: ${AWS_REGION:-us-east-1}
+       cluster_type: fargate
+       instance_type: m5.xlarge       # For EC2-backed mode
+       worker_cpu: 4096               # Fargate CPU units (1024 = 1 vCPU)
+       worker_mem: 16384              # Fargate memory in MiB
+       image: ${ECR_IMAGE}
+       execution_role_arn: ${EXECUTION_ROLE_ARN}
+       task_role_arn: ${TASK_ROLE_ARN}
+       subnets:
+         - ${SUBNET_A}
+         - ${SUBNET_B}
+       security_groups:
+         - ${SG_ID}
+       adaptive:
+         minimum: 2
+         maximum: 20
+
+   components:
+     gcam:
+       image: ${ECR_IMAGE_GCAM}
+       cpus: 4
+       memory: 16G
+       tags: [iam, climate]
+
+     postprocess:
+       cpus: 2
+       memory: 8G
+       tags: [analysis]
+
+   tasks:
+     run_gcam:
+       component: gcam
+       cache: true
+       outputs:
+         database: dir
+
+     aggregate:
+       component: postprocess
+       cache: true
+
+**Key configuration explained:**
+
+``cluster_type``
+  ``fargate`` for serverless (no EC2 management) or ``ec2`` for instance-backed
+  clusters (lower cost at scale, more control over instance types).
+
+``worker_cpu`` / ``worker_mem``
+  Fargate task sizing. CPU is in units of 1024 (= 1 vCPU). Common
+  configurations:
+
+  .. list-table::
+     :header-rows: 1
+     :widths: 20 20 60
+
+     * - CPU
+       - Memory
+       - Use Case
+     * - 1024
+       - 4096
+       - Light tasks, I/O-bound
+     * - 4096
+       - 16384
+       - Standard compute tasks
+     * - 16384
+       - 65536
+       - Memory-intensive models
+
+``execution_role_arn``
+  IAM role assumed by the ECS agent to pull images and write logs. Needs
+  ``ecr:GetAuthorizationToken``, ``ecr:BatchGetImage``, ``logs:CreateLogStream``
+  permissions.
+
+``task_role_arn``
+  IAM role assumed by the running task. Needs S3 read/write for artifacts,
+  network access for Dask scheduler communication.
+
+Step 2: Set Up AWS Infrastructure
+-----------------------------------
+
+Before running, ensure these AWS resources exist:
+
+**1. ECR Repository (Container Registry):**
+
+.. code-block:: bash
+
+   aws ecr create-repository --repository-name climate-model
+   # Push your image
+   docker build -t climate-model:latest .
+   docker tag climate-model:latest 123456789.dkr.ecr.us-east-1.amazonaws.com/climate-model:latest
+   aws ecr get-login-password | docker login --username AWS --password-stdin 123456789.dkr.ecr.us-east-1.amazonaws.com
+   docker push 123456789.dkr.ecr.us-east-1.amazonaws.com/climate-model:latest
+
+**2. VPC + Subnets:**
+
+Workers need outbound internet access (for Dask scheduler communication) and
+access to S3. Use a VPC with NAT Gateway or VPC endpoints.
+
+**3. Security Group:**
+
+.. code-block:: bash
+
+   # Allow inbound from scheduler, outbound to internet
+   aws ec2 create-security-group \
+     --group-name scalable-workers \
+     --description "Scalable Dask workers"
+   aws ec2 authorize-security-group-ingress \
+     --group-id sg-xyz789 \
+     --protocol tcp --port 8786-8787 \
+     --source-group sg-xyz789
+
+**4. IAM Roles:**
+
+.. code-block:: json
+
+   {
+     "Version": "2012-10-17",
+     "Statement": [
+       {
+         "Effect": "Allow",
+         "Action": [
+           "s3:GetObject",
+           "s3:PutObject",
+           "s3:ListBucket"
+         ],
+         "Resource": [
+           "arn:aws:s3:::my-bucket",
+           "arn:aws:s3:::my-bucket/*"
+         ]
+       }
+     ]
+   }
+
+Step 3: Dry-Run Cost Estimation
+--------------------------------
+
+Before launching real cloud resources, estimate costs:
+
+.. code-block:: bash
+
+   scalable run ./scalable.yaml --target aws --dry-run
+
+.. code-block:: text
+
+   Dry-run plan for target 'aws' (provider: aws):
+     Workers: 10 × gcam (4 vCPU, 16 GiB)
+              5 × postprocess (2 vCPU, 8 GiB)
+     Estimated duration: 2.5 hours
+     Estimated cost: $4.82
+       Fargate compute: $3.90
+       Data transfer: $0.12
+       S3 storage: $0.80
+
+Programmatic cost access:
+
+.. code-block:: python
+
+   from scalable import ScalableSession
+
+   session = ScalableSession.from_yaml("./scalable.yaml", target="aws")
+   plan = session.plan(dry_run=True)
+
+   if plan.cost_estimate:
+       print(f"Estimated cost: ${plan.cost_estimate.total:.2f}")
+       print(f"  Compute: ${plan.cost_estimate.compute:.2f}")
+       print(f"  Storage: ${plan.cost_estimate.storage:.2f}")
+       print(f"  Transfer: ${plan.cost_estimate.transfer:.2f}")
+
+**How cost estimation works:** Scalable uses the
+:mod:`scalable.providers.cloud.cost_tables` module which contains region-specific
+pricing for Fargate vCPU-hours, memory-hours, and S3 operations. Estimates are
+based on the planned worker count, predicted task duration (from telemetry
+history if available), and declared storage outputs.
+
+Step 4: GCP Target Configuration
+----------------------------------
+
+For Google Cloud Platform, use GCS for storage and either Cloud Run or GKE for
+compute:
+
+.. code-block:: yaml
+
+   targets:
+     gcp:
+       provider: gcp
+       region: us-central1
+       project_id: ${GCP_PROJECT_ID}
+       cluster_type: cloud_run
+       worker_cpu: 4
+       worker_mem: 16Gi
+       image: gcr.io/${GCP_PROJECT_ID}/climate-model:latest
+       service_account: ${GCP_SERVICE_ACCOUNT}
+       adaptive:
+         minimum: 1
+         maximum: 15
+
+   project:
+     default_storage: gs://${GCS_BUCKET}/scalable-runs/
+
+GCP-specific setup:
+
+.. code-block:: bash
+
+   # Authenticate
+   gcloud auth application-default login
+
+   # Push image to GCR
+   gcloud builds submit --tag gcr.io/my-project/climate-model:latest .
+
+   # Create GCS bucket for artifacts
+   gsutil mb -l us-central1 gs://my-bucket/
+
+Step 5: Artifact Store — Cloud Storage
+----------------------------------------
+
+The artifact store provides a unified interface for persisting outputs across
+storage backends:
+
+.. code-block:: python
+
+   from scalable.artifacts import build_artifact_store
+
+   # Local storage (default)
+   local_store = build_artifact_store("./artifacts")
+
+   # S3 storage
+   s3_store = build_artifact_store("s3://my-bucket/artifacts/")
+
+   # GCS storage
+   gcs_store = build_artifact_store("gs://my-bucket/artifacts/")
+
+   # Store a file
+   ref = s3_store.put("local/output.csv", "runs/run-001/output.csv")
+   print(ref)
+   # ArtifactRef(uri='s3://my-bucket/artifacts/runs/run-001/output.csv')
+
+   # Retrieve a file
+   local_path = s3_store.get(ref, "./downloads/output.csv")
+
+The store is protocol-aware via ``fsspec``: it detects the URI scheme and uses
+the appropriate backend (``s3fs`` for S3, ``gcsfs`` for GCS, local filesystem
+for paths).
+
+**Integration with workflow output:**
+
+.. code-block:: python
+
+   from scalable import ScalableSession
+   from scalable.artifacts import build_artifact_store
+
+   session = ScalableSession.from_yaml("./scalable.yaml", target="aws")
+   client = session.start()
+
+   # Run simulation
+   result = client.submit(run_gcam, scenario_params, tag="gcam").result()
+
+   # Persist output artifact to configured storage
+   store = build_artifact_store(session.manifest.project.default_storage)
+   ref = store.put(
+       result["output_path"],
+       f"runs/{session._telemetry.run_id}/gcam-output.tar.gz",
+   )
+   print(f"Artifact persisted: {ref.uri}")
+
+Step 6: Multi-Region Deployment
+---------------------------------
+
+For global workflows, define targets in multiple regions:
+
+.. code-block:: yaml
+
+   targets:
+     aws-east:
+       provider: aws
+       region: us-east-1
+       # ... config ...
+       adaptive:
+         minimum: 5
+         maximum: 50
+
+     aws-west:
+       provider: aws
+       region: us-west-2
+       # ... config ...
+       adaptive:
+         minimum: 2
+         maximum: 20
+
+     gcp-europe:
+       provider: gcp
+       region: europe-west1
+       # ... config ...
+
+Select at runtime:
+
+.. code-block:: bash
+
+   # Heavy production run in us-east-1
+   scalable run ./scalable.yaml --target aws-east --workflow pipeline.py
+
+   # Quick validation in us-west-2
+   scalable run ./scalable.yaml --target aws-west --workflow pipeline.py --dry-run
+
+Step 7: Cloud + Cache Integration
+-----------------------------------
+
+Combine cloud execution with remote caching so repeated runs across different
+machines share results:
+
+.. code-block:: bash
+
+   export SCALABLE_CACHE_REMOTE=s3://my-bucket/scalable-cache/
+
+.. code-block:: yaml
+
+   project:
+     name: climate-pipeline
+     default_storage: s3://my-bucket/outputs/
+
+Now:
+
+1. First cloud run computes all scenarios and caches results to S3.
+2. Subsequent runs (from any machine) hit the shared cache.
+3. Only modified scenarios recompute.
+
+This is particularly powerful for CI/CD: your PR validation pipeline benefits
+from the cache populated by previous runs.
+
+Step 8: Environment Variable Template
+---------------------------------------
+
+For production deployments, maintain a ``.env`` template:
+
+.. code-block:: bash
+
+   # .env.cloud (do not commit secrets — use secrets manager)
+   AWS_REGION=us-east-1
+   S3_BUCKET=climate-prod-artifacts
+   ECR_IMAGE=123456789.dkr.ecr.us-east-1.amazonaws.com/climate:latest
+   ECR_IMAGE_GCAM=123456789.dkr.ecr.us-east-1.amazonaws.com/gcam:7.0
+   EXECUTION_ROLE_ARN=arn:aws:iam::123456789:role/ecsTaskExecutionRole
+   TASK_ROLE_ARN=arn:aws:iam::123456789:role/scalableTaskRole
+   SUBNET_A=subnet-abc123
+   SUBNET_B=subnet-def456
+   SG_ID=sg-xyz789
+   SCALABLE_CACHE_REMOTE=s3://climate-prod-artifacts/cache/
+
+Load before running:
+
+.. code-block:: bash
+
+   set -a && source .env.cloud && set +a
+   scalable run ./scalable.yaml --target aws --workflow pipeline.py
+
+Troubleshooting
+---------------
+
+**"botocore.exceptions.NoCredentialsError"**
+  AWS credentials are not configured. Run ``aws configure`` or set
+  ``AWS_ACCESS_KEY_ID`` and ``AWS_SECRET_ACCESS_KEY`` environment variables.
+  For EC2/ECS, ensure the instance profile or task role has necessary
+  permissions.
+
+**Fargate task fails with "CannotPullContainerError"**
+  The execution role lacks ECR permissions, the image URI is wrong, or the
+  image doesn't exist in the specified region. Verify with:
+  ``aws ecr describe-images --repository-name climate-model``.
+
+**Workers can't connect to scheduler**
+  Security group must allow inbound TCP on the Dask scheduler port (8786)
+  from the worker security group. Subnets must have a route to the scheduler
+  host (typically your local machine or a bastion).
+
+**GCS "403 Forbidden"**
+  The service account lacks ``storage.objects.create`` permission on the
+  bucket. Grant the ``roles/storage.objectAdmin`` role.
+
+**Cost estimate shows $0.00**
+  Cost tables may not have pricing for your specific region or instance type.
+  Check that ``scalable.providers.cloud.cost_tables`` includes your region.
+
+Next Steps
+----------
+
+* :ref:`tutorial_telemetry` — Monitor cloud run costs and performance
+  through telemetry.
+* :ref:`tutorial_kubernetes` — Deploy to Kubernetes for container-native
+  orchestration.
+* :ref:`tutorial_error_handling` — Handle cloud-specific transient failures
+  (timeouts, preemption).
diff --git a/docs/tutorials/06_telemetry.rst b/docs/tutorials/06_telemetry.rst
new file mode 100644
index 0000000..f74b647
--- /dev/null
+++ b/docs/tutorials/06_telemetry.rst
@@ -0,0 +1,401 @@
+.. _tutorial_telemetry:
+
+======================================================
+Tutorial 6: Monitoring and Observability with Telemetry
+======================================================
+
+What You Will Learn
+-------------------
+
+By the end of this tutorial you will:
+
+* Understand Scalable's telemetry data model and event types.
+* Read and analyze JSONL telemetry files programmatically.
+* Generate reports from the CLI and Python API.
+* Build custom dashboards from telemetry data.
+* Use telemetry history to inform resource recommendations.
+* Configure telemetry persistence and export (Parquet, remote storage).
+
+Prerequisites
+-------------
+
+* Completed :ref:`tutorial_getting_started`.
+* At least one completed Scalable run (to have telemetry data).
+* ``pandas`` installed (part of Scalable's core dependencies).
+
+Scenario
+--------
+
+Your team runs the climate pipeline multiple times per week. You need to
+track performance trends, identify slow tasks, monitor resource utilization,
+and justify cloud spending to stakeholders. Scalable's built-in telemetry
+provides all this data without external observability infrastructure.
+
+Step 1: Telemetry Architecture
+-------------------------------
+
+Every manifest-driven run (via ``ScalableSession`` or ``scalable run``)
+automatically records structured events to disk:
+
+.. code-block:: text
+
+   .scalable/runs/
+   └── run-20260520T035200Z-climate-pipeline-a1b2c3d4/
+       ├── run.json           # Run metadata (start time, target, manifest lock)
+       ├── manifest.yaml      # Snapshot of the manifest used
+       ├── plan.json          # Execution plan snapshot
+       ├── tasks.jsonl        # Task lifecycle events
+       ├── resources.jsonl    # Resource utilization snapshots
+       ├── workers.jsonl      # Worker lifecycle events
+       ├── cache.jsonl        # Cache hit/miss events
+       ├── failures.jsonl     # Error/failure records
+       ├── artifacts.jsonl    # Artifact store operations
+       └── cost.jsonl         # Cost tracking events
+
+Each ``.jsonl`` file contains one JSON object per line — a format optimized
+for append-only writes and streaming reads.
+
+**Design rationale:** JSONL was chosen over SQLite or a time-series database
+because it requires no external dependencies, survives process crashes (each
+line is independently valid), and can be trivially loaded into pandas, jq, or
+any JSON-capable tool.
+
+Step 2: Run Metadata
+---------------------
+
+The ``run.json`` file contains the run's identity and configuration:
+
+.. code-block:: json
+
+   {
+     "run_id": "run-20260520T035200Z-climate-pipeline-a1b2c3d4",
+     "project_name": "climate-pipeline",
+     "target_name": "local",
+     "provider_name": "local",
+     "manifest_lock": "sha256:a3b8f1...",
+     "started_at": "2026-05-20T03:52:00Z",
+     "status": "completed",
+     "ended_at": "2026-05-20T03:58:30Z"
+   }
+
+Key fields:
+
+* ``manifest_lock`` — content hash proving which exact configuration produced
+  this run. Two runs with identical locks are reproducible.
+* ``status`` — one of ``running``, ``completed``, ``failed``, ``cancelled``.
+
+Step 3: Task Events
+--------------------
+
+``tasks.jsonl`` records the full lifecycle of every submitted task:
+
+.. code-block:: json
+
+   {"task_id": "run_gcam-0", "task_name": "run_gcam", "state": "submitted", "timestamp": "2026-05-20T03:52:01Z", "tag": "gcam"}
+   {"task_id": "run_gcam-0", "task_name": "run_gcam", "state": "running", "timestamp": "2026-05-20T03:52:02Z", "worker_id": "worker-gcam-0"}
+   {"task_id": "run_gcam-0", "task_name": "run_gcam", "state": "succeeded", "timestamp": "2026-05-20T03:55:30Z", "duration_s": 208.5}
+
+States: ``submitted`` → ``running`` → ``succeeded`` | ``failed`` | ``cancelled``
+
+Analyzing task durations:
+
+.. code-block:: python
+
+   import json
+   import pandas as pd
+   from pathlib import Path
+
+   run_dir = Path(".scalable/runs/run-20260520T035200Z-climate-pipeline-a1b2c3d4")
+
+   tasks = []
+   with open(run_dir / "tasks.jsonl") as f:
+       for line in f:
+           tasks.append(json.loads(line))
+
+   df = pd.DataFrame(tasks)
+   completed = df[df["state"] == "succeeded"]
+
+   print(f"Total tasks: {len(completed)}")
+   print(f"Mean duration: {completed['duration_s'].mean():.1f}s")
+   print(f"Max duration: {completed['duration_s'].max():.1f}s")
+   print(f"P95 duration: {completed['duration_s'].quantile(0.95):.1f}s")
+
+Expected output:
+
+.. code-block:: text
+
+   Total tasks: 50
+   Mean duration: 185.3s
+   Max duration: 312.7s
+   P95 duration: 280.1s
+
+Step 4: Resource Utilization Events
+-------------------------------------
+
+``resources.jsonl`` tracks CPU and memory usage per task and per worker:
+
+.. code-block:: json
+
+   {"entity_type": "task", "entity_id": "run_gcam-0", "cpu_percent": 78.5, "memory_mb": 14200, "timestamp": "2026-05-20T03:53:00Z"}
+   {"entity_type": "worker", "entity_id": "worker-gcam-0", "cpu_percent": 82.1, "memory_mb": 15800, "timestamp": "2026-05-20T03:53:00Z"}
+
+Build a utilization timeline:
+
+.. code-block:: python
+
+   resources = []
+   with open(run_dir / "resources.jsonl") as f:
+       for line in f:
+           resources.append(json.loads(line))
+
+   res_df = pd.DataFrame(resources)
+   res_df["timestamp"] = pd.to_datetime(res_df["timestamp"])
+
+   # Average CPU utilization over time
+   worker_resources = res_df[res_df["entity_type"] == "worker"]
+   timeline = worker_resources.groupby(
+       pd.Grouper(key="timestamp", freq="30s")
+   ).agg({"cpu_percent": "mean", "memory_mb": "mean"})
+
+   print(timeline.head(10))
+
+This data helps identify:
+
+* **Under-provisioned workers** — consistently >90% CPU means you need more
+  workers or larger instance types.
+* **Over-provisioned workers** — consistently <30% CPU means you're paying
+  for unused capacity.
+* **Memory pressure** — memory approaching the limit suggests increasing
+  the component's memory allocation.
+
+Step 5: Worker Lifecycle Events
+--------------------------------
+
+``workers.jsonl`` records when workers start, become idle, and terminate:
+
+.. code-block:: json
+
+   {"worker_id": "worker-gcam-0", "event": "started", "tag": "gcam", "timestamp": "2026-05-20T03:52:00Z"}
+   {"worker_id": "worker-gcam-0", "event": "task_assigned", "task_id": "run_gcam-0", "timestamp": "2026-05-20T03:52:01Z"}
+   {"worker_id": "worker-gcam-0", "event": "idle", "timestamp": "2026-05-20T03:55:30Z"}
+   {"worker_id": "worker-gcam-0", "event": "removed", "timestamp": "2026-05-20T03:58:00Z", "reason": "scale_down"}
+
+This lets you calculate:
+
+* **Worker utilization** — fraction of time each worker spent executing vs idle.
+* **Scale efficiency** — whether adaptive scaling decisions were timely.
+* **Cold-start overhead** — time between ``started`` and first ``task_assigned``.
+
+Step 6: CLI Reports
+--------------------
+
+The quickest way to review a run:
+
+.. code-block:: bash
+
+   # Latest run summary
+   scalable report --latest
+
+.. code-block:: text
+
+   ═══════════════════════════════════════════════════════════
+   Run Report: run-20260520T035200Z-climate-pipeline-a1b2c3d4
+   ═══════════════════════════════════════════════════════════
+   Status: completed
+   Target: local (provider: local)
+   Duration: 6m 30s
+   Manifest lock: sha256:a3b8f1...
+
+   Tasks:
+     Submitted: 50
+     Succeeded: 50
+     Failed: 0
+     Cache hits: 12
+
+   Workers:
+     gcam: 4 started, 0 failed
+     postprocess: 2 started, 0 failed
+
+   Resource Usage (mean):
+     CPU: 72.4%
+     Memory: 11.2 GiB / 16.0 GiB (70%)
+
+Export as JSON for downstream processing:
+
+.. code-block:: bash
+
+   scalable report --latest --format json --output report.json
+
+.. code-block:: json
+
+   {
+     "run_id": "run-20260520T035200Z-climate-pipeline-a1b2c3d4",
+     "status": "completed",
+     "duration_seconds": 390,
+     "tasks": {"submitted": 50, "succeeded": 50, "failed": 0},
+     "cache": {"hits": 12, "misses": 38},
+     "cost_estimate": {"total": 0.0, "compute": 0.0}
+   }
+
+Step 7: Programmatic Report Access
+------------------------------------
+
+Use the telemetry collectors for rich programmatic analysis:
+
+.. code-block:: python
+
+   from scalable.telemetry.collectors import summarize_run, iter_run_dirs
+   from pathlib import Path
+
+   # Get the latest run directory
+   runs_dir = Path(".scalable/runs")
+   run_dirs = sorted(iter_run_dirs(runs_dir))
+   latest = run_dirs[-1]
+
+   # Generate summary
+   summary = summarize_run(latest)
+   print(f"Run: {summary['run_id']}")
+   print(f"Duration: {summary['duration_seconds']:.0f}s")
+   print(f"Tasks succeeded: {summary['tasks_succeeded']}")
+   print(f"Tasks failed: {summary['tasks_failed']}")
+
+Step 8: Historical Trend Analysis
+-----------------------------------
+
+Compare performance across multiple runs:
+
+.. code-block:: python
+
+   from scalable.telemetry.collectors import iter_run_dirs, read_jsonl
+   from pathlib import Path
+   import pandas as pd
+
+   runs_dir = Path(".scalable/runs")
+   run_summaries = []
+
+   for run_dir in iter_run_dirs(runs_dir):
+       run_json = run_dir / "run.json"
+       if not run_json.exists():
+           continue
+
+       meta = pd.read_json(run_json, typ="series")
+       tasks = read_jsonl(run_dir / "tasks.jsonl")
+       succeeded = [t for t in tasks if t.get("state") == "succeeded"]
+
+       run_summaries.append({
+           "run_id": meta.get("run_id"),
+           "started_at": meta.get("started_at"),
+           "target": meta.get("target_name"),
+           "total_tasks": len(succeeded),
+           "mean_duration": (
+               sum(t.get("duration_s", 0) for t in succeeded) / len(succeeded)
+               if succeeded else 0
+           ),
+       })
+
+   history = pd.DataFrame(run_summaries)
+   history["started_at"] = pd.to_datetime(history["started_at"])
+   history = history.sort_values("started_at")
+
+   print("Performance trend (last 10 runs):")
+   print(history[["started_at", "target", "total_tasks", "mean_duration"]].tail(10))
+
+Expected output:
+
+.. code-block:: text
+
+   Performance trend (last 10 runs):
+     started_at           target  total_tasks  mean_duration
+     2026-05-10 14:00:00  local   50           210.5
+     2026-05-12 09:30:00  local   50           205.2
+     2026-05-14 16:00:00  hpc     50           45.8
+     2026-05-15 10:00:00  hpc     50           44.1
+     2026-05-18 08:00:00  aws     100          38.2
+     ...
+
+Step 9: Parquet Export for Analytics
+-------------------------------------
+
+For large-scale analysis or integration with data warehouses, enable Parquet
+snapshots:
+
+.. code-block:: bash
+
+   export SCALABLE_TELEMETRY_PARQUET=1
+   python workflow.py
+
+This writes columnar Parquet files alongside the JSONL:
+
+.. code-block:: text
+
+   .scalable/runs/run-.../
+   ├── tasks.jsonl
+   ├── tasks.parquet      # ← Parquet snapshot
+   ├── resources.jsonl
+   ├── resources.parquet  # ← Parquet snapshot
+   └── ...
+
+Load directly into pandas or any Parquet-compatible tool:
+
+.. code-block:: python
+
+   import pandas as pd
+   df = pd.read_parquet(".scalable/runs/run-.../tasks.parquet")
+   print(df.describe())
+
+Step 10: Telemetry Configuration
+----------------------------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 15 55
+
+   * - Variable
+     - Default
+     - Effect
+   * - ``SCALABLE_TELEMETRY``
+     - ``1``
+     - Set to ``0`` to disable all telemetry recording.
+   * - ``SCALABLE_TELEMETRY_PARQUET``
+     - ``0``
+     - Set to ``1`` to emit Parquet snapshots at run close.
+   * - ``SCALABLE_RUNS_DIR``
+     - ``.scalable/runs``
+     - Base directory for run telemetry.
+
+**When to disable telemetry:** Unit tests, benchmarking micro-operations, or
+environments where disk I/O is constrained. For production runs, always leave
+telemetry enabled — the overhead is negligible (<1% of total runtime) and the
+data is invaluable for debugging.
+
+Troubleshooting
+---------------
+
+**No telemetry data after a run**
+  Ensure you are using the Session API (``ScalableSession``) or the
+  ``scalable run`` CLI. The legacy imperative API (``SlurmCluster`` directly)
+  does not automatically record telemetry unless you manually configure a
+  ``TelemetryStore``.
+
+**"FileNotFoundError: .scalable/runs"**
+  The runs directory is created automatically on first run. If you're querying
+  before any run has completed, the directory won't exist yet.
+
+**Parquet files not generated**
+  Set ``SCALABLE_TELEMETRY_PARQUET=1`` *before* starting the session. The
+  setting is read at session creation time.
+
+**Report shows "0 tasks" but workflow completed**
+  The telemetry store must be active when tasks are submitted. If you create
+  a ``ScalableClient`` outside a session (e.g., connecting to an existing
+  cluster), telemetry won't be recorded unless explicitly configured.
+
+Next Steps
+----------
+
+* :ref:`tutorial_error_handling` — Use failure events to diagnose and recover
+  from errors.
+* :ref:`tutorial_ml_advanced` — Feed telemetry history into the ML advisor for
+  predictive resource recommendations.
+* :ref:`tutorial_cloud_integration` — Monitor cloud costs through telemetry
+  cost events.
diff --git a/docs/tutorials/07_error_handling.rst b/docs/tutorials/07_error_handling.rst
new file mode 100644
index 0000000..7b556de
--- /dev/null
+++ b/docs/tutorials/07_error_handling.rst
@@ -0,0 +1,522 @@
+.. _tutorial_error_handling:
+
+======================================================
+Tutorial 7: Error Handling and Resilience Patterns
+======================================================
+
+What You Will Learn
+-------------------
+
+By the end of this tutorial you will:
+
+* Understand how Scalable propagates and records errors across distributed
+  workers.
+* Implement retry strategies for transient failures.
+* Use the telemetry failure log to diagnose root causes.
+* Handle worker crashes, timeouts, and preemption gracefully.
+* Build fault-tolerant workflows with partial-result recovery.
+* Use the AI diagnostic assistant to analyze failures.
+
+Prerequisites
+-------------
+
+* Completed :ref:`tutorial_getting_started` and :ref:`tutorial_telemetry`.
+* Scalable installed (``pip install scalable``).
+* For AI diagnosis: ``pip install scalable[ai]``.
+
+Scenario
+--------
+
+Your production pipeline runs 200 climate scenarios overnight. Some scenarios
+fail due to transient issues (network timeouts pulling data, OOM on edge-case
+inputs, worker preemption on shared HPC clusters). You need a workflow that
+tolerates partial failures, recovers what it can, and provides clear
+diagnostics for what went wrong.
+
+Step 1: Understanding Error Propagation
+-----------------------------------------
+
+When a function submitted to Scalable raises an exception, the error is:
+
+1. Captured by the Dask worker.
+2. Serialized and transmitted back to the client.
+3. Recorded in telemetry (``failures.jsonl``).
+4. Re-raised when you call ``.result()`` or ``client.gather()``.
+
+.. code-block:: python
+
+   from scalable import ScalableSession
+
+
+   def flaky_simulation(scenario_id: int) -> dict:
+       """Simulates a function that sometimes fails."""
+       if scenario_id % 7 == 0:
+           raise RuntimeError(f"OOM: scenario {scenario_id} exceeded memory limit")
+       return {"scenario": scenario_id, "result": scenario_id * 42}
+
+
+   session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+   client = session.start()
+
+   futures = [client.submit(flaky_simulation, i, tag="analysis") for i in range(20)]
+
+   # This will raise on the first failed future:
+   try:
+       results = client.gather(futures)
+   except RuntimeError as e:
+       print(f"Workflow failed: {e}")
+
+Step 2: Gathering with Error Tolerance
+----------------------------------------
+
+Instead of failing on the first error, collect results and errors separately:
+
+.. code-block:: python
+
+   from distributed import as_completed
+
+   session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+   client = session.start()
+
+   futures = [client.submit(flaky_simulation, i, tag="analysis") for i in range(20)]
+
+   succeeded = []
+   failed = []
+
+   for future in as_completed(futures):
+       try:
+           result = future.result()
+           succeeded.append(result)
+       except Exception as e:
+           failed.append({
+               "error": str(e),
+               "type": type(e).__name__,
+               "key": future.key,
+           })
+
+   print(f"Succeeded: {len(succeeded)}, Failed: {len(failed)}")
+   for f in failed:
+       print(f"  [{f['type']}] {f['error']}")
+
+   session.close()
+
+Expected output:
+
+.. code-block:: text
+
+   Succeeded: 17, Failed: 3
+     [RuntimeError] OOM: scenario 0 exceeded memory limit
+     [RuntimeError] OOM: scenario 7 exceeded memory limit
+     [RuntimeError] OOM: scenario 14 exceeded memory limit
+
+**Pattern: Partial Success.** This is the recommended approach for batch
+workflows. Gather all results, log failures, and decide whether to proceed
+with partial data or abort.
+
+Step 3: Implementing Retry Logic
+---------------------------------
+
+For transient failures (network issues, preempted workers), retries often
+succeed. Implement exponential backoff:
+
+.. code-block:: python
+
+   import time
+   from distributed import as_completed
+
+
+   def submit_with_retry(client, func, *args, tag, max_retries=3, backoff=2.0):
+       """Submit a function with exponential backoff retry."""
+       last_error = None
+
+       for attempt in range(max_retries + 1):
+           future = client.submit(func, *args, tag=tag)
+           try:
+               return future.result(timeout=300)  # 5-minute timeout
+           except Exception as e:
+               last_error = e
+               if attempt < max_retries:
+                   wait = backoff ** attempt
+                   print(f"  Attempt {attempt + 1} failed: {e}. Retrying in {wait}s...")
+                   time.sleep(wait)
+               else:
+                   raise last_error
+
+
+   # Usage
+   session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+   client = session.start()
+
+   results = []
+   permanent_failures = []
+
+   for scenario_id in range(20):
+       try:
+           result = submit_with_retry(
+               client, flaky_simulation, scenario_id,
+               tag="analysis", max_retries=3
+           )
+           results.append(result)
+       except Exception as e:
+           permanent_failures.append({"scenario": scenario_id, "error": str(e)})
+
+   print(f"Completed: {len(results)}, Permanent failures: {len(permanent_failures)}")
+   session.close()
+
+**When to retry vs. fail fast:**
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 35 35
+
+   * - Failure Type
+     - Strategy
+     - Rationale
+   * - Network timeout
+     - Retry (3x, exponential)
+     - Transient; usually resolves
+   * - OOM (out of memory)
+     - Fail fast or retry with more resources
+     - Persistent; same inputs will fail again
+   * - Worker preemption
+     - Retry (unlimited, with backoff)
+     - External; will succeed when rescheduled
+   * - Input validation error
+     - Fail fast
+     - Bug in data; retrying won't help
+   * - Dependency import error
+     - Fail fast
+     - Container/environment issue
+
+Step 4: Timeout Management
+---------------------------
+
+Long-running tasks need timeouts to prevent runaway processes:
+
+.. code-block:: python
+
+   from concurrent.futures import TimeoutError
+
+   future = client.submit(expensive_simulation, params, tag="gcam")
+
+   try:
+       result = future.result(timeout=3600)  # 1-hour timeout
+   except TimeoutError:
+       print("Task exceeded 1-hour timeout")
+       future.cancel()
+       # Log and continue with remaining tasks
+
+For Slurm-backed workers, walltime provides a hard ceiling:
+
+.. code-block:: yaml
+
+   targets:
+     hpc:
+       provider: slurm
+       walltime: "04:00:00"   # Workers killed after 4 hours
+
+If a worker hits its walltime, Slurm terminates the process. Dask detects the
+lost worker and marks its tasks as failed with a ``KilledWorker`` exception.
+Your error-handling code should treat this as a retryable failure.
+
+Step 5: Telemetry Failure Records
+----------------------------------
+
+Every failure is recorded in ``failures.jsonl``:
+
+.. code-block:: json
+
+   {
+     "failure_class": "RuntimeError",
+     "message": "OOM: scenario 7 exceeded memory limit",
+     "timestamp": "2026-05-20T04:15:30Z",
+     "details": {
+       "phase": "task_execution",
+       "task_id": "run_gcam-7",
+       "worker_id": "worker-gcam-2",
+       "traceback": "Traceback (most recent call last):\n  ..."
+     }
+   }
+
+Analyze failure patterns:
+
+.. code-block:: python
+
+   import json
+   from pathlib import Path
+   from collections import Counter
+
+   run_dir = Path(".scalable/runs/run-20260520T.../")
+   failures = []
+   with open(run_dir / "failures.jsonl") as f:
+       for line in f:
+           failures.append(json.loads(line))
+
+   # Group by failure class
+   by_class = Counter(f["failure_class"] for f in failures)
+   print("Failures by type:")
+   for cls, count in by_class.most_common():
+       print(f"  {cls}: {count}")
+
+   # Find the most common error message pattern
+   by_message = Counter(f["message"].split(":")[0] for f in failures)
+   print("\nTop error patterns:")
+   for msg, count in by_message.most_common(5):
+       print(f"  {msg}: {count}")
+
+Expected output:
+
+.. code-block:: text
+
+   Failures by type:
+     RuntimeError: 8
+     MemoryError: 3
+     TimeoutError: 2
+
+   Top error patterns:
+     OOM: 8
+     MemoryError: 3
+     TimeoutError: 2
+
+Step 6: AI-Assisted Diagnosis
+-------------------------------
+
+When failures are complex, the AI diagnostic assistant (``scalable[ai]``)
+analyzes telemetry and provides human-readable explanations:
+
+.. code-block:: bash
+
+   scalable diagnose --latest --no-ai
+
+.. code-block:: text
+
+   Diagnosis for run-20260520T...-climate-pipeline-a1b2c3d4:
+
+   ⚠ 13 failures detected across 3 categories:
+
+   1. RuntimeError (OOM) — 8 occurrences
+      Pattern: Scenarios with large input datasets (>2GB) exceed the 16G
+      memory allocation for gcam workers.
+      Suggestion: Increase component memory to 32G or chunk large inputs.
+
+   2. MemoryError — 3 occurrences
+      Pattern: Worker process exhausted system memory during pandas concat.
+      Suggestion: Use chunked processing or increase max_workers to spread load.
+
+   3. TimeoutError — 2 occurrences
+      Pattern: Network calls to external data API timed out after 300s.
+      Suggestion: Increase timeout or add retry logic for external calls.
+
+Programmatic API:
+
+.. code-block:: python
+
+   from scalable.ai import diagnose_run
+
+   result = diagnose_run(
+       run_dir=".scalable/runs/run-20260520T.../",
+       no_ai=True,  # Use heuristic analysis (no LLM required)
+   )
+
+   print(result.summary)
+   for finding in result.findings:
+       print(f"  [{finding.severity}] {finding.category}: {finding.suggestion}")
+
+Step 7: Graceful Session Shutdown
+----------------------------------
+
+Proper shutdown ensures telemetry is finalized even when errors occur:
+
+.. code-block:: python
+
+   from scalable import ScalableSession
+
+   session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+
+   try:
+       client = session.start()
+
+       futures = [client.submit(process, i, tag="analysis") for i in range(100)]
+
+       results = []
+       for future in as_completed(futures):
+           try:
+               results.append(future.result())
+           except Exception as e:
+               print(f"Task failed: {e}")
+
+   except Exception as e:
+       print(f"Fatal error: {e}")
+   finally:
+       # ALWAYS close the session — this finalizes telemetry
+       session.close()
+
+The ``session.close()`` method:
+
+1. Shuts down the Dask client.
+2. Records the final run status (``completed`` or ``failed``).
+3. Writes summary statistics to ``run.json``.
+4. Generates Parquet snapshots if enabled.
+5. Resets the telemetry context.
+
+**If you skip ``session.close()``:** Telemetry files remain valid (JSONL is
+append-safe) but the run status stays ``running`` and summary stats won't be
+computed.
+
+Step 8: Fault-Tolerant Pipeline Pattern
+-----------------------------------------
+
+For production pipelines, combine all resilience patterns:
+
+.. code-block:: python
+
+   """Fault-tolerant pipeline with retry, partial success, and diagnostics."""
+
+   from scalable import ScalableSession, cacheable
+   from distributed import as_completed
+   import time
+
+
+   @cacheable(return_type=dict, scenario_id=int)
+   def run_scenario(scenario_id: int) -> dict:
+       """Cached computation — won't re-run on retry if previously succeeded."""
+       # ... expensive computation ...
+       return {"scenario": scenario_id, "result": scenario_id * 42}
+
+
+   def run_pipeline():
+       session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+
+       try:
+           client = session.start()
+           scenarios = list(range(200))
+
+           # Phase 1: Submit all with retry
+           succeeded = {}
+           failed = {}
+           retry_queue = [(s, 0) for s in scenarios]  # (scenario, attempt)
+
+           while retry_queue:
+               batch = retry_queue[:50]  # Process in batches of 50
+               retry_queue = retry_queue[50:]
+
+               futures = {
+                   client.submit(run_scenario, s, tag="analysis"): (s, attempt)
+                   for s, attempt in batch
+               }
+
+               for future in as_completed(futures):
+                   scenario_id, attempt = futures[future]
+                   try:
+                       result = future.result(timeout=600)
+                       succeeded[scenario_id] = result
+                   except Exception as e:
+                       if attempt < 3:
+                           # Retry with backoff
+                           time.sleep(2 ** attempt)
+                           retry_queue.append((scenario_id, attempt + 1))
+                       else:
+                           failed[scenario_id] = str(e)
+
+           # Phase 2: Report results
+           print(f"Pipeline complete: {len(succeeded)} succeeded, {len(failed)} failed")
+
+           if failed:
+               print("Permanent failures:")
+               for s, err in sorted(failed.items()):
+                   print(f"  Scenario {s}: {err}")
+
+           return succeeded
+
+       finally:
+           session.close()
+
+
+   if __name__ == "__main__":
+       results = run_pipeline()
+
+Step 9: Worker Health Monitoring
+---------------------------------
+
+Detect and respond to unhealthy workers:
+
+.. code-block:: python
+
+   # Check worker status during long-running workflows
+   info = client.scheduler_info()
+   workers = info.get("workers", {})
+
+   for addr, worker_info in workers.items():
+       memory_used = worker_info.get("metrics", {}).get("memory", 0)
+       memory_limit = worker_info.get("memory_limit", 1)
+       utilization = memory_used / memory_limit
+
+       if utilization > 0.9:
+           print(f"WARNING: Worker {addr} at {utilization*100:.0f}% memory")
+           # Consider scaling up or migrating tasks
+
+Step 10: Post-Failure Recovery
+-------------------------------
+
+After a failed run, use caching and telemetry to resume efficiently:
+
+.. code-block:: python
+
+   """Resume a pipeline from where it left off."""
+   import json
+   from pathlib import Path
+
+   # Find what succeeded in the previous run
+   prev_run = Path(".scalable/runs/run-20260519T.../")
+   prev_tasks = []
+   with open(prev_run / "tasks.jsonl") as f:
+       for line in f:
+           prev_tasks.append(json.loads(line))
+
+   completed_scenarios = {
+       t["task_id"] for t in prev_tasks if t.get("state") == "succeeded"
+   }
+
+   # Only run what failed or wasn't attempted
+   all_scenarios = set(range(200))
+   remaining = all_scenarios - completed_scenarios
+   print(f"Resuming: {len(remaining)} scenarios remaining (skipping {len(completed_scenarios)} cached)")
+
+   # The @cacheable decorator handles this automatically — even without
+   # explicit resume logic, cached scenarios will return instantly.
+   # This pattern is useful when you want explicit control.
+
+Troubleshooting
+---------------
+
+**"KilledWorker" exception but task should have succeeded**
+  The Slurm job hit its walltime or was preempted. Increase ``walltime`` in
+  the target or reduce per-task computation time by splitting into smaller
+  chunks.
+
+**Retry logic causes duplicate computation**
+  If using ``@cacheable``, retried tasks automatically hit the cache (they
+  won't recompute). Without caching, retries execute the function again. For
+  idempotent functions this is safe; for functions with side effects, add
+  deduplication logic.
+
+**"Cannot serialize" errors on exception propagation**
+  Some custom exception classes aren't serializable. Dask workers must
+  serialize exceptions to send them back to the client. Keep exception
+  classes simple (inherit from built-in exceptions, avoid unpicklable
+  attributes).
+
+**Session status shows "running" after crash**
+  If the process crashes before ``session.close()`` runs, the run status stays
+  ``running``. The telemetry data is still valid — inspect it manually or run
+  ``scalable diagnose --run-id <id>`` to analyze.
+
+Next Steps
+----------
+
+* :ref:`tutorial_kubernetes` — Handle pod evictions and node failures in
+  Kubernetes deployments.
+* :ref:`tutorial_caching` — Use caching to make retries free after partial
+  completion.
+* :ref:`tutorial_ml_advanced` — Let ML-driven advising predict and prevent
+  resource-related failures.
diff --git a/docs/tutorials/08_kubernetes.rst b/docs/tutorials/08_kubernetes.rst
new file mode 100644
index 0000000..6694296
--- /dev/null
+++ b/docs/tutorials/08_kubernetes.rst
@@ -0,0 +1,468 @@
+.. _tutorial_kubernetes:
+
+======================================================
+Tutorial 8: Deployment Workflows with Kubernetes
+======================================================
+
+What You Will Learn
+-------------------
+
+By the end of this tutorial you will:
+
+* Deploy Scalable workflows on Kubernetes using the Dask Kubernetes Operator.
+* Configure namespace isolation, resource quotas, and pod specifications.
+* Use the Kubernetes provider with adaptive scaling.
+* Manage container images and pull secrets.
+* Combine Kubernetes with overlays for multi-environment deployments.
+* Handle pod evictions and node failures.
+
+Prerequisites
+-------------
+
+* Completed :ref:`tutorial_getting_started`, :ref:`tutorial_manifest_system`,
+  and :ref:`tutorial_scaling_strategies`.
+* ``pip install scalable[kubernetes]`` (installs ``dask-kubernetes``,
+  ``kubernetes``).
+* Access to a Kubernetes cluster (local ``minikube``/``kind`` for development,
+  or a managed cluster like GKE/EKS/AKS for production).
+* ``kubectl`` configured with cluster access.
+
+Scenario
+--------
+
+Your organization runs a shared Kubernetes cluster for all scientific
+workloads. You need to deploy the climate pipeline as a Dask cluster within
+your team's namespace, with resource quotas enforced by platform engineering.
+The deployment must support both development (small, fast iterations) and
+production (large-scale, fault-tolerant) modes.
+
+Step 1: Install the Dask Kubernetes Operator
+---------------------------------------------
+
+The Dask Kubernetes Operator manages DaskCluster custom resources in your
+cluster:
+
+.. code-block:: bash
+
+   # Install the operator (cluster-admin required, one-time setup)
+   helm repo add dask https://helm.dask.org
+   helm repo update
+   helm install dask-operator dask/dask-kubernetes-operator \
+     --namespace dask-operator --create-namespace
+
+Verify the operator is running:
+
+.. code-block:: bash
+
+   kubectl get pods -n dask-operator
+
+.. code-block:: text
+
+   NAME                             READY   STATUS    RESTARTS   AGE
+   dask-operator-7f8b6d5c4-x2j9k   1/1     Running   0          2m
+
+Step 2: Configure the Kubernetes Target
+-----------------------------------------
+
+.. code-block:: yaml
+
+   # scalable.yaml
+   version: 1
+   project:
+     name: climate-pipeline-k8s
+     default_storage: gs://${GCS_BUCKET}/scalable-runs/
+
+   targets:
+     k8s-dev:
+       provider: kubernetes
+       namespace: climate-dev
+       image: gcr.io/${GCP_PROJECT}/climate-model:${IMAGE_TAG:-latest}
+       adaptive:
+         minimum: 1
+         maximum: 5
+       overlay: k8s-dev-resources
+
+     k8s-prod:
+       provider: kubernetes
+       namespace: climate-prod
+       image: gcr.io/${GCP_PROJECT}/climate-model:${IMAGE_TAG}
+       adaptive:
+         minimum: 4
+         maximum: 40
+       overlay: k8s-prod-resources
+
+   components:
+     gcam:
+       image: gcr.io/${GCP_PROJECT}/gcam:7.0
+       cpus: 8
+       memory: 32G
+       tags: [iam, climate]
+       env:
+         GCAM_DATA: /data/gcam
+
+     postprocess:
+       image: gcr.io/${GCP_PROJECT}/postprocess:latest
+       cpus: 4
+       memory: 16G
+       tags: [analysis]
+
+   tasks:
+     run_gcam:
+       component: gcam
+       cache: true
+       outputs:
+         database: dir
+
+     aggregate:
+       component: postprocess
+       cache: true
+
+   overlays:
+     k8s-dev-resources:
+       components:
+         gcam:
+           cpus: 2
+           memory: 8G
+         postprocess:
+           cpus: 1
+           memory: 4G
+
+     k8s-prod-resources:
+       components:
+         gcam:
+           cpus: 16
+           memory: 64G
+         postprocess:
+           cpus: 8
+           memory: 32G
+
+Step 3: Namespace Setup
+------------------------
+
+Create isolated namespaces for development and production:
+
+.. code-block:: bash
+
+   # Development namespace
+   kubectl create namespace climate-dev
+   kubectl label namespace climate-dev team=climate env=dev
+
+   # Production namespace
+   kubectl create namespace climate-prod
+   kubectl label namespace climate-prod team=climate env=prod
+
+Apply resource quotas to prevent runaway usage:
+
+.. code-block:: yaml
+
+   # resource-quota.yaml
+   apiVersion: v1
+   kind: ResourceQuota
+   metadata:
+     name: climate-pipeline-quota
+     namespace: climate-prod
+   spec:
+     hard:
+       requests.cpu: "160"
+       requests.memory: "640Gi"
+       limits.cpu: "200"
+       limits.memory: "800Gi"
+       pods: "50"
+
+.. code-block:: bash
+
+   kubectl apply -f resource-quota.yaml
+
+Step 4: Image Pull Secrets
+---------------------------
+
+If your container registry requires authentication:
+
+.. code-block:: bash
+
+   # For GCR (Google Container Registry)
+   kubectl create secret docker-registry gcr-secret \
+     --docker-server=gcr.io \
+     --docker-username=_json_key \
+     --docker-password="$(cat service-account-key.json)" \
+     --namespace climate-prod
+
+   # For ECR (AWS Elastic Container Registry)
+   kubectl create secret docker-registry ecr-secret \
+     --docker-server=123456789.dkr.ecr.us-east-1.amazonaws.com \
+     --docker-username=AWS \
+     --docker-password="$(aws ecr get-login-password)" \
+     --namespace climate-prod
+
+The Kubernetes provider automatically attaches these secrets to worker pods
+when the image URI matches the registry.
+
+Step 5: Run a Development Workflow
+-----------------------------------
+
+.. code-block:: bash
+
+   export GCP_PROJECT=my-gcp-project
+   export GCS_BUCKET=climate-artifacts
+   export IMAGE_TAG=dev-$(git rev-parse --short HEAD)
+
+   # Validate
+   scalable validate ./scalable.yaml
+
+   # Plan (shows pod resource requests)
+   scalable plan ./scalable.yaml --target k8s-dev --dry-run
+
+.. code-block:: text
+
+   Plan created for target 'k8s-dev' (provider: kubernetes)
+   Namespace: climate-dev
+   Workers:
+     gcam: 2 pods (2 cpu, 8G memory)
+     postprocess: 1 pod (1 cpu, 4G memory)
+   Adaptive: min=1, max=5
+
+Run the workflow:
+
+.. code-block:: python
+
+   from scalable import ScalableSession
+
+   session = ScalableSession.from_yaml("./scalable.yaml", target="k8s-dev")
+   plan = session.plan(dry_run=True)
+   client = session.start(plan)
+
+   # Submit tasks — they run in Kubernetes pods
+   futures = [client.submit(run_gcam, s, tag="gcam") for s in range(5)]
+   results = client.gather(futures)
+
+   session.close()
+
+**What happens under the hood:**
+
+1. The :class:`~scalable.providers.kubernetes.KubernetesProvider` creates a
+   ``DaskCluster`` custom resource in the ``climate-dev`` namespace.
+2. The Dask Kubernetes Operator provisions scheduler and worker pods.
+3. Worker pods are labeled with component tags for affinity scheduling.
+4. The adaptive scaler monitors task backlog and scales pods up/down within
+   the configured bounds.
+5. On ``session.close()``, the ``DaskCluster`` resource is deleted, cleaning
+   up all pods.
+
+Step 6: Monitor Pods and Scaling
+---------------------------------
+
+Watch Kubernetes events in real-time:
+
+.. code-block:: bash
+
+   # Watch pods in the namespace
+   kubectl get pods -n climate-dev -w
+
+.. code-block:: text
+
+   NAME                                READY   STATUS    RESTARTS   AGE
+   dask-scheduler-climate-dev-0        1/1     Running   0          30s
+   dask-worker-gcam-0                  1/1     Running   0          25s
+   dask-worker-gcam-1                  1/1     Running   0          25s
+   dask-worker-postprocess-0           1/1     Running   0          25s
+
+   # Scale-up event
+   dask-worker-gcam-2                  0/1     Pending   0          0s
+   dask-worker-gcam-2                  1/1     Running   0          15s
+
+Check the Dask dashboard (port-forward the scheduler):
+
+.. code-block:: bash
+
+   kubectl port-forward -n climate-dev svc/dask-scheduler-climate-dev 8787:8787
+   # Open http://localhost:8787 in your browser
+
+Step 7: Production Deployment
+------------------------------
+
+For production, ensure high availability and fault tolerance:
+
+.. code-block:: bash
+
+   export IMAGE_TAG=v2.1.0  # Pinned release tag
+   scalable run ./scalable.yaml --target k8s-prod --workflow pipeline.py
+
+Production considerations:
+
+**Pod disruption budgets** — Prevent too many workers from being evicted
+simultaneously:
+
+.. code-block:: yaml
+
+   # pdb.yaml
+   apiVersion: policy/v1
+   kind: PodDisruptionBudget
+   metadata:
+     name: dask-workers-pdb
+     namespace: climate-prod
+   spec:
+     minAvailable: "50%"
+     selector:
+       matchLabels:
+         app: dask-worker
+
+.. code-block:: bash
+
+   kubectl apply -f pdb.yaml
+
+**Priority classes** — Ensure your workload gets scheduled before lower-priority
+jobs:
+
+.. code-block:: yaml
+
+   apiVersion: scheduling.k8s.io/v1
+   kind: PriorityClass
+   metadata:
+     name: climate-production
+   value: 1000
+   globalDefault: false
+   description: "Priority for production climate pipeline runs"
+
+Step 8: Handling Pod Evictions
+-------------------------------
+
+Kubernetes may evict pods due to resource pressure or node maintenance.
+Scalable's error handling (see :ref:`tutorial_error_handling`) catches these
+as ``KilledWorker`` exceptions:
+
+.. code-block:: python
+
+   from distributed import as_completed
+
+   session = ScalableSession.from_yaml("./scalable.yaml", target="k8s-prod")
+   client = session.start()
+
+   futures = [client.submit(run_gcam, s, tag="gcam") for s in range(200)]
+
+   results = []
+   retry_queue = []
+
+   for future in as_completed(futures):
+       try:
+           results.append(future.result())
+       except Exception as e:
+           if "KilledWorker" in str(type(e).__name__):
+               # Pod was evicted — retry
+               scenario_id = future.key.split("-")[-1]
+               retry_queue.append(scenario_id)
+           else:
+               print(f"Permanent failure: {e}")
+
+   # Retry evicted tasks
+   if retry_queue:
+       print(f"Retrying {len(retry_queue)} evicted tasks...")
+       retry_futures = [
+           client.submit(run_gcam, s, tag="gcam") for s in retry_queue
+       ]
+       retry_results = client.gather(retry_futures)
+       results.extend(retry_results)
+
+   session.close()
+
+Step 9: CI/CD Integration
+---------------------------
+
+Automate Kubernetes deployments from your CI pipeline:
+
+.. code-block:: yaml
+
+   # .github/workflows/scalable-prod.yaml
+   name: Production Pipeline Run
+   on:
+     workflow_dispatch:
+       inputs:
+         scenarios:
+           description: "Number of scenarios"
+           default: "200"
+
+   jobs:
+     run:
+       runs-on: ubuntu-latest
+       steps:
+         - uses: actions/checkout@v4
+
+         - uses: google-github-actions/auth@v2
+           with:
+             credentials_json: ${{ secrets.GCP_SA_KEY }}
+
+         - uses: google-github-actions/get-gke-credentials@v2
+           with:
+             cluster_name: climate-cluster
+             location: us-central1
+
+         - name: Install Scalable
+           run: pip install scalable[kubernetes,cloud]
+
+         - name: Run Pipeline
+           env:
+             GCP_PROJECT: ${{ vars.GCP_PROJECT }}
+             GCS_BUCKET: ${{ vars.GCS_BUCKET }}
+             IMAGE_TAG: ${{ github.sha }}
+             SCALABLE_TARGET: k8s-prod
+           run: |
+             scalable validate ./scalable.yaml
+             scalable run ./scalable.yaml --target k8s-prod --workflow pipeline.py
+
+Step 10: Local Development with minikube
+------------------------------------------
+
+For local Kubernetes development without a cloud cluster:
+
+.. code-block:: bash
+
+   # Start minikube
+   minikube start --cpus=4 --memory=8192
+
+   # Install Dask operator
+   helm install dask-operator dask/dask-kubernetes-operator
+
+   # Build and load image locally
+   docker build -t climate-model:local .
+   minikube image load climate-model:local
+
+   # Use local image in manifest
+   export IMAGE_TAG=local
+   scalable run ./scalable.yaml --target k8s-dev --workflow workflow.py
+
+This gives you a realistic Kubernetes environment for testing pod scheduling,
+resource limits, and failure modes before deploying to production.
+
+Troubleshooting
+---------------
+
+**Pods stuck in "Pending" state**
+  Check resource availability: ``kubectl describe pod <pod-name> -n <ns>``.
+  Common causes: insufficient cluster capacity, resource quota exceeded, or
+  node selector constraints not met.
+
+**"ImagePullBackOff" error**
+  The image URI is wrong or the pull secret is missing/expired. Verify:
+  ``kubectl get secret -n <ns>`` and check image URI spelling.
+
+**Workers fail to connect to scheduler**
+  Ensure network policies allow pod-to-pod communication within the namespace.
+  The scheduler service must be reachable on port 8786.
+
+**Adaptive scaling not working**
+  Verify the Dask Kubernetes Operator is running and the ``DaskCluster``
+  resource has ``adaptive`` section configured. Check operator logs:
+  ``kubectl logs -n dask-operator deployment/dask-operator``.
+
+**Resource quota prevents scaling**
+  If ``adaptive.maximum`` exceeds what the quota allows, pods will stay
+  pending. Set maximum to a value within your quota limits.
+
+Next Steps
+----------
+
+* :ref:`tutorial_ml_advanced` — Use ML predictions to pre-size Kubernetes pods
+  based on historical resource usage.
+* :ref:`tutorial_error_handling` — Build resilient pipelines that handle pod
+  evictions gracefully.
+* :ref:`tutorial_ai_composition` — Auto-generate Kubernetes manifests from
+  natural language workflow descriptions.
diff --git a/docs/tutorials/09_ml_emulation.rst b/docs/tutorials/09_ml_emulation.rst
new file mode 100644
index 0000000..2add304
--- /dev/null
+++ b/docs/tutorials/09_ml_emulation.rst
@@ -0,0 +1,544 @@
+.. _tutorial_ml_advanced:
+
+======================================================
+Tutorial 9: ML-Driven Scaling and Model Emulation
+======================================================
+
+What You Will Learn
+-------------------
+
+By the end of this tutorial you will:
+
+* Train and use the LearnedAdvisor for ML-backed resource predictions.
+* Configure the AdaptiveScaler with ML-informed decisions.
+* Mark functions as emulatable with the ``@emulatable`` decorator.
+* Build, register, and dispatch surrogate models.
+* Implement confidence-gated routing between emulators and full models.
+* Use active learning to improve emulator accuracy over time.
+
+Prerequisites
+-------------
+
+* Completed :ref:`tutorial_getting_started`, :ref:`tutorial_telemetry`, and
+  :ref:`tutorial_scaling_strategies`.
+* ``pip install scalable[ml]`` (installs ``scikit-learn``, ``dask-ml``,
+  ``joblib``).
+* At least 5 completed telemetry runs (more history → better predictions).
+
+Scenario
+--------
+
+Your pipeline has been running for weeks, accumulating telemetry data. You want
+to leverage this history to (1) automatically predict optimal resource
+allocations for new runs, and (2) replace expensive model invocations with
+fast surrogate models when confidence is high. Both features reduce cost and
+time without sacrificing accuracy.
+
+Part A: ML-Driven Resource Advising
+=====================================
+
+Step 1: The ResourceAdvisor (Baseline)
+---------------------------------------
+
+Before ML, Scalable provides a deterministic, quantile-based advisor:
+
+.. code-block:: python
+
+   from scalable import ResourceAdvisor
+
+   advisor = ResourceAdvisor.from_history("./.scalable/runs")
+   recommendation = advisor.recommend(
+       task="run_gcam",
+       target="local",
+       confidence=0.95,
+   )
+
+   print(f"Recommended workers: {recommendation.workers}")
+   print(f"Resources: {recommendation.resources}")
+   print(f"Confidence: {recommendation.confidence}")
+   print(f"Evidence: {recommendation.evidence}")
+
+Expected output:
+
+.. code-block:: text
+
+   Recommended workers: {'gcam': 4}
+   Resources: {'gcam': {'cpus': 8, 'memory': '32G', 'walltime': '02:30:00'}}
+   Confidence: 0.95
+   Evidence: {'runs_analyzed': 12, 'method': 'quantile', 'percentile': 95}
+
+The deterministic advisor uses simple quantile statistics (P95 of historical
+duration and resource usage). It's reliable but doesn't adapt to input
+characteristics — it treats all invocations of ``run_gcam`` identically.
+
+Step 2: The LearnedAdvisor (ML-Enhanced)
+-----------------------------------------
+
+The :class:`~scalable.ml.learned_advisor.LearnedAdvisor` trains a machine
+learning model on your telemetry to predict resource requirements based on
+task features:
+
+.. code-block:: python
+
+   from scalable import LearnedAdvisor
+
+   # Train from telemetry history
+   advisor = LearnedAdvisor.from_history(
+       "./.scalable/runs",
+       model_type="gradient_boosting",  # or "random_forest", "linear"
+   )
+
+   # Predict resources for a specific task with input features
+   recommendation = advisor.recommend(
+       task="run_gcam",
+       target="hpc",
+       features={
+           "num_scenarios": 50,
+           "input_size_mb": 2048,
+           "time_horizon": 2100,
+       },
+   )
+
+   print(f"Predicted workers: {recommendation.workers}")
+   print(f"Predicted resources: {recommendation.resources}")
+   print(f"Model confidence: {recommendation.confidence:.2f}")
+
+Expected output:
+
+.. code-block:: text
+
+   Predicted workers: {'gcam': 8}
+   Predicted resources: {'gcam': {'cpus': 16, 'memory': '48G', 'walltime': '03:15:00'}}
+   Model confidence: 0.87
+
+**How it works:**
+
+1. The advisor scans telemetry run directories for completed tasks.
+2. It extracts features: task name, input sizes, component resources, target
+   type, historical duration, peak memory.
+3. A gradient boosting model (or random forest) is trained to predict optimal
+   resource allocation given input features.
+4. Predictions include confidence intervals — low confidence triggers
+   fallback to the deterministic advisor.
+
+Step 3: Model Types and Trade-Offs
+------------------------------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 20 30 25 25
+
+   * - Model Type
+     - Accuracy
+     - Training Speed
+     - When to Use
+   * - ``linear``
+     - Low
+     - Fast (<1s)
+     - Few runs, simple patterns
+   * - ``random_forest``
+     - Medium
+     - Moderate (5–30s)
+     - Moderate history, non-linear patterns
+   * - ``gradient_boosting``
+     - High
+     - Slow (30–120s)
+     - Rich history (50+ runs), complex patterns
+
+Choose via CLI:
+
+.. code-block:: bash
+
+   # Use the ML advisor from CLI
+   scalable advise --task run_gcam --model-type gradient_boosting --format json
+
+.. code-block:: json
+
+   {
+     "task": "run_gcam",
+     "workers": {"gcam": 8},
+     "resources": {"gcam": {"cpus": 16, "memory": "48G", "walltime": "03:15:00"}},
+     "confidence": 0.87,
+     "model_type": "gradient_boosting"
+   }
+
+Step 4: AdaptiveScaler with ML Predictions
+--------------------------------------------
+
+Combine the LearnedAdvisor with real-time scaling:
+
+.. code-block:: python
+
+   from scalable import AdaptiveScaler, LearnedAdvisor, ScalableSession
+
+   # Train advisor
+   advisor = LearnedAdvisor.from_history("./.scalable/runs", model_type="gradient_boosting")
+
+   # Create adaptive scaler backed by ML predictions
+   scaler = AdaptiveScaler(
+       advisor=advisor,
+       min_workers={"gcam": 2, "postprocess": 1},
+       max_workers={"gcam": 30, "postprocess": 10},
+       scale_up_threshold=0.7,
+       scale_down_threshold=0.3,
+       cooldown_seconds=90,
+   )
+
+   session = ScalableSession.from_yaml("./scalable.yaml", target="aws")
+   client = session.start()
+
+   # Submit work in batches and let the scaler decide
+   for batch in scenario_batches:
+       futures = [client.submit(run_gcam, s, tag="gcam") for s in batch]
+
+       decision = scaler.evaluate(
+           pending_tasks=[{"tag": "gcam", "features": {"input_size_mb": s.size}} for s in batch],
+           active_workers={"gcam": 10},
+           recent_completions=[{"tag": "gcam", "duration_s": 180}],
+       )
+
+       if decision.has_changes:
+           print(f"ML-informed scaling: {decision.reasoning}")
+           print(f"  Confidence: {decision.confidence:.2f}")
+           print(f"  Predicted completion: {decision.predicted_completion_time:.0f}s")
+
+   session.close()
+
+The ML-backed scaler considers:
+
+* Current queue depth and worker utilization.
+* Predicted task duration from the learned model.
+* Historical scaling patterns (what worked before).
+* Cost constraints (from the ``max_workers`` ceiling).
+
+Step 5: Hyperparameter Tuning
+------------------------------
+
+For optimal predictions, tune the ML model:
+
+.. code-block:: python
+
+   from scalable.ml import HyperparameterSearch
+
+   search = HyperparameterSearch(
+       runs_dir="./.scalable/runs",
+       model_type="gradient_boosting",
+       cv_folds=5,
+   )
+
+   best_params = search.run()
+   print(f"Best parameters: {best_params}")
+   print(f"Cross-validation score: {search.best_score:.3f}")
+
+   # Use best parameters
+   advisor = LearnedAdvisor.from_history(
+       "./.scalable/runs",
+       model_type="gradient_boosting",
+       model_params=best_params,
+   )
+
+Part B: Model Emulation
+=========================
+
+Step 6: The @emulatable Decorator
+-----------------------------------
+
+The :func:`~scalable.emulation.decorator.emulatable` decorator marks expensive
+functions as candidates for surrogate model replacement:
+
+.. code-block:: python
+
+   from scalable import emulatable
+
+
+   @emulatable(
+       tag="gcam",
+       inputs=["carbon_price", "population", "gdp"],
+       outputs=["emissions", "energy_price"],
+       uncertainty="required",
+       fallback="full_model",
+       domain={
+           "carbon_price": (0, 500),
+           "population": (7e9, 12e9),
+           "gdp": (50e12, 200e12),
+       },
+       confidence_threshold=0.9,
+   )
+   def run_gcam_scenario(carbon_price, population, gdp):
+       """Run a full GCAM scenario — takes 30+ minutes."""
+       # ... expensive climate model execution ...
+       return {"emissions": 35.2, "energy_price": 0.12}
+
+Decorator parameters:
+
+``tag``
+  Component tag for worker routing when falling back to the full model.
+
+``inputs``
+  Named input parameters the emulator expects. Order matters for training data.
+
+``outputs``
+  Named output values the emulator produces.
+
+``uncertainty``
+  * ``"required"`` — Emulator must provide calibrated uncertainty bounds.
+    Predictions without bounds are rejected.
+  * ``"optional"`` — Point estimates are accepted.
+  * ``"none"`` — No uncertainty checking.
+
+``fallback``
+  Strategy when the emulator is unavailable or confidence is low:
+  * ``"full_model"`` — Execute the original function.
+  * ``"error"`` — Raise an exception.
+  * ``"cached"`` — Try the disk cache.
+
+``domain``
+  Input validation bounds. Predictions outside the domain always fall back to
+  the full model (extrapolation is unreliable).
+
+``confidence_threshold``
+  Minimum emulator confidence for accepting a prediction.
+
+Step 7: Training an Emulator
+------------------------------
+
+Collect training data by running the full model on a design-of-experiments
+grid, then train a surrogate:
+
+.. code-block:: python
+
+   from scalable.emulation import EmulatorRegistry
+   import numpy as np
+
+   # Generate training data (Latin Hypercube or similar)
+   np.random.seed(42)
+   training_inputs = {
+       "carbon_price": np.random.uniform(0, 500, size=100),
+       "population": np.random.uniform(7e9, 12e9, size=100),
+       "gdp": np.random.uniform(50e12, 200e12, size=100),
+   }
+
+   # Run the full model for each sample (expensive!)
+   training_outputs = []
+   for i in range(100):
+       result = run_gcam_scenario(
+           carbon_price=training_inputs["carbon_price"][i],
+           population=training_inputs["population"][i],
+           gdp=training_inputs["gdp"][i],
+       )
+       training_outputs.append(result)
+
+   # Register the trained emulator
+   registry = EmulatorRegistry(".scalable/emulators")
+   registry.register(
+       function_name="run_gcam_scenario",
+       training_inputs=training_inputs,
+       training_outputs=training_outputs,
+       model_type="gaussian_process",  # Provides uncertainty estimates
+   )
+
+   print(f"Emulator registered: {registry.list()}")
+
+Step 8: Confidence-Gated Dispatch
+----------------------------------
+
+The :class:`~scalable.emulation.dispatch.EmulatorDispatch` routes calls between
+the emulator and full model based on confidence:
+
+.. code-block:: python
+
+   from scalable.emulation import EmulatorDispatch, EmulatorRegistry
+
+   registry = EmulatorRegistry(".scalable/emulators")
+   dispatch = EmulatorDispatch(registry, confidence_threshold=0.9)
+
+   # High-confidence prediction (within training domain)
+   result = dispatch.predict(
+       "run_gcam_scenario",
+       inputs={"carbon_price": 100, "population": 8e9, "gdp": 80e12},
+   )
+   print(f"Source: {result.source}")        # "emulator"
+   print(f"Confidence: {result.confidence:.3f}")  # 0.95
+   print(f"Prediction: {result.values}")    # {'emissions': 34.8, 'energy_price': 0.11}
+   print(f"Uncertainty: {result.uncertainty}")  # {'emissions': ±1.2, 'energy_price': ±0.02}
+
+   # Low-confidence prediction (edge of domain)
+   result = dispatch.predict(
+       "run_gcam_scenario",
+       inputs={"carbon_price": 490, "population": 11.5e9, "gdp": 190e12},
+   )
+   print(f"Source: {result.source}")        # "full_model" (fell back)
+   print(f"Confidence: {result.confidence:.3f}")  # 0.72 (below threshold)
+
+**Dispatch logic:**
+
+.. code-block:: text
+
+   ┌─────────────────┐     ┌──────────────┐
+   │  Input arrives  │────▶│ Domain check │
+   └─────────────────┘     └──────┬───────┘
+                                   │
+                          In domain?│
+                      ┌────Yes─────┼────No─────┐
+                      │            │            │
+               ┌──────▼──────┐    │    ┌───────▼───────┐
+               │  Emulator   │    │    │  Full model   │
+               │  predict    │    │    │  (fallback)   │
+               └──────┬──────┘    │    └───────────────┘
+                      │            │
+            Confidence > threshold?│
+                 ┌──Yes──┼───No────┐
+                 │       │         │
+          ┌──────▼──┐    │  ┌──────▼───────┐
+          │ Accept  │    │  │  Full model  │
+          │ result  │    │  │  (fallback)  │
+          └─────────┘    │  └──────────────┘
+                         │
+
+Step 9: Active Learning
+------------------------
+
+Improve emulator accuracy by strategically selecting which points to run with
+the full model:
+
+.. code-block:: python
+
+   from scalable.emulation import ActiveLearner
+
+   learner = ActiveLearner(
+       registry=registry,
+       function_name="run_gcam_scenario",
+       acquisition="uncertainty",  # Sample where emulator is least confident
+       batch_size=10,
+   )
+
+   # Get the next batch of points to evaluate with the full model
+   next_points = learner.suggest()
+   print(f"Suggested {len(next_points)} points for full model evaluation:")
+   for point in next_points[:3]:
+       print(f"  carbon_price={point['carbon_price']:.0f}, "
+             f"population={point['population']:.2e}, "
+             f"gdp={point['gdp']:.2e}")
+
+   # Run full model on suggested points
+   new_results = []
+   for point in next_points:
+       result = run_gcam_scenario(**point)
+       new_results.append(result)
+
+   # Update the emulator with new data
+   learner.update(next_points, new_results)
+   print(f"Emulator updated. New training size: {learner.training_size}")
+   print(f"Estimated accuracy improvement: {learner.accuracy_gain:.1%}")
+
+Active learning acquisition strategies:
+
+* ``"uncertainty"`` — Sample where prediction uncertainty is highest.
+* ``"expected_improvement"`` — Sample where model is likely wrong.
+* ``"random"`` — Uniform random (baseline comparison).
+
+Step 10: Emulation in Production Workflows
+--------------------------------------------
+
+Integrate emulation into your pipeline for massive speedups:
+
+.. code-block:: python
+
+   from scalable import ScalableSession, emulatable, cacheable
+   from scalable.emulation import EmulatorDispatch, EmulatorRegistry
+
+
+   @emulatable(
+       tag="gcam",
+       inputs=["carbon_price", "population"],
+       outputs=["emissions"],
+       uncertainty="required",
+       fallback="full_model",
+       confidence_threshold=0.9,
+   )
+   @cacheable(return_type=dict, carbon_price=float, population=float)
+   def run_scenario(carbon_price: float, population: float) -> dict:
+       """Full model — 30 min per call."""
+       # ... expensive computation ...
+       return {"emissions": carbon_price * 0.1 + population * 1e-10}
+
+
+   def run_pipeline():
+       session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+       client = session.start()
+
+       registry = EmulatorRegistry(".scalable/emulators")
+       dispatch = EmulatorDispatch(registry, confidence_threshold=0.9)
+
+       results = []
+       emulated_count = 0
+       full_model_count = 0
+
+       for cp in range(0, 500, 10):
+           for pop in [8e9, 9e9, 10e9]:
+               # Try emulator first
+               result = dispatch.predict(
+                   "run_scenario",
+                   inputs={"carbon_price": cp, "population": pop},
+               )
+
+               if result.source == "emulator":
+                   results.append(result.values)
+                   emulated_count += 1
+               else:
+                   # Fall back to full model via distributed workers
+                   fut = client.submit(run_scenario, cp, pop, tag="gcam")
+                   results.append(fut.result())
+                   full_model_count += 1
+
+       print(f"Total scenarios: {emulated_count + full_model_count}")
+       print(f"  Emulated: {emulated_count} ({emulated_count/(emulated_count+full_model_count)*100:.0f}%)")
+       print(f"  Full model: {full_model_count}")
+       print(f"  Time saved: ~{emulated_count * 30} minutes")
+
+       session.close()
+
+Expected output:
+
+.. code-block:: text
+
+   Total scenarios: 150
+     Emulated: 128 (85%)
+     Full model: 22
+     Time saved: ~3840 minutes
+
+Troubleshooting
+---------------
+
+**LearnedAdvisor predictions are poor**
+  Ensure you have sufficient telemetry history (at least 10–20 completed runs
+  with varied inputs). With fewer runs, the deterministic ``ResourceAdvisor``
+  is more reliable.
+
+**"ImportError: scikit-learn not installed"**
+  Install the ML extra: ``pip install scalable[ml]``.
+
+**Emulator confidence is always low**
+  The training domain may not cover your query inputs. Run active learning to
+  expand coverage, or check that your domain bounds in ``@emulatable`` match
+  the actual input range.
+
+**"EmulatorRegistry: no emulator registered for function"**
+  You must train and register an emulator before dispatch can use it. See
+  Step 7 for the registration process.
+
+**Active learning suggests the same points repeatedly**
+  The learner converges when uncertainty is uniformly low across the domain.
+  If it's suggesting the same points, your emulator may already be well-trained.
+  Check ``learner.mean_uncertainty`` — if it's below your threshold, no
+  further training is needed.
+
+Next Steps
+----------
+
+* :ref:`tutorial_ai_composition` — Use AI assistants to generate workflow
+  configurations that incorporate emulation.
+* :ref:`tutorial_telemetry` — Track emulator vs. full model usage in telemetry
+  for cost analysis.
+* :ref:`tutorial_cloud_integration` — Deploy emulator-backed workflows to cloud
+  for maximum cost savings.
diff --git a/docs/tutorials/10_ai_composition.rst b/docs/tutorials/10_ai_composition.rst
new file mode 100644
index 0000000..6c6ec59
--- /dev/null
+++ b/docs/tutorials/10_ai_composition.rst
@@ -0,0 +1,574 @@
+.. _tutorial_ai_composition:
+
+======================================================
+Tutorial 10: AI-Assisted Workflow Composition
+======================================================
+
+What You Will Learn
+-------------------
+
+By the end of this tutorial you will:
+
+* Use the AI assistant suite to accelerate workflow development.
+* Onboard new model components with ``scalable init-component``.
+* Diagnose run failures with ``scalable diagnose``.
+* Generate human-readable explanations of execution plans.
+* Compose new workflows from natural language descriptions.
+* Migrate manifests between providers with ``scalable migrate``.
+* Understand heuristic mode vs. LLM-enhanced mode.
+
+Prerequisites
+-------------
+
+* Completed :ref:`tutorial_getting_started` and :ref:`tutorial_manifest_system`.
+* ``pip install scalable[ai]`` (installs ``jinja2``, ``rich``).
+* For LLM-enhanced mode (optional): an API key for OpenAI or a running
+  Ollama instance.
+
+Scenario
+--------
+
+Your team is onboarding a new model (Stitches) into the climate pipeline.
+You need to configure its component definition, write task bindings, and
+eventually migrate the entire pipeline from Slurm to Kubernetes. The AI
+assistants automate tedious configuration tasks and provide expert guidance
+without requiring deep Scalable expertise.
+
+Step 1: Heuristic vs. LLM Modes
+---------------------------------
+
+All AI assistants work in two modes:
+
+**Heuristic mode (``--no-ai``, default when ``SCALABLE_AI_BACKEND=none``):**
+
+* Uses deterministic rules, templates, and pattern matching.
+* No external API calls. Works offline.
+* Fast, reproducible, and auditable.
+* Best for CI/CD and automated pipelines.
+
+**LLM-enhanced mode (``SCALABLE_AI_BACKEND=openai`` or ``ollama``):**
+
+* Augments heuristics with a language model for richer explanations and
+  more creative workflow composition.
+* Requires API credentials and network access.
+* May produce varied output across invocations.
+* Best for interactive development and exploration.
+
+Configure the backend:
+
+.. code-block:: bash
+
+   # Heuristic only (default)
+   export SCALABLE_AI_BACKEND=none
+
+   # OpenAI
+   export SCALABLE_AI_BACKEND=openai
+   export SCALABLE_AI_MODEL=gpt-4
+   export OPENAI_API_KEY=sk-...
+
+   # Ollama (local)
+   export SCALABLE_AI_BACKEND=ollama
+   export SCALABLE_AI_MODEL=llama3
+   export SCALABLE_AI_ENDPOINT=http://localhost:11434
+
+Step 2: Onboarding a New Component
+------------------------------------
+
+The ``init-component`` command analyzes a model directory and generates a
+component configuration:
+
+.. code-block:: bash
+
+   scalable init-component ./path/to/stitches --name stitches --no-ai
+
+.. code-block:: text
+
+   Analyzing ./path/to/stitches...
+   Detected:
+     Language: R (via rpy2)
+     Dependencies: stitches, dplyr, tidyr
+     Entry point: ./run_stitches.R
+     Estimated resources: 6 CPUs, 50G memory
+
+   Generated component configuration:
+
+   components:
+     stitches:
+       image: ghcr.io/jgcri/stitches:latest
+       cpus: 6
+       memory: 50G
+       tags: [climate, downscaling]
+       env:
+         R_LIBS_USER: /opt/R/library
+
+   Suggested task binding:
+
+   tasks:
+     run_stitches:
+       component: stitches
+       cache: true
+
+   Written to: ./stitches/scalable-component.yaml
+
+**What the analyzer checks:**
+
+* Language detection (Python imports, R scripts, compiled binaries).
+* Dependency scanning (``requirements.txt``, ``DESCRIPTION``, ``Makefile``).
+* Resource estimation from file sizes and known model profiles.
+* Container image inference from Dockerfiles or registry naming conventions.
+
+Python API:
+
+.. code-block:: python
+
+   from scalable.ai import onboard_component
+
+   result = onboard_component(
+       "./path/to/stitches",
+       name="stitches",
+       no_ai=True,
+   )
+
+   print(result.component_yaml)
+   print(result.task_yaml)
+   print(result.recommendations)
+
+Step 3: Diagnosing Run Failures
+---------------------------------
+
+After a failed run, use the diagnostic assistant to identify root causes:
+
+.. code-block:: bash
+
+   scalable diagnose --latest --no-ai
+
+.. code-block:: text
+
+   ═══════════════════════════════════════════════════════════
+   Diagnosis: run-20260520T041500Z-climate-pipeline-f8e2a1b3
+   ═══════════════════════════════════════════════════════════
+
+   Status: failed (13 task failures)
+
+   Root Cause Analysis:
+   ─────────────────────
+   PRIMARY: Memory exhaustion (8 of 13 failures)
+     Pattern: Tasks processing scenarios with >500 grid cells exhaust
+     the 16G memory limit during the spatial interpolation step.
+     Evidence: All OOM failures occur in run_gcam tasks with
+     input_grid_cells > 500.
+
+   SECONDARY: Network timeouts (3 of 13 failures)
+     Pattern: External data API (api.climate-data.org) returning 503
+     between 04:15-04:20 UTC.
+     Evidence: All timeout failures cluster within a 5-minute window.
+
+   TERTIARY: Serialization error (2 of 13 failures)
+     Pattern: Return value contains unpicklable threading.Lock object.
+     Evidence: TypeError in dill serialization.
+
+   Recommendations:
+   ─────────────────
+   1. Increase gcam component memory to 32G (or use overlay for
+      high-resolution scenarios).
+   2. Add retry logic with exponential backoff for external API calls.
+   3. Remove threading.Lock from return values — use result dict only.
+
+Programmatic access:
+
+.. code-block:: python
+
+   from scalable.ai import diagnose_run
+
+   result = diagnose_run(
+       run_dir=".scalable/runs/run-20260520T041500Z.../",
+       no_ai=True,
+   )
+
+   print(f"Root cause: {result.summary}")
+   for finding in result.findings:
+       print(f"  [{finding.severity}] {finding.category}")
+       print(f"    Pattern: {finding.pattern}")
+       print(f"    Suggestion: {finding.suggestion}")
+
+Step 4: Explaining Execution Plans
+------------------------------------
+
+Make execution plans understandable for non-technical stakeholders:
+
+.. code-block:: bash
+
+   # Generate a plan
+   scalable plan ./scalable.yaml --target aws --dry-run --output plan.json
+
+   # Explain it in plain language
+   scalable explain plan.json
+
+.. code-block:: text
+
+   Plan Explanation
+   ═════════════════
+
+   This plan will execute the "climate-pipeline" project on AWS (Fargate)
+   in the us-east-1 region.
+
+   What will happen:
+   1. A Dask cluster will be created with 10 workers running GCAM (4 vCPU,
+      16 GiB each) and 5 workers for post-processing (2 vCPU, 8 GiB each).
+   2. Workers auto-scale between 2 (minimum) and 20 (maximum) based on
+      task backlog.
+   3. Results will be cached to avoid recomputation on retry.
+   4. Outputs will be stored in s3://my-bucket/scalable-runs/.
+
+   Estimated cost: $4.82 (2.5 hours of Fargate compute + S3 storage)
+
+   Risks:
+   • Network connectivity between scheduler and workers depends on VPC
+     routing. Verify subnets have NAT gateway access.
+   • Fargate cold-start adds 30-90s to first task execution.
+
+Python API:
+
+.. code-block:: python
+
+   from scalable.ai import explain_plan
+
+   result = explain_plan("plan.json")
+   print(result.explanation)
+   print(result.risks)
+   print(result.cost_summary)
+
+Step 5: Composing Workflows from Natural Language
+---------------------------------------------------
+
+The most powerful AI assistant — generate complete workflow configurations
+from descriptions:
+
+.. code-block:: bash
+
+   scalable compose "Run GCAM reference scenario for SSP2, \
+     then run Stitches to downscale daily climate data, \
+     then aggregate results by region and produce summary plots"
+
+.. code-block:: text
+
+   Generated workflow:
+   ═══════════════════
+
+   # workflow.py
+   from scalable import ScalableSession, cacheable
+
+   @cacheable(return_type=dict, ssp=str)
+   def run_gcam_reference(ssp: str) -> dict:
+       """Run GCAM reference scenario for the given SSP."""
+       # TODO: Implement GCAM execution logic
+       return {"database_path": f"./output/gcam_{ssp}/"}
+
+   @cacheable(return_type=dict, gcam_output=str)
+   def run_stitches_downscale(gcam_output: str) -> dict:
+       """Downscale GCAM output to daily climate using Stitches."""
+       # TODO: Implement Stitches execution logic
+       return {"daily_climate_path": f"./output/stitches/{gcam_output}"}
+
+   @cacheable(return_type=dict, climate_path=str)
+   def aggregate_and_plot(climate_path: str) -> dict:
+       """Aggregate by region and produce summary plots."""
+       # TODO: Implement aggregation logic
+       return {"summary_path": "./output/summary/"}
+
+   def main():
+       session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+       client = session.start()
+
+       # Stage 1: GCAM
+       gcam_result = client.submit(run_gcam_reference, "SSP2", tag="gcam").result()
+
+       # Stage 2: Stitches downscaling
+       stitches_result = client.submit(
+           run_stitches_downscale,
+           gcam_result["database_path"],
+           tag="stitches",
+       ).result()
+
+       # Stage 3: Aggregation
+       final = client.submit(
+           aggregate_and_plot,
+           stitches_result["daily_climate_path"],
+           tag="postprocess",
+       ).result()
+
+       print(f"Pipeline complete: {final['summary_path']}")
+       session.close()
+
+   if __name__ == "__main__":
+       main()
+
+   ═══════════════════
+   Suggested manifest additions:
+
+   components:
+     gcam:
+       cpus: 8
+       memory: 32G
+       tags: [iam, climate]
+     stitches:
+       cpus: 6
+       memory: 50G
+       tags: [climate, downscaling]
+     postprocess:
+       cpus: 2
+       memory: 8G
+       tags: [analysis]
+
+   tasks:
+     run_gcam_reference:
+       component: gcam
+       cache: true
+     run_stitches_downscale:
+       component: stitches
+       cache: true
+     aggregate_and_plot:
+       component: postprocess
+       cache: true
+
+Python API for programmatic composition:
+
+.. code-block:: python
+
+   from scalable.ai import compose_workflow
+
+   result = compose_workflow(
+       "Run GCAM for SSP1-5, then Stitches for each, then aggregate"
+   )
+
+   print(result.workflow_code)
+   print(result.manifest_additions)
+   print(result.dependencies)
+
+Step 6: Migrating Between Providers
+-------------------------------------
+
+Move your workflow from one provider to another:
+
+.. code-block:: bash
+
+   scalable migrate scalable.yaml --to-provider kubernetes
+
+.. code-block:: text
+
+   Migration: slurm → kubernetes
+   ══════════════════════════════
+
+   Changes required:
+   1. Target 'hpc' → new target 'k8s'
+      - Remove: queue, account, walltime, interface
+      - Add: namespace, image, adaptive
+
+   2. Components need container images:
+      - gcam: needs 'image' field (suggest: gcr.io/project/gcam:7.0)
+      - postprocess: needs 'image' field (suggest: gcr.io/project/postprocess:latest)
+
+   3. Environment changes:
+      - Slurm module loads → container pre-installed
+      - File system mounts → PVC or GCS bucket
+
+   Generated manifest:
+
+   targets:
+     k8s:
+       provider: kubernetes
+       namespace: climate-prod
+       image: gcr.io/my-project/climate-model:latest
+       adaptive:
+         minimum: 2
+         maximum: 20
+
+   components:
+     gcam:
+       image: gcr.io/my-project/gcam:7.0
+       cpus: 8
+       memory: 32G
+       tags: [iam, climate]
+       env:
+         GCAM_DATA: /data/gcam
+
+     postprocess:
+       image: gcr.io/my-project/postprocess:latest
+       cpus: 4
+       memory: 16G
+       tags: [analysis]
+
+   Migration notes:
+   • Apptainer mounts must be converted to Kubernetes PVC mounts.
+   • Walltime is replaced by pod timeout annotations.
+   • Network interface (ib0) is irrelevant for Kubernetes — remove.
+
+Python API:
+
+.. code-block:: python
+
+   from scalable.ai import migrate_manifest
+
+   result = migrate_manifest(
+       "scalable.yaml",
+       to_provider="kubernetes",
+   )
+
+   print(result.migrated_yaml)
+   print(result.changes_summary)
+   print(result.migration_notes)
+
+Step 7: Integration into Development Workflow
+----------------------------------------------
+
+Combine AI assistants into a smooth development loop:
+
+.. code-block:: bash
+
+   # 1. Onboard a new model
+   scalable init-component ./new-model --name new-model
+
+   # 2. Compose a workflow incorporating it
+   scalable compose "Run existing pipeline then feed results to new-model"
+
+   # 3. Validate the generated configuration
+   scalable validate ./scalable.yaml
+
+   # 4. Plan and review (explain for team review)
+   scalable plan ./scalable.yaml --target local --dry-run --output plan.json
+   scalable explain plan.json
+
+   # 5. Run locally
+   scalable run ./scalable.yaml --target local --workflow workflow.py
+
+   # 6. If it fails, diagnose
+   scalable diagnose --latest
+
+   # 7. When ready for production, migrate
+   scalable migrate scalable.yaml --to-provider kubernetes
+
+Step 8: Customizing AI Heuristics
+----------------------------------
+
+The heuristic mode uses rule-based templates that you can inspect and
+influence:
+
+.. code-block:: python
+
+   from scalable.ai.heuristics import (
+       detect_language,
+       estimate_resources,
+       suggest_component_config,
+   )
+
+   # Language detection
+   lang = detect_language("./path/to/model")
+   print(f"Detected: {lang}")  # "python", "r", "compiled"
+
+   # Resource estimation from known model profiles
+   resources = estimate_resources(
+       model_name="gcam",
+       input_size_mb=2048,
+       num_scenarios=50,
+   )
+   print(f"Estimated: {resources}")
+   # {'cpus': 8, 'memory': '32G', 'walltime': '03:00:00'}
+
+The heuristics are deterministic — same input always produces same output.
+This makes them suitable for automated CI/CD pipelines where reproducibility
+matters.
+
+Step 9: LLM-Enhanced Mode
+---------------------------
+
+For richer, context-aware responses, enable an LLM backend:
+
+.. code-block:: bash
+
+   export SCALABLE_AI_BACKEND=openai
+   export SCALABLE_AI_MODEL=gpt-4
+   export OPENAI_API_KEY=sk-...
+
+   # Now compose generates more detailed, context-aware workflows
+   scalable compose "Build a multi-model ensemble that runs GCAM, Hector, \
+     and MAGICC in parallel, compares their climate projections, and \
+     produces a weighted average based on historical skill scores"
+
+LLM-enhanced mode adds:
+
+* More detailed code comments and documentation.
+* Context-aware parameter suggestions based on model documentation.
+* Richer error explanations with links to relevant resources.
+* More creative workflow architectures for complex descriptions.
+
+**Important:** LLM output is non-deterministic. For reproducible pipelines,
+always use ``--no-ai`` (heuristic mode) in CI/CD.
+
+Step 10: Validating AI-Generated Output
+-----------------------------------------
+
+Always validate AI-generated configurations before running:
+
+.. code-block:: python
+
+   from scalable.ai import compose_workflow
+   from scalable import ScalableSession
+
+   # Generate workflow
+   result = compose_workflow("Run GCAM for all SSPs then aggregate")
+
+   # Write generated manifest additions
+   # (merge with your existing scalable.yaml)
+
+   # Validate the result
+   session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+   report = session.validate()
+
+   if not report.ok:
+       print("Generated config has issues:")
+       for issue in report.errors:
+           print(f"  [{issue.code}] {issue.path}: {issue.message}")
+       # Fix issues and re-validate
+   else:
+       print("Generated config is valid — ready to run")
+
+Troubleshooting
+---------------
+
+**"ImportError: jinja2 not installed"**
+  Install the AI extra: ``pip install scalable[ai]``.
+
+**AI assistant gives unhelpful generic responses**
+  In heuristic mode, the assistant relies on pattern matching. Provide more
+  specific input — e.g., a directory with actual code rather than an empty
+  scaffold.
+
+**LLM mode is slow**
+  LLM API calls typically take 5–30 seconds. For quick iteration, use
+  ``--no-ai`` for heuristic mode and only switch to LLM mode for complex
+  composition tasks.
+
+**"SCALABLE_AI_BACKEND=openai but no OPENAI_API_KEY"**
+  Set your API key: ``export OPENAI_API_KEY=sk-...``. The error is raised
+  at call time, not import time.
+
+**Generated workflow has TODO placeholders**
+  The AI generates a skeleton with ``# TODO`` markers where domain-specific
+  logic belongs. Fill in the function bodies with your actual model execution
+  code.
+
+**Migration suggests incompatible changes**
+  Migration is advisory — it shows what needs to change but cannot verify that
+  cloud infrastructure exists. Always validate the migrated manifest and test
+  with ``--dry-run`` before production deployment.
+
+Next Steps
+----------
+
+* :ref:`tutorial_getting_started` — If you're new, start from the beginning
+  for full context.
+* :ref:`tutorial_manifest_system` — Deep-dive into the manifest schema that
+  AI assistants generate.
+* :ref:`tutorial_kubernetes` — Deploy AI-generated Kubernetes configurations.
+* :ref:`tutorial_ml_advanced` — Combine AI composition with ML-driven
+  resource optimization.
diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst
new file mode 100644
index 0000000..07d9831
--- /dev/null
+++ b/docs/tutorials/index.rst
@@ -0,0 +1,136 @@
+.. _tutorials:
+
+======================================================
+Tutorials
+======================================================
+
+Hands-on, step-by-step guides that walk you through Scalable's features from
+first installation to advanced production workflows. Each tutorial builds on a
+realistic scenario, includes full code examples with expected output, and ends
+with suggested next steps.
+
+Getting Started
+---------------
+
+.. toctree::
+   :maxdepth: 1
+
+   01_getting_started
+   02_manifest_system
+
+These introductory tutorials assume no prior Scalable experience. Start here
+if you are new to the framework.
+
+Core Capabilities
+-----------------
+
+.. toctree::
+   :maxdepth: 1
+
+   03_scaling_strategies
+   04_caching_performance
+   05_cloud_integration
+   06_telemetry
+   07_error_handling
+
+These tutorials cover Scalable's primary feature set. They assume you have
+completed the Getting Started tutorials and have a working local environment.
+
+Advanced Topics
+---------------
+
+.. toctree::
+   :maxdepth: 1
+
+   08_kubernetes
+   09_ml_emulation
+   10_ai_composition
+
+These tutorials explore Scalable's advanced and differentiating capabilities.
+They assume familiarity with the core features and, in some cases, access to
+external infrastructure (Kubernetes clusters, cloud accounts).
+
+Recommended Learning Path
+--------------------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 10 50 40
+
+   * - #
+     - Tutorial
+     - You'll Learn
+   * - 1
+     - :ref:`tutorial_getting_started`
+     - Install, configure, run your first workflow
+   * - 2
+     - :ref:`tutorial_manifest_system`
+     - Manifest schema, targets, overlays, validation
+   * - 3
+     - :ref:`tutorial_scaling_strategies`
+     - Providers, manual/adaptive/policy scaling
+   * - 4
+     - :ref:`tutorial_caching`
+     - @cacheable, FileType/DirType, remote cache
+   * - 5
+     - :ref:`tutorial_cloud_integration`
+     - AWS Fargate, GCP, cost estimation, artifacts
+   * - 6
+     - :ref:`tutorial_telemetry`
+     - JSONL events, reports, historical analysis
+   * - 7
+     - :ref:`tutorial_error_handling`
+     - Retry, partial success, diagnostics
+   * - 8
+     - :ref:`tutorial_kubernetes`
+     - Dask Operator, namespaces, pod management
+   * - 9
+     - :ref:`tutorial_ml_advanced`
+     - LearnedAdvisor, AdaptiveScaler, @emulatable
+   * - 10
+     - :ref:`tutorial_ai_composition`
+     - init-component, diagnose, compose, migrate
+
+Prerequisites by Tutorial
+--------------------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 30 40
+
+   * - Tutorial
+     - Install Extra
+     - External Requirements
+   * - 1–4
+     - ``pip install scalable``
+     - None (local only)
+   * - 5
+     - ``pip install scalable[cloud]``
+     - AWS/GCP credentials
+   * - 6–7
+     - ``pip install scalable``
+     - None
+   * - 8
+     - ``pip install scalable[kubernetes]``
+     - Kubernetes cluster + kubectl
+   * - 9
+     - ``pip install scalable[ml]``
+     - 5+ telemetry runs
+   * - 10
+     - ``pip install scalable[ai]``
+     - None (optional: LLM API key)
+
+Conventions Used
+-----------------
+
+Throughout these tutorials:
+
+* All code examples use Python 3.11+ syntax.
+* Shell commands assume a Unix-like environment (macOS/Linux). Windows
+  equivalents are noted where they differ.
+* The project name ``climate-pipeline`` and component names ``gcam``,
+  ``stitches``, ``postprocess`` appear consistently across tutorials as a
+  running example.
+* Environment variables use the ``${VAR:-default}`` pattern for portability.
+* Expected output blocks show representative output — exact values (timestamps,
+  hashes, run IDs) will differ on your machine.
diff --git a/pyproject.toml b/pyproject.toml
index 73ce484..208be07 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "scalable"
-version = "2.0.0a5"
+version = "2.0.0"
 description = "Assist with running models on job queing systems like Slurm"
 authors = [
     { name = "Shashank Lamba" },

From 61129295650c327f4dd75af3645c2902786d4e79 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 00:21:37 -0400
Subject: [PATCH 29/47] jupyter notebook tutorials

---
 notebooks/01_getting_started.ipynb     | 390 ++++++++++++++++
 notebooks/02_manifest_system.ipynb     | 458 ++++++++++++++++++
 notebooks/03_scaling_strategies.ipynb  | 415 +++++++++++++++++
 notebooks/04_caching_performance.ipynb | 503 ++++++++++++++++++++
 notebooks/05_cloud_integration.ipynb   | 445 ++++++++++++++++++
 notebooks/06_telemetry.ipynb           | 435 ++++++++++++++++++
 notebooks/07_error_handling.ipynb      | 504 ++++++++++++++++++++
 notebooks/08_kubernetes.ipynb          | 498 ++++++++++++++++++++
 notebooks/09_ml_emulation.ipynb        | 515 +++++++++++++++++++++
 notebooks/10_ai_composition.ipynb      | 613 +++++++++++++++++++++++++
 notebooks/README.md                    |  46 ++
 11 files changed, 4822 insertions(+)
 create mode 100644 notebooks/01_getting_started.ipynb
 create mode 100644 notebooks/02_manifest_system.ipynb
 create mode 100644 notebooks/03_scaling_strategies.ipynb
 create mode 100644 notebooks/04_caching_performance.ipynb
 create mode 100644 notebooks/05_cloud_integration.ipynb
 create mode 100644 notebooks/06_telemetry.ipynb
 create mode 100644 notebooks/07_error_handling.ipynb
 create mode 100644 notebooks/08_kubernetes.ipynb
 create mode 100644 notebooks/09_ml_emulation.ipynb
 create mode 100644 notebooks/10_ai_composition.ipynb
 create mode 100644 notebooks/README.md

diff --git a/notebooks/01_getting_started.ipynb b/notebooks/01_getting_started.ipynb
new file mode 100644
index 0000000..836289d
--- /dev/null
+++ b/notebooks/01_getting_started.ipynb
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial 1: Getting Started with Scalable\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "By the end of this notebook you will:\n",
+    "\n",
+    "- Install Scalable and verify its dependencies\n",
+    "- Create a minimal `scalable.yaml` manifest\n",
+    "- Validate, plan, and execute a local workflow end-to-end\n",
+    "- Inspect the telemetry output of a successful run\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Python 3.11+\n",
+    "- `pip install scalable` (run the cell below if not already installed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install Scalable (skip if already installed)\n",
+    "# !pip install scalable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Verify installation\n",
+    "import scalable\n",
+    "print(f\"Scalable version: {scalable.__version__}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Create a Project Directory and Manifest\n",
+    "\n",
+    "The manifest (`scalable.yaml`) is the declarative single source of truth for your workflow.\n",
+    "It describes:\n",
+    "- **Project** metadata\n",
+    "- **Targets** (execution environments)\n",
+    "- **Components** (resource profiles for workloads)\n",
+    "- **Tasks** (named work units bound to components)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "# Create a temporary project directory for this tutorial\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-tutorial-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write the manifest\n",
+    "manifest_content = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: hello-scalable\n",
+    "\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 2\n",
+    "    threads_per_worker: 1\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "\n",
+    "components:\n",
+    "  analysis:\n",
+    "    cpus: 1\n",
+    "    memory: 1G\n",
+    "\n",
+    "tasks:\n",
+    "  run_analysis:\n",
+    "    component: analysis\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(manifest_content)\n",
+    "\n",
+    "print(\"Manifest written to scalable.yaml\")\n",
+    "print(\"---\")\n",
+    "print(manifest_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Understanding the Manifest\n",
+    "\n",
+    "| Section | Purpose |\n",
+    "|---------|--------|\n",
+    "| `version: 1` | Schema version (only `1` currently supported) |\n",
+    "| `project.name` | Identifies the project in telemetry and artifacts |\n",
+    "| `targets.local` | Uses the built-in `LocalProvider` (Dask LocalCluster) |\n",
+    "| `components.analysis` | Declares 1 CPU / 1 GB resource profile |\n",
+    "| `tasks.run_analysis` | Binds a work unit to the `analysis` component |\n",
+    "\n",
+    "> **Trade-off:** `processes: false` runs Dask workers as threads (fast startup, no serialization overhead) but provides no memory isolation between tasks."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Validate the Manifest\n",
+    "\n",
+    "Validation checks structural and semantic correctness before running anything."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable import ScalableSession\n",
+    "\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "report = session.validate()\n",
+    "\n",
+    "if report.ok:\n",
+    "    print(\"✓ Manifest is valid (0 errors, 0 warnings)\")\n",
+    "else:\n",
+    "    for issue in report.errors:\n",
+    "        print(f\"ERROR [{issue.code}] {issue.path}: {issue.message}\")\n",
+    "    for issue in report.warnings:\n",
+    "        print(f\"WARN  [{issue.code}] {issue.path}: {issue.message}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Plan the Execution\n",
+    "\n",
+    "Planning produces a dry-run plan — a description of what *would* happen without allocating real resources."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plan = session.plan(dry_run=True)\n",
+    "\n",
+    "print(f\"Target: {plan.target_name}\")\n",
+    "print(f\"Provider: {plan.provider_name}\")\n",
+    "print(f\"Manifest lock: {plan.manifest_lock}\")\n",
+    "print(f\"Scale plan: {plan.scale_plan}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `manifest_lock` is a content-addressable hash. Two plans with the same lock were derived from byte-identical configurations — this guarantees reproducibility."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Write and Run a Workflow\n",
+    "\n",
+    "Now let's define a simple computation and execute it through Scalable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "\n",
+    "\n",
+    "def analyze(scenario_id: int) -> dict:\n",
+    "    \"\"\"Simulate an expensive computation.\"\"\"\n",
+    "    time.sleep(0.5)  # Shortened for notebook\n",
+    "    return {\"scenario\": scenario_id, \"result\": scenario_id * 42}\n",
+    "\n",
+    "\n",
+    "# Start the cluster\n",
+    "client = session.start(plan)\n",
+    "print(f\"Client connected: {client}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Submit tasks tagged to the 'analysis' component\n",
+    "futures = []\n",
+    "for i in range(5):\n",
+    "    fut = client.submit(analyze, i, tag=\"analysis\")\n",
+    "    futures.append(fut)\n",
+    "\n",
+    "print(f\"Submitted {len(futures)} tasks\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gather results\n",
+    "results = client.gather(futures)\n",
+    "\n",
+    "for r in results:\n",
+    "    print(r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What Happened Under the Hood\n",
+    "\n",
+    "1. `ScalableSession.from_yaml` parsed the manifest and built a `DeploymentSpec`\n",
+    "2. `session.plan()` validated and computed resource allocation (2 workers × 1 CPU / 1G)\n",
+    "3. `session.start()` created a Dask `LocalCluster` with the specified workers\n",
+    "4. Each `client.submit(..., tag=\"analysis\")` routed the function to matching workers\n",
+    "5. Results were gathered back to the client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: Close the Session and Inspect Telemetry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Always close the session to finalize telemetry\n",
+    "session.close()\n",
+    "print(\"Session closed.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check telemetry output\n",
+    "from pathlib import Path\n",
+    "\n",
+    "runs_dir = Path(\".scalable/runs\")\n",
+    "if runs_dir.exists():\n",
+    "    run_dirs = sorted(runs_dir.iterdir())\n",
+    "    if run_dirs:\n",
+    "        latest_run = run_dirs[-1]\n",
+    "        print(f\"Latest run: {latest_run.name}\")\n",
+    "        print(f\"\\nContents:\")\n",
+    "        for f in sorted(latest_run.iterdir()):\n",
+    "            size = f.stat().st_size\n",
+    "            print(f\"  {f.name} ({size} bytes)\")\n",
+    "else:\n",
+    "    print(\"No telemetry data found (telemetry may be disabled)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read run metadata\n",
+    "import json\n",
+    "\n",
+    "if runs_dir.exists() and run_dirs:\n",
+    "    run_json = latest_run / \"run.json\"\n",
+    "    if run_json.exists():\n",
+    "        with open(run_json) as f:\n",
+    "            meta = json.load(f)\n",
+    "        print(\"Run metadata:\")\n",
+    "        for key, value in meta.items():\n",
+    "            print(f\"  {key}: {value}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: Environment Variables\n",
+    "\n",
+    "Scalable is configured through environment variables for deployment flexibility:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable.common import settings\n",
+    "\n",
+    "print(\"Current settings:\")\n",
+    "print(f\"  Cache dir: {settings.cache_dir}\")\n",
+    "print(f\"  Seed: {settings.seed}\")\n",
+    "print(f\"  Manifest path: {settings.manifest_path}\")\n",
+    "print(f\"  Runs dir: {settings.runs_dir}\")\n",
+    "print(f\"  Telemetry enabled: {settings.telemetry_enabled}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this tutorial you:\n",
+    "1. Created a minimal `scalable.yaml` manifest\n",
+    "2. Validated it for structural correctness\n",
+    "3. Generated a dry-run execution plan\n",
+    "4. Ran a workflow through the Session API\n",
+    "5. Inspected telemetry output\n",
+    "\n",
+    "## Next Steps\n",
+    "\n",
+    "- **Tutorial 2**: Deep-dive into the manifest system (targets, overlays, env vars)\n",
+    "- **Tutorial 4**: Add `@cacheable` to skip redundant computation\n",
+    "- **Tutorial 6**: Analyze telemetry data for performance insights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cleanup\n",
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(\"Tutorial workspace cleaned up.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/02_manifest_system.ipynb b/notebooks/02_manifest_system.ipynb
new file mode 100644
index 0000000..2582bc4
--- /dev/null
+++ b/notebooks/02_manifest_system.ipynb
@@ -0,0 +1,458 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial 2: Mastering the Manifest System\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- Every section of a `scalable.yaml` manifest in depth\n",
+    "- Environment variable expansion for portable manifests\n",
+    "- Multiple targets for local, HPC, and cloud\n",
+    "- Component configuration with images, mounts, and tags\n",
+    "- Overlays for environment-specific customization\n",
+    "- Programmatic validation and error interpretation\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed Tutorial 1\n",
+    "- `pip install scalable`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-manifest-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Multi-Target Manifest\n",
+    "\n",
+    "A single manifest can describe your entire promotion path: develop locally → validate on HPC → deploy to cloud."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "manifest_content = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: climate-pipeline\n",
+    "  default_storage: ./outputs\n",
+    "\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 4\n",
+    "    threads_per_worker: 2\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "\n",
+    "  hpc:\n",
+    "    provider: slurm\n",
+    "    queue: batch\n",
+    "    account: GCIMS\n",
+    "    walltime: \"04:00:00\"\n",
+    "    interface: ib0\n",
+    "    overlay: hpc-large\n",
+    "\n",
+    "components:\n",
+    "  gcam:\n",
+    "    cpus: 4\n",
+    "    memory: 16G\n",
+    "    tags: [iam, climate]\n",
+    "\n",
+    "  postprocess:\n",
+    "    cpus: 2\n",
+    "    memory: 4G\n",
+    "    tags: [analysis]\n",
+    "\n",
+    "tasks:\n",
+    "  run_gcam:\n",
+    "    component: gcam\n",
+    "    cache: true\n",
+    "    outputs:\n",
+    "      database: dir\n",
+    "\n",
+    "  aggregate:\n",
+    "    component: postprocess\n",
+    "    cache: true\n",
+    "\n",
+    "overlays:\n",
+    "  hpc-large:\n",
+    "    components:\n",
+    "      gcam:\n",
+    "        cpus: 16\n",
+    "        memory: 64G\n",
+    "      postprocess:\n",
+    "        cpus: 8\n",
+    "        memory: 32G\n",
+    "\n",
+    "  hpc-debug:\n",
+    "    components:\n",
+    "      gcam:\n",
+    "        cpus: 2\n",
+    "        memory: 4G\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(manifest_content)\n",
+    "\n",
+    "print(\"Multi-target manifest written.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Parse and Inspect the Manifest\n",
+    "\n",
+    "The parser handles YAML loading, env var expansion, and schema enforcement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable.manifest.parser import load_manifest\n",
+    "\n",
+    "manifest = load_manifest(\"./scalable.yaml\")\n",
+    "\n",
+    "print(f\"Project name: {manifest.project.name}\")\n",
+    "print(f\"Targets: {list(manifest.targets.keys())}\")\n",
+    "print(f\"Components: {list(manifest.components.keys())}\")\n",
+    "print(f\"Tasks: {list(manifest.tasks.keys())}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Inspect a target\n",
+    "local_target = manifest.targets[\"local\"]\n",
+    "print(f\"Local target provider: {local_target.provider}\")\n",
+    "print(f\"Local target options: {local_target.options}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Inspect a component\n",
+    "gcam_component = manifest.components[\"gcam\"]\n",
+    "print(f\"GCAM cpus: {gcam_component.cpus}\")\n",
+    "print(f\"GCAM memory: {gcam_component.memory}\")\n",
+    "print(f\"GCAM tags: {gcam_component.tags}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Environment Variable Expansion\n",
+    "\n",
+    "Manifests support `${VAR}` and `${VAR:-default}` syntax for portability."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable.manifest.parser import expand_env_vars\n",
+    "\n",
+    "# Simulate environment variable expansion\n",
+    "os.environ[\"MY_PROJECT\"] = \"climate-demo\"\n",
+    "\n",
+    "# ${VAR} expansion\n",
+    "result = expand_env_vars(\"${MY_PROJECT}\")\n",
+    "print(f\"${{MY_PROJECT}} → {result}\")\n",
+    "\n",
+    "# ${VAR:-default} expansion (variable set)\n",
+    "result = expand_env_vars(\"${MY_PROJECT:-fallback}\")\n",
+    "print(f\"${{MY_PROJECT:-fallback}} → {result}\")\n",
+    "\n",
+    "# ${VAR:-default} expansion (variable not set)\n",
+    "result = expand_env_vars(\"${UNSET_VAR:-my-default}\")\n",
+    "print(f\"${{UNSET_VAR:-my-default}} → {result}\")\n",
+    "\n",
+    "# Nested structures\n",
+    "data = {\"project\": {\"name\": \"${MY_PROJECT}\"}, \"path\": \"${UNSET_VAR:-./data}\"}\n",
+    "expanded = expand_env_vars(data)\n",
+    "print(f\"\\nExpanded dict: {expanded}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Validation\n",
+    "\n",
+    "The validator checks structural correctness, component references, and provider-specific constraints."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable import ScalableSession\n",
+    "\n",
+    "# Valid manifest\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "report = session.validate()\n",
+    "\n",
+    "print(f\"Valid: {report.ok}\")\n",
+    "print(f\"Errors: {len(report.errors)}\")\n",
+    "print(f\"Warnings: {len(report.warnings)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now let's create an invalid manifest to see error reporting\n",
+    "invalid_manifest = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: broken\n",
+    "\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: -1\n",
+    "    containers: none\n",
+    "\n",
+    "components:\n",
+    "  worker:\n",
+    "    cpus: 1\n",
+    "    memory: 1G\n",
+    "\n",
+    "tasks:\n",
+    "  run_task:\n",
+    "    component: nonexistent_component\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"broken.yaml\", \"w\") as f:\n",
+    "    f.write(invalid_manifest)\n",
+    "\n",
+    "try:\n",
+    "    broken_session = ScalableSession.from_yaml(\"./broken.yaml\", target=\"local\")\n",
+    "    broken_report = broken_session.validate()\n",
+    "    \n",
+    "    print(f\"Valid: {broken_report.ok}\")\n",
+    "    for issue in broken_report.errors:\n",
+    "        print(f\"  ERROR [{issue.code}] {issue.path}: {issue.message}\")\n",
+    "    for issue in broken_report.warnings:\n",
+    "        print(f\"  WARN  [{issue.code}] {issue.path}: {issue.message}\")\n",
+    "except Exception as e:\n",
+    "    print(f\"Parse error: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: Overlays\n",
+    "\n",
+    "Overlays define named configuration deltas that are merged when a target references them.\n",
+    "\n",
+    "In our manifest, the `hpc` target uses `overlay: hpc-large`, which overrides `gcam.cpus` from 4 → 16 and `gcam.memory` from 16G → 64G."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable.manifest.overlays import apply_overlay\n",
+    "\n",
+    "# Show the base component values\n",
+    "print(\"Base components:\")\n",
+    "for name, comp in manifest.components.items():\n",
+    "    print(f\"  {name}: cpus={comp.cpus}, memory={comp.memory}\")\n",
+    "\n",
+    "# Show overlay definitions\n",
+    "print(f\"\\nOverlays defined: {list(manifest.raw.get('overlays', {}).keys())}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Demonstrate overlay application\n",
+    "raw_data = manifest.raw.copy()\n",
+    "overlay_name = \"hpc-large\"\n",
+    "overlay_data = raw_data.get(\"overlays\", {}).get(overlay_name, {})\n",
+    "\n",
+    "print(f\"Overlay '{overlay_name}' changes:\")\n",
+    "for comp_name, overrides in overlay_data.get(\"components\", {}).items():\n",
+    "    print(f\"  {comp_name}: {overrides}\")\n",
+    "\n",
+    "print(f\"\\nAfter applying '{overlay_name}':\")\n",
+    "print(f\"  gcam: cpus=16, memory=64G\")\n",
+    "print(f\"  postprocess: cpus=8, memory=32G\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: Target Selection at Runtime\n",
+    "\n",
+    "Target resolution order:\n",
+    "1. Explicit `target=` argument\n",
+    "2. `SCALABLE_TARGET` environment variable\n",
+    "3. Error (no implicit default)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Explicit selection\n",
+    "session_local = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "print(f\"Selected target: {session_local.target_name}\")\n",
+    "print(f\"Provider: {session_local.spec.provider_name}\")\n",
+    "\n",
+    "# Environment variable selection\n",
+    "os.environ[\"SCALABLE_TARGET\"] = \"local\"\n",
+    "session_env = ScalableSession.from_yaml(\"./scalable.yaml\")\n",
+    "print(f\"\\nEnv-selected target: {session_env.target_name}\")\n",
+    "del os.environ[\"SCALABLE_TARGET\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7: DeploymentSpec — The Provider-Neutral Request\n",
+    "\n",
+    "The `DeploymentSpec` bridges the manifest and the provider. It contains everything a provider needs to build a cluster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spec = session_local.spec\n",
+    "\n",
+    "print(f\"DeploymentSpec:\")\n",
+    "print(f\"  target_name: {spec.target_name}\")\n",
+    "print(f\"  provider_name: {spec.provider_name}\")\n",
+    "print(f\"  components: {list(spec.components.keys())}\")\n",
+    "print(f\"  tasks: {list(spec.tasks.keys())}\")\n",
+    "print(f\"  target options: {spec.target.options}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 8: Planning with Objectives\n",
+    "\n",
+    "The Session API supports policy-driven planning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Default plan\n",
+    "plan_default = session_local.plan(dry_run=True)\n",
+    "print(f\"Default plan: {plan_default.scale_plan}\")\n",
+    "\n",
+    "# Cost-minimizing plan\n",
+    "plan_cost = session_local.plan(objective=\"minimize cost\", policy=\"safe\")\n",
+    "print(f\"Cost-optimized plan: {plan_cost.scale_plan}\")\n",
+    "\n",
+    "# Time-minimizing plan\n",
+    "plan_time = session_local.plan(objective=\"minimize time\", policy=\"aggressive\")\n",
+    "print(f\"Time-optimized plan: {plan_time.scale_plan}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this tutorial you learned:\n",
+    "1. Multi-target manifest structure (local + HPC in one file)\n",
+    "2. Environment variable expansion (`${VAR:-default}` syntax)\n",
+    "3. Component definitions with resource profiles and tags\n",
+    "4. Overlays for environment-specific resource overrides\n",
+    "5. Programmatic validation with error code interpretation\n",
+    "6. Target selection and DeploymentSpec\n",
+    "7. Policy-driven planning (cost vs time vs balance)\n",
+    "\n",
+    "## Next Steps\n",
+    "\n",
+    "- **Tutorial 3**: Scaling strategies across providers\n",
+    "- **Tutorial 5**: Cloud integration with AWS/GCP targets\n",
+    "- **Tutorial 4**: Performance optimization with caching"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cleanup\n",
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(\"Cleaned up.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/03_scaling_strategies.ipynb b/notebooks/03_scaling_strategies.ipynb
new file mode 100644
index 0000000..29b2534
--- /dev/null
+++ b/notebooks/03_scaling_strategies.ipynb
@@ -0,0 +1,415 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial 3: Scaling Strategies with Providers\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- Scalable's provider architecture and how it abstracts backends\n",
+    "- Configure the Local provider for development\n",
+    "- Session-based scaling with objectives and policies\n",
+    "- Heterogeneous worker pools with multiple components\n",
+    "- Adaptive scaling concepts\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed Tutorials 1 and 2\n",
+    "- `pip install scalable`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "import time\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-scaling-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: The Provider Architecture\n",
+    "\n",
+    "Every provider implements the `DeploymentProvider` protocol:\n",
+    "\n",
+    "```\n",
+    "Manifest → DeploymentSpec → Provider → Cluster\n",
+    "                                          ↓\n",
+    "                            Local / Slurm / Cloud / K8s\n",
+    "```\n",
+    "\n",
+    "Your workflow code is **provider-agnostic** — the same `client.submit(func, arg, tag=\"gcam\")` works identically regardless of backend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# List registered providers\n",
+    "from scalable.providers.registry import iter_provider_names\n",
+    "\n",
+    "print(\"Registered providers:\")\n",
+    "for name in iter_provider_names(include_entrypoints=True):\n",
+    "    print(f\"  - {name}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Local Provider — Development & CI\n",
+    "\n",
+    "The `LocalProvider` wraps Dask's `LocalCluster`. It's the fastest way to iterate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "manifest_content = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: scaling-demo\n",
+    "\n",
+    "targets:\n",
+    "  local-threads:\n",
+    "    provider: local\n",
+    "    max_workers: 4\n",
+    "    threads_per_worker: 2\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "\n",
+    "  local-processes:\n",
+    "    provider: local\n",
+    "    max_workers: 2\n",
+    "    threads_per_worker: 1\n",
+    "    processes: true\n",
+    "    containers: none\n",
+    "\n",
+    "components:\n",
+    "  compute:\n",
+    "    cpus: 2\n",
+    "    memory: 4G\n",
+    "    tags: [heavy]\n",
+    "\n",
+    "  io:\n",
+    "    cpus: 1\n",
+    "    memory: 1G\n",
+    "    tags: [light]\n",
+    "\n",
+    "tasks:\n",
+    "  simulate:\n",
+    "    component: compute\n",
+    "    cache: true\n",
+    "\n",
+    "  aggregate:\n",
+    "    component: io\n",
+    "    cache: false\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(manifest_content)\n",
+    "\n",
+    "print(\"Manifest with two local targets (threads vs processes) written.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Threads vs Processes\n",
+    "\n",
+    "| Mode | Startup | Isolation | Best For |\n",
+    "|------|---------|-----------|----------|\n",
+    "| `processes: false` | Fast | None (shared memory) | I/O-bound tasks, quick iteration |\n",
+    "| `processes: true` | Slower | Full (separate process) | CPU-bound tasks, GIL-heavy code |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable import ScalableSession\n",
+    "\n",
+    "# Threaded mode (fast startup)\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local-threads\")\n",
+    "plan = session.plan(dry_run=True)\n",
+    "\n",
+    "print(f\"Target: {plan.target_name}\")\n",
+    "print(f\"Scale plan: {plan.scale_plan}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Heterogeneous Worker Pools\n",
+    "\n",
+    "Real workflows need different resource profiles running simultaneously. Submit to each pool independently using tags."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def heavy_computation(scenario_id: int) -> dict:\n",
+    "    \"\"\"CPU-intensive simulation.\"\"\"\n",
+    "    time.sleep(0.5)\n",
+    "    return {\"scenario\": scenario_id, \"result\": scenario_id ** 2}\n",
+    "\n",
+    "\n",
+    "def light_aggregation(results: list) -> dict:\n",
+    "    \"\"\"I/O-bound aggregation.\"\"\"\n",
+    "    total = sum(r[\"result\"] for r in results)\n",
+    "    return {\"total\": total, \"count\": len(results)}\n",
+    "\n",
+    "\n",
+    "# Start the cluster\n",
+    "client = session.start(plan)\n",
+    "print(f\"Cluster started with {plan.scale_plan}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Phase 1: Heavy compute tasks go to 'compute' workers\n",
+    "compute_futures = [\n",
+    "    client.submit(heavy_computation, i, tag=\"compute\")\n",
+    "    for i in range(8)\n",
+    "]\n",
+    "print(f\"Submitted {len(compute_futures)} compute tasks\")\n",
+    "\n",
+    "# Wait for compute results\n",
+    "compute_results = client.gather(compute_futures)\n",
+    "print(f\"Compute results: {compute_results[:3]}...\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Phase 2: Light aggregation on 'io' workers\n",
+    "agg_future = client.submit(light_aggregation, compute_results, tag=\"io\")\n",
+    "agg_result = agg_future.result()\n",
+    "\n",
+    "print(f\"Aggregation result: {agg_result}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Why Heterogeneous Pools?\n",
+    "\n",
+    "This pattern avoids over-provisioning:\n",
+    "- Expensive 4 GB workers handle heavy computation\n",
+    "- Cheap 1 GB workers handle aggregation\n",
+    "- You only pay for the resources each task actually needs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Policy-Driven Planning\n",
+    "\n",
+    "The Session API supports objectives that automatically influence worker allocation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "session.close()\n",
+    "\n",
+    "# Create a fresh session to demonstrate planning\n",
+    "session2 = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local-threads\")\n",
+    "\n",
+    "# Minimize cost: fewest workers\n",
+    "plan_cost = session2.plan(objective=\"minimize cost\", policy=\"safe\")\n",
+    "print(f\"Cost-optimized: {plan_cost.scale_plan}\")\n",
+    "\n",
+    "# Minimize time: max parallelism\n",
+    "plan_time = session2.plan(objective=\"minimize time\", policy=\"aggressive\")\n",
+    "print(f\"Time-optimized: {plan_time.scale_plan}\")\n",
+    "\n",
+    "# Balance: midpoint\n",
+    "plan_balanced = session2.plan(objective=\"balance\", policy=\"safe\")\n",
+    "print(f\"Balanced: {plan_balanced.scale_plan}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: AdaptiveScaler Concepts\n",
+    "\n",
+    "For long-running workflows where task load varies, the `AdaptiveScaler` monitors queue depth and adjusts workers dynamically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    from scalable import AdaptiveScaler\n",
+    "    \n",
+    "    scaler = AdaptiveScaler(\n",
+    "        min_workers={\"compute\": 1, \"io\": 1},\n",
+    "        max_workers={\"compute\": 10, \"io\": 5},\n",
+    "        scale_up_threshold=0.8,\n",
+    "        scale_down_threshold=0.2,\n",
+    "        cooldown_seconds=30,\n",
+    "    )\n",
+    "    \n",
+    "    # Simulate evaluation with pending tasks\n",
+    "    decision = scaler.evaluate(\n",
+    "        pending_tasks=[{\"tag\": \"compute\"} for _ in range(20)],\n",
+    "        active_workers={\"compute\": 2, \"io\": 1},\n",
+    "    )\n",
+    "    \n",
+    "    print(f\"Scale decision:\")\n",
+    "    print(f\"  Has changes: {decision.has_changes}\")\n",
+    "    print(f\"  Workers to add: {decision.workers_to_add}\")\n",
+    "    print(f\"  Workers to remove: {decision.workers_to_remove}\")\n",
+    "    print(f\"  Reasoning: {decision.reasoning}\")\n",
+    "    print(f\"  Confidence: {decision.confidence:.2f}\")\n",
+    "    \n",
+    "except ImportError:\n",
+    "    print(\"AdaptiveScaler requires scalable[ml]. Install with:\")\n",
+    "    print(\"  pip install scalable[ml]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: Scaling Trade-offs\n",
+    "\n",
+    "```\n",
+    "Aggressive (max workers)\n",
+    "├── Fastest completion\n",
+    "├── Highest cost\n",
+    "└── Risk: idle workers during low-load phases\n",
+    "\n",
+    "Conservative (min workers)\n",
+    "├── Lowest cost\n",
+    "├── Slowest completion\n",
+    "└── Risk: queue buildup during bursts\n",
+    "\n",
+    "Adaptive (dynamic scaling)\n",
+    "├── Best cost-performance ratio\n",
+    "├── Requires cooldown tuning\n",
+    "└── Latency: scale-up takes time\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Demonstrate the impact of worker count on throughput\n",
+    "from scalable import ScalableSession\n",
+    "import time\n",
+    "\n",
+    "def timed_workload(n_tasks, target_name):\n",
+    "    \"\"\"Run n_tasks and measure total time.\"\"\"\n",
+    "    sess = ScalableSession.from_yaml(\"./scalable.yaml\", target=target_name)\n",
+    "    client = sess.start()\n",
+    "    \n",
+    "    start = time.time()\n",
+    "    futures = [client.submit(heavy_computation, i, tag=\"compute\") for i in range(n_tasks)]\n",
+    "    results = client.gather(futures)\n",
+    "    elapsed = time.time() - start\n",
+    "    \n",
+    "    sess.close()\n",
+    "    return elapsed\n",
+    "\n",
+    "# 4-worker threaded (from local-threads)\n",
+    "time_4w = timed_workload(8, \"local-threads\")\n",
+    "print(f\"4 workers, 8 tasks: {time_4w:.2f}s\")\n",
+    "\n",
+    "# 2-worker process (from local-processes)\n",
+    "time_2w = timed_workload(8, \"local-processes\")\n",
+    "print(f\"2 workers, 8 tasks: {time_2w:.2f}s\")\n",
+    "\n",
+    "print(f\"\\nSpeedup: {time_2w/time_4w:.1f}x\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this tutorial you learned:\n",
+    "1. The provider architecture abstracts execution backends\n",
+    "2. LocalProvider supports both threaded and process-based workers\n",
+    "3. Heterogeneous pools match resources to task requirements\n",
+    "4. Policy-driven planning automates worker count decisions\n",
+    "5. AdaptiveScaler provides real-time scaling recommendations\n",
+    "\n",
+    "## Next Steps\n",
+    "\n",
+    "- **Tutorial 4**: Cache expensive computations\n",
+    "- **Tutorial 5**: Deploy to AWS/GCP cloud providers\n",
+    "- **Tutorial 8**: Kubernetes provider with pod management"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(\"Cleaned up.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/04_caching_performance.ipynb b/notebooks/04_caching_performance.ipynb
new file mode 100644
index 0000000..28442ed
--- /dev/null
+++ b/notebooks/04_caching_performance.ipynb
@@ -0,0 +1,503 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial 4: Performance Optimization and Caching\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- Use `@cacheable` to skip redundant computation\n",
+    "- Understand how Scalable hashes function arguments\n",
+    "- Handle file-based inputs with `FileType` and `DirType`\n",
+    "- Force recomputation and invalidate caches\n",
+    "- Monitor cache hit/miss rates\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed Tutorial 1\n",
+    "- `pip install scalable`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "import time\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-caching-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")\n",
+    "\n",
+    "# Set cache dir for this tutorial\n",
+    "os.environ[\"SCALABLE_CACHE_DIR\"] = os.path.join(project_dir, \"cache\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Basic @cacheable\n",
+    "\n",
+    "The `@cacheable` decorator intercepts function calls, computes a cache key from the function name and arguments, and returns cached results when available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable import cacheable\n",
+    "\n",
+    "\n",
+    "@cacheable(return_type=dict, scenario_id=int)\n",
+    "def expensive_simulation(scenario_id: int) -> dict:\n",
+    "    \"\"\"Simulates an expensive computation.\"\"\"\n",
+    "    time.sleep(1)  # Simulate 1 second of work\n",
+    "    return {\"scenario\": scenario_id, \"result\": scenario_id * 42}\n",
+    "\n",
+    "\n",
+    "print(\"Function defined with @cacheable\")\n",
+    "print(f\"Cache directory: {os.environ['SCALABLE_CACHE_DIR']}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First call — cache MISS (takes ~1 second)\n",
+    "start = time.time()\n",
+    "result1 = expensive_simulation(42)\n",
+    "elapsed1 = time.time() - start\n",
+    "\n",
+    "print(f\"First call: {result1}\")\n",
+    "print(f\"Time: {elapsed1:.3f}s (cache MISS)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Second call — cache HIT (instant)\n",
+    "start = time.time()\n",
+    "result2 = expensive_simulation(42)\n",
+    "elapsed2 = time.time() - start\n",
+    "\n",
+    "print(f\"Second call: {result2}\")\n",
+    "print(f\"Time: {elapsed2:.3f}s (cache HIT)\")\n",
+    "print(f\"\\nSpeedup: {elapsed1/max(elapsed2, 0.001):.0f}x\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Different argument — cache MISS\n",
+    "start = time.time()\n",
+    "result3 = expensive_simulation(99)\n",
+    "elapsed3 = time.time() - start\n",
+    "\n",
+    "print(f\"New argument: {result3}\")\n",
+    "print(f\"Time: {elapsed3:.3f}s (cache MISS — different key)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How It Works\n",
+    "\n",
+    "1. Arguments are serialized with `dill` and hashed with `xxhash` (seeded by `SCALABLE_SEED`)\n",
+    "2. Function name + argument hash = composite cache key\n",
+    "3. On hit: stored result is deserialized and returned\n",
+    "4. On miss: function executes, result is stored"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Type Annotations for Reliable Hashing\n",
+    "\n",
+    "Explicit types ensure deterministic cache keys across Python type variations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@cacheable(return_type=str, name=str, count=int)\n",
+    "def greet(name: str, count: int) -> str:\n",
+    "    \"\"\"Generate a greeting.\"\"\"\n",
+    "    return f\"Hello {name}! (x{count})\"\n",
+    "\n",
+    "\n",
+    "# Consistent hashing regardless of source type\n",
+    "r1 = greet(\"World\", 3)\n",
+    "r2 = greet(\"World\", 3)  # Cache hit\n",
+    "print(f\"Result: {r1}\")\n",
+    "print(f\"Same result from cache: {r1 == r2}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Hashing Files and Directories\n",
+    "\n",
+    "Scientific workflows often operate on input files. `FileType` hashes file **content** (not paths)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable import cacheable, FileType\n",
+    "\n",
+    "# Create a sample input file\n",
+    "with open(\"input_data.csv\", \"w\") as f:\n",
+    "    f.write(\"scenario,temperature,precipitation\\n\")\n",
+    "    f.write(\"1,300,1200\\n\")\n",
+    "    f.write(\"2,310,1100\\n\")\n",
+    "\n",
+    "print(\"Created input_data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@cacheable(return_type=dict, data_file=FileType)\n",
+    "def process_data(data_file: str) -> dict:\n",
+    "    \"\"\"Process a data file. Cache key includes file contents.\"\"\"\n",
+    "    time.sleep(0.5)\n",
+    "    with open(data_file) as f:\n",
+    "        lines = f.readlines()\n",
+    "    return {\"records\": len(lines) - 1, \"file\": data_file}\n",
+    "\n",
+    "\n",
+    "# First call — hashes file content\n",
+    "start = time.time()\n",
+    "r1 = process_data(\"input_data.csv\")\n",
+    "print(f\"First call: {r1} ({time.time()-start:.3f}s)\")\n",
+    "\n",
+    "# Second call — same file content = cache hit\n",
+    "start = time.time()\n",
+    "r2 = process_data(\"input_data.csv\")\n",
+    "print(f\"Second call: {r2} ({time.time()-start:.3f}s) — HIT\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Modify the file — cache miss (content changed)\n",
+    "with open(\"input_data.csv\", \"a\") as f:\n",
+    "    f.write(\"3,305,1300\\n\")\n",
+    "\n",
+    "start = time.time()\n",
+    "r3 = process_data(\"input_data.csv\")\n",
+    "print(f\"After modification: {r3} ({time.time()-start:.3f}s) — MISS (content changed)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Type Hashing Strategies\n",
+    "\n",
+    "| Type | Strategy |\n",
+    "|------|----------|\n",
+    "| `FileType` | Streams file in 1MB chunks through xxhash. Includes basename. |\n",
+    "| `DirType` | Walks directory, hashes each file path + content (sorted). |\n",
+    "| `str` | Hashes UTF-8 bytes directly. |\n",
+    "| `int` | Hashes byte representation. |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Forcing Recomputation\n",
+    "\n",
+    "Use `recompute=True` to invalidate a specific function's cache (e.g., after fixing a bug)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@cacheable(return_type=dict, recompute=True, scenario_id=int)\n",
+    "def fixed_simulation(scenario_id: int) -> dict:\n",
+    "    \"\"\"Always recomputes — ignores cache.\"\"\"\n",
+    "    return {\"scenario\": scenario_id, \"result\": scenario_id * 1.7}  # Fixed formula\n",
+    "\n",
+    "\n",
+    "# Both calls execute the function (recompute=True)\n",
+    "r1 = fixed_simulation(42)\n",
+    "r2 = fixed_simulation(42)\n",
+    "print(f\"Call 1: {r1}\")\n",
+    "print(f\"Call 2: {r2}\")\n",
+    "print(\"Both computed fresh (recompute=True bypasses cache reads)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: Minimal @cacheable Form\n",
+    "\n",
+    "For quick prototyping, `@cacheable` works without explicit types:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@cacheable\n",
+    "def quick_add(x, y):\n",
+    "    \"\"\"Minimal cacheable form — no explicit types.\"\"\"\n",
+    "    time.sleep(0.3)\n",
+    "    return x + y\n",
+    "\n",
+    "\n",
+    "start = time.time()\n",
+    "r1 = quick_add(10, 20)\n",
+    "print(f\"First: {r1} ({time.time()-start:.3f}s)\")\n",
+    "\n",
+    "start = time.time()\n",
+    "r2 = quick_add(10, 20)\n",
+    "print(f\"Second: {r2} ({time.time()-start:.3f}s) — cache hit\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Recommendation:** Always use explicit types for production code. The minimal form is acceptable for experiments where cache key stability isn't critical."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: Cache Directory Structure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "cache_dir = Path(os.environ[\"SCALABLE_CACHE_DIR\"])\n",
+    "\n",
+    "if cache_dir.exists():\n",
+    "    print(f\"Cache directory: {cache_dir}\")\n",
+    "    total_size = sum(f.stat().st_size for f in cache_dir.rglob(\"*\") if f.is_file())\n",
+    "    file_count = sum(1 for f in cache_dir.rglob(\"*\") if f.is_file())\n",
+    "    print(f\"Files: {file_count}\")\n",
+    "    print(f\"Total size: {total_size / 1024:.1f} KB\")\n",
+    "else:\n",
+    "    print(\"Cache directory not yet created\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7: Cache Invalidation Strategies\n",
+    "\n",
+    "| Strategy | Scope | When to Use |\n",
+    "|----------|-------|-------------|\n",
+    "| `recompute=True` | Single function | After fixing a bug in one function |\n",
+    "| Change `SCALABLE_SEED` | Global | After major refactoring |\n",
+    "| Version the function name | Single function | Between known-breaking changes |\n",
+    "| Delete cache directory | Global | Nuclear option |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Strategy: Version the function name\n",
+    "@cacheable(return_type=dict, scenario_id=int)\n",
+    "def run_model_v2(scenario_id: int) -> dict:\n",
+    "    \"\"\"v2 has a different cache key than v1 by virtue of its name.\"\"\"\n",
+    "    return {\"scenario\": scenario_id, \"result\": scenario_id * 3.14}\n",
+    "\n",
+    "\n",
+    "r = run_model_v2(42)\n",
+    "print(f\"v2 result: {r}\")\n",
+    "print(\"v1 cache entries remain but are now 'stale' (different function name)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 8: Caching in a Distributed Workflow\n",
+    "\n",
+    "Combine `@cacheable` with the Session API for production-grade caching."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable import ScalableSession, cacheable\n",
+    "\n",
+    "# Write manifest\n",
+    "manifest = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: caching-demo\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 2\n",
+    "    threads_per_worker: 1\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "components:\n",
+    "  worker:\n",
+    "    cpus: 1\n",
+    "    memory: 1G\n",
+    "tasks:\n",
+    "  compute:\n",
+    "    component: worker\n",
+    "    cache: true\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(manifest)\n",
+    "\n",
+    "\n",
+    "@cacheable(return_type=dict, n=int)\n",
+    "def cached_task(n: int) -> dict:\n",
+    "    \"\"\"A cacheable distributed task.\"\"\"\n",
+    "    time.sleep(0.5)\n",
+    "    return {\"n\": n, \"square\": n**2}\n",
+    "\n",
+    "\n",
+    "print(\"Ready for distributed caching demo\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run 1: All cache misses\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "client = session.start()\n",
+    "\n",
+    "start = time.time()\n",
+    "futures = [client.submit(cached_task, i, tag=\"worker\") for i in range(6)]\n",
+    "results = client.gather(futures)\n",
+    "run1_time = time.time() - start\n",
+    "\n",
+    "print(f\"Run 1 (all misses): {run1_time:.2f}s\")\n",
+    "print(f\"Results: {results[:3]}...\")\n",
+    "session.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run 2: All cache hits (same arguments)\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "client = session.start()\n",
+    "\n",
+    "start = time.time()\n",
+    "futures = [client.submit(cached_task, i, tag=\"worker\") for i in range(6)]\n",
+    "results = client.gather(futures)\n",
+    "run2_time = time.time() - start\n",
+    "\n",
+    "print(f\"Run 2 (all hits): {run2_time:.2f}s\")\n",
+    "print(f\"Speedup: {run1_time/max(run2_time, 0.01):.1f}x\")\n",
+    "session.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1. `@cacheable` prevents redundant computation across retries\n",
+    "2. Explicit type annotations ensure stable cache keys\n",
+    "3. `FileType`/`DirType` hash file **content**, not paths\n",
+    "4. `recompute=True` forces fresh execution for debugging\n",
+    "5. Cache + distributed = major time savings on repeated runs\n",
+    "\n",
+    "## Next Steps\n",
+    "\n",
+    "- **Tutorial 5**: Remote cache sharing with S3/GCS\n",
+    "- **Tutorial 6**: Monitor cache hit rates in telemetry\n",
+    "- **Tutorial 7**: Handle cache corruption gracefully"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "if \"SCALABLE_CACHE_DIR\" in os.environ:\n",
+    "    del os.environ[\"SCALABLE_CACHE_DIR\"]\n",
+    "print(\"Cleaned up.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/05_cloud_integration.ipynb b/notebooks/05_cloud_integration.ipynb
new file mode 100644
index 0000000..e307704
--- /dev/null
+++ b/notebooks/05_cloud_integration.ipynb
@@ -0,0 +1,445 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial 5: Cloud Integration with AWS and GCP\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- Configure AWS Fargate and GCP targets in a manifest\n",
+    "- Use the artifact store for cloud storage (S3, GCS)\n",
+    "- Estimate costs with dry-run planning\n",
+    "- Structure multi-cloud manifests\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed Tutorials 1-2\n",
+    "- `pip install scalable[cloud]`\n",
+    "- AWS/GCP credentials (or follow along conceptually)\n",
+    "\n",
+    "> **Note:** This notebook demonstrates configuration and planning. Actual cloud deployment requires valid credentials and infrastructure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-cloud-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: AWS Target Configuration\n",
+    "\n",
+    "The AWS provider uses `dask-cloudprovider` to launch Dask workers on Fargate or EC2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aws_manifest = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: climate-model-aws\n",
+    "  default_storage: s3://my-bucket/scalable-runs/\n",
+    "\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 2\n",
+    "    threads_per_worker: 1\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "\n",
+    "  aws:\n",
+    "    provider: aws\n",
+    "    region: us-east-1\n",
+    "    cluster_type: fargate\n",
+    "    worker_cpu: 4096\n",
+    "    worker_mem: 16384\n",
+    "    image: 123456789.dkr.ecr.us-east-1.amazonaws.com/climate:latest\n",
+    "    execution_role_arn: arn:aws:iam::123456789:role/ecsTaskExecutionRole\n",
+    "    task_role_arn: arn:aws:iam::123456789:role/scalableTaskRole\n",
+    "    subnets:\n",
+    "      - subnet-abc123\n",
+    "      - subnet-def456\n",
+    "    security_groups:\n",
+    "      - sg-xyz789\n",
+    "    adaptive:\n",
+    "      minimum: 2\n",
+    "      maximum: 20\n",
+    "\n",
+    "components:\n",
+    "  gcam:\n",
+    "    image: 123456789.dkr.ecr.us-east-1.amazonaws.com/gcam:7.0\n",
+    "    cpus: 4\n",
+    "    memory: 16G\n",
+    "    tags: [iam, climate]\n",
+    "\n",
+    "  postprocess:\n",
+    "    cpus: 2\n",
+    "    memory: 8G\n",
+    "    tags: [analysis]\n",
+    "\n",
+    "tasks:\n",
+    "  run_gcam:\n",
+    "    component: gcam\n",
+    "    cache: true\n",
+    "    outputs:\n",
+    "      database: dir\n",
+    "\n",
+    "  aggregate:\n",
+    "    component: postprocess\n",
+    "    cache: true\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(aws_manifest)\n",
+    "\n",
+    "print(\"AWS manifest written.\")\n",
+    "print(\"\\nKey configuration:\")\n",
+    "print(\"  cluster_type: fargate (serverless containers)\")\n",
+    "print(\"  worker_cpu: 4096 (= 4 vCPU)\")\n",
+    "print(\"  worker_mem: 16384 (= 16 GiB)\")\n",
+    "print(\"  adaptive: min=2, max=20 workers\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fargate CPU/Memory Configurations\n",
+    "\n",
+    "| CPU (units) | Memory (MiB) | Use Case |\n",
+    "|-------------|--------------|----------|\n",
+    "| 1024 | 4096 | Light tasks, I/O-bound |\n",
+    "| 4096 | 16384 | Standard compute |\n",
+    "| 16384 | 65536 | Memory-intensive models |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Validate and Plan (Local Target)\n",
+    "\n",
+    "We can validate and plan against the local target without AWS credentials."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable import ScalableSession\n",
+    "\n",
+    "# Use the local target for validation\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "report = session.validate()\n",
+    "\n",
+    "print(f\"Manifest valid: {report.ok}\")\n",
+    "if not report.ok:\n",
+    "    for e in report.errors:\n",
+    "        print(f\"  ERROR: {e.message}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plan for local execution\n",
+    "plan = session.plan(dry_run=True)\n",
+    "print(f\"Target: {plan.target_name}\")\n",
+    "print(f\"Provider: {plan.provider_name}\")\n",
+    "print(f\"Scale plan: {plan.scale_plan}\")\n",
+    "print(f\"Manifest lock: {plan.manifest_lock}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Cost Estimation\n",
+    "\n",
+    "Scalable includes cost tables for cloud providers. Let's explore the cost estimation module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable.costing import CostEstimate\n",
+    "\n",
+    "# CostEstimate is included in dry-run plan output for cloud targets\n",
+    "print(\"CostEstimate fields:\")\n",
+    "print(f\"  - total: Total estimated cost\")\n",
+    "print(f\"  - compute: Fargate/EC2 compute cost\")\n",
+    "print(f\"  - storage: S3/GCS storage cost\")\n",
+    "print(f\"  - transfer: Data transfer cost\")\n",
+    "\n",
+    "# Check if cost estimate is available\n",
+    "if hasattr(plan, 'cost_estimate') and plan.cost_estimate:\n",
+    "    print(f\"\\nEstimated cost: ${plan.cost_estimate.total:.2f}\")\n",
+    "else:\n",
+    "    print(\"\\nCost estimation available for cloud targets (aws, gcp)\")\n",
+    "    print(\"Run: scalable run ./scalable.yaml --target aws --dry-run\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Artifact Store\n",
+    "\n",
+    "The artifact store provides unified storage across local and cloud backends."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable.artifacts import build_artifact_store\n",
+    "\n",
+    "# Local artifact store (always available)\n",
+    "local_store = build_artifact_store(\"./artifacts\")\n",
+    "print(f\"Local store: {local_store}\")\n",
+    "print(f\"Store type: {type(local_store).__name__}\")\n",
+    "\n",
+    "# Create a sample output file\n",
+    "os.makedirs(\"output\", exist_ok=True)\n",
+    "with open(\"output/results.csv\", \"w\") as f:\n",
+    "    f.write(\"scenario,emissions\\n1,35.2\\n2,28.7\\n\")\n",
+    "\n",
+    "# Store an artifact\n",
+    "ref = local_store.put(\"output/results.csv\", \"runs/demo/results.csv\")\n",
+    "print(f\"\\nStored artifact: {ref}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Retrieve the artifact\n",
+    "retrieved_path = local_store.get(ref, \"./downloads/results.csv\")\n",
+    "print(f\"Retrieved to: {retrieved_path}\")\n",
+    "\n",
+    "with open(retrieved_path) as f:\n",
+    "    print(f\"Contents: {f.read()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cloud Storage (S3/GCS)\n",
+    "\n",
+    "With `scalable[cloud]` installed, the same API works with remote URIs:\n",
+    "\n",
+    "```python\n",
+    "# S3\n",
+    "s3_store = build_artifact_store(\"s3://my-bucket/artifacts/\")\n",
+    "ref = s3_store.put(\"local/output.csv\", \"runs/run-001/output.csv\")\n",
+    "\n",
+    "# GCS\n",
+    "gcs_store = build_artifact_store(\"gs://my-bucket/artifacts/\")\n",
+    "ref = gcs_store.put(\"local/output.csv\", \"runs/run-001/output.csv\")\n",
+    "```\n",
+    "\n",
+    "The store auto-detects the URI scheme and uses `fsspec` backends (`s3fs`, `gcsfs`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: GCP Target Configuration\n",
+    "\n",
+    "For reference, here's a GCP/GKE manifest:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gcp_manifest = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: climate-model-gke\n",
+    "  default_storage: gs://my-bucket/scalable-runs/\n",
+    "\n",
+    "targets:\n",
+    "  gke:\n",
+    "    provider: kubernetes\n",
+    "    namespace: climate-prod\n",
+    "    image: gcr.io/my-project/climate-model:latest\n",
+    "    adaptive:\n",
+    "      minimum: 2\n",
+    "      maximum: 20\n",
+    "\n",
+    "components:\n",
+    "  gcam:\n",
+    "    image: gcr.io/my-project/gcam:7.0\n",
+    "    cpus: 8\n",
+    "    memory: 32G\n",
+    "    tags: [iam, climate]\n",
+    "    env:\n",
+    "      GCAM_DATA: /data/gcam\n",
+    "\n",
+    "  postprocess:\n",
+    "    image: gcr.io/my-project/postprocess:latest\n",
+    "    cpus: 4\n",
+    "    memory: 16G\n",
+    "\n",
+    "tasks:\n",
+    "  run_gcam:\n",
+    "    component: gcam\n",
+    "    cache: true\n",
+    "  aggregate:\n",
+    "    component: postprocess\n",
+    "    cache: true\n",
+    "\"\"\"\n",
+    "\n",
+    "print(\"GCP/GKE manifest structure:\")\n",
+    "print(gcp_manifest)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: Environment Variable Template\n",
+    "\n",
+    "Production deployments use environment variables for credentials and configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "env_template = \"\"\"\\\n",
+    "# .env.cloud (do NOT commit secrets)\n",
+    "AWS_REGION=us-east-1\n",
+    "S3_BUCKET=climate-prod-artifacts\n",
+    "ECR_IMAGE=123456789.dkr.ecr.us-east-1.amazonaws.com/climate:latest\n",
+    "EXECUTION_ROLE_ARN=arn:aws:iam::123456789:role/ecsTaskExecutionRole\n",
+    "TASK_ROLE_ARN=arn:aws:iam::123456789:role/scalableTaskRole\n",
+    "SUBNET_A=subnet-abc123\n",
+    "SUBNET_B=subnet-def456\n",
+    "SG_ID=sg-xyz789\n",
+    "SCALABLE_CACHE_REMOTE=s3://climate-prod-artifacts/cache/\n",
+    "\"\"\"\n",
+    "\n",
+    "print(\"Environment variable template for cloud deployment:\")\n",
+    "print(env_template)\n",
+    "print(\"Usage: set -a && source .env.cloud && set +a\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7: Cloud + Cache Integration\n",
+    "\n",
+    "Combine cloud execution with remote caching so repeated runs across different machines share results:\n",
+    "\n",
+    "```bash\n",
+    "export SCALABLE_CACHE_REMOTE=s3://my-bucket/scalable-cache/\n",
+    "```\n",
+    "\n",
+    "Cache lookup order:\n",
+    "1. Local disk (fast, per-machine)\n",
+    "2. Remote store (slower, shared across team)\n",
+    "3. Execute function (slowest, produces new entry)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Demonstrate the settings that control remote caching\n",
+    "from scalable.common import settings\n",
+    "\n",
+    "print(\"Cache-related settings:\")\n",
+    "print(f\"  Local cache dir: {settings.cache_dir}\")\n",
+    "print(f\"  Remote cache URI: {settings.cache_remote_uri}\")\n",
+    "print(f\"  Default storage: {settings.default_storage}\")\n",
+    "print(f\"\\nTo enable remote cache:\")\n",
+    "print(f\"  export SCALABLE_CACHE_REMOTE=s3://bucket/cache/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1. AWS Fargate provides serverless Dask workers with auto-scaling\n",
+    "2. GCP/GKE uses Kubernetes provider with container-native execution\n",
+    "3. Artifact store (`build_artifact_store`) works identically for local/S3/GCS\n",
+    "4. Cost estimation is built into dry-run planning\n",
+    "5. Environment variables keep credentials out of manifests\n",
+    "6. Remote caching enables cross-machine result sharing\n",
+    "\n",
+    "## Next Steps\n",
+    "\n",
+    "- **Tutorial 6**: Monitor cloud costs and performance through telemetry\n",
+    "- **Tutorial 8**: Full Kubernetes deployment walkthrough\n",
+    "- **Tutorial 7**: Handle cloud-specific transient failures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(\"Cleaned up.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/06_telemetry.ipynb b/notebooks/06_telemetry.ipynb
new file mode 100644
index 0000000..d14fd7c
--- /dev/null
+++ b/notebooks/06_telemetry.ipynb
@@ -0,0 +1,435 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial 6: Monitoring and Observability with Telemetry\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- Understand Scalable's telemetry data model\n",
+    "- Read and analyze JSONL telemetry files\n",
+    "- Generate reports from the Python API\n",
+    "- Build performance analysis from run history\n",
+    "- Configure telemetry options\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed Tutorial 1\n",
+    "- `pip install scalable`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "import time\n",
+    "import json\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-telemetry-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Generate Telemetry Data\n",
+    "\n",
+    "Let's run a workflow to produce telemetry records."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable import ScalableSession\n",
+    "\n",
+    "manifest = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: telemetry-demo\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 2\n",
+    "    threads_per_worker: 1\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "components:\n",
+    "  worker:\n",
+    "    cpus: 1\n",
+    "    memory: 1G\n",
+    "tasks:\n",
+    "  compute:\n",
+    "    component: worker\n",
+    "    cache: true\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(manifest)\n",
+    "\n",
+    "print(\"Manifest written.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_task(n: int) -> dict:\n",
+    "    \"\"\"A task that takes variable time.\"\"\"\n",
+    "    time.sleep(0.2 + (n % 3) * 0.1)  # Variable duration\n",
+    "    return {\"n\": n, \"result\": n ** 2}\n",
+    "\n",
+    "\n",
+    "# Run a workflow\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "client = session.start()\n",
+    "\n",
+    "futures = [client.submit(compute_task, i, tag=\"worker\") for i in range(10)]\n",
+    "results = client.gather(futures)\n",
+    "\n",
+    "session.close()\n",
+    "print(f\"Workflow complete: {len(results)} tasks\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Telemetry Directory Structure\n",
+    "\n",
+    "Every run produces structured JSONL files under `.scalable/runs/`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "runs_dir = Path(\".scalable/runs\")\n",
+    "\n",
+    "if runs_dir.exists():\n",
+    "    run_dirs = sorted(runs_dir.iterdir())\n",
+    "    latest_run = run_dirs[-1]\n",
+    "    \n",
+    "    print(f\"Run directory: {latest_run.name}\")\n",
+    "    print(f\"\\nFiles:\")\n",
+    "    for f in sorted(latest_run.iterdir()):\n",
+    "        size = f.stat().st_size\n",
+    "        print(f\"  {f.name:20s} {size:>6} bytes\")\n",
+    "else:\n",
+    "    print(\"No telemetry found (check SCALABLE_TELEMETRY=1)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Read Run Metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_json = latest_run / \"run.json\"\n",
+    "\n",
+    "if run_json.exists():\n",
+    "    with open(run_json) as f:\n",
+    "        meta = json.load(f)\n",
+    "    \n",
+    "    print(\"Run Metadata:\")\n",
+    "    print(f\"  Run ID: {meta.get('run_id', 'N/A')}\")\n",
+    "    print(f\"  Project: {meta.get('project_name', 'N/A')}\")\n",
+    "    print(f\"  Target: {meta.get('target_name', 'N/A')}\")\n",
+    "    print(f\"  Provider: {meta.get('provider_name', 'N/A')}\")\n",
+    "    print(f\"  Status: {meta.get('status', 'N/A')}\")\n",
+    "    print(f\"  Started: {meta.get('started_at', 'N/A')}\")\n",
+    "    print(f\"  Manifest lock: {meta.get('manifest_lock', 'N/A')[:20]}...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Analyze Task Events"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "tasks_file = latest_run / \"tasks.jsonl\"\n",
+    "\n",
+    "if tasks_file.exists() and tasks_file.stat().st_size > 0:\n",
+    "    tasks = []\n",
+    "    with open(tasks_file) as f:\n",
+    "        for line in f:\n",
+    "            if line.strip():\n",
+    "                tasks.append(json.loads(line))\n",
+    "    \n",
+    "    df = pd.DataFrame(tasks)\n",
+    "    print(f\"Task events: {len(df)}\")\n",
+    "    print(f\"Columns: {list(df.columns)}\")\n",
+    "    print(f\"\\nStates: {df['state'].value_counts().to_dict() if 'state' in df.columns else 'N/A'}\")\n",
+    "    \n",
+    "    if 'duration_s' in df.columns:\n",
+    "        completed = df[df['state'] == 'succeeded']\n",
+    "        if not completed.empty:\n",
+    "            print(f\"\\nDuration statistics:\")\n",
+    "            print(f\"  Mean: {completed['duration_s'].mean():.3f}s\")\n",
+    "            print(f\"  Max: {completed['duration_s'].max():.3f}s\")\n",
+    "            print(f\"  Min: {completed['duration_s'].min():.3f}s\")\n",
+    "else:\n",
+    "    print(\"No task events recorded.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: Worker Events"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "workers_file = latest_run / \"workers.jsonl\"\n",
+    "\n",
+    "if workers_file.exists() and workers_file.stat().st_size > 0:\n",
+    "    workers = []\n",
+    "    with open(workers_file) as f:\n",
+    "        for line in f:\n",
+    "            if line.strip():\n",
+    "                workers.append(json.loads(line))\n",
+    "    \n",
+    "    print(f\"Worker events: {len(workers)}\")\n",
+    "    for w in workers[:5]:\n",
+    "        print(f\"  {w}\")\n",
+    "else:\n",
+    "    print(\"No worker events recorded.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: Resource Utilization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resources_file = latest_run / \"resources.jsonl\"\n",
+    "\n",
+    "if resources_file.exists() and resources_file.stat().st_size > 0:\n",
+    "    resources = []\n",
+    "    with open(resources_file) as f:\n",
+    "        for line in f:\n",
+    "            if line.strip():\n",
+    "                resources.append(json.loads(line))\n",
+    "    \n",
+    "    res_df = pd.DataFrame(resources)\n",
+    "    print(f\"Resource events: {len(res_df)}\")\n",
+    "    print(f\"Columns: {list(res_df.columns)}\")\n",
+    "    \n",
+    "    if 'cpu_percent' in res_df.columns:\n",
+    "        print(f\"\\nCPU utilization: {res_df['cpu_percent'].mean():.1f}% mean\")\n",
+    "    if 'memory_mb' in res_df.columns:\n",
+    "        print(f\"Memory usage: {res_df['memory_mb'].mean():.0f} MB mean\")\n",
+    "else:\n",
+    "    print(\"No resource events recorded.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7: Using the Telemetry Collectors API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable.telemetry.collectors import iter_run_dirs, read_jsonl\n",
+    "\n",
+    "# Iterate all run directories\n",
+    "print(\"All runs:\")\n",
+    "for run_dir in iter_run_dirs(runs_dir):\n",
+    "    run_json_path = run_dir / \"run.json\"\n",
+    "    if run_json_path.exists():\n",
+    "        with open(run_json_path) as f:\n",
+    "            meta = json.load(f)\n",
+    "        print(f\"  {meta.get('run_id', run_dir.name)}: {meta.get('status', '?')}\")\n",
+    "\n",
+    "# Read task events using helper\n",
+    "task_records = read_jsonl(latest_run / \"tasks.jsonl\")\n",
+    "print(f\"\\nTask records via read_jsonl: {len(task_records)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 8: Multiple Runs for Trend Analysis\n",
+    "\n",
+    "Let's run the workflow a few more times to see trends."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run 2 more times with slightly different workloads\n",
+    "for run_num in range(2):\n",
+    "    sess = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "    cl = sess.start()\n",
+    "    \n",
+    "    n_tasks = 5 + run_num * 3\n",
+    "    futs = [cl.submit(compute_task, i, tag=\"worker\") for i in range(n_tasks)]\n",
+    "    cl.gather(futs)\n",
+    "    sess.close()\n",
+    "    \n",
+    "    print(f\"Run {run_num + 2} complete ({n_tasks} tasks)\")\n",
+    "    time.sleep(0.5)\n",
+    "\n",
+    "print(f\"\\nTotal runs: {len(list(iter_run_dirs(runs_dir)))}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Analyze trends across runs\n",
+    "summaries = []\n",
+    "\n",
+    "for run_dir in iter_run_dirs(runs_dir):\n",
+    "    run_json_path = run_dir / \"run.json\"\n",
+    "    if not run_json_path.exists():\n",
+    "        continue\n",
+    "    \n",
+    "    with open(run_json_path) as f:\n",
+    "        meta = json.load(f)\n",
+    "    \n",
+    "    task_records = read_jsonl(run_dir / \"tasks.jsonl\")\n",
+    "    succeeded = [t for t in task_records if t.get(\"state\") == \"succeeded\"]\n",
+    "    \n",
+    "    summaries.append({\n",
+    "        \"run_id\": meta.get(\"run_id\", \"\")[:30],\n",
+    "        \"status\": meta.get(\"status\"),\n",
+    "        \"tasks\": len(succeeded),\n",
+    "        \"mean_duration\": (\n",
+    "            sum(t.get(\"duration_s\", 0) for t in succeeded) / len(succeeded)\n",
+    "            if succeeded else 0\n",
+    "        ),\n",
+    "    })\n",
+    "\n",
+    "history_df = pd.DataFrame(summaries)\n",
+    "print(\"Run History:\")\n",
+    "print(history_df.to_string(index=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 9: Telemetry Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable.common import settings\n",
+    "\n",
+    "print(\"Telemetry settings:\")\n",
+    "print(f\"  Enabled: {settings.telemetry_enabled} (SCALABLE_TELEMETRY)\")\n",
+    "print(f\"  Parquet export: {settings.telemetry_parquet} (SCALABLE_TELEMETRY_PARQUET)\")\n",
+    "print(f\"  Runs directory: {settings.runs_dir} (SCALABLE_RUNS_DIR)\")\n",
+    "print(f\"\\nTo disable: export SCALABLE_TELEMETRY=0\")\n",
+    "print(f\"To enable Parquet: export SCALABLE_TELEMETRY_PARQUET=1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1. Every manifest-driven run records JSONL telemetry automatically\n",
+    "2. `run.json` contains metadata (ID, target, status, timestamps)\n",
+    "3. `tasks.jsonl` records full task lifecycle with durations\n",
+    "4. `resources.jsonl` tracks CPU/memory utilization\n",
+    "5. `workers.jsonl` records worker lifecycle events\n",
+    "6. `iter_run_dirs` and `read_jsonl` provide programmatic access\n",
+    "7. Historical analysis reveals performance trends\n",
+    "\n",
+    "## Next Steps\n",
+    "\n",
+    "- **Tutorial 7**: Use failure events for error diagnosis\n",
+    "- **Tutorial 9**: Feed telemetry to ML advisor for predictions\n",
+    "- **Tutorial 5**: Monitor cloud costs through telemetry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(\"Cleaned up.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/07_error_handling.ipynb b/notebooks/07_error_handling.ipynb
new file mode 100644
index 0000000..4a61a71
--- /dev/null
+++ b/notebooks/07_error_handling.ipynb
@@ -0,0 +1,504 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial 7: Error Handling and Resilience Patterns\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- How Scalable propagates errors from distributed workers\n",
+    "- Gather results with error tolerance (partial success)\n",
+    "- Implement retry logic with exponential backoff\n",
+    "- Build fault-tolerant pipeline patterns\n",
+    "- Analyze failure telemetry\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed Tutorials 1 and 6\n",
+    "- `pip install scalable`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "import time\n",
+    "import json\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-errors-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")\n",
+    "\n",
+    "# Write manifest\n",
+    "manifest = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: error-handling-demo\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 2\n",
+    "    threads_per_worker: 1\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "components:\n",
+    "  worker:\n",
+    "    cpus: 1\n",
+    "    memory: 1G\n",
+    "tasks:\n",
+    "  compute:\n",
+    "    component: worker\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(manifest)\n",
+    "print(\"Manifest written.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Understanding Error Propagation\n",
+    "\n",
+    "When a function raises an exception on a Dask worker, the error is captured, serialized, and re-raised on the client."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable import ScalableSession\n",
+    "\n",
+    "\n",
+    "def flaky_task(scenario_id: int) -> dict:\n",
+    "    \"\"\"A function that fails for certain inputs.\"\"\"\n",
+    "    if scenario_id % 5 == 0:\n",
+    "        raise RuntimeError(f\"OOM: scenario {scenario_id} exceeded memory limit\")\n",
+    "    return {\"scenario\": scenario_id, \"result\": scenario_id * 42}\n",
+    "\n",
+    "\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "client = session.start()\n",
+    "\n",
+    "# Submit a task that will fail\n",
+    "future = client.submit(flaky_task, 0, tag=\"worker\")\n",
+    "\n",
+    "try:\n",
+    "    result = future.result()\n",
+    "except RuntimeError as e:\n",
+    "    print(f\"Caught error: {type(e).__name__}: {e}\")\n",
+    "    print(\"The error was raised on the worker and propagated back to the client.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Gathering with Error Tolerance\n",
+    "\n",
+    "Instead of failing on the first error, collect results and errors separately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from distributed import as_completed\n",
+    "\n",
+    "# Submit a batch with some failures expected\n",
+    "futures = [client.submit(flaky_task, i, tag=\"worker\") for i in range(15)]\n",
+    "\n",
+    "succeeded = []\n",
+    "failed = []\n",
+    "\n",
+    "for future in as_completed(futures):\n",
+    "    try:\n",
+    "        result = future.result()\n",
+    "        succeeded.append(result)\n",
+    "    except Exception as e:\n",
+    "        failed.append({\n",
+    "            \"error\": str(e),\n",
+    "            \"type\": type(e).__name__,\n",
+    "        })\n",
+    "\n",
+    "print(f\"Succeeded: {len(succeeded)}\")\n",
+    "print(f\"Failed: {len(failed)}\")\n",
+    "print(f\"\\nFailure details:\")\n",
+    "for f in failed:\n",
+    "    print(f\"  [{f['type']}] {f['error']}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Retry with Exponential Backoff\n",
+    "\n",
+    "For transient failures, retries with backoff often succeed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "\n",
+    "def sometimes_fails(scenario_id: int) -> dict:\n",
+    "    \"\"\"Transient failure — succeeds on retry with 70% probability.\"\"\"\n",
+    "    if random.random() < 0.3:\n",
+    "        raise ConnectionError(f\"Timeout fetching data for scenario {scenario_id}\")\n",
+    "    return {\"scenario\": scenario_id, \"result\": scenario_id * 42}\n",
+    "\n",
+    "\n",
+    "def submit_with_retry(client, func, *args, tag, max_retries=3, backoff=1.0):\n",
+    "    \"\"\"Submit with exponential backoff retry.\"\"\"\n",
+    "    last_error = None\n",
+    "    \n",
+    "    for attempt in range(max_retries + 1):\n",
+    "        future = client.submit(func, *args, tag=tag)\n",
+    "        try:\n",
+    "            return future.result(timeout=10)\n",
+    "        except Exception as e:\n",
+    "            last_error = e\n",
+    "            if attempt < max_retries:\n",
+    "                wait = backoff * (2 ** attempt)\n",
+    "                print(f\"  Attempt {attempt+1} failed: {e}. Retrying in {wait:.1f}s...\")\n",
+    "                time.sleep(wait)\n",
+    "    \n",
+    "    raise last_error\n",
+    "\n",
+    "\n",
+    "# Run with retries\n",
+    "random.seed(42)\n",
+    "print(\"Submitting with retry logic:\")\n",
+    "\n",
+    "results = []\n",
+    "permanent_failures = []\n",
+    "\n",
+    "for i in range(5):\n",
+    "    try:\n",
+    "        r = submit_with_retry(client, sometimes_fails, i, tag=\"worker\", max_retries=3, backoff=0.1)\n",
+    "        results.append(r)\n",
+    "        print(f\"  Scenario {i}: success\")\n",
+    "    except Exception as e:\n",
+    "        permanent_failures.append({\"scenario\": i, \"error\": str(e)})\n",
+    "        print(f\"  Scenario {i}: PERMANENT FAILURE after retries\")\n",
+    "\n",
+    "print(f\"\\nCompleted: {len(results)}, Permanent failures: {len(permanent_failures)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: When to Retry vs. Fail Fast\n",
+    "\n",
+    "| Failure Type | Strategy | Rationale |\n",
+    "|-------------|----------|----------|\n",
+    "| Network timeout | Retry (3x, exponential) | Transient; usually resolves |\n",
+    "| OOM (out of memory) | Fail fast | Persistent; same inputs will fail again |\n",
+    "| Worker preemption | Retry (unlimited) | External; will succeed when rescheduled |\n",
+    "| Input validation | Fail fast | Bug in data; retrying won't help |\n",
+    "| Import error | Fail fast | Container/environment issue |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def classify_error(error: Exception) -> str:\n",
+    "    \"\"\"Classify an error as retryable or permanent.\"\"\"\n",
+    "    retryable_types = (ConnectionError, TimeoutError, OSError)\n",
+    "    retryable_patterns = [\"timeout\", \"connection\", \"temporary\", \"retry\"]\n",
+    "    \n",
+    "    if isinstance(error, retryable_types):\n",
+    "        return \"retryable\"\n",
+    "    \n",
+    "    msg = str(error).lower()\n",
+    "    if any(p in msg for p in retryable_patterns):\n",
+    "        return \"retryable\"\n",
+    "    \n",
+    "    return \"permanent\"\n",
+    "\n",
+    "\n",
+    "# Test classification\n",
+    "test_errors = [\n",
+    "    ConnectionError(\"Connection timeout\"),\n",
+    "    RuntimeError(\"OOM: exceeded memory limit\"),\n",
+    "    ValueError(\"Invalid input parameter\"),\n",
+    "    OSError(\"Temporary failure in name resolution\"),\n",
+    "]\n",
+    "\n",
+    "for err in test_errors:\n",
+    "    classification = classify_error(err)\n",
+    "    print(f\"  {type(err).__name__}: '{err}' → {classification}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: Fault-Tolerant Pipeline Pattern\n",
+    "\n",
+    "Combine partial success, retry, and caching for production resilience."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable import cacheable\n",
+    "\n",
+    "os.environ[\"SCALABLE_CACHE_DIR\"] = os.path.join(project_dir, \"cache\")\n",
+    "\n",
+    "\n",
+    "@cacheable(return_type=dict, scenario_id=int)\n",
+    "def cached_simulation(scenario_id: int) -> dict:\n",
+    "    \"\"\"Cached — won't re-run on retry if previously succeeded.\"\"\"\n",
+    "    time.sleep(0.2)\n",
+    "    if scenario_id == 7:\n",
+    "        raise RuntimeError(\"Scenario 7 always fails\")\n",
+    "    return {\"scenario\": scenario_id, \"result\": scenario_id * 42}\n",
+    "\n",
+    "\n",
+    "def fault_tolerant_pipeline(n_scenarios=10, max_retries=2):\n",
+    "    \"\"\"Run a pipeline that tolerates partial failures.\"\"\"\n",
+    "    session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "    \n",
+    "    try:\n",
+    "        cl = session.start()\n",
+    "        \n",
+    "        succeeded = {}\n",
+    "        failed = {}\n",
+    "        retry_queue = [(s, 0) for s in range(n_scenarios)]\n",
+    "        \n",
+    "        while retry_queue:\n",
+    "            batch = retry_queue[:5]\n",
+    "            retry_queue = retry_queue[5:]\n",
+    "            \n",
+    "            futures = {\n",
+    "                cl.submit(cached_simulation, s, tag=\"worker\"): (s, attempt)\n",
+    "                for s, attempt in batch\n",
+    "            }\n",
+    "            \n",
+    "            for future in as_completed(futures):\n",
+    "                scenario_id, attempt = futures[future]\n",
+    "                try:\n",
+    "                    result = future.result()\n",
+    "                    succeeded[scenario_id] = result\n",
+    "                except Exception as e:\n",
+    "                    if attempt < max_retries and classify_error(e) == \"retryable\":\n",
+    "                        retry_queue.append((scenario_id, attempt + 1))\n",
+    "                    else:\n",
+    "                        failed[scenario_id] = str(e)\n",
+    "        \n",
+    "        return succeeded, failed\n",
+    "    finally:\n",
+    "        session.close()\n",
+    "\n",
+    "\n",
+    "succeeded, failed = fault_tolerant_pipeline()\n",
+    "print(f\"Pipeline complete:\")\n",
+    "print(f\"  Succeeded: {len(succeeded)}\")\n",
+    "print(f\"  Failed: {len(failed)}\")\n",
+    "if failed:\n",
+    "    print(f\"  Permanent failures:\")\n",
+    "    for s, err in sorted(failed.items()):\n",
+    "        print(f\"    Scenario {s}: {err}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: Graceful Session Shutdown\n",
+    "\n",
+    "Always use `try/finally` to ensure telemetry is finalized."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def safe_workflow():\n",
+    "    \"\"\"Pattern: always close the session.\"\"\"\n",
+    "    session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "    \n",
+    "    try:\n",
+    "        client = session.start()\n",
+    "        \n",
+    "        # Do work...\n",
+    "        futures = [client.submit(cached_simulation, i, tag=\"worker\") for i in range(5)]\n",
+    "        \n",
+    "        results = []\n",
+    "        for future in as_completed(futures):\n",
+    "            try:\n",
+    "                results.append(future.result())\n",
+    "            except Exception as e:\n",
+    "                print(f\"  Task failed: {e}\")\n",
+    "        \n",
+    "        return results\n",
+    "    \n",
+    "    except Exception as e:\n",
+    "        print(f\"Fatal error: {e}\")\n",
+    "        return []\n",
+    "    \n",
+    "    finally:\n",
+    "        # ALWAYS close — finalizes telemetry\n",
+    "        session.close()\n",
+    "        print(\"  Session closed (telemetry finalized)\")\n",
+    "\n",
+    "\n",
+    "results = safe_workflow()\n",
+    "print(f\"Got {len(results)} results\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7: Analyzing Failure Telemetry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from collections import Counter\n",
+    "from scalable.telemetry.collectors import iter_run_dirs, read_jsonl\n",
+    "\n",
+    "runs_dir = Path(\".scalable/runs\")\n",
+    "\n",
+    "if runs_dir.exists():\n",
+    "    all_failures = []\n",
+    "    \n",
+    "    for run_dir in iter_run_dirs(runs_dir):\n",
+    "        failures_file = run_dir / \"failures.jsonl\"\n",
+    "        if failures_file.exists():\n",
+    "            all_failures.extend(read_jsonl(failures_file))\n",
+    "    \n",
+    "    if all_failures:\n",
+    "        print(f\"Total failures across all runs: {len(all_failures)}\")\n",
+    "        \n",
+    "        by_class = Counter(f.get(\"failure_class\", \"Unknown\") for f in all_failures)\n",
+    "        print(f\"\\nBy type:\")\n",
+    "        for cls, count in by_class.most_common():\n",
+    "            print(f\"  {cls}: {count}\")\n",
+    "    else:\n",
+    "        print(\"No failure events recorded in telemetry.\")\n",
+    "else:\n",
+    "    print(\"No telemetry data available.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 8: Timeout Management"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from concurrent.futures import TimeoutError as FuturesTimeout\n",
+    "\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "client = session.start()\n",
+    "\n",
+    "\n",
+    "def slow_task(n: int) -> dict:\n",
+    "    \"\"\"A task that takes too long.\"\"\"\n",
+    "    time.sleep(5)  # 5 seconds\n",
+    "    return {\"n\": n}\n",
+    "\n",
+    "\n",
+    "future = client.submit(slow_task, 1, tag=\"worker\")\n",
+    "\n",
+    "try:\n",
+    "    # 2-second timeout\n",
+    "    result = future.result(timeout=2)\n",
+    "except Exception as e:\n",
+    "    print(f\"Timeout handling: {type(e).__name__}\")\n",
+    "    print(f\"  Task exceeded timeout — cancelling\")\n",
+    "    future.cancel()\n",
+    "\n",
+    "session.close()\n",
+    "print(\"Session closed.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1. Errors propagate from workers to client via serialization\n",
+    "2. Use `as_completed` for partial-success gathering\n",
+    "3. Classify errors as retryable vs permanent\n",
+    "4. Exponential backoff prevents thundering herd on retries\n",
+    "5. `@cacheable` makes retries free for previously-succeeded tasks\n",
+    "6. Always use `try/finally` with `session.close()`\n",
+    "7. Telemetry records all failures for post-hoc analysis\n",
+    "\n",
+    "## Next Steps\n",
+    "\n",
+    "- **Tutorial 8**: Handle Kubernetes pod evictions\n",
+    "- **Tutorial 4**: Cache to make retries instantaneous\n",
+    "- **Tutorial 9**: ML predictions to prevent resource-related failures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "if \"SCALABLE_CACHE_DIR\" in os.environ:\n",
+    "    del os.environ[\"SCALABLE_CACHE_DIR\"]\n",
+    "print(\"Cleaned up.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/08_kubernetes.ipynb b/notebooks/08_kubernetes.ipynb
new file mode 100644
index 0000000..73c44b0
--- /dev/null
+++ b/notebooks/08_kubernetes.ipynb
@@ -0,0 +1,498 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial 8: Deployment Workflows with Kubernetes\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- Configure the Kubernetes provider in a manifest\n",
+    "- Understand the Dask Kubernetes Operator architecture\n",
+    "- Set up namespace isolation and resource quotas\n",
+    "- Use overlays for dev/prod Kubernetes environments\n",
+    "- Handle pod evictions and node failures\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed Tutorials 1-3\n",
+    "- `pip install scalable[kubernetes]`\n",
+    "- Kubernetes cluster access (or follow along conceptually)\n",
+    "\n",
+    "> **Note:** This notebook demonstrates configuration and concepts. Actual deployment requires a running Kubernetes cluster with the Dask Operator installed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-k8s-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Kubernetes Provider Architecture\n",
+    "\n",
+    "```\n",
+    "ScalableSession\n",
+    "    └── KubernetesProvider\n",
+    "           └── Creates DaskCluster CR (Custom Resource)\n",
+    "                  └── Dask Kubernetes Operator\n",
+    "                         ├── Scheduler Pod\n",
+    "                         ├── Worker Pod (gcam-0)\n",
+    "                         ├── Worker Pod (gcam-1)\n",
+    "                         └── Worker Pod (postprocess-0)\n",
+    "```\n",
+    "\n",
+    "The operator manages pod lifecycle, health checks, and scaling."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Multi-Environment Kubernetes Manifest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "k8s_manifest = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: climate-pipeline-k8s\n",
+    "  default_storage: gs://climate-artifacts/scalable-runs/\n",
+    "\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 2\n",
+    "    threads_per_worker: 1\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "\n",
+    "  k8s-dev:\n",
+    "    provider: kubernetes\n",
+    "    namespace: climate-dev\n",
+    "    image: gcr.io/my-project/climate-model:latest\n",
+    "    adaptive:\n",
+    "      minimum: 1\n",
+    "      maximum: 5\n",
+    "    overlay: k8s-dev-resources\n",
+    "\n",
+    "  k8s-prod:\n",
+    "    provider: kubernetes\n",
+    "    namespace: climate-prod\n",
+    "    image: gcr.io/my-project/climate-model:v2.1.0\n",
+    "    adaptive:\n",
+    "      minimum: 4\n",
+    "      maximum: 40\n",
+    "    overlay: k8s-prod-resources\n",
+    "\n",
+    "components:\n",
+    "  gcam:\n",
+    "    image: gcr.io/my-project/gcam:7.0\n",
+    "    cpus: 4\n",
+    "    memory: 16G\n",
+    "    tags: [iam, climate]\n",
+    "    env:\n",
+    "      GCAM_DATA: /data/gcam\n",
+    "\n",
+    "  postprocess:\n",
+    "    image: gcr.io/my-project/postprocess:latest\n",
+    "    cpus: 2\n",
+    "    memory: 8G\n",
+    "    tags: [analysis]\n",
+    "\n",
+    "tasks:\n",
+    "  run_gcam:\n",
+    "    component: gcam\n",
+    "    cache: true\n",
+    "    outputs:\n",
+    "      database: dir\n",
+    "\n",
+    "  aggregate:\n",
+    "    component: postprocess\n",
+    "    cache: true\n",
+    "\n",
+    "overlays:\n",
+    "  k8s-dev-resources:\n",
+    "    components:\n",
+    "      gcam:\n",
+    "        cpus: 2\n",
+    "        memory: 8G\n",
+    "      postprocess:\n",
+    "        cpus: 1\n",
+    "        memory: 4G\n",
+    "\n",
+    "  k8s-prod-resources:\n",
+    "    components:\n",
+    "      gcam:\n",
+    "        cpus: 16\n",
+    "        memory: 64G\n",
+    "      postprocess:\n",
+    "        cpus: 8\n",
+    "        memory: 32G\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(k8s_manifest)\n",
+    "\n",
+    "print(\"Kubernetes manifest written with dev/prod overlays.\")\n",
+    "print(\"\\nTargets:\")\n",
+    "print(\"  local     → development (no K8s needed)\")\n",
+    "print(\"  k8s-dev   → Kubernetes dev namespace (small pods)\")\n",
+    "print(\"  k8s-prod  → Kubernetes prod namespace (large pods)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Validate Against Local Target\n",
+    "\n",
+    "We can validate the manifest structure without K8s access by using the local target."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable import ScalableSession\n",
+    "\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "report = session.validate()\n",
+    "\n",
+    "print(f\"Manifest valid: {report.ok}\")\n",
+    "print(f\"Errors: {len(report.errors)}\")\n",
+    "print(f\"Warnings: {len(report.warnings)}\")\n",
+    "\n",
+    "for w in report.warnings:\n",
+    "    print(f\"  WARN: {w.message}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Check Kubernetes Provider Availability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    from scalable.providers.kubernetes import KubernetesProvider\n",
+    "    print(\"✓ KubernetesProvider is available\")\n",
+    "    print(f\"  Provider name: {KubernetesProvider.name}\")\n",
+    "except ImportError:\n",
+    "    print(\"✗ KubernetesProvider not available\")\n",
+    "    print(\"  Install with: pip install scalable[kubernetes]\")\n",
+    "    print(\"  (requires dask-kubernetes and kubernetes packages)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: Kubernetes Resource Specifications\n",
+    "\n",
+    "Components map directly to pod resource requests/limits in Kubernetes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable.manifest.parser import load_manifest\n",
+    "\n",
+    "manifest = load_manifest(\"./scalable.yaml\")\n",
+    "\n",
+    "print(\"Component → Pod Resource Mapping:\")\n",
+    "print(\"=\"*50)\n",
+    "\n",
+    "for name, comp in manifest.components.items():\n",
+    "    print(f\"\\n  Component: {name}\")\n",
+    "    print(f\"  Image: {comp.image or '(inherited from target)'}\")\n",
+    "    print(f\"  Resources:\")\n",
+    "    print(f\"    requests.cpu: {comp.cpus}\")\n",
+    "    print(f\"    requests.memory: {comp.memory}\")\n",
+    "    print(f\"    limits.cpu: {comp.cpus}\")\n",
+    "    print(f\"    limits.memory: {comp.memory}\")\n",
+    "    print(f\"  Labels:\")\n",
+    "    print(f\"    scalable.io/component: {name}\")\n",
+    "    if comp.tags:\n",
+    "        print(f\"    scalable.io/tags: {','.join(comp.tags)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: Namespace and Quota Planning\n",
+    "\n",
+    "In production Kubernetes, resource quotas prevent runaway usage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate total resource requirements for production\n",
+    "prod_overlay = manifest.raw.get(\"overlays\", {}).get(\"k8s-prod-resources\", {})\n",
+    "prod_components = prod_overlay.get(\"components\", {})\n",
+    "\n",
+    "# Merge base + overlay\n",
+    "max_workers = 40  # From k8s-prod target\n",
+    "\n",
+    "gcam_cpus = prod_components.get(\"gcam\", {}).get(\"cpus\", 4)\n",
+    "gcam_mem = prod_components.get(\"gcam\", {}).get(\"memory\", \"16G\")\n",
+    "pp_cpus = prod_components.get(\"postprocess\", {}).get(\"cpus\", 2)\n",
+    "pp_mem = prod_components.get(\"postprocess\", {}).get(\"memory\", \"8G\")\n",
+    "\n",
+    "# Assume 80% gcam, 20% postprocess\n",
+    "gcam_workers = int(max_workers * 0.8)\n",
+    "pp_workers = max_workers - gcam_workers\n",
+    "\n",
+    "total_cpu = gcam_workers * gcam_cpus + pp_workers * pp_cpus\n",
+    "\n",
+    "print(\"Production Resource Quota Planning:\")\n",
+    "print(f\"  Max workers: {max_workers}\")\n",
+    "print(f\"  GCAM workers: {gcam_workers} × {gcam_cpus} CPU = {gcam_workers * gcam_cpus} CPU\")\n",
+    "print(f\"  Postprocess workers: {pp_workers} × {pp_cpus} CPU = {pp_workers * pp_cpus} CPU\")\n",
+    "print(f\"  Total CPU needed: {total_cpu}\")\n",
+    "print(f\"\\n  Recommended quota (with 25% headroom):\")\n",
+    "print(f\"    requests.cpu: {int(total_cpu * 1.25)}\")\n",
+    "print(f\"    pods: {max_workers + 5}  (workers + scheduler + buffer)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate ResourceQuota YAML\n",
+    "quota_yaml = f\"\"\"\\\n",
+    "apiVersion: v1\n",
+    "kind: ResourceQuota\n",
+    "metadata:\n",
+    "  name: climate-pipeline-quota\n",
+    "  namespace: climate-prod\n",
+    "spec:\n",
+    "  hard:\n",
+    "    requests.cpu: \"{int(total_cpu * 1.25)}\"\n",
+    "    requests.memory: \"1280Gi\"\n",
+    "    limits.cpu: \"{int(total_cpu * 1.5)}\"\n",
+    "    limits.memory: \"1600Gi\"\n",
+    "    pods: \"{max_workers + 5}\"\n",
+    "\"\"\"\n",
+    "\n",
+    "print(\"Generated ResourceQuota:\")\n",
+    "print(quota_yaml)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7: Handling Pod Evictions\n",
+    "\n",
+    "Kubernetes may evict pods. Scalable catches these as `KilledWorker` exceptions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Pattern: Retry evicted tasks\n",
+    "eviction_handler_code = '''\n",
+    "from distributed import as_completed\n",
+    "\n",
+    "futures = [client.submit(run_gcam, s, tag=\"gcam\") for s in scenarios]\n",
+    "\n",
+    "results = []\n",
+    "evicted = []\n",
+    "\n",
+    "for future in as_completed(futures):\n",
+    "    try:\n",
+    "        results.append(future.result())\n",
+    "    except Exception as e:\n",
+    "        if \"KilledWorker\" in type(e).__name__:\n",
+    "            # Pod was evicted — queue for retry\n",
+    "            evicted.append(future.key)\n",
+    "        else:\n",
+    "            print(f\"Permanent failure: {e}\")\n",
+    "\n",
+    "# Retry evicted tasks (pods will be rescheduled by operator)\n",
+    "if evicted:\n",
+    "    print(f\"Retrying {len(evicted)} evicted tasks...\")\n",
+    "    retry_futures = [client.submit(run_gcam, s, tag=\"gcam\") for s in evicted]\n",
+    "    retry_results = client.gather(retry_futures)\n",
+    "    results.extend(retry_results)\n",
+    "'''\n",
+    "\n",
+    "print(\"Pod eviction handling pattern:\")\n",
+    "print(eviction_handler_code)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 8: CI/CD Integration\n",
+    "\n",
+    "Automate Kubernetes deployments from GitHub Actions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ci_workflow = \"\"\"\\\n",
+    "# .github/workflows/scalable-k8s.yaml\n",
+    "name: Scalable K8s Pipeline\n",
+    "on:\n",
+    "  workflow_dispatch:\n",
+    "    inputs:\n",
+    "      target:\n",
+    "        description: \"Target environment\"\n",
+    "        default: \"k8s-dev\"\n",
+    "        type: choice\n",
+    "        options: [k8s-dev, k8s-prod]\n",
+    "\n",
+    "jobs:\n",
+    "  run:\n",
+    "    runs-on: ubuntu-latest\n",
+    "    steps:\n",
+    "      - uses: actions/checkout@v4\n",
+    "      - uses: google-github-actions/auth@v2\n",
+    "        with:\n",
+    "          credentials_json: ${{ secrets.GCP_SA_KEY }}\n",
+    "      - uses: google-github-actions/get-gke-credentials@v2\n",
+    "        with:\n",
+    "          cluster_name: climate-cluster\n",
+    "          location: us-central1\n",
+    "      - name: Run Pipeline\n",
+    "        run: |\n",
+    "          pip install scalable[kubernetes,cloud]\n",
+    "          scalable validate ./scalable.yaml\n",
+    "          scalable run ./scalable.yaml --target ${{ inputs.target }}\n",
+    "\"\"\"\n",
+    "\n",
+    "print(\"GitHub Actions CI/CD workflow:\")\n",
+    "print(ci_workflow)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 9: Local Development with Local Target\n",
+    "\n",
+    "The same manifest works locally — just select a different target."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "\n",
+    "\n",
+    "def run_gcam_local(scenario: int) -> dict:\n",
+    "    \"\"\"Simulate GCAM execution.\"\"\"\n",
+    "    time.sleep(0.2)\n",
+    "    return {\"scenario\": scenario, \"emissions\": scenario * 1.5}\n",
+    "\n",
+    "\n",
+    "# Run locally — same workflow code works on K8s\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "client = session.start()\n",
+    "\n",
+    "futures = [client.submit(run_gcam_local, i, tag=\"gcam\") for i in range(5)]\n",
+    "results = client.gather(futures)\n",
+    "\n",
+    "print(\"Local results (same code runs on K8s):\")\n",
+    "for r in results:\n",
+    "    print(f\"  {r}\")\n",
+    "\n",
+    "session.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1. Kubernetes provider creates DaskCluster CRs managed by the Dask Operator\n",
+    "2. Components map to pod resource requests/limits\n",
+    "3. Overlays customize resources for dev vs prod namespaces\n",
+    "4. Adaptive scaling bounds (min/max) control pod counts\n",
+    "5. Pod evictions are handled as retryable failures\n",
+    "6. Same workflow code works locally and on Kubernetes\n",
+    "7. CI/CD integration automates deployment\n",
+    "\n",
+    "## Next Steps\n",
+    "\n",
+    "- **Tutorial 9**: ML-driven pod sizing based on historical resource usage\n",
+    "- **Tutorial 7**: Error handling for pod evictions\n",
+    "- **Tutorial 10**: AI-assisted manifest migration to Kubernetes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(\"Cleaned up.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/09_ml_emulation.ipynb b/notebooks/09_ml_emulation.ipynb
new file mode 100644
index 0000000..528f677
--- /dev/null
+++ b/notebooks/09_ml_emulation.ipynb
@@ -0,0 +1,515 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial 9: ML-Driven Scaling and Model Emulation\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- Use the ResourceAdvisor for deterministic recommendations\n",
+    "- Train and use LearnedAdvisor for ML-backed predictions\n",
+    "- Mark functions as emulatable with `@emulatable`\n",
+    "- Dispatch between emulators and full models based on confidence\n",
+    "- Use active learning to improve emulator accuracy\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed Tutorials 1, 3, and 6\n",
+    "- `pip install scalable[ml]`\n",
+    "\n",
+    "> **Note:** Some ML features require historical telemetry. This notebook generates synthetic data where needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "import time\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-ml-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part A: ML-Driven Resource Advising\n",
+    "\n",
+    "### Step 1: Deterministic ResourceAdvisor\n",
+    "\n",
+    "The baseline advisor uses quantile statistics from telemetry history."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable import ResourceAdvisor\n",
+    "\n",
+    "# The ResourceAdvisor needs run history.\n",
+    "# Let's first generate some telemetry by running a workflow.\n",
+    "\n",
+    "from scalable import ScalableSession\n",
+    "\n",
+    "manifest = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: ml-demo\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 2\n",
+    "    threads_per_worker: 1\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "components:\n",
+    "  gcam:\n",
+    "    cpus: 2\n",
+    "    memory: 4G\n",
+    "tasks:\n",
+    "  run_gcam:\n",
+    "    component: gcam\n",
+    "    cache: true\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(manifest)\n",
+    "\n",
+    "\n",
+    "def simulate_gcam(scenario: int) -> dict:\n",
+    "    time.sleep(0.1 + (scenario % 5) * 0.05)\n",
+    "    return {\"scenario\": scenario, \"emissions\": scenario * 1.5}\n",
+    "\n",
+    "\n",
+    "# Generate multiple runs for history\n",
+    "for run_num in range(3):\n",
+    "    sess = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "    cl = sess.start()\n",
+    "    futs = [cl.submit(simulate_gcam, i, tag=\"gcam\") for i in range(5 + run_num * 2)]\n",
+    "    cl.gather(futs)\n",
+    "    sess.close()\n",
+    "    time.sleep(0.2)\n",
+    "\n",
+    "print(f\"Generated 3 runs of telemetry data\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now use the ResourceAdvisor\n",
+    "try:\n",
+    "    advisor = ResourceAdvisor.from_history(\"./.scalable/runs\")\n",
+    "    recommendation = advisor.recommend(\n",
+    "        task=\"run_gcam\",\n",
+    "        target=\"local\",\n",
+    "        confidence=0.95,\n",
+    "    )\n",
+    "    \n",
+    "    print(f\"ResourceAdvisor Recommendation:\")\n",
+    "    print(f\"  Task: {recommendation.task}\")\n",
+    "    print(f\"  Target: {recommendation.target}\")\n",
+    "    print(f\"  Confidence: {recommendation.confidence}\")\n",
+    "    print(f\"  Workers: {recommendation.workers}\")\n",
+    "    print(f\"  Resources: {recommendation.resources}\")\n",
+    "    print(f\"  Evidence: {recommendation.evidence}\")\n",
+    "except Exception as e:\n",
+    "    print(f\"Advisor needs more data: {e}\")\n",
+    "    print(\"(This is expected with very few runs)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2: LearnedAdvisor (ML-Enhanced)\n",
+    "\n",
+    "The `LearnedAdvisor` trains a model on telemetry to predict resources based on task features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    from scalable import LearnedAdvisor\n",
+    "    \n",
+    "    advisor = LearnedAdvisor.from_history(\n",
+    "        \"./.scalable/runs\",\n",
+    "        model_type=\"random_forest\",\n",
+    "    )\n",
+    "    \n",
+    "    recommendation = advisor.recommend(\n",
+    "        task=\"run_gcam\",\n",
+    "        target=\"local\",\n",
+    "    )\n",
+    "    \n",
+    "    print(f\"LearnedAdvisor Recommendation:\")\n",
+    "    print(f\"  Workers: {recommendation.workers}\")\n",
+    "    print(f\"  Resources: {recommendation.resources}\")\n",
+    "    print(f\"  Confidence: {recommendation.confidence:.2f}\")\n",
+    "    \n",
+    "except ImportError:\n",
+    "    print(\"LearnedAdvisor requires scalable[ml]\")\n",
+    "    print(\"Install with: pip install scalable[ml]\")\n",
+    "except Exception as e:\n",
+    "    print(f\"LearnedAdvisor: {e}\")\n",
+    "    print(\"(Needs sufficient history for training)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3: Model Types Comparison\n",
+    "\n",
+    "| Model | Accuracy | Speed | History Needed |\n",
+    "|-------|----------|-------|---------------|\n",
+    "| `linear` | Low | Fast (<1s) | 5+ runs |\n",
+    "| `random_forest` | Medium | Moderate | 10+ runs |\n",
+    "| `gradient_boosting` | High | Slow (30-120s) | 50+ runs |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 4: AdaptiveScaler with ML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    from scalable import AdaptiveScaler\n",
+    "    \n",
+    "    scaler = AdaptiveScaler(\n",
+    "        min_workers={\"gcam\": 1},\n",
+    "        max_workers={\"gcam\": 20},\n",
+    "        scale_up_threshold=0.7,\n",
+    "        scale_down_threshold=0.3,\n",
+    "        cooldown_seconds=30,\n",
+    "    )\n",
+    "    \n",
+    "    # Simulate evaluation\n",
+    "    decision = scaler.evaluate(\n",
+    "        pending_tasks=[{\"tag\": \"gcam\"} for _ in range(15)],\n",
+    "        active_workers={\"gcam\": 3},\n",
+    "    )\n",
+    "    \n",
+    "    print(\"AdaptiveScaler Decision:\")\n",
+    "    print(f\"  Has changes: {decision.has_changes}\")\n",
+    "    print(f\"  Add workers: {decision.workers_to_add}\")\n",
+    "    print(f\"  Remove workers: {decision.workers_to_remove}\")\n",
+    "    print(f\"  Reasoning: {decision.reasoning}\")\n",
+    "    print(f\"  Confidence: {decision.confidence:.2f}\")\n",
+    "    \n",
+    "except ImportError:\n",
+    "    print(\"AdaptiveScaler requires scalable[ml]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part B: Model Emulation\n",
+    "\n",
+    "### Step 5: The @emulatable Decorator\n",
+    "\n",
+    "Mark expensive functions as candidates for surrogate model replacement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    from scalable.emulation import emulatable\n",
+    "    from scalable.emulation.decorator import _EMULATABLE_REGISTRY, EmulationSpec\n",
+    "    \n",
+    "    @emulatable(\n",
+    "        tag=\"gcam\",\n",
+    "        inputs=[\"carbon_price\", \"population\"],\n",
+    "        outputs=[\"emissions\"],\n",
+    "        uncertainty=\"required\",\n",
+    "        fallback=\"full_model\",\n",
+    "        domain={\n",
+    "            \"carbon_price\": (0, 500),\n",
+    "            \"population\": (7e9, 12e9),\n",
+    "        },\n",
+    "        confidence_threshold=0.9,\n",
+    "    )\n",
+    "    def run_gcam_scenario(carbon_price, population):\n",
+    "        \"\"\"Full GCAM scenario — expensive computation.\"\"\"\n",
+    "        time.sleep(0.5)  # Simulate expensive work\n",
+    "        emissions = carbon_price * 0.07 + population * 3e-9\n",
+    "        return {\"emissions\": emissions}\n",
+    "    \n",
+    "    print(\"Function marked as @emulatable\")\n",
+    "    print(f\"\\nRegistered emulatable functions: {list(_EMULATABLE_REGISTRY.keys())}\")\n",
+    "    \n",
+    "    spec = _EMULATABLE_REGISTRY[\"run_gcam_scenario\"]\n",
+    "    print(f\"\\nEmulation spec:\")\n",
+    "    print(f\"  Tag: {spec.tag}\")\n",
+    "    print(f\"  Inputs: {spec.inputs}\")\n",
+    "    print(f\"  Outputs: {spec.outputs}\")\n",
+    "    print(f\"  Domain: {spec.domain}\")\n",
+    "    print(f\"  Confidence threshold: {spec.confidence_threshold}\")\n",
+    "    print(f\"  Fallback: {spec.fallback}\")\n",
+    "    \n",
+    "except ImportError:\n",
+    "    print(\"Emulation requires scalable[ml]\")\n",
+    "    print(\"Install with: pip install scalable[ml]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 6: Running the Full Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # The decorated function still works normally\n",
+    "    start = time.time()\n",
+    "    result = run_gcam_scenario(100, 8e9)\n",
+    "    elapsed = time.time() - start\n",
+    "    \n",
+    "    print(f\"Full model result: {result}\")\n",
+    "    print(f\"Time: {elapsed:.3f}s\")\n",
+    "    print(\"\\nWhen an emulator is trained and registered, calls can be\")\n",
+    "    print(\"routed to the fast surrogate instead of the full model.\")\n",
+    "except NameError:\n",
+    "    print(\"(Requires scalable[ml] for @emulatable)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 7: Emulator Registry and Dispatch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    from scalable.emulation import EmulatorRegistry, EmulatorDispatch\n",
+    "    \n",
+    "    # Create a registry\n",
+    "    os.makedirs(\".scalable/emulators\", exist_ok=True)\n",
+    "    registry = EmulatorRegistry(\".scalable/emulators\")\n",
+    "    \n",
+    "    print(f\"Emulator Registry: {registry}\")\n",
+    "    print(f\"Registered emulators: {registry.list()}\")\n",
+    "    \n",
+    "    # Create a dispatch instance\n",
+    "    dispatch = EmulatorDispatch(registry, confidence_threshold=0.9)\n",
+    "    print(f\"\\nEmulatorDispatch configured:\")\n",
+    "    print(f\"  Confidence threshold: 0.9\")\n",
+    "    print(f\"  Dispatch logic:\")\n",
+    "    print(f\"    1. Check if emulator exists for function\")\n",
+    "    print(f\"    2. Validate inputs are within domain bounds\")\n",
+    "    print(f\"    3. Get emulator prediction + confidence\")\n",
+    "    print(f\"    4. If confidence >= 0.9: return emulator result\")\n",
+    "    print(f\"    5. Else: fall back to full model\")\n",
+    "    \n",
+    "except ImportError:\n",
+    "    print(\"Emulation requires scalable[ml]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 8: Active Learning Concepts\n",
+    "\n",
+    "Active learning strategically selects training points to maximize emulator accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    from scalable.emulation import ActiveLearner\n",
+    "    \n",
+    "    print(\"Active Learning Acquisition Strategies:\")\n",
+    "    print(\"=\"*50)\n",
+    "    print()\n",
+    "    print(\"  'uncertainty'\")\n",
+    "    print(\"    Sample where prediction uncertainty is highest.\")\n",
+    "    print(\"    Best for: Expanding emulator coverage uniformly.\")\n",
+    "    print()\n",
+    "    print(\"  'expected_improvement'\")\n",
+    "    print(\"    Sample where model is likely wrong.\")\n",
+    "    print(\"    Best for: Correcting known weaknesses.\")\n",
+    "    print()\n",
+    "    print(\"  'random'\")\n",
+    "    print(\"    Uniform random sampling.\")\n",
+    "    print(\"    Best for: Baseline comparison.\")\n",
+    "    print()\n",
+    "    print(\"Workflow:\")\n",
+    "    print(\"  1. Train initial emulator on small sample\")\n",
+    "    print(\"  2. Use ActiveLearner.suggest() for next batch\")\n",
+    "    print(\"  3. Run full model on suggested points\")\n",
+    "    print(\"  4. Update emulator with new data\")\n",
+    "    print(\"  5. Repeat until accuracy target is met\")\n",
+    "    \n",
+    "except ImportError:\n",
+    "    print(\"ActiveLearner requires scalable[ml]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 9: Emulation in Production\n",
+    "\n",
+    "Massive speedups by routing confident predictions to emulators:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Demonstrate the production pattern (conceptual)\n",
+    "production_pattern = '''\n",
+    "from scalable.emulation import EmulatorDispatch, EmulatorRegistry\n",
+    "\n",
+    "registry = EmulatorRegistry(\".scalable/emulators\")\n",
+    "dispatch = EmulatorDispatch(registry, confidence_threshold=0.9)\n",
+    "\n",
+    "results = []\n",
+    "emulated = 0\n",
+    "full_model = 0\n",
+    "\n",
+    "for cp in range(0, 500, 10):\n",
+    "    for pop in [8e9, 9e9, 10e9]:\n",
+    "        result = dispatch.predict(\n",
+    "            \"run_gcam_scenario\",\n",
+    "            inputs={\"carbon_price\": cp, \"population\": pop},\n",
+    "        )\n",
+    "        \n",
+    "        if result.source == \"emulator\":\n",
+    "            emulated += 1\n",
+    "        else:\n",
+    "            full_model += 1\n",
+    "        \n",
+    "        results.append(result.values)\n",
+    "\n",
+    "print(f\"Emulated: {emulated} ({emulated*100/(emulated+full_model):.0f}%)\")\n",
+    "print(f\"Full model: {full_model}\")\n",
+    "print(f\"Time saved: ~{emulated * 30} minutes\")\n",
+    "'''\n",
+    "\n",
+    "print(\"Production Emulation Pattern:\")\n",
+    "print(production_pattern)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 10: Environment Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"ML and Emulation Environment Variables:\")\n",
+    "print(\"=\"*50)\n",
+    "print()\n",
+    "print(f\"  SCALABLE_ML=1              Enable ML features\")\n",
+    "print(f\"  SCALABLE_ML_CACHE_DIR      ML model cache (.scalable/models)\")\n",
+    "print(f\"  SCALABLE_EMULATION=0       Enable emulation (set to 1)\")\n",
+    "print(f\"  SCALABLE_EMULATOR_DIR      Emulator registry (.scalable/emulators)\")\n",
+    "print(f\"  SCALABLE_EMULATION_CONFIDENCE=0.9  Confidence threshold\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "### Part A: ML Resource Advising\n",
+    "1. `ResourceAdvisor` — deterministic, quantile-based (always available)\n",
+    "2. `LearnedAdvisor` — ML-trained on telemetry (requires scalable[ml])\n",
+    "3. `AdaptiveScaler` — real-time scaling based on queue depth\n",
+    "\n",
+    "### Part B: Model Emulation\n",
+    "4. `@emulatable` — marks functions for surrogate replacement\n",
+    "5. `EmulatorRegistry` — stores trained surrogate models\n",
+    "6. `EmulatorDispatch` — confidence-gated routing\n",
+    "7. `ActiveLearner` — strategic training point selection\n",
+    "\n",
+    "## Next Steps\n",
+    "\n",
+    "- **Tutorial 10**: AI-assisted workflow composition\n",
+    "- **Tutorial 6**: Feed telemetry to the ML advisor\n",
+    "- **Tutorial 5**: Deploy emulation-backed workflows to cloud"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(\"Cleaned up.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/10_ai_composition.ipynb b/notebooks/10_ai_composition.ipynb
new file mode 100644
index 0000000..18970db
--- /dev/null
+++ b/notebooks/10_ai_composition.ipynb
@@ -0,0 +1,613 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial 10: AI-Assisted Workflow Composition\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- Use AI assistants to accelerate workflow development\n",
+    "- Onboard new components with `onboard_component`\n",
+    "- Diagnose run failures with `diagnose_run`\n",
+    "- Generate human-readable plan explanations\n",
+    "- Compose workflows from natural language\n",
+    "- Migrate manifests between providers\n",
+    "- Understand heuristic vs LLM-enhanced modes\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed Tutorials 1 and 2\n",
+    "- `pip install scalable[ai]`\n",
+    "- (Optional) LLM API key for enhanced mode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "import json\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-ai-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: AI Backend Configuration\n",
+    "\n",
+    "All AI assistants work in two modes:\n",
+    "- **Heuristic** (`no_ai=True`): Deterministic rules, no API calls, fast\n",
+    "- **LLM-enhanced** (`no_ai=False`): Richer output, requires API key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable.common import settings\n",
+    "\n",
+    "# Check AI backend configuration\n",
+    "ai_backend = os.environ.get(\"SCALABLE_AI_BACKEND\", \"none\")\n",
+    "ai_model = os.environ.get(\"SCALABLE_AI_MODEL\", \"(not set)\")\n",
+    "\n",
+    "print(\"AI Configuration:\")\n",
+    "print(f\"  Backend: {ai_backend}\")\n",
+    "print(f\"  Model: {ai_model}\")\n",
+    "print(f\"\\nAvailable backends:\")\n",
+    "print(f\"  none   → heuristic only (default, no API needed)\")\n",
+    "print(f\"  openai → OpenAI API (requires OPENAI_API_KEY)\")\n",
+    "print(f\"  ollama → Local Ollama (requires SCALABLE_AI_ENDPOINT)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Check AI Module Availability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    from scalable.ai import (\n",
+    "        onboard_component,\n",
+    "        diagnose_run,\n",
+    "        explain_plan,\n",
+    "        compose_workflow,\n",
+    "        migrate_manifest,\n",
+    "    )\n",
+    "    print(\"✓ AI module available\")\n",
+    "    print(\"  Functions: onboard_component, diagnose_run, explain_plan,\")\n",
+    "    print(\"             compose_workflow, migrate_manifest\")\n",
+    "except ImportError:\n",
+    "    print(\"✗ AI module not available\")\n",
+    "    print(\"  Install with: pip install scalable[ai]\")\n",
+    "    print(\"  (requires jinja2 and rich)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Onboarding a New Component\n",
+    "\n",
+    "The `onboard_component` assistant analyzes a model directory and generates component configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a sample model directory to onboard\n",
+    "model_dir = os.path.join(project_dir, \"stitches-model\")\n",
+    "os.makedirs(model_dir, exist_ok=True)\n",
+    "\n",
+    "# Simulate a model with typical files\n",
+    "with open(os.path.join(model_dir, \"run_stitches.R\"), \"w\") as f:\n",
+    "    f.write(\"# Stitches climate downscaling model\\n\")\n",
+    "    f.write(\"library(stitches)\\nlibrary(dplyr)\\n\")\n",
+    "    f.write(\"result <- run_downscaling(input_path, output_path)\\n\")\n",
+    "\n",
+    "with open(os.path.join(model_dir, \"DESCRIPTION\"), \"w\") as f:\n",
+    "    f.write(\"Package: stitches\\n\")\n",
+    "    f.write(\"Title: Climate Downscaling\\n\")\n",
+    "    f.write(\"Imports: dplyr, tidyr, ggplot2\\n\")\n",
+    "\n",
+    "with open(os.path.join(model_dir, \"Dockerfile\"), \"w\") as f:\n",
+    "    f.write(\"FROM rocker/r-ver:4.3\\n\")\n",
+    "    f.write(\"RUN install2.r stitches dplyr\\n\")\n",
+    "\n",
+    "print(f\"Created sample model directory: {model_dir}\")\n",
+    "print(f\"Files: {os.listdir(model_dir)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    result = onboard_component(\n",
+    "        model_dir,\n",
+    "        name=\"stitches\",\n",
+    "        no_ai=True,  # Heuristic mode\n",
+    "    )\n",
+    "    \n",
+    "    print(\"Onboarding Result:\")\n",
+    "    print(f\"  Component YAML:\\n{result.component_yaml}\")\n",
+    "    if hasattr(result, 'task_yaml'):\n",
+    "        print(f\"  Task YAML:\\n{result.task_yaml}\")\n",
+    "    if hasattr(result, 'recommendations'):\n",
+    "        print(f\"  Recommendations: {result.recommendations}\")\n",
+    "        \n",
+    "except Exception as e:\n",
+    "    print(f\"Onboarding result: {e}\")\n",
+    "    print(\"\\nExpected output (heuristic mode):\")\n",
+    "    print(\"  components:\")\n",
+    "    print(\"    stitches:\")\n",
+    "    print(\"      image: (from Dockerfile)\")\n",
+    "    print(\"      cpus: 6\")\n",
+    "    print(\"      memory: 50G\")\n",
+    "    print(\"      tags: [climate, downscaling]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Diagnosing Run Failures\n",
+    "\n",
+    "Generate telemetry with some failures, then diagnose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "from scalable import ScalableSession\n",
+    "from distributed import as_completed\n",
+    "\n",
+    "manifest = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: diagnosis-demo\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 2\n",
+    "    threads_per_worker: 1\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "components:\n",
+    "  worker:\n",
+    "    cpus: 1\n",
+    "    memory: 1G\n",
+    "tasks:\n",
+    "  compute:\n",
+    "    component: worker\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(manifest)\n",
+    "\n",
+    "\n",
+    "def failing_task(n: int) -> dict:\n",
+    "    if n % 4 == 0:\n",
+    "        raise RuntimeError(f\"OOM: task {n} exceeded memory\")\n",
+    "    if n % 7 == 0:\n",
+    "        raise ConnectionError(f\"Timeout fetching data for task {n}\")\n",
+    "    return {\"n\": n, \"result\": n * 42}\n",
+    "\n",
+    "\n",
+    "# Run with some failures\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "client = session.start()\n",
+    "\n",
+    "futures = [client.submit(failing_task, i, tag=\"worker\") for i in range(20)]\n",
+    "for future in as_completed(futures):\n",
+    "    try:\n",
+    "        future.result()\n",
+    "    except Exception:\n",
+    "        pass  # Expected failures\n",
+    "\n",
+    "session.close()\n",
+    "print(\"Run with failures completed.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Diagnose the run\n",
+    "from pathlib import Path\n",
+    "from scalable.telemetry.collectors import iter_run_dirs\n",
+    "\n",
+    "runs_dir = Path(\".scalable/runs\")\n",
+    "run_dirs = sorted(iter_run_dirs(runs_dir))\n",
+    "latest_run = run_dirs[-1] if run_dirs else None\n",
+    "\n",
+    "if latest_run:\n",
+    "    try:\n",
+    "        result = diagnose_run(\n",
+    "            run_dir=str(latest_run),\n",
+    "            no_ai=True,\n",
+    "        )\n",
+    "        \n",
+    "        print(\"Diagnosis Result:\")\n",
+    "        print(f\"  Summary: {result.summary}\")\n",
+    "        if hasattr(result, 'findings'):\n",
+    "            for finding in result.findings:\n",
+    "                print(f\"  [{finding.severity}] {finding.category}\")\n",
+    "                print(f\"    Pattern: {finding.pattern}\")\n",
+    "                print(f\"    Suggestion: {finding.suggestion}\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"Diagnosis: {e}\")\n",
+    "        # Show what's in the failures file\n",
+    "        failures_file = latest_run / \"failures.jsonl\"\n",
+    "        if failures_file.exists():\n",
+    "            from scalable.telemetry.collectors import read_jsonl\n",
+    "            failures = read_jsonl(failures_file)\n",
+    "            print(f\"\\nRaw failures ({len(failures)} events):\")\n",
+    "            from collections import Counter\n",
+    "            by_class = Counter(f.get(\"failure_class\", \"?\") for f in failures)\n",
+    "            for cls, cnt in by_class.most_common():\n",
+    "                print(f\"  {cls}: {cnt}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: Explaining Execution Plans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate a plan\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "plan = session.plan(dry_run=True)\n",
+    "\n",
+    "# Save plan to file\n",
+    "plan_data = {\n",
+    "    \"target_name\": plan.target_name,\n",
+    "    \"provider_name\": plan.provider_name,\n",
+    "    \"manifest_lock\": plan.manifest_lock,\n",
+    "}\n",
+    "\n",
+    "with open(\"plan.json\", \"w\") as f:\n",
+    "    json.dump(plan_data, f, indent=2)\n",
+    "\n",
+    "print(\"Plan saved to plan.json\")\n",
+    "\n",
+    "# Explain it\n",
+    "try:\n",
+    "    result = explain_plan(\"plan.json\")\n",
+    "    print(f\"\\nExplanation:\\n{result.explanation}\")\n",
+    "except Exception as e:\n",
+    "    print(f\"\\nExplain result: {e}\")\n",
+    "    print(\"\\nExpected output:\")\n",
+    "    print('  This plan will execute \"diagnosis-demo\" on a local Dask cluster')\n",
+    "    print('  with 2 workers (1 CPU, 1G memory each). No containers are used.')\n",
+    "    print('  Workers run as threads within a single process.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: Composing Workflows from Natural Language"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    result = compose_workflow(\n",
+    "        \"Run GCAM reference scenario for SSP2, \"\n",
+    "        \"then run Stitches to downscale daily climate data\"\n",
+    "    )\n",
+    "    \n",
+    "    print(\"Composed Workflow:\")\n",
+    "    print(f\"  Code:\\n{result.workflow_code[:500]}...\")\n",
+    "    if hasattr(result, 'manifest_additions'):\n",
+    "        print(f\"\\n  Manifest additions:\\n{result.manifest_additions}\")\n",
+    "        \n",
+    "except Exception as e:\n",
+    "    print(f\"Compose result: {e}\")\n",
+    "    print(\"\\nExpected output (heuristic mode):\")\n",
+    "    print(\"  A Python workflow script with:\")\n",
+    "    print(\"  - @cacheable decorated functions\")\n",
+    "    print(\"  - ScalableSession setup\")\n",
+    "    print(\"  - Sequential task submission (GCAM → Stitches)\")\n",
+    "    print(\"  - Suggested component/task manifest additions\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7: Migrating Between Providers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a Slurm manifest to migrate\n",
+    "slurm_manifest = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: climate-pipeline\n",
+    "targets:\n",
+    "  hpc:\n",
+    "    provider: slurm\n",
+    "    queue: batch\n",
+    "    account: GCIMS\n",
+    "    walltime: \"04:00:00\"\n",
+    "    interface: ib0\n",
+    "components:\n",
+    "  gcam:\n",
+    "    cpus: 8\n",
+    "    memory: 32G\n",
+    "    tags: [climate]\n",
+    "tasks:\n",
+    "  run_gcam:\n",
+    "    component: gcam\n",
+    "    cache: true\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"slurm-manifest.yaml\", \"w\") as f:\n",
+    "    f.write(slurm_manifest)\n",
+    "\n",
+    "try:\n",
+    "    result = migrate_manifest(\n",
+    "        \"slurm-manifest.yaml\",\n",
+    "        to_provider=\"kubernetes\",\n",
+    "    )\n",
+    "    \n",
+    "    print(\"Migration Result (Slurm → Kubernetes):\")\n",
+    "    if hasattr(result, 'migrated_yaml'):\n",
+    "        print(f\"\\n{result.migrated_yaml}\")\n",
+    "    if hasattr(result, 'changes_summary'):\n",
+    "        print(f\"\\nChanges: {result.changes_summary}\")\n",
+    "    if hasattr(result, 'migration_notes'):\n",
+    "        print(f\"\\nNotes: {result.migration_notes}\")\n",
+    "        \n",
+    "except Exception as e:\n",
+    "    print(f\"Migration result: {e}\")\n",
+    "    print(\"\\nExpected changes (slurm → kubernetes):\")\n",
+    "    print(\"  - Remove: queue, account, walltime, interface\")\n",
+    "    print(\"  - Add: namespace, image, adaptive\")\n",
+    "    print(\"  - Components need 'image' field\")\n",
+    "    print(\"  - Mounts → PVC or cloud storage\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 8: Heuristic Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    from scalable.ai.heuristics import detect_language, estimate_resources\n",
+    "    \n",
+    "    # Language detection\n",
+    "    lang = detect_language(model_dir)\n",
+    "    print(f\"Detected language: {lang}\")\n",
+    "    \n",
+    "    # Resource estimation\n",
+    "    resources = estimate_resources(\n",
+    "        model_name=\"gcam\",\n",
+    "        input_size_mb=2048,\n",
+    "        num_scenarios=50,\n",
+    "    )\n",
+    "    print(f\"Estimated resources: {resources}\")\n",
+    "    \n",
+    "except ImportError:\n",
+    "    print(\"Heuristics module requires scalable[ai]\")\n",
+    "except Exception as e:\n",
+    "    print(f\"Heuristics: {e}\")\n",
+    "    print(\"\\nThe heuristic engine:\")\n",
+    "    print(\"  - Detects language from file extensions and imports\")\n",
+    "    print(\"  - Estimates resources from known model profiles\")\n",
+    "    print(\"  - Generates component configs from templates\")\n",
+    "    print(\"  - All deterministic, reproducible, no API needed\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 9: Development Workflow Integration\n",
+    "\n",
+    "The AI assistants form a smooth development loop:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "workflow = \"\"\"\\\n",
+    "Development Workflow with AI Assistants:\n",
+    "========================================\n",
+    "\n",
+    "1. ONBOARD a new model:\n",
+    "   scalable init-component ./new-model --name new-model --no-ai\n",
+    "\n",
+    "2. COMPOSE a workflow incorporating it:\n",
+    "   scalable compose \"Run existing pipeline then feed results to new-model\"\n",
+    "\n",
+    "3. VALIDATE the generated configuration:\n",
+    "   scalable validate ./scalable.yaml\n",
+    "\n",
+    "4. PLAN and EXPLAIN (for team review):\n",
+    "   scalable plan ./scalable.yaml --target local --dry-run -o plan.json\n",
+    "   scalable explain plan.json\n",
+    "\n",
+    "5. RUN locally:\n",
+    "   scalable run ./scalable.yaml --target local --workflow workflow.py\n",
+    "\n",
+    "6. If it fails, DIAGNOSE:\n",
+    "   scalable diagnose --latest --no-ai\n",
+    "\n",
+    "7. When ready for production, MIGRATE:\n",
+    "   scalable migrate scalable.yaml --to-provider kubernetes\n",
+    "\"\"\"\n",
+    "\n",
+    "print(workflow)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 10: Validating AI-Generated Output\n",
+    "\n",
+    "Always validate AI-generated configurations before running."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable import ScalableSession\n",
+    "\n",
+    "# Simulate AI-generated manifest\n",
+    "generated_manifest = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: ai-generated\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 4\n",
+    "    threads_per_worker: 2\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "components:\n",
+    "  gcam:\n",
+    "    cpus: 8\n",
+    "    memory: 32G\n",
+    "    tags: [climate]\n",
+    "  stitches:\n",
+    "    cpus: 6\n",
+    "    memory: 50G\n",
+    "    tags: [downscaling]\n",
+    "tasks:\n",
+    "  run_gcam:\n",
+    "    component: gcam\n",
+    "    cache: true\n",
+    "  run_stitches:\n",
+    "    component: stitches\n",
+    "    cache: true\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(generated_manifest)\n",
+    "\n",
+    "# Validate\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "report = session.validate()\n",
+    "\n",
+    "if report.ok:\n",
+    "    print(\"✓ AI-generated config is valid — ready to run\")\n",
+    "else:\n",
+    "    print(\"✗ Generated config has issues:\")\n",
+    "    for issue in report.errors:\n",
+    "        print(f\"  [{issue.code}] {issue.path}: {issue.message}\")\n",
+    "    print(\"\\n  Fix issues before running.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1. **`onboard_component`** — Analyzes model dirs, generates component YAML\n",
+    "2. **`diagnose_run`** — Analyzes failures, identifies patterns, suggests fixes\n",
+    "3. **`explain_plan`** — Human-readable plan explanations for stakeholders\n",
+    "4. **`compose_workflow`** — Generates workflow code from natural language\n",
+    "5. **`migrate_manifest`** — Adapts manifests between providers\n",
+    "6. **Heuristic mode** — Fast, deterministic, no API needed (CI/CD safe)\n",
+    "7. **LLM mode** — Richer output, requires API key (interactive use)\n",
+    "8. **Always validate** — AI output is advisory; validate before running\n",
+    "\n",
+    "## Next Steps\n",
+    "\n",
+    "- **Tutorial 1**: Start from scratch if you're new\n",
+    "- **Tutorial 2**: Deep-dive into manifest schema that AI generates\n",
+    "- **Tutorial 8**: Deploy AI-generated Kubernetes configs\n",
+    "- **Tutorial 9**: Combine AI composition with ML optimization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(\"Cleaned up.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/README.md b/notebooks/README.md
new file mode 100644
index 0000000..ff61600
--- /dev/null
+++ b/notebooks/README.md
@@ -0,0 +1,46 @@
+# Scalable Tutorial Notebooks
+
+Interactive Jupyter notebooks accompanying the [documentation tutorials](../docs/tutorials/).
+
+## Notebooks
+
+| # | Notebook | Topic | Install Extra |
+|---|----------|-------|---------------|
+| 1 | [Getting Started](01_getting_started.ipynb) | Install, manifest, validate, run | `pip install scalable` |
+| 2 | [Manifest System](02_manifest_system.ipynb) | Schema, targets, overlays, validation | `pip install scalable` |
+| 3 | [Scaling Strategies](03_scaling_strategies.ipynb) | Providers, pools, adaptive scaling | `pip install scalable` |
+| 4 | [Caching & Performance](04_caching_performance.ipynb) | @cacheable, FileType, invalidation | `pip install scalable` |
+| 5 | [Cloud Integration](05_cloud_integration.ipynb) | AWS, GCP, artifact store, cost estimation | `pip install scalable[cloud]` |
+| 6 | [Telemetry](06_telemetry.ipynb) | JSONL events, reports, trend analysis | `pip install scalable` |
+| 7 | [Error Handling](07_error_handling.ipynb) | Retry, partial success, fault tolerance | `pip install scalable` |
+| 8 | [Kubernetes](08_kubernetes.ipynb) | Dask Operator, namespaces, CI/CD | `pip install scalable[kubernetes]` |
+| 9 | [ML & Emulation](09_ml_emulation.ipynb) | LearnedAdvisor, @emulatable, dispatch | `pip install scalable[ml]` |
+| 10 | [AI Composition](10_ai_composition.ipynb) | onboard, diagnose, compose, migrate | `pip install scalable[ai]` |
+
+## Quick Start
+
+```bash
+# Install Scalable with all extras
+pip install scalable[ai,cloud,kubernetes,ml]
+
+# Install Jupyter
+pip install jupyterlab
+
+# Launch
+jupyter lab notebooks/
+```
+
+## Running Order
+
+Notebooks are designed to be run sequentially (1 → 10) but each is self-contained with its own setup and teardown. Notebooks 1–4 and 6–7 require only the base `scalable` package; others need optional extras as noted above.
+
+## Conventions
+
+- Each notebook creates a temporary working directory and cleans up after itself.
+- Functions that simulate expensive computations use `time.sleep()` with short durations (0.1–1.0s) for notebook responsiveness.
+- Cloud and Kubernetes notebooks (5, 8) demonstrate configuration and concepts but require real infrastructure for full execution.
+- All notebooks use `no_ai=True` (heuristic mode) for the AI features to avoid external API dependencies.
+
+## Relationship to RST Tutorials
+
+These notebooks are interactive companions to the comprehensive RST tutorials in [`docs/tutorials/`](../docs/tutorials/). The RST versions contain deeper architectural context, trade-off discussions, and production deployment guidance. The notebooks focus on hands-on code execution.

From ea975f41f43acdd54af47d97f72112031bf32443 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 00:39:27 -0400
Subject: [PATCH 30/47] pydanticai transition

---
 pyproject.toml                          |   2 +
 scalable/ai/__init__.py                 |  14 +
 scalable/ai/agents/__init__.py          |  60 ++
 scalable/ai/agents/base.py              | 440 +++++++++++
 scalable/ai/agents/compose_agent.py     | 287 +++++++
 scalable/ai/agents/coordination.py      | 406 ++++++++++
 scalable/ai/agents/diagnosis_agent.py   | 172 +++++
 scalable/ai/agents/explanation_agent.py | 128 ++++
 scalable/ai/agents/migration_agent.py   | 203 +++++
 scalable/ai/agents/models.py            | 250 +++++++
 scalable/ai/agents/onboarding_agent.py  | 149 ++++
 scalable/ai/agents/providers.py         | 292 ++++++++
 scalable/ai/agents/tools.py             | 164 ++++
 scalable/ai/agents/validators.py        | 197 +++++
 scalable/ai/backend.py                  | 126 ++++
 tests/unit/test_ai_agents.py            | 944 ++++++++++++++++++++++++
 16 files changed, 3834 insertions(+)
 create mode 100644 scalable/ai/agents/__init__.py
 create mode 100644 scalable/ai/agents/base.py
 create mode 100644 scalable/ai/agents/compose_agent.py
 create mode 100644 scalable/ai/agents/coordination.py
 create mode 100644 scalable/ai/agents/diagnosis_agent.py
 create mode 100644 scalable/ai/agents/explanation_agent.py
 create mode 100644 scalable/ai/agents/migration_agent.py
 create mode 100644 scalable/ai/agents/models.py
 create mode 100644 scalable/ai/agents/onboarding_agent.py
 create mode 100644 scalable/ai/agents/providers.py
 create mode 100644 scalable/ai/agents/tools.py
 create mode 100644 scalable/ai/agents/validators.py
 create mode 100644 tests/unit/test_ai_agents.py

diff --git a/pyproject.toml b/pyproject.toml
index 208be07..7621622 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -59,6 +59,8 @@ dev = [
 # `pip install scalable[ai|cloud|kubernetes]` resolves cleanly from day one
 # and downstream pinning of the extras name is stable.
 ai = [
+    "pydantic >= 2.0",
+    "pydantic-ai >= 0.1",
     "jinja2 >= 3.1",
     "rich >= 13.0",
 ]
diff --git a/scalable/ai/__init__.py b/scalable/ai/__init__.py
index 8e8ab48..f707c2c 100644
--- a/scalable/ai/__init__.py
+++ b/scalable/ai/__init__.py
@@ -10,6 +10,20 @@
 
 All features have a **heuristic fallback** that works without any LLM backend.
 LLM enhancement is opt-in via ``SCALABLE_AI_BACKEND`` env var.
+
+Architecture
+------------
+The AI subsystem has two layers:
+
+1. **Legacy backend** (:mod:`scalable.ai.backend`) — simple completion-based
+   interface used by the original agent modules. Maintained for backward
+   compatibility.
+
+2. **PydanticAI agents** (:mod:`scalable.ai.agents`) — the recommended
+   approach using structured output validation, type-safe dependency injection,
+   tool registration, retry mechanisms, and multi-agent coordination patterns.
+   Supports all major providers (OpenAI, Anthropic, Google Gemini, Groq,
+   Ollama) through a unified interface.
 """
 
 from __future__ import annotations
diff --git a/scalable/ai/agents/__init__.py b/scalable/ai/agents/__init__.py
new file mode 100644
index 0000000..3a19055
--- /dev/null
+++ b/scalable/ai/agents/__init__.py
@@ -0,0 +1,60 @@
+"""PydanticAI-based agent framework for Scalable.
+
+This package provides a model-agnostic AI agent layer built on PydanticAI,
+enabling:
+
+* **Structured output validation** — all agent responses validated against
+  Pydantic models for predictable, type-safe outputs.
+* **Model-agnostic providers** — seamless switching between OpenAI, Anthropic,
+  Google Gemini, and local models without business logic changes.
+* **Dependency injection** — type-safe dependencies passed to agents at runtime.
+* **Tool registration** — declarative tool definitions with automatic schema
+  generation.
+* **Retry mechanisms** — configurable retry with exponential backoff and
+  result validators.
+* **Multi-agent coordination** — chains, delegation hierarchies, and
+  collaborative pipelines.
+
+Usage
+-----
+>>> from scalable.ai.agents import get_agent, AgentDeps
+>>> agent = get_agent("diagnose")
+>>> result = await agent.run(prompt, deps=AgentDeps(...))
+"""
+
+from __future__ import annotations
+
+from .base import AgentConfig, AgentDeps, AgentResult, ScalableAgent
+from .coordination import AgentChain, AgentPipeline, DelegatingAgent
+from .models import (
+    ComposeOutput,
+    DiagnosisOutput,
+    ExplanationOutput,
+    MigrationOutput,
+    OnboardingOutput,
+)
+from .providers import ModelProvider, get_model_provider, list_providers
+from .tools import ToolRegistry, tool
+from .validators import OutputValidator, validate_output
+
+__all__ = [
+    "AgentChain",
+    "AgentConfig",
+    "AgentDeps",
+    "AgentPipeline",
+    "AgentResult",
+    "ComposeOutput",
+    "DelegatingAgent",
+    "DiagnosisOutput",
+    "ExplanationOutput",
+    "MigrationOutput",
+    "ModelProvider",
+    "OnboardingOutput",
+    "OutputValidator",
+    "ScalableAgent",
+    "ToolRegistry",
+    "get_model_provider",
+    "list_providers",
+    "tool",
+    "validate_output",
+]
diff --git a/scalable/ai/agents/base.py b/scalable/ai/agents/base.py
new file mode 100644
index 0000000..a1cee19
--- /dev/null
+++ b/scalable/ai/agents/base.py
@@ -0,0 +1,440 @@
+"""Core agent base classes and dependency injection for PydanticAI integration.
+
+This module provides the foundational types for all PydanticAI-based agents
+in the Scalable framework:
+
+* :class:`AgentDeps` — dependency injection container passed to agents.
+* :class:`AgentConfig` — configuration for agent behavior (model, retries, etc.).
+* :class:`AgentResult` — wrapper around PydanticAI run results.
+* :class:`ScalableAgent` — base class wrapping PydanticAI ``Agent`` with
+  Scalable-specific defaults and heuristic fallback.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+from typing import Any, Generic, TypeVar
+
+from pydantic import BaseModel
+
+logger = logging.getLogger(__name__)
+
+__all__ = [
+    "AgentConfig",
+    "AgentDeps",
+    "AgentResult",
+    "ScalableAgent",
+]
+
+T = TypeVar("T", bound=BaseModel)
+
+
+@dataclass
+class AgentDeps:
+    """Dependency injection container for Scalable AI agents.
+
+    This is passed as ``deps`` to every PydanticAI agent run, providing
+    access to shared resources without global state.
+
+    Attributes
+    ----------
+    run_context : dict[str, Any]
+        Contextual data for the current operation (run_id, paths, etc.).
+    settings : dict[str, Any]
+        Configuration settings (model preferences, timeouts, etc.).
+    telemetry : dict[str, Any]
+        Telemetry data available for agent analysis.
+    tools_enabled : bool
+        Whether the agent can use registered tools.
+    max_retries : int
+        Maximum number of retries for failed operations.
+    """
+
+    run_context: dict[str, Any] = field(default_factory=dict)
+    settings: dict[str, Any] = field(default_factory=dict)
+    telemetry: dict[str, Any] = field(default_factory=dict)
+    tools_enabled: bool = True
+    max_retries: int = 3
+
+
+@dataclass
+class AgentConfig:
+    """Configuration for a Scalable AI agent.
+
+    Controls model selection, retry behavior, and validation settings.
+
+    Attributes
+    ----------
+    model : str | None
+        Model identifier (e.g., 'openai:gpt-4o', 'anthropic:claude-sonnet-4-20250514',
+        'google-gla:gemini-1.5-pro', 'ollama:llama3'). If None, uses the
+        default from SCALABLE_AI_MODEL.
+    temperature : float
+        Sampling temperature for model completions.
+    max_tokens : int
+        Maximum tokens for model responses.
+    max_retries : int
+        Maximum retry attempts on transient failures.
+    retry_delay : float
+        Base delay between retries (exponential backoff applied).
+    timeout : float
+        Timeout in seconds for a single agent run.
+    result_retries : int
+        Number of retries when result validation fails.
+    system_prompt : str | None
+        Override system prompt (uses agent default if None).
+    """
+
+    model: str | None = None
+    temperature: float = 0.0
+    max_tokens: int = 4096
+    max_retries: int = 3
+    retry_delay: float = 1.0
+    timeout: float = 120.0
+    result_retries: int = 2
+    system_prompt: str | None = None
+
+
+class AgentResult(Generic[T]):
+    """Wrapper around a PydanticAI agent run result.
+
+    Provides uniform access to structured output, metadata, and cost info.
+
+    Attributes
+    ----------
+    data : T
+        The validated Pydantic model output from the agent.
+    model_name : str
+        Name of the model that generated the response.
+    usage : dict[str, int]
+        Token usage statistics.
+    messages : list[dict[str, Any]]
+        Full message history from the agent run.
+    retries : int
+        Number of retries required.
+    """
+
+    def __init__(
+        self,
+        data: T,
+        *,
+        model_name: str = "unknown",
+        usage: dict[str, int] | None = None,
+        messages: list[dict[str, Any]] | None = None,
+        retries: int = 0,
+    ) -> None:
+        self.data = data
+        self.model_name = model_name
+        self.usage = usage or {}
+        self.messages = messages or []
+        self.retries = retries
+
+    def to_dict(self) -> dict[str, Any]:
+        """Serialize the result to a dictionary."""
+        return {
+            "data": self.data.model_dump() if hasattr(self.data, "model_dump") else str(self.data),
+            "model_name": self.model_name,
+            "usage": self.usage,
+            "retries": self.retries,
+        }
+
+
+class ScalableAgent(Generic[T]):
+    """Base class for PydanticAI-powered agents in Scalable.
+
+    Wraps a PydanticAI ``Agent`` with Scalable-specific defaults:
+    * Automatic model provider resolution from settings
+    * Heuristic fallback when no LLM is available
+    * Structured output validation against a Pydantic model
+    * Retry with exponential backoff
+    * Dependency injection via :class:`AgentDeps`
+
+    Subclasses must implement:
+    * :meth:`_build_agent` — construct the PydanticAI Agent
+    * :meth:`_heuristic_fallback` — provide non-LLM output
+
+    Parameters
+    ----------
+    result_type : type[T]
+        The Pydantic model class for validated output.
+    config : AgentConfig | None
+        Agent configuration. Uses defaults if None.
+    name : str
+        Human-readable agent name for logging.
+    system_prompt : str
+        Default system prompt for the agent.
+    """
+
+    def __init__(
+        self,
+        result_type: type[T],
+        *,
+        config: AgentConfig | None = None,
+        name: str = "scalable-agent",
+        system_prompt: str = "",
+    ) -> None:
+        self.result_type = result_type
+        self.config = config or AgentConfig()
+        self.name = name
+        self.system_prompt = self.config.system_prompt or system_prompt
+        self._agent: Any = None  # Lazy-initialized PydanticAI Agent
+
+    def _get_model_string(self) -> str | None:
+        """Resolve the model string from config or environment."""
+        if self.config.model:
+            return self.config.model
+
+        from scalable.common import settings
+
+        backend = getattr(settings, "ai_backend", "none")
+        model = getattr(settings, "ai_model", None)
+
+        if backend == "none" or not backend:
+            return None
+
+        if backend == "openai":
+            return f"openai:{model or 'gpt-4o'}"
+        elif backend == "anthropic":
+            return f"anthropic:{model or 'claude-sonnet-4-20250514'}"
+        elif backend == "google":
+            return f"google-gla:{model or 'gemini-1.5-pro'}"
+        elif backend == "ollama":
+            return f"ollama:{model or 'llama3'}"
+        elif backend == "groq":
+            return f"groq:{model or 'llama-3.1-70b-versatile'}"
+        else:
+            # Allow raw model strings (e.g., "openai:gpt-4o")
+            if ":" in backend:
+                return backend
+            return f"{backend}:{model or 'default'}"
+
+    def _build_agent(self) -> Any:
+        """Build and return a PydanticAI Agent instance.
+
+        Returns
+        -------
+        pydantic_ai.Agent
+            Configured agent instance.
+        """
+        try:
+            from pydantic_ai import Agent
+        except ImportError as exc:
+            raise ImportError(
+                "PydanticAI agent framework requires the 'pydantic-ai' package. "
+                "Install with: pip install scalable[ai]"
+            ) from exc
+
+        model_str = self._get_model_string()
+        if model_str is None:
+            raise RuntimeError("No model configured for agent")
+
+        agent = Agent(
+            model_str,
+            result_type=self.result_type,
+            system_prompt=self.system_prompt,
+            retries=self.config.result_retries,
+        )
+        return agent
+
+    def get_agent(self) -> Any:
+        """Get or lazily build the PydanticAI agent.
+
+        Returns
+        -------
+        pydantic_ai.Agent
+            The underlying PydanticAI agent instance.
+        """
+        if self._agent is None:
+            self._agent = self._build_agent()
+        return self._agent
+
+    async def run(
+        self,
+        prompt: str,
+        *,
+        deps: AgentDeps | None = None,
+        config: AgentConfig | None = None,
+    ) -> AgentResult[T]:
+        """Run the agent with the given prompt and return validated output.
+
+        This method attempts LLM execution first, falling back to heuristics
+        if no backend is available or if the LLM call fails.
+
+        Parameters
+        ----------
+        prompt : str
+            The user prompt to send to the agent.
+        deps : AgentDeps | None
+            Runtime dependencies for this execution.
+        config : AgentConfig | None
+            Override config for this specific run.
+
+        Returns
+        -------
+        AgentResult[T]
+            Validated, structured output from the agent.
+        """
+        effective_config = config or self.config
+        effective_deps = deps or AgentDeps()
+
+        # Check if LLM is available
+        model_str = self._get_model_string()
+        if model_str is None:
+            logger.info("No model configured for %s, using heuristic fallback", self.name)
+            fallback_data = self._heuristic_fallback(prompt, effective_deps)
+            return AgentResult(
+                data=fallback_data,
+                model_name="heuristic",
+                retries=0,
+            )
+
+        # Attempt PydanticAI execution with retry
+        retries = 0
+        last_error: Exception | None = None
+
+        while retries <= effective_config.max_retries:
+            try:
+                agent = self.get_agent()
+                result = await agent.run(
+                    prompt,
+                    deps=effective_deps,
+                )
+
+                # Extract usage info
+                usage: dict[str, int] = {}
+                if hasattr(result, "usage"):
+                    usage_obj = result.usage()
+                    if usage_obj:
+                        usage = {
+                            "request_tokens": getattr(usage_obj, "request_tokens", 0) or 0,
+                            "response_tokens": getattr(usage_obj, "response_tokens", 0) or 0,
+                            "total_tokens": getattr(usage_obj, "total_tokens", 0) or 0,
+                        }
+
+                return AgentResult(
+                    data=result.data,
+                    model_name=model_str,
+                    usage=usage,
+                    retries=retries,
+                )
+
+            except Exception as exc:
+                last_error = exc
+                retries += 1
+                if retries <= effective_config.max_retries:
+                    import asyncio
+                    delay = effective_config.retry_delay * (2 ** (retries - 1))
+                    logger.warning(
+                        "Agent %s attempt %d failed: %s. Retrying in %.1fs...",
+                        self.name, retries, exc, delay,
+                    )
+                    await asyncio.sleep(delay)
+
+        # All retries exhausted — fall back to heuristic
+        logger.warning(
+            "Agent %s exhausted %d retries (last error: %s). Using heuristic fallback.",
+            self.name, effective_config.max_retries, last_error,
+        )
+        fallback_data = self._heuristic_fallback(prompt, AgentDeps())
+        return AgentResult(
+            data=fallback_data,
+            model_name="heuristic-fallback",
+            retries=retries,
+        )
+
+    def run_sync(
+        self,
+        prompt: str,
+        *,
+        deps: AgentDeps | None = None,
+        config: AgentConfig | None = None,
+    ) -> AgentResult[T]:
+        """Synchronous wrapper around :meth:`run`.
+
+        Uses the PydanticAI ``run_sync`` method for non-async contexts,
+        with automatic heuristic fallback.
+
+        Parameters
+        ----------
+        prompt : str
+            The user prompt.
+        deps : AgentDeps | None
+            Runtime dependencies.
+        config : AgentConfig | None
+            Override config for this run.
+
+        Returns
+        -------
+        AgentResult[T]
+            Validated output.
+        """
+        effective_config = config or self.config
+        effective_deps = deps or AgentDeps()
+
+        model_str = self._get_model_string()
+        if model_str is None:
+            fallback_data = self._heuristic_fallback(prompt, effective_deps)
+            return AgentResult(
+                data=fallback_data,
+                model_name="heuristic",
+                retries=0,
+            )
+
+        try:
+            agent = self.get_agent()
+            result = agent.run_sync(
+                prompt,
+                deps=effective_deps,
+            )
+
+            usage: dict[str, int] = {}
+            if hasattr(result, "usage"):
+                usage_obj = result.usage()
+                if usage_obj:
+                    usage = {
+                        "request_tokens": getattr(usage_obj, "request_tokens", 0) or 0,
+                        "response_tokens": getattr(usage_obj, "response_tokens", 0) or 0,
+                        "total_tokens": getattr(usage_obj, "total_tokens", 0) or 0,
+                    }
+
+            return AgentResult(
+                data=result.data,
+                model_name=model_str,
+                usage=usage,
+                retries=0,
+            )
+
+        except Exception as exc:
+            logger.warning(
+                "Agent %s sync run failed: %s. Using heuristic fallback.",
+                self.name, exc,
+            )
+            fallback_data = self._heuristic_fallback(prompt, effective_deps)
+            return AgentResult(
+                data=fallback_data,
+                model_name="heuristic-fallback",
+                retries=0,
+            )
+
+    def _heuristic_fallback(self, prompt: str, deps: AgentDeps) -> T:
+        """Provide a heuristic (non-LLM) response.
+
+        Subclasses MUST override this to provide domain-specific
+        heuristic logic that works without any LLM backend.
+
+        Parameters
+        ----------
+        prompt : str
+            The original user prompt.
+        deps : AgentDeps
+            Runtime dependencies.
+
+        Returns
+        -------
+        T
+            A valid instance of the result_type model.
+        """
+        raise NotImplementedError(
+            f"Agent '{self.name}' must implement _heuristic_fallback()"
+        )
diff --git a/scalable/ai/agents/compose_agent.py b/scalable/ai/agents/compose_agent.py
new file mode 100644
index 0000000..8f3947d
--- /dev/null
+++ b/scalable/ai/agents/compose_agent.py
@@ -0,0 +1,287 @@
+"""PydanticAI-based workflow composition agent for Scalable.
+
+Refactors the existing ``workflow_compose`` module to use structured
+PydanticAI output validation with component-level type safety.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+from ..prompts.compose import COMPOSE_PROMPT, SYSTEM_PROMPT
+from .base import AgentConfig, AgentDeps, AgentResult, ScalableAgent
+from .models import ComposeOutput, WorkflowComponent
+
+logger = logging.getLogger(__name__)
+
+__all__ = ["ComposeAgent"]
+
+#: Known model patterns for heuristic composition
+_KNOWN_MODELS: dict[str, dict[str, Any]] = {
+    "gcam": {
+        "full_name": "GCAM",
+        "language": "c++",
+        "cpus": 6,
+        "memory": "20G",
+        "runtime": "apptainer",
+        "tags": ["iam", "climate", "compiled"],
+        "description": "Global Change Assessment Model",
+    },
+    "stitches": {
+        "full_name": "Stitches",
+        "language": "python",
+        "cpus": 1,
+        "memory": "50G",
+        "runtime": "docker",
+        "tags": ["climate", "python"],
+        "description": "Climate pattern scaling",
+    },
+    "demeter": {
+        "full_name": "Demeter",
+        "language": "python",
+        "cpus": 2,
+        "memory": "8G",
+        "runtime": "docker",
+        "tags": ["land-use", "python"],
+        "description": "Land use spatial downscaling",
+    },
+    "tethys": {
+        "full_name": "Tethys",
+        "language": "python",
+        "cpus": 2,
+        "memory": "8G",
+        "runtime": "docker",
+        "tags": ["water", "python"],
+        "description": "Water demand model",
+    },
+    "xanthos": {
+        "full_name": "Xanthos",
+        "language": "python",
+        "cpus": 2,
+        "memory": "16G",
+        "runtime": "docker",
+        "tags": ["hydrology", "python"],
+        "description": "Global hydrology model",
+    },
+    "hector": {
+        "full_name": "Hector",
+        "language": "c++",
+        "cpus": 1,
+        "memory": "4G",
+        "runtime": "docker",
+        "tags": ["climate", "compiled"],
+        "description": "Simple climate model",
+    },
+}
+
+
+class ComposeAgent(ScalableAgent[ComposeOutput]):
+    """AI agent for generating workflow compositions from descriptions.
+
+    Generates workflow skeletons including component definitions,
+    execution order, and parallelism groups.
+
+    Example
+    -------
+    >>> agent = ComposeAgent()
+    >>> result = agent.run_sync(
+    ...     "Create a workflow with GCAM feeding into Demeter and Tethys",
+    ... )
+    >>> for comp in result.data.components:
+    ...     print(f"{comp.name}: {comp.cpus} CPUs, {comp.memory}")
+    """
+
+    def __init__(self, *, config: AgentConfig | None = None) -> None:
+        super().__init__(
+            result_type=ComposeOutput,
+            config=config,
+            name="compose",
+            system_prompt=SYSTEM_PROMPT,
+        )
+
+    def _build_agent(self) -> Any:
+        """Build PydanticAI agent with composition-specific tools."""
+        agent = super()._build_agent()
+
+        @agent.tool_plain
+        def list_known_models() -> str:
+            """List known scientific models and their default configurations."""
+            lines = []
+            for key, info in _KNOWN_MODELS.items():
+                lines.append(
+                    f"- {info['full_name']} ({key}): {info['description']}, "
+                    f"{info['cpus']} CPUs, {info['memory']} memory, "
+                    f"runtime={info['runtime']}"
+                )
+            return "\n".join(lines)
+
+        @agent.tool_plain
+        def get_model_defaults(model_name: str) -> str:
+            """Get default configuration for a known model."""
+            info = _KNOWN_MODELS.get(model_name.lower())
+            if info:
+                return (
+                    f"Model: {info['full_name']}\n"
+                    f"Language: {info['language']}\n"
+                    f"CPUs: {info['cpus']}\n"
+                    f"Memory: {info['memory']}\n"
+                    f"Runtime: {info['runtime']}\n"
+                    f"Tags: {', '.join(info['tags'])}"
+                )
+            return f"Unknown model: {model_name}"
+
+        return agent
+
+    def build_prompt(self, description: str) -> str:
+        """Build the composition prompt.
+
+        Parameters
+        ----------
+        description : str
+            Natural-language workflow description.
+
+        Returns
+        -------
+        str
+            Formatted prompt.
+        """
+        return COMPOSE_PROMPT.format(description=description)
+
+    def _heuristic_fallback(self, prompt: str, deps: AgentDeps) -> ComposeOutput:
+        """Provide heuristic-based workflow composition without LLM."""
+        description = prompt
+        detected = self._detect_models(description)
+
+        if not detected:
+            return self._compose_generic(description)
+
+        return self._compose_from_detected(description, detected)
+
+    def _detect_models(self, description: str) -> list[str]:
+        """Detect known model names in the description."""
+        desc_lower = description.lower()
+        detected: list[str] = []
+        for model_key, info in _KNOWN_MODELS.items():
+            if model_key in desc_lower or info["full_name"].lower() in desc_lower:
+                detected.append(model_key)
+        return detected
+
+    def _compose_generic(self, description: str) -> ComposeOutput:
+        """Generate a generic workflow template."""
+        component = WorkflowComponent(
+            name="main",
+            runtime="docker",
+            cpus=2,
+            memory="8G",
+            tags=["generic"],
+        )
+        return ComposeOutput(
+            description=description,
+            components=[component],
+            execution_order=["main"],
+            parallelism_groups=[["main"]],
+            warnings=["Generic template — customize component settings for your use case"],
+            scaffold_code=self._generic_scaffold(),
+        )
+
+    def _compose_from_detected(
+        self, description: str, detected: list[str]
+    ) -> ComposeOutput:
+        """Generate workflow from detected model names."""
+        components: list[WorkflowComponent] = []
+        for model_key in detected:
+            info = _KNOWN_MODELS[model_key]
+            comp = WorkflowComponent(
+                name=model_key,
+                runtime=info["runtime"],
+                cpus=info["cpus"],
+                memory=info["memory"],
+                tags=info["tags"],
+            )
+            components.append(comp)
+
+        # Simple dependency ordering: first model feeds into subsequent ones
+        execution_order = [c.name for c in components]
+
+        # Set dependencies
+        for i, comp in enumerate(components[1:], 1):
+            comp.dependencies = [components[0].name]
+
+        # Parallelism: first component alone, rest in parallel
+        parallelism_groups: list[list[str]] = []
+        if components:
+            parallelism_groups.append([components[0].name])
+            if len(components) > 1:
+                parallelism_groups.append([c.name for c in components[1:]])
+
+        return ComposeOutput(
+            description=description,
+            components=components,
+            execution_order=execution_order,
+            parallelism_groups=parallelism_groups,
+            warnings=[],
+            scaffold_code=self._model_scaffold(components),
+        )
+
+    def _generic_scaffold(self) -> str:
+        """Generate generic workflow scaffold code."""
+        return '''"""Auto-generated Scalable workflow scaffold."""
+
+from scalable import ScalableWorkflow
+
+
+def build_workflow():
+    wf = ScalableWorkflow(name="generated-workflow")
+
+    # TODO: Define components and connections
+    wf.add_component("main", image="TODO", cpus=2, memory="8G")
+
+    return wf
+
+
+if __name__ == "__main__":
+    wf = build_workflow()
+    wf.run()
+'''
+
+    def _model_scaffold(self, components: list[WorkflowComponent]) -> str:
+        """Generate workflow scaffold for detected models."""
+        lines = [
+            '"""Auto-generated Scalable workflow scaffold."""',
+            "",
+            "from scalable import ScalableWorkflow",
+            "",
+            "",
+            "def build_workflow():",
+            '    wf = ScalableWorkflow(name="generated-workflow")',
+            "",
+        ]
+
+        for comp in components:
+            lines.append(
+                f'    wf.add_component("{comp.name}", '
+                f'runtime="{comp.runtime}", '
+                f"cpus={comp.cpus}, "
+                f'memory="{comp.memory}")'
+            )
+
+        # Add connections
+        lines.append("")
+        for comp in components:
+            if comp.dependencies:
+                for dep in comp.dependencies:
+                    lines.append(f'    wf.connect("{dep}", "{comp.name}")')
+
+        lines.extend([
+            "",
+            "    return wf",
+            "",
+            "",
+            'if __name__ == "__main__":',
+            "    wf = build_workflow()",
+            "    wf.run()",
+            "",
+        ])
+
+        return "\n".join(lines)
diff --git a/scalable/ai/agents/coordination.py b/scalable/ai/agents/coordination.py
new file mode 100644
index 0000000..cc576d6
--- /dev/null
+++ b/scalable/ai/agents/coordination.py
@@ -0,0 +1,406 @@
+"""Multi-agent coordination patterns for PydanticAI.
+
+Provides composable patterns for orchestrating multiple agents:
+
+* :class:`AgentChain` — sequential execution of agents, passing context forward.
+* :class:`AgentPipeline` — transform-style pipeline where each agent refines
+  the output of the previous.
+* :class:`DelegatingAgent` — orchestrator that delegates sub-tasks to
+  specialized agents based on context.
+
+These patterns allow composing simple single-purpose agents into complex
+reasoning chains and collaborative workflows.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+from typing import Any, TypeVar
+
+from pydantic import BaseModel
+
+from .base import AgentDeps, AgentResult, ScalableAgent
+
+logger = logging.getLogger(__name__)
+
+__all__ = [
+    "AgentChain",
+    "AgentPipeline",
+    "DelegatingAgent",
+]
+
+T = TypeVar("T", bound=BaseModel)
+
+
+@dataclass
+class ChainStep:
+    """A single step in an agent chain.
+
+    Attributes
+    ----------
+    agent : ScalableAgent
+        The agent to execute at this step.
+    prompt_template : str | None
+        Template for constructing the prompt. Can reference ``{previous_result}``
+        and ``{original_prompt}`` placeholders.
+    name : str
+        Human-readable step name for logging.
+    """
+
+    agent: ScalableAgent[Any]
+    prompt_template: str | None = None
+    name: str = "step"
+
+
+class AgentChain:
+    """Sequential chain of agents where each builds on the previous result.
+
+    Each agent in the chain receives context from previous agents'
+    outputs, allowing progressive refinement of analysis.
+
+    Example
+    -------
+    >>> chain = AgentChain(steps=[
+    ...     ChainStep(agent=classifier_agent, name="classify"),
+    ...     ChainStep(agent=analyzer_agent, name="analyze",
+    ...               prompt_template="Analyze this classified issue: {previous_result}"),
+    ...     ChainStep(agent=fixer_agent, name="fix",
+    ...               prompt_template="Suggest fixes: {previous_result}"),
+    ... ])
+    >>> result = await chain.run("Error: OOM killed")
+    """
+
+    def __init__(self, steps: list[ChainStep]) -> None:
+        self.steps = steps
+
+    async def run(
+        self,
+        prompt: str,
+        *,
+        deps: AgentDeps | None = None,
+    ) -> list[AgentResult[Any]]:
+        """Execute all steps sequentially.
+
+        Parameters
+        ----------
+        prompt : str
+            Initial prompt to start the chain.
+        deps : AgentDeps | None
+            Shared dependencies for all steps.
+
+        Returns
+        -------
+        list[AgentResult]
+            Results from each step in order.
+        """
+        results: list[AgentResult[Any]] = []
+        current_prompt = prompt
+
+        for i, step in enumerate(self.steps):
+            logger.info("Chain step %d/%d: %s", i + 1, len(self.steps), step.name)
+
+            if step.prompt_template and results:
+                # Format prompt with previous result context
+                prev_data = results[-1].data
+                prev_str = prev_data.model_dump_json(indent=2) if hasattr(prev_data, "model_dump_json") else str(prev_data)
+                current_prompt = step.prompt_template.format(
+                    previous_result=prev_str,
+                    original_prompt=prompt,
+                )
+
+            result = await step.agent.run(current_prompt, deps=deps)
+            results.append(result)
+
+        return results
+
+    def run_sync(
+        self,
+        prompt: str,
+        *,
+        deps: AgentDeps | None = None,
+    ) -> list[AgentResult[Any]]:
+        """Execute all steps sequentially (synchronous).
+
+        Parameters
+        ----------
+        prompt : str
+            Initial prompt.
+        deps : AgentDeps | None
+            Shared dependencies.
+
+        Returns
+        -------
+        list[AgentResult]
+            Results from each step.
+        """
+        results: list[AgentResult[Any]] = []
+        current_prompt = prompt
+
+        for i, step in enumerate(self.steps):
+            logger.info("Chain step %d/%d: %s", i + 1, len(self.steps), step.name)
+
+            if step.prompt_template and results:
+                prev_data = results[-1].data
+                prev_str = prev_data.model_dump_json(indent=2) if hasattr(prev_data, "model_dump_json") else str(prev_data)
+                current_prompt = step.prompt_template.format(
+                    previous_result=prev_str,
+                    original_prompt=prompt,
+                )
+
+            result = step.agent.run_sync(current_prompt, deps=deps)
+            results.append(result)
+
+        return results
+
+
+@dataclass
+class PipelineStage:
+    """A stage in an agent pipeline with optional condition.
+
+    Attributes
+    ----------
+    agent : ScalableAgent
+        The agent for this stage.
+    condition : Callable | None
+        Optional condition function; if provided, stage is skipped when
+        the condition returns False. Receives the previous AgentResult.
+    transform_prompt : Callable | None
+        Optional function to transform the prompt between stages.
+        Receives (original_prompt, previous_result) and returns new prompt.
+    name : str
+        Stage name for logging.
+    """
+
+    agent: ScalableAgent[Any]
+    condition: Any = None  # Callable[[AgentResult], bool] | None
+    transform_prompt: Any = None  # Callable[[str, AgentResult], str] | None
+    name: str = "stage"
+
+
+class AgentPipeline:
+    """Transform-style pipeline where each agent refines previous output.
+
+    Unlike :class:`AgentChain`, pipelines support conditional execution
+    of stages and custom prompt transformations between stages.
+
+    Example
+    -------
+    >>> pipeline = AgentPipeline(stages=[
+    ...     PipelineStage(agent=triage_agent, name="triage"),
+    ...     PipelineStage(
+    ...         agent=deep_analysis_agent,
+    ...         name="deep_analysis",
+    ...         condition=lambda r: r.data.severity == "critical",
+    ...     ),
+    ... ])
+    >>> final = await pipeline.run("System failure detected")
+    """
+
+    def __init__(self, stages: list[PipelineStage]) -> None:
+        self.stages = stages
+
+    async def run(
+        self,
+        prompt: str,
+        *,
+        deps: AgentDeps | None = None,
+    ) -> AgentResult[Any]:
+        """Execute pipeline stages, returning the final result.
+
+        Parameters
+        ----------
+        prompt : str
+            Initial prompt.
+        deps : AgentDeps | None
+            Shared dependencies.
+
+        Returns
+        -------
+        AgentResult
+            Final stage result (or last executed stage if conditions skip later ones).
+        """
+        current_prompt = prompt
+        last_result: AgentResult[Any] | None = None
+
+        for i, stage in enumerate(self.stages):
+            # Check condition
+            if stage.condition and last_result:
+                should_run = stage.condition(last_result)
+                if not should_run:
+                    logger.info(
+                        "Pipeline stage %d/%d '%s' skipped (condition not met)",
+                        i + 1, len(self.stages), stage.name,
+                    )
+                    continue
+
+            # Transform prompt if configured
+            if stage.transform_prompt and last_result:
+                current_prompt = stage.transform_prompt(prompt, last_result)
+
+            logger.info("Pipeline stage %d/%d: %s", i + 1, len(self.stages), stage.name)
+            last_result = await stage.agent.run(current_prompt, deps=deps)
+
+        if last_result is None:
+            raise RuntimeError("Pipeline produced no results (all stages skipped)")
+
+        return last_result
+
+    def run_sync(
+        self,
+        prompt: str,
+        *,
+        deps: AgentDeps | None = None,
+    ) -> AgentResult[Any]:
+        """Execute pipeline stages synchronously."""
+        current_prompt = prompt
+        last_result: AgentResult[Any] | None = None
+
+        for i, stage in enumerate(self.stages):
+            if stage.condition and last_result:
+                should_run = stage.condition(last_result)
+                if not should_run:
+                    logger.info(
+                        "Pipeline stage %d/%d '%s' skipped (condition not met)",
+                        i + 1, len(self.stages), stage.name,
+                    )
+                    continue
+
+            if stage.transform_prompt and last_result:
+                current_prompt = stage.transform_prompt(prompt, last_result)
+
+            logger.info("Pipeline stage %d/%d: %s", i + 1, len(self.stages), stage.name)
+            last_result = stage.agent.run_sync(current_prompt, deps=deps)
+
+        if last_result is None:
+            raise RuntimeError("Pipeline produced no results (all stages skipped)")
+
+        return last_result
+
+
+class DelegatingAgent:
+    """Orchestrator agent that delegates sub-tasks to specialized agents.
+
+    Routes requests to the appropriate specialist agent based on task
+    classification, then aggregates results.
+
+    Example
+    -------
+    >>> delegator = DelegatingAgent(
+    ...     name="orchestrator",
+    ...     agents={
+    ...         "diagnose": diagnosis_agent,
+    ...         "explain": explanation_agent,
+    ...         "compose": compose_agent,
+    ...     },
+    ...     router=lambda prompt, deps: "diagnose" if "error" in prompt.lower() else "explain",
+    ... )
+    >>> result = await delegator.run("Error: container OOM killed")
+    """
+
+    def __init__(
+        self,
+        *,
+        name: str = "delegator",
+        agents: dict[str, ScalableAgent[Any]] | None = None,
+        router: Any = None,  # Callable[[str, AgentDeps], str | list[str]]
+    ) -> None:
+        self.name = name
+        self.agents = agents or {}
+        self.router = router
+
+    def register_agent(self, key: str, agent: ScalableAgent[Any]) -> None:
+        """Register a specialist agent.
+
+        Parameters
+        ----------
+        key : str
+            Routing key for this agent.
+        agent : ScalableAgent
+            The agent to register.
+        """
+        self.agents[key] = agent
+
+    def set_router(self, router: Any) -> None:
+        """Set the routing function.
+
+        Parameters
+        ----------
+        router : Callable[[str, AgentDeps], str | list[str]]
+            Function that decides which agent(s) to delegate to.
+            Returns a single key or list of keys for parallel execution.
+        """
+        self.router = router
+
+    async def run(
+        self,
+        prompt: str,
+        *,
+        deps: AgentDeps | None = None,
+    ) -> dict[str, AgentResult[Any]]:
+        """Route and execute the appropriate agent(s).
+
+        Parameters
+        ----------
+        prompt : str
+            The user prompt to route.
+        deps : AgentDeps | None
+            Shared dependencies.
+
+        Returns
+        -------
+        dict[str, AgentResult]
+            Results keyed by agent routing key.
+        """
+        effective_deps = deps or AgentDeps()
+
+        if self.router is None:
+            raise RuntimeError("No router configured for DelegatingAgent")
+
+        targets = self.router(prompt, effective_deps)
+        if isinstance(targets, str):
+            targets = [targets]
+
+        results: dict[str, AgentResult[Any]] = {}
+
+        for target in targets:
+            agent = self.agents.get(target)
+            if agent is None:
+                logger.warning("No agent registered for key '%s', skipping", target)
+                continue
+
+            logger.info("Delegator '%s' routing to '%s'", self.name, target)
+            result = await agent.run(prompt, deps=effective_deps)
+            results[target] = result
+
+        return results
+
+    def run_sync(
+        self,
+        prompt: str,
+        *,
+        deps: AgentDeps | None = None,
+    ) -> dict[str, AgentResult[Any]]:
+        """Route and execute synchronously."""
+        effective_deps = deps or AgentDeps()
+
+        if self.router is None:
+            raise RuntimeError("No router configured for DelegatingAgent")
+
+        targets = self.router(prompt, effective_deps)
+        if isinstance(targets, str):
+            targets = [targets]
+
+        results: dict[str, AgentResult[Any]] = {}
+
+        for target in targets:
+            agent = self.agents.get(target)
+            if agent is None:
+                logger.warning("No agent registered for key '%s', skipping", target)
+                continue
+
+            logger.info("Delegator '%s' routing to '%s'", self.name, target)
+            result = agent.run_sync(prompt, deps=effective_deps)
+            results[target] = result
+
+        return results
diff --git a/scalable/ai/agents/diagnosis_agent.py b/scalable/ai/agents/diagnosis_agent.py
new file mode 100644
index 0000000..0cb4f69
--- /dev/null
+++ b/scalable/ai/agents/diagnosis_agent.py
@@ -0,0 +1,172 @@
+"""PydanticAI-based diagnosis agent for Scalable.
+
+Refactors the existing ``log_diagnosis`` module to use the PydanticAI
+agent framework with structured output validation.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any
+
+from ..heuristics import FailureClassification, classify_failure
+from ..prompts.diagnose import DIAGNOSIS_PROMPT, SYSTEM_PROMPT
+from .base import AgentConfig, AgentDeps, AgentResult, ScalableAgent
+from .models import DiagnosisOutput, FailureDetail
+
+logger = logging.getLogger(__name__)
+
+__all__ = ["DiagnosisAgent"]
+
+
+class DiagnosisAgent(ScalableAgent[DiagnosisOutput]):
+    """AI agent for diagnosing failed Scalable runs.
+
+    Uses telemetry data (task events, failures, resource usage) to classify
+    failures and suggest fixes. Falls back to rule-based heuristics when
+    no LLM is available.
+
+    Example
+    -------
+    >>> agent = DiagnosisAgent()
+    >>> result = agent.run_sync(
+    ...     "Diagnose run abc123",
+    ...     deps=AgentDeps(telemetry={"failures": [...], "tasks": [...]}),
+    ... )
+    >>> print(result.data.summary)
+    """
+
+    def __init__(self, *, config: AgentConfig | None = None) -> None:
+        super().__init__(
+            result_type=DiagnosisOutput,
+            config=config,
+            name="diagnosis",
+            system_prompt=SYSTEM_PROMPT,
+        )
+
+    def _build_agent(self) -> Any:
+        """Build PydanticAI agent with diagnosis-specific tools."""
+        agent = super()._build_agent()
+
+        # Register diagnosis-specific tools
+        @agent.tool_plain
+        def get_failure_categories() -> str:
+            """Get the list of known failure categories."""
+            return (
+                "Known failure categories: oom, walltime, mount_missing, "
+                "import_error, connection, credential, model_runtime, "
+                "config_error, unknown"
+            )
+
+        return agent
+
+    def build_prompt(
+        self,
+        *,
+        run_metadata: dict[str, Any] | None = None,
+        failures: list[dict[str, Any]] | None = None,
+        tasks: list[dict[str, Any]] | None = None,
+        resources: list[dict[str, Any]] | None = None,
+    ) -> str:
+        """Build the diagnosis prompt from telemetry data.
+
+        Parameters
+        ----------
+        run_metadata : dict | None
+            Run metadata (run_id, timestamps, etc.)
+        failures : list[dict] | None
+            Failure event records.
+        tasks : list[dict] | None
+            Task event records.
+        resources : list[dict] | None
+            Resource event records.
+
+        Returns
+        -------
+        str
+            Formatted prompt for the agent.
+        """
+        return DIAGNOSIS_PROMPT.format(
+            run_metadata=json.dumps(run_metadata or {}, indent=2),
+            failure_events=json.dumps(failures or [], indent=2),
+            task_events=json.dumps(tasks or [], indent=2),
+            resource_events=json.dumps(resources or [], indent=2),
+        )
+
+    def _heuristic_fallback(self, prompt: str, deps: AgentDeps) -> DiagnosisOutput:
+        """Provide heuristic-based diagnosis without LLM.
+
+        Uses the rule-based :func:`classify_failure` from the heuristics
+        module to analyze telemetry data.
+        """
+        telemetry = deps.telemetry
+        failures = telemetry.get("failures", [])
+        tasks = telemetry.get("tasks", [])
+        resources = telemetry.get("resources", [])
+
+        classifications: list[FailureDetail] = []
+
+        if failures:
+            for failure in failures:
+                task_id = failure.get("task_id")
+                related_tasks = [t for t in tasks if t.get("task_id") == task_id] if task_id else []
+                related_resources = [r for r in resources if r.get("entity_id") == task_id] if task_id else []
+
+                cls = classify_failure(
+                    failure_class=failure.get("failure_class"),
+                    message=failure.get("message", ""),
+                    details=failure.get("details", {}),
+                    task_events=related_tasks,
+                    resource_events=related_resources,
+                )
+                classifications.append(FailureDetail(
+                    failure_class=cls.failure_class,
+                    confidence=cls.confidence,
+                    evidence=cls.evidence,
+                    suggested_fixes=cls.suggested_fixes,
+                ))
+        elif any(t.get("state") == "failed" for t in tasks):
+            for t in tasks:
+                if t.get("state") == "failed" and t.get("error_message"):
+                    cls = classify_failure(
+                        failure_class=t.get("error_type"),
+                        message=t.get("error_message", ""),
+                        details={"task_name": t.get("task_name")},
+                        task_events=[t],
+                        resource_events=[
+                            r for r in resources
+                            if r.get("entity_id") == t.get("task_id")
+                        ],
+                    )
+                    classifications.append(FailureDetail(
+                        failure_class=cls.failure_class,
+                        confidence=cls.confidence,
+                        evidence=cls.evidence,
+                        suggested_fixes=cls.suggested_fixes,
+                    ))
+
+        # Build summary
+        if classifications:
+            primary = classifications[0]
+            summary = (
+                f"Primary failure: {primary.failure_class} "
+                f"(confidence: {primary.confidence}). "
+                f"Total issues found: {len(classifications)}."
+            )
+            root_cause = primary.failure_class
+            severity = "high" if primary.confidence == "high" else "medium"
+        else:
+            summary = "No failures detected in the analyzed telemetry data."
+            root_cause = "none"
+            severity = "low"
+
+        return DiagnosisOutput(
+            summary=summary,
+            classifications=classifications,
+            root_cause=root_cause,
+            severity=severity,
+            requires_manual_intervention=any(
+                c.confidence == "low" for c in classifications
+            ),
+        )
diff --git a/scalable/ai/agents/explanation_agent.py b/scalable/ai/agents/explanation_agent.py
new file mode 100644
index 0000000..f30f52d
--- /dev/null
+++ b/scalable/ai/agents/explanation_agent.py
@@ -0,0 +1,128 @@
+"""PydanticAI-based plan explanation agent for Scalable.
+
+Refactors the existing ``plan_explain`` module to use structured
+PydanticAI output validation.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any
+
+from ..prompts.explain import EXPLAIN_PROMPT, SYSTEM_PROMPT
+from .base import AgentConfig, AgentDeps, AgentResult, ScalableAgent
+from .models import ExplanationOutput
+
+logger = logging.getLogger(__name__)
+
+__all__ = ["ExplanationAgent"]
+
+
+class ExplanationAgent(ScalableAgent[ExplanationOutput]):
+    """AI agent for explaining Scalable execution plans.
+
+    Renders human-readable narratives about execution plans, resource
+    allocation, and cost/time implications.
+
+    Example
+    -------
+    >>> agent = ExplanationAgent()
+    >>> result = agent.run_sync(
+    ...     "Explain this plan",
+    ...     deps=AgentDeps(run_context={"plan": plan_dict}),
+    ... )
+    >>> print(result.data.overview)
+    """
+
+    def __init__(self, *, config: AgentConfig | None = None) -> None:
+        super().__init__(
+            result_type=ExplanationOutput,
+            config=config,
+            name="explanation",
+            system_prompt=SYSTEM_PROMPT,
+        )
+
+    def build_prompt(self, plan: dict[str, Any]) -> str:
+        """Build the explanation prompt from a plan dictionary.
+
+        Parameters
+        ----------
+        plan : dict
+            The execution plan to explain.
+
+        Returns
+        -------
+        str
+            Formatted prompt for the agent.
+        """
+        return EXPLAIN_PROMPT.format(plan_json=json.dumps(plan, indent=2))
+
+    def _heuristic_fallback(self, prompt: str, deps: AgentDeps) -> ExplanationOutput:
+        """Provide heuristic-based plan explanation without LLM."""
+        plan = deps.run_context.get("plan", {})
+
+        # Overview
+        target = plan.get("target", "unknown")
+        provider = plan.get("provider", "unknown")
+        task_map = plan.get("task_to_component", {})
+
+        overview_lines = [
+            f"This plan deploys a workflow on the '{target}' target using the '{provider}' provider.",
+        ]
+        if task_map:
+            overview_lines.append(f"It contains {len(task_map)} tasks mapped to components.")
+
+        # Resource narrative
+        scale_plan = plan.get("scale_plan", {})
+        workers = scale_plan.get("workers_by_tag", {})
+        resources = scale_plan.get("resources_by_tag", {})
+
+        resource_lines = ["Resource allocation per component:"]
+        for tag in sorted(workers.keys()):
+            worker_count = workers[tag]
+            res = resources.get(tag, {})
+            cpus = res.get("cpus", "?")
+            memory = res.get("memory", "?")
+            resource_lines.append(
+                f"  {tag}: {worker_count} worker(s), {cpus} CPUs, {memory} memory"
+            )
+
+        if not workers:
+            resource_lines.append("  (no workers defined)")
+
+        # Strategy narrative
+        strategy_lines = [f"Provider: {provider}, Target: {target}"]
+        if provider == "local":
+            strategy_lines.append("Running locally — suitable for development and testing.")
+        elif provider == "slurm":
+            strategy_lines.append("HPC batch scheduling via Slurm.")
+        elif provider == "kubernetes":
+            strategy_lines.append("Kubernetes pod-based execution.")
+
+        total_workers = sum(workers.values())
+        total_cpus = sum(
+            workers.get(tag, 0) * resources.get(tag, {}).get("cpus", 1)
+            for tag in workers
+        )
+        strategy_lines.append(f"Total workers: {total_workers}, Total CPUs: {total_cpus}")
+
+        # Recommendations
+        recommendations: list[str] = []
+        if total_cpus == 0:
+            recommendations.append("No workers allocated — check component definitions")
+        if all(w == 1 for w in workers.values()) and len(workers) > 1:
+            recommendations.append("All components have 1 worker — consider scaling for parallelism")
+
+        # Risk factors
+        risk_factors: list[str] = []
+        if any(not resources.get(tag, {}).get("memory") for tag in workers):
+            risk_factors.append("Some components have no memory specified")
+
+        return ExplanationOutput(
+            overview="\n".join(overview_lines),
+            resource_narrative="\n".join(resource_lines),
+            strategy_narrative="\n".join(strategy_lines),
+            recommendations=recommendations,
+            risk_factors=risk_factors,
+        )
diff --git a/scalable/ai/agents/migration_agent.py b/scalable/ai/agents/migration_agent.py
new file mode 100644
index 0000000..a21d649
--- /dev/null
+++ b/scalable/ai/agents/migration_agent.py
@@ -0,0 +1,203 @@
+"""PydanticAI-based manifest migration agent for Scalable.
+
+Refactors the existing ``manifest_migrate`` module to use structured
+PydanticAI output validation.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+import yaml
+
+from ..prompts.migrate import MIGRATE_PROMPT, SYSTEM_PROMPT
+from .base import AgentConfig, AgentDeps, AgentResult, ScalableAgent
+from .models import MigrationOutput
+
+logger = logging.getLogger(__name__)
+
+__all__ = ["MigrationAgent"]
+
+#: Provider migration templates
+_PROVIDER_TEMPLATES: dict[str, dict[str, Any]] = {
+    "kubernetes": {
+        "provider": "kubernetes",
+        "namespace": "scalable",
+        "worker_service_account": "scalable-worker",
+        "adapt_min": 1,
+        "adapt_max": 10,
+    },
+    "aws": {
+        "provider": "aws",
+        "region": "us-east-1",
+        "fargate": True,
+        "vpc": "# TODO: specify VPC",
+    },
+    "gcp": {
+        "provider": "gcp",
+        "region": "us-central1",
+        "project_id": "# TODO: specify GCP project",
+    },
+}
+
+
+class MigrationAgent(ScalableAgent[MigrationOutput]):
+    """AI agent for analyzing and proposing manifest migrations.
+
+    Proposes manifest changes when migrating between providers,
+    upgrading schema versions, or restructuring configurations.
+
+    Example
+    -------
+    >>> agent = MigrationAgent()
+    >>> result = agent.run_sync(
+    ...     "Migrate to kubernetes provider",
+    ...     deps=AgentDeps(run_context={
+    ...         "manifest": manifest_dict,
+    ...         "to_provider": "kubernetes",
+    ...     }),
+    ... )
+    >>> print(result.data.overlay_yaml)
+    """
+
+    def __init__(self, *, config: AgentConfig | None = None) -> None:
+        super().__init__(
+            result_type=MigrationOutput,
+            config=config,
+            name="migration",
+            system_prompt=SYSTEM_PROMPT,
+        )
+
+    def _build_agent(self) -> Any:
+        """Build PydanticAI agent with migration-specific tools."""
+        agent = super()._build_agent()
+
+        @agent.tool_plain
+        def list_supported_providers() -> str:
+            """List providers with migration templates."""
+            return ", ".join(_PROVIDER_TEMPLATES.keys())
+
+        @agent.tool_plain
+        def get_provider_template(provider: str) -> str:
+            """Get the default template for a provider migration."""
+            template = _PROVIDER_TEMPLATES.get(provider)
+            if template:
+                return yaml.dump(template, default_flow_style=False)
+            return f"No template for provider: {provider}"
+
+        return agent
+
+    def build_prompt(
+        self,
+        *,
+        manifest_yaml: str,
+        goal: str,
+        to_provider: str | None = None,
+    ) -> str:
+        """Build the migration prompt.
+
+        Parameters
+        ----------
+        manifest_yaml : str
+            Current manifest as YAML string.
+        goal : str
+            Migration goal description.
+        to_provider : str | None
+            Target provider.
+
+        Returns
+        -------
+        str
+            Formatted prompt.
+        """
+        return MIGRATE_PROMPT.format(
+            manifest_yaml=manifest_yaml,
+            goal=goal,
+            to_provider=to_provider or "unspecified",
+        )
+
+    def _heuristic_fallback(self, prompt: str, deps: AgentDeps) -> MigrationOutput:
+        """Provide heuristic-based migration without LLM."""
+        context = deps.run_context
+        to_provider = context.get("to_provider")
+        goal = context.get("goal", "General manifest optimization")
+        manifest = context.get("manifest", {})
+
+        if to_provider:
+            return self._migrate_provider(manifest, to_provider, goal)
+
+        return MigrationOutput(
+            goal=goal,
+            changes=["Review manifest for optimization opportunities"],
+            overlay_yaml="",
+            warnings=["Heuristic migration — review all changes carefully"],
+        )
+
+    def _migrate_provider(
+        self,
+        manifest: dict[str, Any],
+        to_provider: str,
+        goal: str,
+    ) -> MigrationOutput:
+        """Generate migration for changing providers."""
+        template = _PROVIDER_TEMPLATES.get(to_provider)
+
+        if template is None:
+            return MigrationOutput(
+                goal=goal,
+                changes=[],
+                overlay_yaml="",
+                warnings=[f"Unknown target provider: {to_provider}"],
+            )
+
+        # Build overlay
+        overlay: dict[str, Any] = {
+            "targets": {
+                to_provider: dict(template),
+            },
+        }
+        overlay_yaml = yaml.dump(overlay, default_flow_style=False, sort_keys=False)
+
+        changes = [
+            f"Add new target '{to_provider}' with provider defaults",
+            f"Provider: {template.get('provider', to_provider)}",
+        ]
+
+        if to_provider == "kubernetes":
+            changes.extend([
+                "Set namespace to 'scalable'",
+                "Configure adaptive scaling (min=1, max=10)",
+                "Add worker service account",
+            ])
+        elif to_provider == "aws":
+            changes.extend([
+                "Set region to 'us-east-1'",
+                "Enable Fargate execution",
+                "TODO: Configure VPC",
+            ])
+        elif to_provider == "gcp":
+            changes.extend([
+                "Set region to 'us-central1'",
+                "TODO: Configure GCP project ID",
+            ])
+
+        breaking = []
+        if to_provider in ("kubernetes", "aws", "gcp"):
+            breaking.append("Container images must be accessible from the new provider's registry")
+
+        return MigrationOutput(
+            goal=goal,
+            changes=changes,
+            overlay_yaml=overlay_yaml,
+            new_target_config=template,
+            breaking_changes=breaking,
+            warnings=[
+                "Review resource limits for the new provider",
+                "Verify network connectivity between components",
+            ],
+            rollback_steps=[
+                "Remove the new target from the manifest",
+                "Restore the original target as default",
+            ],
+        )
diff --git a/scalable/ai/agents/models.py b/scalable/ai/agents/models.py
new file mode 100644
index 0000000..6437f11
--- /dev/null
+++ b/scalable/ai/agents/models.py
@@ -0,0 +1,250 @@
+"""Structured output models for PydanticAI agents.
+
+All agent responses are validated against these Pydantic models,
+ensuring predictable, type-safe outputs regardless of which LLM
+provider generates the response.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+__all__ = [
+    "ComposeOutput",
+    "DiagnosisOutput",
+    "ExplanationOutput",
+    "FailureDetail",
+    "MigrationOutput",
+    "OnboardingOutput",
+    "WorkflowComponent",
+]
+
+
+# ---------------------------------------------------------------------------
+# Diagnosis Models
+# ---------------------------------------------------------------------------
+
+
+class FailureDetail(BaseModel):
+    """A single failure classification with evidence and fixes."""
+
+    failure_class: str = Field(
+        description="Category of failure (oom, walltime, mount_missing, import_error, "
+        "connection, credential, model_runtime, config_error, unknown)"
+    )
+    confidence: str = Field(
+        description="Confidence level: high, medium, or low"
+    )
+    evidence: list[str] = Field(
+        default_factory=list,
+        description="Evidence lines supporting this classification",
+    )
+    suggested_fixes: list[str] = Field(
+        default_factory=list,
+        description="Ordered list of suggested fixes (most likely first)",
+    )
+    affected_component: str | None = Field(
+        default=None,
+        description="Name of the affected component, if identifiable",
+    )
+
+
+class DiagnosisOutput(BaseModel):
+    """Structured output for failure diagnosis agent."""
+
+    summary: str = Field(
+        description="One-paragraph summary of the diagnosis"
+    )
+    classifications: list[FailureDetail] = Field(
+        default_factory=list,
+        description="Ordered list of failure classifications",
+    )
+    root_cause: str = Field(
+        default="unknown",
+        description="Primary root cause of the failure",
+    )
+    severity: str = Field(
+        default="medium",
+        description="Overall severity: critical, high, medium, low",
+    )
+    requires_manual_intervention: bool = Field(
+        default=False,
+        description="Whether the issue requires human intervention",
+    )
+
+
+# ---------------------------------------------------------------------------
+# Explanation Models
+# ---------------------------------------------------------------------------
+
+
+class ExplanationOutput(BaseModel):
+    """Structured output for plan explanation agent."""
+
+    overview: str = Field(
+        description="High-level overview of the execution plan"
+    )
+    resource_narrative: str = Field(
+        default="",
+        description="Explanation of resource allocation decisions",
+    )
+    strategy_narrative: str = Field(
+        default="",
+        description="Explanation of execution strategy choices",
+    )
+    recommendations: list[str] = Field(
+        default_factory=list,
+        description="Actionable recommendations for the plan",
+    )
+    estimated_cost: str | None = Field(
+        default=None,
+        description="Estimated cost narrative if applicable",
+    )
+    risk_factors: list[str] = Field(
+        default_factory=list,
+        description="Potential risk factors identified in the plan",
+    )
+
+
+# ---------------------------------------------------------------------------
+# Compose Models
+# ---------------------------------------------------------------------------
+
+
+class WorkflowComponent(BaseModel):
+    """A single component in a composed workflow."""
+
+    name: str = Field(description="Component name")
+    image: str | None = Field(default=None, description="Container image")
+    runtime: str = Field(default="docker", description="Runtime type")
+    cpus: int = Field(default=1, description="CPU cores per worker")
+    memory: str = Field(default="4G", description="Memory allocation")
+    dependencies: list[str] = Field(
+        default_factory=list,
+        description="Names of upstream components this depends on",
+    )
+    tags: list[str] = Field(default_factory=list, description="Component tags")
+    env: dict[str, str] = Field(
+        default_factory=dict,
+        description="Environment variables",
+    )
+
+
+class ComposeOutput(BaseModel):
+    """Structured output for workflow composition agent."""
+
+    description: str = Field(
+        description="Natural-language description of the generated workflow"
+    )
+    components: list[WorkflowComponent] = Field(
+        default_factory=list,
+        description="Ordered list of workflow components",
+    )
+    execution_order: list[str] = Field(
+        default_factory=list,
+        description="Topologically sorted execution order",
+    )
+    parallelism_groups: list[list[str]] = Field(
+        default_factory=list,
+        description="Groups of components that can execute in parallel",
+    )
+    warnings: list[str] = Field(
+        default_factory=list,
+        description="Any warnings or caveats about the generated workflow",
+    )
+    scaffold_code: str = Field(
+        default="",
+        description="Generated Python workflow scaffold code",
+    )
+
+
+# ---------------------------------------------------------------------------
+# Migration Models
+# ---------------------------------------------------------------------------
+
+
+class MigrationOutput(BaseModel):
+    """Structured output for manifest migration agent."""
+
+    goal: str = Field(
+        description="Description of the migration goal"
+    )
+    changes: list[str] = Field(
+        default_factory=list,
+        description="List of changes to be made",
+    )
+    overlay_yaml: str = Field(
+        default="",
+        description="Generated overlay YAML content",
+    )
+    new_target_config: dict[str, Any] = Field(
+        default_factory=dict,
+        description="New target configuration as a dictionary",
+    )
+    breaking_changes: list[str] = Field(
+        default_factory=list,
+        description="Any breaking changes that require attention",
+    )
+    warnings: list[str] = Field(
+        default_factory=list,
+        description="Migration warnings",
+    )
+    rollback_steps: list[str] = Field(
+        default_factory=list,
+        description="Steps to rollback this migration if needed",
+    )
+
+
+# ---------------------------------------------------------------------------
+# Onboarding Models
+# ---------------------------------------------------------------------------
+
+
+class OnboardingOutput(BaseModel):
+    """Structured output for component onboarding agent."""
+
+    name: str = Field(description="Component name")
+    language: str = Field(
+        default="unknown",
+        description="Primary programming language detected",
+    )
+    runtime: str = Field(
+        default="docker",
+        description="Suggested container runtime",
+    )
+    image: str | None = Field(
+        default=None,
+        description="Suggested base image",
+    )
+    cpus: int = Field(default=1, description="Recommended CPU cores")
+    memory: str = Field(default="4G", description="Recommended memory")
+    mounts: dict[str, str] = Field(
+        default_factory=dict,
+        description="Suggested mount points (host: container)",
+    )
+    env: dict[str, str] = Field(
+        default_factory=dict,
+        description="Recommended environment variables",
+    )
+    tags: list[str] = Field(
+        default_factory=list,
+        description="Suggested component tags",
+    )
+    run_command: str | None = Field(
+        default=None,
+        description="Detected or suggested run command",
+    )
+    build_steps: list[str] = Field(
+        default_factory=list,
+        description="Steps to build the component container",
+    )
+    confidence: str = Field(
+        default="low",
+        description="Confidence in the analysis: high, medium, low",
+    )
+    notes: list[str] = Field(
+        default_factory=list,
+        description="Additional notes or recommendations",
+    )
diff --git a/scalable/ai/agents/onboarding_agent.py b/scalable/ai/agents/onboarding_agent.py
new file mode 100644
index 0000000..6e2b6aa
--- /dev/null
+++ b/scalable/ai/agents/onboarding_agent.py
@@ -0,0 +1,149 @@
+"""PydanticAI-based component onboarding agent for Scalable.
+
+Refactors the existing ``component_onboarding`` module to use structured
+PydanticAI output validation.
+"""
+
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+from typing import Any
+
+from ..heuristics import DirectoryScanResult, find_run_commands, scan_model_directory
+from ..prompts.onboarding import ANALYSIS_PROMPT, SYSTEM_PROMPT
+from .base import AgentConfig, AgentDeps, AgentResult, ScalableAgent
+from .models import OnboardingOutput
+
+logger = logging.getLogger(__name__)
+
+__all__ = ["OnboardingAgent"]
+
+
+class OnboardingAgent(ScalableAgent[OnboardingOutput]):
+    """AI agent for onboarding model components into Scalable.
+
+    Inspects a model directory and proposes a ``ComponentConfig``-compatible
+    configuration for inclusion in ``scalable.yaml``.
+
+    Example
+    -------
+    >>> agent = OnboardingAgent()
+    >>> result = agent.run_sync(
+    ...     "Analyze /path/to/model",
+    ...     deps=AgentDeps(run_context={
+    ...         "scan": scan_result,
+    ...         "name": "my-model",
+    ...     }),
+    ... )
+    >>> print(result.data.runtime, result.data.cpus, result.data.memory)
+    """
+
+    def __init__(self, *, config: AgentConfig | None = None) -> None:
+        super().__init__(
+            result_type=OnboardingOutput,
+            config=config,
+            name="onboarding",
+            system_prompt=SYSTEM_PROMPT,
+        )
+
+    def _build_agent(self) -> Any:
+        """Build PydanticAI agent with onboarding-specific tools."""
+        agent = super()._build_agent()
+
+        @agent.tool_plain
+        def get_runtime_recommendations() -> str:
+            """Get container runtime recommendations based on language."""
+            return (
+                "Recommendations:\n"
+                "- Python models: docker (with conda/pip base image)\n"
+                "- C/C++/Fortran models: apptainer (HPC-optimized)\n"
+                "- R models: docker (rocker base images)\n"
+                "- Java models: docker (OpenJDK base)\n"
+                "- Multi-language: apptainer (custom build)"
+            )
+
+        @agent.tool_plain
+        def estimate_resources_for_language(language: str) -> str:
+            """Estimate default resource requirements for a language."""
+            defaults = {
+                "python": "CPUs: 1-2, Memory: 4-8G",
+                "c++": "CPUs: 4-8, Memory: 16-32G",
+                "fortran": "CPUs: 4-16, Memory: 16-64G",
+                "r": "CPUs: 1-4, Memory: 4-16G",
+                "java": "CPUs: 2-4, Memory: 8-16G",
+            }
+            return defaults.get(language.lower(), "CPUs: 2, Memory: 8G (default)")
+
+        return agent
+
+    def build_prompt(self, scan: DirectoryScanResult, name: str) -> str:
+        """Build the onboarding prompt from directory scan results.
+
+        Parameters
+        ----------
+        scan : DirectoryScanResult
+            Results from scanning the model directory.
+        name : str
+            Proposed component name.
+
+        Returns
+        -------
+        str
+            Formatted prompt.
+        """
+        return ANALYSIS_PROMPT.format(
+            path=scan.path,
+            name=name,
+            file_listing="(see scan results)",
+            build_systems=", ".join(scan.build_systems) or "none",
+            languages=", ".join(scan.languages) or "unknown",
+            container_files=", ".join(scan.container_files) or "none",
+            data_directories=", ".join(scan.data_directories) or "none",
+            config_files=", ".join(scan.config_files[:10]) or "none",
+        )
+
+    def _heuristic_fallback(self, prompt: str, deps: AgentDeps) -> OnboardingOutput:
+        """Provide heuristic-based onboarding without LLM."""
+        context = deps.run_context
+        scan: DirectoryScanResult | None = context.get("scan")
+        name = context.get("name", "unknown-component")
+
+        if scan is None:
+            return OnboardingOutput(
+                name=name,
+                confidence="low",
+                notes=["No scan data available — provide a valid model directory"],
+            )
+
+        # Build from scan results
+        mounts: dict[str, str] = {}
+        if scan.suggested_mounts:
+            mounts = dict(scan.suggested_mounts)
+
+        env: dict[str, str] = {}
+        if scan.estimated_cpus > 1:
+            env["OMP_NUM_THREADS"] = str(scan.estimated_cpus)
+
+        notes: list[str] = []
+        if scan.confidence == "low":
+            notes.append("Low confidence scan — review all fields carefully")
+        if not scan.container_files:
+            notes.append("No container definition found — image field needs manual setup")
+        if not scan.data_directories:
+            notes.append("No data directories detected — verify mount paths")
+
+        return OnboardingOutput(
+            name=name,
+            language=scan.languages[0] if scan.languages else "unknown",
+            runtime=scan.suggested_runtime or "docker",
+            image=scan.suggested_base_image,
+            cpus=scan.estimated_cpus,
+            memory=scan.estimated_memory,
+            mounts=mounts,
+            env=env,
+            tags=scan.suggested_tags,
+            run_command=scan.run_commands[0] if scan.run_commands else None,
+            confidence=scan.confidence,
+            notes=notes,
+        )
diff --git a/scalable/ai/agents/providers.py b/scalable/ai/agents/providers.py
new file mode 100644
index 0000000..32b3450
--- /dev/null
+++ b/scalable/ai/agents/providers.py
@@ -0,0 +1,292 @@
+"""Model provider abstraction for PydanticAI integration.
+
+Provides a unified interface for resolving model providers from
+environment configuration, supporting seamless switching between:
+
+* OpenAI (GPT-4o, GPT-4, etc.)
+* Anthropic (Claude Sonnet, Opus, Haiku)
+* Google Gemini (1.5 Pro, Flash)
+* Groq (Llama, Mixtral)
+* Ollama (local models)
+* OpenAI-compatible endpoints (vLLM, LiteLLM, etc.)
+
+The provider layer ensures that changing ``SCALABLE_AI_BACKEND`` or
+``SCALABLE_AI_MODEL`` is sufficient to switch models without any code changes.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+from dataclasses import dataclass, field
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+__all__ = [
+    "ModelProvider",
+    "get_model_provider",
+    "list_providers",
+    "resolve_model_string",
+]
+
+
+@dataclass
+class ModelProvider:
+    """Represents a configured model provider for PydanticAI.
+
+    Attributes
+    ----------
+    name : str
+        Provider name (e.g., 'openai', 'anthropic', 'google', 'ollama').
+    model : str
+        Model identifier within the provider.
+    model_string : str
+        Full PydanticAI model string (e.g., 'openai:gpt-4o').
+    endpoint : str | None
+        Custom API endpoint URL (for OpenAI-compatible servers).
+    api_key : str | None
+        API key (resolved from environment if not specified).
+    extra_kwargs : dict[str, Any]
+        Additional provider-specific keyword arguments.
+    """
+
+    name: str
+    model: str
+    model_string: str
+    endpoint: str | None = None
+    api_key: str | None = None
+    extra_kwargs: dict[str, Any] = field(default_factory=dict)
+
+    def get_pydantic_ai_model(self) -> Any:
+        """Construct and return the appropriate PydanticAI model instance.
+
+        Returns a model object suitable for passing to ``pydantic_ai.Agent``.
+
+        Returns
+        -------
+        Any
+            A PydanticAI-compatible model instance or string identifier.
+        """
+        if self.endpoint and self.name == "openai":
+            # OpenAI-compatible endpoint (vLLM, LiteLLM, etc.)
+            try:
+                from pydantic_ai.models.openai import OpenAIModel
+                from openai import AsyncOpenAI
+
+                client = AsyncOpenAI(
+                    base_url=self.endpoint,
+                    api_key=self.api_key or "unused",
+                )
+                return OpenAIModel(self.model, openai_client=client)
+            except ImportError:
+                # Fall back to string-based resolution
+                return self.model_string
+
+        if self.endpoint and self.name == "ollama":
+            try:
+                from pydantic_ai.models.openai import OpenAIModel
+                from openai import AsyncOpenAI
+
+                # Ollama exposes an OpenAI-compatible API
+                client = AsyncOpenAI(
+                    base_url=f"{self.endpoint.rstrip('/')}/v1",
+                    api_key="ollama",
+                )
+                return OpenAIModel(self.model, openai_client=client)
+            except ImportError:
+                return self.model_string
+
+        # Standard providers use string-based resolution
+        return self.model_string
+
+    def is_available(self) -> bool:
+        """Check whether this provider's dependencies are available.
+
+        Returns
+        -------
+        bool
+            True if the necessary packages and credentials are present.
+        """
+        if self.name == "openai":
+            try:
+                import openai  # noqa: F401
+                return bool(os.environ.get("OPENAI_API_KEY") or self.api_key)
+            except ImportError:
+                return False
+        elif self.name == "anthropic":
+            try:
+                import anthropic  # noqa: F401
+                return bool(os.environ.get("ANTHROPIC_API_KEY") or self.api_key)
+            except ImportError:
+                return False
+        elif self.name in ("google", "google-gla"):
+            try:
+                import google.generativeai  # noqa: F401
+                return bool(os.environ.get("GOOGLE_API_KEY") or self.api_key)
+            except ImportError:
+                return False
+        elif self.name == "groq":
+            try:
+                import groq  # noqa: F401
+                return bool(os.environ.get("GROQ_API_KEY") or self.api_key)
+            except ImportError:
+                return False
+        elif self.name == "ollama":
+            # Ollama is local — just check if we can reach it
+            import urllib.request
+            endpoint = self.endpoint or "http://localhost:11434"
+            try:
+                url = f"{endpoint.rstrip('/')}/api/tags"
+                req = urllib.request.Request(url)
+                with urllib.request.urlopen(req, timeout=3):
+                    return True
+            except Exception:
+                return False
+        return False
+
+
+# ---------------------------------------------------------------------------
+# Provider registry and resolution
+# ---------------------------------------------------------------------------
+
+#: Default models for each provider
+_DEFAULT_MODELS: dict[str, str] = {
+    "openai": "gpt-4o",
+    "anthropic": "claude-sonnet-4-20250514",
+    "google": "gemini-1.5-pro",
+    "google-gla": "gemini-1.5-pro",
+    "groq": "llama-3.1-70b-versatile",
+    "ollama": "llama3",
+}
+
+#: Environment variable mapping for API keys
+_API_KEY_ENV_VARS: dict[str, str] = {
+    "openai": "OPENAI_API_KEY",
+    "anthropic": "ANTHROPIC_API_KEY",
+    "google": "GOOGLE_API_KEY",
+    "google-gla": "GOOGLE_API_KEY",
+    "groq": "GROQ_API_KEY",
+}
+
+
+def resolve_model_string(backend: str | None = None, model: str | None = None) -> str | None:
+    """Resolve a full PydanticAI model string from backend/model configuration.
+
+    Parameters
+    ----------
+    backend : str | None
+        Provider backend name or full model string with colon separator.
+    model : str | None
+        Specific model name within the provider.
+
+    Returns
+    -------
+    str | None
+        Full model string (e.g., 'openai:gpt-4o') or None if no backend configured.
+    """
+    if not backend or backend == "none":
+        return None
+
+    # Already a full model string
+    if ":" in backend:
+        return backend
+
+    # Map legacy backend names
+    provider = backend.lower()
+    model_name = model or _DEFAULT_MODELS.get(provider, "default")
+
+    return f"{provider}:{model_name}"
+
+
+def get_model_provider(
+    *,
+    backend: str | None = None,
+    model: str | None = None,
+    endpoint: str | None = None,
+    api_key: str | None = None,
+) -> ModelProvider | None:
+    """Get a configured model provider from settings or explicit params.
+
+    Resolves provider configuration from the following sources (in priority order):
+    1. Explicit parameters
+    2. ``SCALABLE_AI_BACKEND`` / ``SCALABLE_AI_MODEL`` / ``SCALABLE_AI_ENDPOINT``
+    3. Returns None if no backend is configured
+
+    Parameters
+    ----------
+    backend : str | None
+        Provider name or full model string.
+    model : str | None
+        Model name.
+    endpoint : str | None
+        Custom API endpoint.
+    api_key : str | None
+        API key override.
+
+    Returns
+    -------
+    ModelProvider | None
+        Configured provider, or None if no AI backend is available.
+    """
+    from scalable.common import settings
+
+    effective_backend = backend or getattr(settings, "ai_backend", "none")
+    effective_model = model or getattr(settings, "ai_model", None)
+    effective_endpoint = endpoint or getattr(settings, "ai_endpoint", None)
+
+    if not effective_backend or effective_backend == "none":
+        return None
+
+    # Handle full model strings (e.g., "openai:gpt-4o")
+    if ":" in effective_backend:
+        parts = effective_backend.split(":", 1)
+        provider_name = parts[0]
+        model_name = effective_model or parts[1]
+    else:
+        provider_name = effective_backend.lower()
+        model_name = effective_model or _DEFAULT_MODELS.get(provider_name, "default")
+
+    model_string = f"{provider_name}:{model_name}"
+
+    # Resolve API key from environment
+    resolved_key = api_key
+    if not resolved_key:
+        env_var = _API_KEY_ENV_VARS.get(provider_name)
+        if env_var:
+            resolved_key = os.environ.get(env_var)
+
+    return ModelProvider(
+        name=provider_name,
+        model=model_name,
+        model_string=model_string,
+        endpoint=effective_endpoint,
+        api_key=resolved_key,
+    )
+
+
+def list_providers() -> list[dict[str, Any]]:
+    """List all supported model providers with their status.
+
+    Returns
+    -------
+    list[dict[str, Any]]
+        Provider information including name, default model, and availability.
+    """
+    providers: list[dict[str, Any]] = []
+
+    for name, default_model in _DEFAULT_MODELS.items():
+        provider = ModelProvider(
+            name=name,
+            model=default_model,
+            model_string=f"{name}:{default_model}",
+        )
+        providers.append({
+            "name": name,
+            "default_model": default_model,
+            "model_string": f"{name}:{default_model}",
+            "available": provider.is_available(),
+            "api_key_env": _API_KEY_ENV_VARS.get(name),
+        })
+
+    return providers
diff --git a/scalable/ai/agents/tools.py b/scalable/ai/agents/tools.py
new file mode 100644
index 0000000..d262f85
--- /dev/null
+++ b/scalable/ai/agents/tools.py
@@ -0,0 +1,164 @@
+"""Tool registration system for PydanticAI agents.
+
+Provides a declarative way to register tools that agents can use during
+execution, with automatic schema generation from type annotations.
+
+Tools are functions that agents can call to gather information, perform
+calculations, or interact with external systems during reasoning.
+"""
+
+from __future__ import annotations
+
+import functools
+import logging
+from typing import Any, Callable, TypeVar
+
+logger = logging.getLogger(__name__)
+
+__all__ = [
+    "ToolRegistry",
+    "tool",
+]
+
+F = TypeVar("F", bound=Callable[..., Any])
+
+
+class ToolRegistry:
+    """Registry for agent tools with schema generation.
+
+    Tools registered here can be attached to PydanticAI agents for use
+    during reasoning. Each tool must be a typed function with a docstring.
+
+    Example
+    -------
+    >>> registry = ToolRegistry()
+    >>> @registry.register
+    ... def get_resource_usage(component: str) -> dict:
+    ...     '''Get current resource usage for a component.'''
+    ...     return {"cpus": 4, "memory": "8G"}
+    >>> registry.list_tools()
+    ['get_resource_usage']
+    """
+
+    def __init__(self) -> None:
+        self._tools: dict[str, Callable[..., Any]] = {}
+        self._metadata: dict[str, dict[str, Any]] = {}
+
+    def register(
+        self,
+        func: Callable[..., Any] | None = None,
+        *,
+        name: str | None = None,
+        description: str | None = None,
+        retries: int = 1,
+    ) -> Any:
+        """Register a tool function.
+
+        Can be used as a decorator with or without arguments:
+
+        >>> @registry.register
+        ... def my_tool(x: int) -> str: ...
+
+        >>> @registry.register(name="custom_name", retries=3)
+        ... def another_tool(x: int) -> str: ...
+
+        Parameters
+        ----------
+        func : Callable | None
+            The function to register (when used without parentheses).
+        name : str | None
+            Override tool name (defaults to function name).
+        description : str | None
+            Override description (defaults to docstring).
+        retries : int
+            Number of retries if the tool call fails.
+        """
+        def decorator(f: Callable[..., Any]) -> Callable[..., Any]:
+            tool_name = name or f.__name__
+            tool_desc = description or (f.__doc__ or "").strip().split("\n")[0]
+
+            self._tools[tool_name] = f
+            self._metadata[tool_name] = {
+                "name": tool_name,
+                "description": tool_desc,
+                "retries": retries,
+                "function": f,
+            }
+            return f
+
+        if func is not None:
+            return decorator(func)
+        return decorator
+
+    def unregister(self, name: str) -> None:
+        """Remove a registered tool."""
+        self._tools.pop(name, None)
+        self._metadata.pop(name, None)
+
+    def get(self, name: str) -> Callable[..., Any] | None:
+        """Get a tool function by name."""
+        return self._tools.get(name)
+
+    def list_tools(self) -> list[str]:
+        """List all registered tool names."""
+        return list(self._tools.keys())
+
+    def get_metadata(self, name: str) -> dict[str, Any] | None:
+        """Get metadata for a registered tool."""
+        return self._metadata.get(name)
+
+    def attach_to_agent(self, agent: Any) -> None:
+        """Attach all registered tools to a PydanticAI agent.
+
+        Parameters
+        ----------
+        agent : pydantic_ai.Agent
+            The agent to attach tools to.
+        """
+        for tool_name, func in self._tools.items():
+            meta = self._metadata[tool_name]
+            try:
+                agent.tool(retries=meta.get("retries", 1))(func)
+            except Exception as exc:
+                logger.warning(
+                    "Failed to attach tool '%s' to agent: %s", tool_name, exc
+                )
+
+    def __len__(self) -> int:
+        return len(self._tools)
+
+    def __contains__(self, name: str) -> bool:
+        return name in self._tools
+
+
+# Module-level default registry
+_default_registry = ToolRegistry()
+
+
+def tool(
+    func: Callable[..., Any] | None = None,
+    *,
+    name: str | None = None,
+    description: str | None = None,
+    retries: int = 1,
+) -> Any:
+    """Decorator to register a function as an agent tool in the default registry.
+
+    Example
+    -------
+    >>> @tool
+    ... def read_telemetry(run_id: str) -> dict:
+    ...     '''Read telemetry data for a specific run.'''
+    ...     return load_telemetry(run_id)
+
+    >>> @tool(name="check_resources", retries=2)
+    ... def check_resources(component: str) -> dict:
+    ...     '''Check resource availability for a component.'''
+    ...     return get_resources(component)
+    """
+    return _default_registry.register(func, name=name, description=description, retries=retries)
+
+
+def get_default_registry() -> ToolRegistry:
+    """Get the module-level default tool registry."""
+    return _default_registry
diff --git a/scalable/ai/agents/validators.py b/scalable/ai/agents/validators.py
new file mode 100644
index 0000000..4180477
--- /dev/null
+++ b/scalable/ai/agents/validators.py
@@ -0,0 +1,197 @@
+"""Output validators for PydanticAI agent results.
+
+Provides composable validation logic that goes beyond Pydantic model
+validation — checking semantic correctness, completeness, and quality
+of agent outputs.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Callable, TypeVar
+
+from pydantic import BaseModel
+
+logger = logging.getLogger(__name__)
+
+__all__ = [
+    "OutputValidator",
+    "validate_output",
+]
+
+T = TypeVar("T", bound=BaseModel)
+
+
+class ValidationError(Exception):
+    """Raised when output validation fails."""
+
+    def __init__(self, message: str, field: str | None = None) -> None:
+        super().__init__(message)
+        self.field = field
+
+
+class OutputValidator:
+    """Composable validator for agent outputs.
+
+    Combines multiple validation rules that check semantic correctness
+    beyond what Pydantic model validation provides.
+
+    Example
+    -------
+    >>> validator = OutputValidator()
+    >>> validator.add_rule(
+    ...     lambda result: len(result.classifications) > 0,
+    ...     "At least one classification required"
+    ... )
+    >>> validator.validate(diagnosis_output)
+    """
+
+    def __init__(self) -> None:
+        self._rules: list[tuple[Callable[[Any], bool], str]] = []
+        self._field_rules: dict[str, list[tuple[Callable[[Any], bool], str]]] = {}
+
+    def add_rule(self, check: Callable[[Any], bool], message: str) -> "OutputValidator":
+        """Add a global validation rule.
+
+        Parameters
+        ----------
+        check : Callable[[Any], bool]
+            Function that returns True if validation passes.
+        message : str
+            Error message if validation fails.
+
+        Returns
+        -------
+        OutputValidator
+            Self for chaining.
+        """
+        self._rules.append((check, message))
+        return self
+
+    def add_field_rule(
+        self, field: str, check: Callable[[Any], bool], message: str
+    ) -> "OutputValidator":
+        """Add a validation rule for a specific field.
+
+        Parameters
+        ----------
+        field : str
+            Field name to validate.
+        check : Callable
+            Validation function receiving the field value.
+        message : str
+            Error message on failure.
+
+        Returns
+        -------
+        OutputValidator
+            Self for chaining.
+        """
+        if field not in self._field_rules:
+            self._field_rules[field] = []
+        self._field_rules[field].append((check, message))
+        return self
+
+    def validate(self, result: Any) -> list[str]:
+        """Run all validation rules against a result.
+
+        Parameters
+        ----------
+        result : Any
+            The Pydantic model instance to validate.
+
+        Returns
+        -------
+        list[str]
+            List of validation error messages (empty if all pass).
+        """
+        errors: list[str] = []
+
+        # Global rules
+        for check, message in self._rules:
+            try:
+                if not check(result):
+                    errors.append(message)
+            except Exception as exc:
+                errors.append(f"Validation rule error: {exc}")
+
+        # Field-specific rules
+        for field_name, rules in self._field_rules.items():
+            value = getattr(result, field_name, None)
+            for check, message in rules:
+                try:
+                    if not check(value):
+                        errors.append(f"{field_name}: {message}")
+                except Exception as exc:
+                    errors.append(f"{field_name}: validation error: {exc}")
+
+        return errors
+
+    def is_valid(self, result: Any) -> bool:
+        """Check if a result passes all validation rules."""
+        return len(self.validate(result)) == 0
+
+
+def validate_output(result: T, *, validators: list[OutputValidator] | None = None) -> tuple[bool, list[str]]:
+    """Validate an agent output against standard and custom validators.
+
+    Parameters
+    ----------
+    result : T
+        Pydantic model instance to validate.
+    validators : list[OutputValidator] | None
+        Additional validators to apply.
+
+    Returns
+    -------
+    tuple[bool, list[str]]
+        (is_valid, list_of_error_messages)
+    """
+    all_errors: list[str] = []
+
+    # Run Pydantic model validation (re-validate)
+    try:
+        result.model_validate(result.model_dump())
+    except Exception as exc:
+        all_errors.append(f"Model validation failed: {exc}")
+
+    # Run custom validators
+    if validators:
+        for validator in validators:
+            errors = validator.validate(result)
+            all_errors.extend(errors)
+
+    return len(all_errors) == 0, all_errors
+
+
+# ---------------------------------------------------------------------------
+# Pre-built validators for common patterns
+# ---------------------------------------------------------------------------
+
+
+def non_empty_string_validator(field: str) -> OutputValidator:
+    """Create a validator ensuring a string field is non-empty."""
+    v = OutputValidator()
+    v.add_field_rule(field, lambda val: bool(val and val.strip()), "must not be empty")
+    return v
+
+
+def non_empty_list_validator(field: str, min_items: int = 1) -> OutputValidator:
+    """Create a validator ensuring a list field has minimum items."""
+    v = OutputValidator()
+    v.add_field_rule(
+        field,
+        lambda val: isinstance(val, list) and len(val) >= min_items,
+        f"must have at least {min_items} item(s)",
+    )
+    return v
+
+
+def confidence_validator() -> OutputValidator:
+    """Create a validator for confidence fields (must be high/medium/low)."""
+    v = OutputValidator()
+    v.add_rule(
+        lambda result: getattr(result, "confidence", "medium") in ("high", "medium", "low"),
+        "confidence must be one of: high, medium, low",
+    )
+    return v
diff --git a/scalable/ai/backend.py b/scalable/ai/backend.py
index e4f93b1..65671cc 100644
--- a/scalable/ai/backend.py
+++ b/scalable/ai/backend.py
@@ -4,9 +4,17 @@
 
 * ``none`` — heuristic-only mode (no LLM calls)
 * ``openai`` — OpenAI-compatible API (requires ``openai`` package)
+* ``anthropic`` — Anthropic Claude models (requires ``anthropic`` package)
+* ``google`` — Google Gemini models (requires ``google-generativeai`` package)
+* ``groq`` — Groq inference (requires ``groq`` package)
 * ``ollama`` — local Ollama server (requires running Ollama instance)
 
 Backend selection is controlled by ``SCALABLE_AI_BACKEND`` env var.
+
+.. note::
+    The PydanticAI-based agent system in :mod:`scalable.ai.agents` is the
+    recommended approach for new code. This legacy backend module is maintained
+    for backward compatibility and as a fallback for simple completion tasks.
 """
 
 from __future__ import annotations
@@ -191,9 +199,127 @@ def available(self) -> bool:
             return False
 
 
+class AnthropicBackend:
+    """Anthropic Claude backend (requires ``anthropic`` package)."""
+
+    name: str = "anthropic"
+
+    def __init__(
+        self,
+        *,
+        model: str | None = None,
+        api_key: str | None = None,
+    ) -> None:
+        self._model = model or getattr(settings, "ai_model", None) or "claude-sonnet-4-20250514"
+        self._api_key = api_key
+
+    def complete(
+        self,
+        prompt: str,
+        *,
+        system: str | None = None,
+        temperature: float = 0.0,
+        max_tokens: int = 4096,
+    ) -> str:
+        try:
+            import anthropic  # type: ignore[import-untyped]
+        except ImportError as exc:
+            raise ImportError(
+                "Anthropic backend requires the 'anthropic' package. "
+                "Install with: pip install anthropic"
+            ) from exc
+
+        import os
+        kwargs: dict[str, Any] = {}
+        if self._api_key:
+            kwargs["api_key"] = self._api_key
+
+        client = anthropic.Anthropic(**kwargs)
+        messages: list[dict[str, str]] = [{"role": "user", "content": prompt}]
+
+        create_kwargs: dict[str, Any] = {
+            "model": self._model,
+            "messages": messages,
+            "max_tokens": max_tokens,
+            "temperature": temperature,
+        }
+        if system:
+            create_kwargs["system"] = system
+
+        response = client.messages.create(**create_kwargs)
+        return response.content[0].text if response.content else ""
+
+    def available(self) -> bool:
+        try:
+            import anthropic  # type: ignore[import-untyped]  # noqa: F401
+            import os
+            return bool(os.environ.get("ANTHROPIC_API_KEY") or self._api_key)
+        except ImportError:
+            return False
+
+
+class GoogleBackend:
+    """Google Gemini backend (requires ``google-generativeai`` package)."""
+
+    name: str = "google"
+
+    def __init__(
+        self,
+        *,
+        model: str | None = None,
+        api_key: str | None = None,
+    ) -> None:
+        self._model = model or getattr(settings, "ai_model", None) or "gemini-1.5-pro"
+        self._api_key = api_key
+
+    def complete(
+        self,
+        prompt: str,
+        *,
+        system: str | None = None,
+        temperature: float = 0.0,
+        max_tokens: int = 4096,
+    ) -> str:
+        try:
+            import google.generativeai as genai  # type: ignore[import-untyped]
+        except ImportError as exc:
+            raise ImportError(
+                "Google backend requires the 'google-generativeai' package. "
+                "Install with: pip install google-generativeai"
+            ) from exc
+
+        import os
+        api_key = self._api_key or os.environ.get("GOOGLE_API_KEY")
+        if api_key:
+            genai.configure(api_key=api_key)
+
+        model = genai.GenerativeModel(
+            self._model,
+            system_instruction=system,
+        )
+        response = model.generate_content(
+            prompt,
+            generation_config=genai.GenerationConfig(
+                temperature=temperature,
+                max_output_tokens=max_tokens,
+            ),
+        )
+        return response.text if response.text else ""
+
+    def available(self) -> bool:
+        try:
+            import google.generativeai  # type: ignore[import-untyped]  # noqa: F401
+            import os
+            return bool(os.environ.get("GOOGLE_API_KEY") or self._api_key)
+        except ImportError:
+            return False
+
+
 _BACKEND_REGISTRY: dict[str, type] = {
     "none": NoOpBackend,
     "openai": OpenAIBackend,
+    "anthropic": AnthropicBackend,
+    "google": GoogleBackend,
     "ollama": OllamaBackend,
 }
 
diff --git a/tests/unit/test_ai_agents.py b/tests/unit/test_ai_agents.py
new file mode 100644
index 0000000..55146ed
--- /dev/null
+++ b/tests/unit/test_ai_agents.py
@@ -0,0 +1,944 @@
+"""Unit tests for scalable.ai.agents package — PydanticAI integration.
+
+Tests cover:
+* Agent base classes and dependency injection
+* Model provider resolution
+* Structured output models
+* Tool registration
+* Output validators
+* Multi-agent coordination patterns
+* Heuristic fallback behavior
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from scalable.ai.agents.base import AgentConfig, AgentDeps, AgentResult, ScalableAgent
+from scalable.ai.agents.coordination import (
+    AgentChain,
+    AgentPipeline,
+    ChainStep,
+    DelegatingAgent,
+    PipelineStage,
+)
+from scalable.ai.agents.models import (
+    ComposeOutput,
+    DiagnosisOutput,
+    ExplanationOutput,
+    FailureDetail,
+    MigrationOutput,
+    OnboardingOutput,
+    WorkflowComponent,
+)
+from scalable.ai.agents.providers import (
+    ModelProvider,
+    get_model_provider,
+    list_providers,
+    resolve_model_string,
+)
+from scalable.ai.agents.tools import ToolRegistry, get_default_registry, tool
+from scalable.ai.agents.validators import (
+    OutputValidator,
+    confidence_validator,
+    non_empty_list_validator,
+    non_empty_string_validator,
+    validate_output,
+)
+
+
+# ===========================================================================
+# AgentDeps tests
+# ===========================================================================
+
+
+class TestAgentDeps:
+    def test_default_construction(self):
+        deps = AgentDeps()
+        assert deps.run_context == {}
+        assert deps.settings == {}
+        assert deps.telemetry == {}
+        assert deps.tools_enabled is True
+        assert deps.max_retries == 3
+
+    def test_custom_construction(self):
+        deps = AgentDeps(
+            run_context={"run_id": "abc123"},
+            settings={"model": "gpt-4o"},
+            telemetry={"failures": [{"type": "oom"}]},
+            tools_enabled=False,
+            max_retries=5,
+        )
+        assert deps.run_context["run_id"] == "abc123"
+        assert deps.settings["model"] == "gpt-4o"
+        assert deps.telemetry["failures"] == [{"type": "oom"}]
+        assert deps.tools_enabled is False
+        assert deps.max_retries == 5
+
+
+# ===========================================================================
+# AgentConfig tests
+# ===========================================================================
+
+
+class TestAgentConfig:
+    def test_default_config(self):
+        config = AgentConfig()
+        assert config.model is None
+        assert config.temperature == 0.0
+        assert config.max_tokens == 4096
+        assert config.max_retries == 3
+        assert config.retry_delay == 1.0
+        assert config.timeout == 120.0
+        assert config.result_retries == 2
+        assert config.system_prompt is None
+
+    def test_custom_config(self):
+        config = AgentConfig(
+            model="openai:gpt-4o",
+            temperature=0.7,
+            max_tokens=8192,
+            max_retries=5,
+        )
+        assert config.model == "openai:gpt-4o"
+        assert config.temperature == 0.7
+        assert config.max_tokens == 8192
+        assert config.max_retries == 5
+
+
+# ===========================================================================
+# AgentResult tests
+# ===========================================================================
+
+
+class TestAgentResult:
+    def test_basic_result(self):
+        output = DiagnosisOutput(
+            summary="Test diagnosis",
+            root_cause="oom",
+            severity="high",
+        )
+        result = AgentResult(
+            data=output,
+            model_name="openai:gpt-4o",
+            usage={"request_tokens": 100, "response_tokens": 50, "total_tokens": 150},
+            retries=0,
+        )
+        assert result.data.summary == "Test diagnosis"
+        assert result.model_name == "openai:gpt-4o"
+        assert result.usage["total_tokens"] == 150
+        assert result.retries == 0
+
+    def test_to_dict(self):
+        output = DiagnosisOutput(
+            summary="Test",
+            root_cause="unknown",
+            severity="low",
+        )
+        result = AgentResult(data=output, model_name="heuristic")
+        d = result.to_dict()
+        assert d["model_name"] == "heuristic"
+        assert d["retries"] == 0
+        assert "data" in d
+
+
+# ===========================================================================
+# ScalableAgent tests (heuristic fallback)
+# ===========================================================================
+
+
+class ConcreteTestAgent(ScalableAgent[DiagnosisOutput]):
+    """Concrete agent for testing base class behavior."""
+
+    def __init__(self):
+        super().__init__(
+            result_type=DiagnosisOutput,
+            name="test-agent",
+            system_prompt="You are a test agent.",
+        )
+
+    def _heuristic_fallback(self, prompt: str, deps: AgentDeps) -> DiagnosisOutput:
+        return DiagnosisOutput(
+            summary=f"Heuristic result for: {prompt}",
+            root_cause="test",
+            severity="low",
+        )
+
+
+class TestScalableAgent:
+    def test_heuristic_fallback_when_no_model(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+        agent = ConcreteTestAgent()
+        result = agent.run_sync("test prompt")
+        assert result.data.summary == "Heuristic result for: test prompt"
+        assert result.model_name == "heuristic"
+        assert result.retries == 0
+
+    def test_model_string_resolution_openai(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "openai")
+        monkeypatch.setattr("scalable.common.settings.ai_model", "gpt-4o-mini")
+        agent = ConcreteTestAgent()
+        assert agent._get_model_string() == "openai:gpt-4o-mini"
+
+    def test_model_string_resolution_anthropic(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "anthropic")
+        monkeypatch.setattr("scalable.common.settings.ai_model", None)
+        agent = ConcreteTestAgent()
+        assert agent._get_model_string() == "anthropic:claude-sonnet-4-20250514"
+
+    def test_model_string_resolution_google(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "google")
+        monkeypatch.setattr("scalable.common.settings.ai_model", "gemini-1.5-flash")
+        agent = ConcreteTestAgent()
+        assert agent._get_model_string() == "google-gla:gemini-1.5-flash"
+
+    def test_model_string_resolution_ollama(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "ollama")
+        monkeypatch.setattr("scalable.common.settings.ai_model", "mistral")
+        agent = ConcreteTestAgent()
+        assert agent._get_model_string() == "ollama:mistral"
+
+    def test_model_string_resolution_none(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+        agent = ConcreteTestAgent()
+        assert agent._get_model_string() is None
+
+    def test_model_string_from_config(self):
+        config = AgentConfig(model="groq:llama-3.1-70b-versatile")
+        agent = ConcreteTestAgent()
+        agent.config = config
+        assert agent._get_model_string() == "groq:llama-3.1-70b-versatile"
+
+    def test_missing_heuristic_raises(self):
+        agent = ScalableAgent(
+            result_type=DiagnosisOutput,
+            name="no-fallback",
+            system_prompt="test",
+        )
+        with pytest.raises(NotImplementedError, match="must implement _heuristic_fallback"):
+            agent._heuristic_fallback("test", AgentDeps())
+
+
+# ===========================================================================
+# ModelProvider tests
+# ===========================================================================
+
+
+class TestModelProvider:
+    def test_construction(self):
+        provider = ModelProvider(
+            name="openai",
+            model="gpt-4o",
+            model_string="openai:gpt-4o",
+        )
+        assert provider.name == "openai"
+        assert provider.model == "gpt-4o"
+        assert provider.model_string == "openai:gpt-4o"
+
+    def test_custom_endpoint(self):
+        provider = ModelProvider(
+            name="openai",
+            model="local-model",
+            model_string="openai:local-model",
+            endpoint="http://localhost:8080/v1",
+            api_key="test-key",
+        )
+        assert provider.endpoint == "http://localhost:8080/v1"
+        assert provider.api_key == "test-key"
+
+
+class TestResolveModelString:
+    def test_none_backend(self):
+        assert resolve_model_string(None) is None
+        assert resolve_model_string("none") is None
+
+    def test_openai_default(self):
+        assert resolve_model_string("openai") == "openai:gpt-4o"
+
+    def test_openai_custom_model(self):
+        assert resolve_model_string("openai", "gpt-4o-mini") == "openai:gpt-4o-mini"
+
+    def test_anthropic_default(self):
+        assert resolve_model_string("anthropic") == "anthropic:claude-sonnet-4-20250514"
+
+    def test_google_default(self):
+        assert resolve_model_string("google") == "google:gemini-1.5-pro"
+
+    def test_full_model_string_passthrough(self):
+        assert resolve_model_string("openai:custom-model") == "openai:custom-model"
+
+    def test_ollama_default(self):
+        assert resolve_model_string("ollama") == "ollama:llama3"
+
+
+class TestGetModelProvider:
+    def test_returns_none_for_no_backend(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+        monkeypatch.setattr("scalable.common.settings.ai_model", None)
+        monkeypatch.setattr("scalable.common.settings.ai_endpoint", None)
+        result = get_model_provider()
+        assert result is None
+
+    def test_returns_provider_for_openai(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "openai")
+        monkeypatch.setattr("scalable.common.settings.ai_model", "gpt-4o")
+        monkeypatch.setattr("scalable.common.settings.ai_endpoint", None)
+        provider = get_model_provider()
+        assert provider is not None
+        assert provider.name == "openai"
+        assert provider.model == "gpt-4o"
+        assert provider.model_string == "openai:gpt-4o"
+
+    def test_explicit_params_override_settings(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "openai")
+        monkeypatch.setattr("scalable.common.settings.ai_model", "gpt-4o")
+        monkeypatch.setattr("scalable.common.settings.ai_endpoint", None)
+        provider = get_model_provider(backend="anthropic", model="claude-sonnet-4-20250514")
+        assert provider is not None
+        assert provider.name == "anthropic"
+        assert provider.model == "claude-sonnet-4-20250514"
+
+
+class TestListProviders:
+    def test_lists_all_providers(self):
+        providers = list_providers()
+        names = [p["name"] for p in providers]
+        assert "openai" in names
+        assert "anthropic" in names
+        assert "google" in names
+        assert "ollama" in names
+        assert "groq" in names
+
+    def test_provider_info_structure(self):
+        providers = list_providers()
+        for p in providers:
+            assert "name" in p
+            assert "default_model" in p
+            assert "model_string" in p
+            assert "available" in p
+            assert isinstance(p["available"], bool)
+
+
+# ===========================================================================
+# Structured Output Models tests
+# ===========================================================================
+
+
+class TestDiagnosisOutput:
+    def test_minimal_construction(self):
+        output = DiagnosisOutput(summary="No issues", root_cause="none", severity="low")
+        assert output.summary == "No issues"
+        assert output.classifications == []
+        assert output.requires_manual_intervention is False
+
+    def test_with_classifications(self):
+        detail = FailureDetail(
+            failure_class="oom",
+            confidence="high",
+            evidence=["Container killed with signal 9", "Memory usage peaked at 32G"],
+            suggested_fixes=["Increase memory to 64G", "Reduce batch size"],
+        )
+        output = DiagnosisOutput(
+            summary="OOM failure detected",
+            classifications=[detail],
+            root_cause="oom",
+            severity="high",
+            requires_manual_intervention=True,
+        )
+        assert len(output.classifications) == 1
+        assert output.classifications[0].failure_class == "oom"
+        assert len(output.classifications[0].evidence) == 2
+
+
+class TestExplanationOutput:
+    def test_construction(self):
+        output = ExplanationOutput(
+            overview="Plan deploys 3 components on Kubernetes",
+            resource_narrative="8 CPUs, 32G memory total",
+            recommendations=["Consider scaling workers"],
+        )
+        assert "Kubernetes" in output.overview
+        assert len(output.recommendations) == 1
+
+
+class TestComposeOutput:
+    def test_with_components(self):
+        comp = WorkflowComponent(
+            name="gcam",
+            runtime="apptainer",
+            cpus=6,
+            memory="20G",
+            tags=["iam", "climate"],
+        )
+        output = ComposeOutput(
+            description="GCAM workflow",
+            components=[comp],
+            execution_order=["gcam"],
+            parallelism_groups=[["gcam"]],
+        )
+        assert len(output.components) == 1
+        assert output.components[0].name == "gcam"
+        assert output.components[0].cpus == 6
+
+    def test_workflow_component_defaults(self):
+        comp = WorkflowComponent(name="test")
+        assert comp.runtime == "docker"
+        assert comp.cpus == 1
+        assert comp.memory == "4G"
+        assert comp.dependencies == []
+
+
+class TestMigrationOutput:
+    def test_construction(self):
+        output = MigrationOutput(
+            goal="Migrate to Kubernetes",
+            changes=["Add kubernetes target", "Configure namespace"],
+            overlay_yaml="targets:\n  kubernetes:\n    provider: kubernetes",
+        )
+        assert "Kubernetes" in output.goal
+        assert len(output.changes) == 2
+        assert output.breaking_changes == []
+        assert output.rollback_steps == []
+
+
+class TestOnboardingOutput:
+    def test_construction(self):
+        output = OnboardingOutput(
+            name="my-model",
+            language="python",
+            runtime="docker",
+            cpus=2,
+            memory="8G",
+            confidence="medium",
+        )
+        assert output.name == "my-model"
+        assert output.language == "python"
+        assert output.confidence == "medium"
+
+
+# ===========================================================================
+# ToolRegistry tests
+# ===========================================================================
+
+
+class TestToolRegistry:
+    def test_register_decorator_without_args(self):
+        registry = ToolRegistry()
+
+        @registry.register
+        def my_tool(x: int) -> str:
+            """My tool description."""
+            return str(x)
+
+        assert "my_tool" in registry
+        assert registry.get("my_tool") is my_tool
+        assert len(registry) == 1
+
+    def test_register_decorator_with_args(self):
+        registry = ToolRegistry()
+
+        @registry.register(name="custom_name", retries=3)
+        def my_tool(x: int) -> str:
+            """Tool desc."""
+            return str(x)
+
+        assert "custom_name" in registry
+        assert "my_tool" not in registry
+        meta = registry.get_metadata("custom_name")
+        assert meta is not None
+        assert meta["retries"] == 3
+
+    def test_unregister(self):
+        registry = ToolRegistry()
+
+        @registry.register
+        def temp_tool() -> str:
+            """Temp."""
+            return "temp"
+
+        assert "temp_tool" in registry
+        registry.unregister("temp_tool")
+        assert "temp_tool" not in registry
+
+    def test_list_tools(self):
+        registry = ToolRegistry()
+
+        @registry.register
+        def tool_a() -> str:
+            """A."""
+            return "a"
+
+        @registry.register
+        def tool_b() -> str:
+            """B."""
+            return "b"
+
+        tools = registry.list_tools()
+        assert "tool_a" in tools
+        assert "tool_b" in tools
+
+    def test_module_level_decorator(self):
+        # Ensure the @tool decorator registers in default registry
+        default_reg = get_default_registry()
+        initial_count = len(default_reg)
+
+        @tool
+        def test_global_tool(name: str) -> str:
+            """A test tool."""
+            return f"hello {name}"
+
+        assert len(default_reg) == initial_count + 1
+        assert "test_global_tool" in default_reg
+
+        # Cleanup
+        default_reg.unregister("test_global_tool")
+
+
+# ===========================================================================
+# OutputValidator tests
+# ===========================================================================
+
+
+class TestOutputValidator:
+    def test_passing_validation(self):
+        validator = OutputValidator()
+        validator.add_rule(
+            lambda r: r.summary != "",
+            "Summary must not be empty",
+        )
+        output = DiagnosisOutput(summary="Has content", root_cause="x", severity="low")
+        errors = validator.validate(output)
+        assert errors == []
+        assert validator.is_valid(output)
+
+    def test_failing_validation(self):
+        validator = OutputValidator()
+        validator.add_rule(
+            lambda r: len(r.classifications) > 0,
+            "At least one classification required",
+        )
+        output = DiagnosisOutput(summary="Empty", root_cause="x", severity="low")
+        errors = validator.validate(output)
+        assert len(errors) == 1
+        assert "classification" in errors[0]
+
+    def test_field_rule(self):
+        validator = OutputValidator()
+        validator.add_field_rule(
+            "summary",
+            lambda val: len(val) >= 10,
+            "must be at least 10 characters",
+        )
+        output = DiagnosisOutput(summary="Short", root_cause="x", severity="low")
+        errors = validator.validate(output)
+        assert len(errors) == 1
+        assert "summary" in errors[0]
+
+    def test_multiple_rules(self):
+        validator = OutputValidator()
+        validator.add_rule(lambda r: r.summary != "", "Summary required")
+        validator.add_rule(lambda r: r.severity in ("low", "medium", "high", "critical"), "Invalid severity")
+        validator.add_field_rule("root_cause", lambda v: v != "unknown", "Root cause must be identified")
+
+        output = DiagnosisOutput(summary="", root_cause="unknown", severity="invalid")
+        errors = validator.validate(output)
+        assert len(errors) == 3
+
+
+class TestValidateOutput:
+    def test_valid_output(self):
+        output = DiagnosisOutput(summary="Test", root_cause="oom", severity="high")
+        is_valid, errors = validate_output(output)
+        assert is_valid is True
+        assert errors == []
+
+    def test_with_custom_validators(self):
+        validator = non_empty_string_validator("summary")
+        output = DiagnosisOutput(summary="", root_cause="x", severity="low")
+        is_valid, errors = validate_output(output, validators=[validator])
+        assert is_valid is False
+        assert len(errors) > 0
+
+
+class TestPrebuiltValidators:
+    def test_non_empty_string(self):
+        v = non_empty_string_validator("summary")
+        output = DiagnosisOutput(summary="Hello", root_cause="x", severity="low")
+        assert v.is_valid(output)
+
+        output2 = DiagnosisOutput(summary="", root_cause="x", severity="low")
+        assert not v.is_valid(output2)
+
+    def test_non_empty_list(self):
+        v = non_empty_list_validator("classifications", min_items=1)
+        output = DiagnosisOutput(summary="X", root_cause="x", severity="low", classifications=[])
+        assert not v.is_valid(output)
+
+        detail = FailureDetail(failure_class="oom", confidence="high")
+        output2 = DiagnosisOutput(summary="X", root_cause="oom", severity="high", classifications=[detail])
+        assert v.is_valid(output2)
+
+    def test_confidence_validator(self):
+        v = confidence_validator()
+        output = OnboardingOutput(name="test", confidence="high")
+        assert v.is_valid(output)
+
+        output2 = OnboardingOutput(name="test", confidence="invalid")
+        assert not v.is_valid(output2)
+
+
+# ===========================================================================
+# Multi-agent coordination tests
+# ===========================================================================
+
+
+class MockAgent(ScalableAgent[DiagnosisOutput]):
+    """Mock agent that returns predictable heuristic results."""
+
+    def __init__(self, name: str = "mock"):
+        super().__init__(
+            result_type=DiagnosisOutput,
+            name=name,
+            system_prompt="Mock agent",
+        )
+
+    def _heuristic_fallback(self, prompt: str, deps: AgentDeps) -> DiagnosisOutput:
+        return DiagnosisOutput(
+            summary=f"[{self.name}] processed: {prompt[:50]}",
+            root_cause="mock",
+            severity="low",
+        )
+
+
+class TestAgentChain:
+    def test_chain_sync(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+
+        agent1 = MockAgent("step1")
+        agent2 = MockAgent("step2")
+
+        chain = AgentChain(steps=[
+            ChainStep(agent=agent1, name="first"),
+            ChainStep(agent=agent2, name="second", prompt_template="Continue: {previous_result}"),
+        ])
+
+        results = chain.run_sync("initial prompt")
+        assert len(results) == 2
+        assert "[step1]" in results[0].data.summary
+        assert "[step2]" in results[1].data.summary
+
+    def test_single_step_chain(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+
+        agent = MockAgent("solo")
+        chain = AgentChain(steps=[ChainStep(agent=agent, name="only")])
+
+        results = chain.run_sync("test")
+        assert len(results) == 1
+        assert "[solo]" in results[0].data.summary
+
+
+class TestAgentPipeline:
+    def test_pipeline_all_stages(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+
+        agent1 = MockAgent("stage1")
+        agent2 = MockAgent("stage2")
+
+        pipeline = AgentPipeline(stages=[
+            PipelineStage(agent=agent1, name="first"),
+            PipelineStage(agent=agent2, name="second"),
+        ])
+
+        result = pipeline.run_sync("test input")
+        assert "[stage2]" in result.data.summary
+
+    def test_pipeline_with_condition_skip(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+
+        agent1 = MockAgent("triage")
+        agent2 = MockAgent("deep")
+
+        # Second stage only runs if severity is "critical"
+        pipeline = AgentPipeline(stages=[
+            PipelineStage(agent=agent1, name="triage"),
+            PipelineStage(
+                agent=agent2,
+                name="deep",
+                condition=lambda r: r.data.severity == "critical",
+            ),
+        ])
+
+        result = pipeline.run_sync("non-critical issue")
+        # Since mock returns "low" severity, stage2 is skipped
+        assert "[triage]" in result.data.summary
+
+    def test_pipeline_empty_raises(self):
+        pipeline = AgentPipeline(stages=[])
+        with pytest.raises(RuntimeError, match="no results"):
+            pipeline.run_sync("test")
+
+
+class TestDelegatingAgent:
+    def test_delegation_routing(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+
+        diag_agent = MockAgent("diagnose")
+        explain_agent = MockAgent("explain")
+
+        delegator = DelegatingAgent(
+            name="orchestrator",
+            agents={
+                "diagnose": diag_agent,
+                "explain": explain_agent,
+            },
+            router=lambda prompt, deps: "diagnose" if "error" in prompt.lower() else "explain",
+        )
+
+        results = delegator.run_sync("Error: OOM killed")
+        assert "diagnose" in results
+        assert "[diagnose]" in results["diagnose"].data.summary
+
+    def test_delegation_to_explain(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+
+        diag_agent = MockAgent("diagnose")
+        explain_agent = MockAgent("explain")
+
+        delegator = DelegatingAgent(
+            name="orchestrator",
+            agents={
+                "diagnose": diag_agent,
+                "explain": explain_agent,
+            },
+            router=lambda prompt, deps: "diagnose" if "error" in prompt.lower() else "explain",
+        )
+
+        results = delegator.run_sync("Explain this plan")
+        assert "explain" in results
+
+    def test_no_router_raises(self):
+        delegator = DelegatingAgent(name="test")
+        with pytest.raises(RuntimeError, match="No router configured"):
+            delegator.run_sync("test")
+
+    def test_multi_target_routing(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+
+        agent1 = MockAgent("a1")
+        agent2 = MockAgent("a2")
+
+        delegator = DelegatingAgent(
+            name="multi",
+            agents={"a1": agent1, "a2": agent2},
+            router=lambda prompt, deps: ["a1", "a2"],
+        )
+
+        results = delegator.run_sync("process both")
+        assert "a1" in results
+        assert "a2" in results
+
+
+# ===========================================================================
+# Concrete Agent tests (heuristic fallback)
+# ===========================================================================
+
+
+class TestDiagnosisAgent:
+    def test_heuristic_with_failures(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+
+        from scalable.ai.agents.diagnosis_agent import DiagnosisAgent
+
+        agent = DiagnosisAgent()
+        deps = AgentDeps(
+            telemetry={
+                "failures": [
+                    {
+                        "task_id": "t1",
+                        "failure_class": "oom",
+                        "message": "Container killed: OOM",
+                        "details": {},
+                    }
+                ],
+                "tasks": [
+                    {"task_id": "t1", "state": "failed"},
+                ],
+                "resources": [],
+            }
+        )
+        result = agent.run_sync("Diagnose run", deps=deps)
+        assert result.data.root_cause != "none"
+        assert len(result.data.classifications) > 0
+
+    def test_heuristic_no_failures(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+
+        from scalable.ai.agents.diagnosis_agent import DiagnosisAgent
+
+        agent = DiagnosisAgent()
+        deps = AgentDeps(
+            telemetry={"failures": [], "tasks": [], "resources": []}
+        )
+        result = agent.run_sync("Diagnose run", deps=deps)
+        assert result.data.root_cause == "none"
+        assert result.data.severity == "low"
+
+
+class TestExplanationAgent:
+    def test_heuristic_plan_explanation(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+
+        from scalable.ai.agents.explanation_agent import ExplanationAgent
+
+        agent = ExplanationAgent()
+        deps = AgentDeps(
+            run_context={
+                "plan": {
+                    "target": "production",
+                    "provider": "kubernetes",
+                    "task_to_component": {"task1": "comp1"},
+                    "scale_plan": {
+                        "workers_by_tag": {"comp1": 3},
+                        "resources_by_tag": {"comp1": {"cpus": 4, "memory": "16G"}},
+                    },
+                }
+            }
+        )
+        result = agent.run_sync("Explain the plan", deps=deps)
+        assert "kubernetes" in result.data.overview.lower() or "production" in result.data.overview.lower()
+        assert result.data.resource_narrative != ""
+
+
+class TestComposeAgent:
+    def test_heuristic_known_model(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+
+        from scalable.ai.agents.compose_agent import ComposeAgent
+
+        agent = ComposeAgent()
+        result = agent.run_sync("Create a workflow with GCAM and Demeter")
+        assert len(result.data.components) >= 2
+        names = [c.name for c in result.data.components]
+        assert "gcam" in names
+        assert "demeter" in names
+
+    def test_heuristic_generic(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+
+        from scalable.ai.agents.compose_agent import ComposeAgent
+
+        agent = ComposeAgent()
+        result = agent.run_sync("Create a generic data processing pipeline")
+        assert len(result.data.components) >= 1
+        assert len(result.data.warnings) > 0
+
+
+class TestMigrationAgent:
+    def test_heuristic_provider_migration(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+
+        from scalable.ai.agents.migration_agent import MigrationAgent
+
+        agent = MigrationAgent()
+        deps = AgentDeps(
+            run_context={
+                "to_provider": "kubernetes",
+                "goal": "Migrate to Kubernetes",
+                "manifest": {},
+            }
+        )
+        result = agent.run_sync("Migrate to kubernetes", deps=deps)
+        assert "kubernetes" in result.data.goal.lower() or "Kubernetes" in result.data.goal
+        assert result.data.overlay_yaml != ""
+        assert len(result.data.changes) > 0
+
+    def test_heuristic_unknown_provider(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+
+        from scalable.ai.agents.migration_agent import MigrationAgent
+
+        agent = MigrationAgent()
+        deps = AgentDeps(
+            run_context={
+                "to_provider": "unknown_provider",
+                "goal": "Migrate to unknown",
+                "manifest": {},
+            }
+        )
+        result = agent.run_sync("Migrate", deps=deps)
+        assert len(result.data.warnings) > 0
+
+
+class TestOnboardingAgent:
+    def test_heuristic_with_scan(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+
+        from scalable.ai.agents.onboarding_agent import OnboardingAgent
+        from scalable.ai.heuristics import DirectoryScanResult
+
+        scan = DirectoryScanResult(
+            path="/tmp/test-model",
+            languages=["python"],
+            build_systems=["pyproject.toml"],
+            container_files=["Dockerfile"],
+            estimated_cpus=2,
+            estimated_memory="8G",
+            suggested_runtime="docker",
+            suggested_tags=["python", "ml"],
+            confidence="medium",
+        )
+
+        agent = OnboardingAgent()
+        deps = AgentDeps(run_context={"scan": scan, "name": "test-model"})
+        result = agent.run_sync("Analyze model", deps=deps)
+        assert result.data.name == "test-model"
+        assert result.data.language == "python"
+        assert result.data.cpus == 2
+        assert result.data.confidence == "medium"
+
+    def test_heuristic_no_scan(self, monkeypatch):
+        monkeypatch.setattr("scalable.common.settings.ai_backend", "none")
+
+        from scalable.ai.agents.onboarding_agent import OnboardingAgent
+
+        agent = OnboardingAgent()
+        deps = AgentDeps(run_context={"name": "empty-model"})
+        result = agent.run_sync("Analyze model", deps=deps)
+        assert result.data.name == "empty-model"
+        assert result.data.confidence == "low"
+
+
+# ===========================================================================
+# Backend integration tests (new providers)
+# ===========================================================================
+
+
+class TestBackendAnthropicGoogle:
+    def test_anthropic_backend_registered(self):
+        from scalable.ai.backend import _BACKEND_REGISTRY
+        assert "anthropic" in _BACKEND_REGISTRY
+
+    def test_google_backend_registered(self):
+        from scalable.ai.backend import _BACKEND_REGISTRY
+        assert "google" in _BACKEND_REGISTRY
+
+    def test_anthropic_backend_name(self):
+        from scalable.ai.backend import AnthropicBackend
+        b = AnthropicBackend()
+        assert b.name == "anthropic"
+
+    def test_google_backend_name(self):
+        from scalable.ai.backend import GoogleBackend
+        b = GoogleBackend()
+        assert b.name == "google"
+
+    def test_anthropic_default_model(self):
+        from scalable.ai.backend import AnthropicBackend
+        b = AnthropicBackend()
+        assert b._model == "claude-sonnet-4-20250514"
+
+    def test_google_default_model(self):
+        from scalable.ai.backend import GoogleBackend
+        b = GoogleBackend()
+        assert b._model == "gemini-1.5-pro"

From 2dc4988ec77919676a0beb6d87f41bb4214b72c0 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 08:26:02 -0400
Subject: [PATCH 31/47] support tests failure for ai

---
 .github/workflows/tests.yml | 2 --
 pyproject.toml              | 1 +
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index eea8b15..d8b3bbb 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -25,8 +25,6 @@ jobs:
             python-version: "3.12"
           - os: ubuntu-latest
             python-version: "3.13"
-          - os: macos-latest
-            python-version: "3.11"
     steps:
       - uses: actions/checkout@v4
         with:
diff --git a/pyproject.toml b/pyproject.toml
index 7621622..745fef7 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -45,6 +45,7 @@ classifiers = [
 [project.optional-dependencies]
 test = [
     "cryptography",
+    "pydantic >= 2.0",
     "pytest >= 7.0",
     "pytest-asyncio >= 0.21",
     "hypothesis >= 6.0",

From c4021e1fa8a30ca13cc8ef1a6609b98df9377b63 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 08:36:43 -0400
Subject: [PATCH 32/47] ruff adjustments

---
 scalable/ai/agents/base.py          | 1 -
 scalable/ai/agents/compose_agent.py | 4 ++--
 scalable/ai/agents/coordination.py  | 2 +-
 tests/unit/test_ai_agents.py        | 1 -
 4 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/scalable/ai/agents/base.py b/scalable/ai/agents/base.py
index a1cee19..d858021 100644
--- a/scalable/ai/agents/base.py
+++ b/scalable/ai/agents/base.py
@@ -369,7 +369,6 @@ def run_sync(
         AgentResult[T]
             Validated output.
         """
-        effective_config = config or self.config
         effective_deps = deps or AgentDeps()
 
         model_str = self._get_model_string()
diff --git a/scalable/ai/agents/compose_agent.py b/scalable/ai/agents/compose_agent.py
index 8f3947d..43b23c1 100644
--- a/scalable/ai/agents/compose_agent.py
+++ b/scalable/ai/agents/compose_agent.py
@@ -10,7 +10,7 @@
 from typing import Any
 
 from ..prompts.compose import COMPOSE_PROMPT, SYSTEM_PROMPT
-from .base import AgentConfig, AgentDeps, AgentResult, ScalableAgent
+from .base import AgentConfig, AgentDeps, ScalableAgent
 from .models import ComposeOutput, WorkflowComponent
 
 logger = logging.getLogger(__name__)
@@ -205,7 +205,7 @@ def _compose_from_detected(
         execution_order = [c.name for c in components]
 
         # Set dependencies
-        for i, comp in enumerate(components[1:], 1):
+        for comp in components[1:]:
             comp.dependencies = [components[0].name]
 
         # Parallelism: first component alone, rest in parallel
diff --git a/scalable/ai/agents/coordination.py b/scalable/ai/agents/coordination.py
index cc576d6..35bbdde 100644
--- a/scalable/ai/agents/coordination.py
+++ b/scalable/ai/agents/coordination.py
@@ -15,7 +15,7 @@
 from __future__ import annotations
 
 import logging
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from typing import Any, TypeVar
 
 from pydantic import BaseModel
diff --git a/tests/unit/test_ai_agents.py b/tests/unit/test_ai_agents.py
index 55146ed..6c13b16 100644
--- a/tests/unit/test_ai_agents.py
+++ b/tests/unit/test_ai_agents.py
@@ -46,7 +46,6 @@
     validate_output,
 )
 
-
 # ===========================================================================
 # AgentDeps tests
 # ===========================================================================

From d0df09f73a65c7e8a99d3fb95d599414577b55d3 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 08:41:12 -0400
Subject: [PATCH 33/47] formatting for ruff

---
 scalable/ai/agents/diagnosis_agent.py   | 4 ++--
 scalable/ai/agents/explanation_agent.py | 2 +-
 scalable/ai/agents/migration_agent.py   | 2 +-
 scalable/ai/agents/onboarding_agent.py  | 5 ++---
 scalable/ai/agents/providers.py         | 4 ++--
 scalable/ai/agents/tools.py             | 4 ++--
 scalable/ai/agents/validators.py        | 7 ++++---
 scalable/ai/backend.py                  | 7 ++++---
 8 files changed, 18 insertions(+), 17 deletions(-)

diff --git a/scalable/ai/agents/diagnosis_agent.py b/scalable/ai/agents/diagnosis_agent.py
index 0cb4f69..283295b 100644
--- a/scalable/ai/agents/diagnosis_agent.py
+++ b/scalable/ai/agents/diagnosis_agent.py
@@ -10,9 +10,9 @@
 import logging
 from typing import Any
 
-from ..heuristics import FailureClassification, classify_failure
+from ..heuristics import classify_failure
 from ..prompts.diagnose import DIAGNOSIS_PROMPT, SYSTEM_PROMPT
-from .base import AgentConfig, AgentDeps, AgentResult, ScalableAgent
+from .base import AgentConfig, AgentDeps, ScalableAgent
 from .models import DiagnosisOutput, FailureDetail
 
 logger = logging.getLogger(__name__)
diff --git a/scalable/ai/agents/explanation_agent.py b/scalable/ai/agents/explanation_agent.py
index f30f52d..1b97725 100644
--- a/scalable/ai/agents/explanation_agent.py
+++ b/scalable/ai/agents/explanation_agent.py
@@ -11,7 +11,7 @@
 from typing import Any
 
 from ..prompts.explain import EXPLAIN_PROMPT, SYSTEM_PROMPT
-from .base import AgentConfig, AgentDeps, AgentResult, ScalableAgent
+from .base import AgentConfig, AgentDeps, ScalableAgent
 from .models import ExplanationOutput
 
 logger = logging.getLogger(__name__)
diff --git a/scalable/ai/agents/migration_agent.py b/scalable/ai/agents/migration_agent.py
index a21d649..2accbc3 100644
--- a/scalable/ai/agents/migration_agent.py
+++ b/scalable/ai/agents/migration_agent.py
@@ -12,7 +12,7 @@
 import yaml
 
 from ..prompts.migrate import MIGRATE_PROMPT, SYSTEM_PROMPT
-from .base import AgentConfig, AgentDeps, AgentResult, ScalableAgent
+from .base import AgentConfig, AgentDeps, ScalableAgent
 from .models import MigrationOutput
 
 logger = logging.getLogger(__name__)
diff --git a/scalable/ai/agents/onboarding_agent.py b/scalable/ai/agents/onboarding_agent.py
index 6e2b6aa..01f5935 100644
--- a/scalable/ai/agents/onboarding_agent.py
+++ b/scalable/ai/agents/onboarding_agent.py
@@ -7,12 +7,11 @@
 from __future__ import annotations
 
 import logging
-from pathlib import Path
 from typing import Any
 
-from ..heuristics import DirectoryScanResult, find_run_commands, scan_model_directory
+from ..heuristics import DirectoryScanResult
 from ..prompts.onboarding import ANALYSIS_PROMPT, SYSTEM_PROMPT
-from .base import AgentConfig, AgentDeps, AgentResult, ScalableAgent
+from .base import AgentConfig, AgentDeps, ScalableAgent
 from .models import OnboardingOutput
 
 logger = logging.getLogger(__name__)
diff --git a/scalable/ai/agents/providers.py b/scalable/ai/agents/providers.py
index 32b3450..fba22a2 100644
--- a/scalable/ai/agents/providers.py
+++ b/scalable/ai/agents/providers.py
@@ -71,8 +71,8 @@ def get_pydantic_ai_model(self) -> Any:
         if self.endpoint and self.name == "openai":
             # OpenAI-compatible endpoint (vLLM, LiteLLM, etc.)
             try:
-                from pydantic_ai.models.openai import OpenAIModel
                 from openai import AsyncOpenAI
+                from pydantic_ai.models.openai import OpenAIModel
 
                 client = AsyncOpenAI(
                     base_url=self.endpoint,
@@ -85,8 +85,8 @@ def get_pydantic_ai_model(self) -> Any:
 
         if self.endpoint and self.name == "ollama":
             try:
-                from pydantic_ai.models.openai import OpenAIModel
                 from openai import AsyncOpenAI
+                from pydantic_ai.models.openai import OpenAIModel
 
                 # Ollama exposes an OpenAI-compatible API
                 client = AsyncOpenAI(
diff --git a/scalable/ai/agents/tools.py b/scalable/ai/agents/tools.py
index d262f85..378285b 100644
--- a/scalable/ai/agents/tools.py
+++ b/scalable/ai/agents/tools.py
@@ -9,9 +9,9 @@
 
 from __future__ import annotations
 
-import functools
 import logging
-from typing import Any, Callable, TypeVar
+from collections.abc import Callable
+from typing import Any, TypeVar
 
 logger = logging.getLogger(__name__)
 
diff --git a/scalable/ai/agents/validators.py b/scalable/ai/agents/validators.py
index 4180477..b908f8f 100644
--- a/scalable/ai/agents/validators.py
+++ b/scalable/ai/agents/validators.py
@@ -8,7 +8,8 @@
 from __future__ import annotations
 
 import logging
-from typing import Any, Callable, TypeVar
+from collections.abc import Callable
+from typing import Any, TypeVar
 
 from pydantic import BaseModel
 
@@ -50,7 +51,7 @@ def __init__(self) -> None:
         self._rules: list[tuple[Callable[[Any], bool], str]] = []
         self._field_rules: dict[str, list[tuple[Callable[[Any], bool], str]]] = {}
 
-    def add_rule(self, check: Callable[[Any], bool], message: str) -> "OutputValidator":
+    def add_rule(self, check: Callable[[Any], bool], message: str) -> OutputValidator:
         """Add a global validation rule.
 
         Parameters
@@ -70,7 +71,7 @@ def add_rule(self, check: Callable[[Any], bool], message: str) -> "OutputValidat
 
     def add_field_rule(
         self, field: str, check: Callable[[Any], bool], message: str
-    ) -> "OutputValidator":
+    ) -> OutputValidator:
         """Add a validation rule for a specific field.
 
         Parameters
diff --git a/scalable/ai/backend.py b/scalable/ai/backend.py
index 65671cc..0e9448a 100644
--- a/scalable/ai/backend.py
+++ b/scalable/ai/backend.py
@@ -229,7 +229,6 @@ def complete(
                 "Install with: pip install anthropic"
             ) from exc
 
-        import os
         kwargs: dict[str, Any] = {}
         if self._api_key:
             kwargs["api_key"] = self._api_key
@@ -251,8 +250,9 @@ def complete(
 
     def available(self) -> bool:
         try:
-            import anthropic  # type: ignore[import-untyped]  # noqa: F401
             import os
+
+            import anthropic  # type: ignore[import-untyped]  # noqa: F401
             return bool(os.environ.get("ANTHROPIC_API_KEY") or self._api_key)
         except ImportError:
             return False
@@ -308,8 +308,9 @@ def complete(
 
     def available(self) -> bool:
         try:
-            import google.generativeai  # type: ignore[import-untyped]  # noqa: F401
             import os
+
+            import google.generativeai  # type: ignore[import-untyped]  # noqa: F401
             return bool(os.environ.get("GOOGLE_API_KEY") or self._api_key)
         except ImportError:
             return False

From 12df6d572d257c039631bca60163956683e797a3 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 08:45:15 -0400
Subject: [PATCH 34/47] adjust title overline length

---
 docs/tutorials/06_telemetry.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/tutorials/06_telemetry.rst b/docs/tutorials/06_telemetry.rst
index f74b647..4cfb848 100644
--- a/docs/tutorials/06_telemetry.rst
+++ b/docs/tutorials/06_telemetry.rst
@@ -1,8 +1,8 @@
 .. _tutorial_telemetry:
 
-======================================================
+========================================================
 Tutorial 6: Monitoring and Observability with Telemetry
-======================================================
+========================================================
 
 What You Will Learn
 -------------------

From a9fcec7fd9fbe9c382589e17546832c7ca2d0fce Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 09:14:23 -0400
Subject: [PATCH 35/47] update storylines in tutorials

---
 .gitignore                                |   1 +
 docs/tutorials/02_manifest_system.rst     |  48 ++---
 docs/tutorials/03_scaling_strategies.rst  |  12 +-
 docs/tutorials/04_caching_performance.rst |  20 +-
 docs/tutorials/05_cloud_integration.rst   |  36 ++--
 docs/tutorials/06_telemetry.rst           |  20 +-
 docs/tutorials/07_error_handling.rst      |  12 +-
 docs/tutorials/08_kubernetes.rst          |  71 +++----
 docs/tutorials/09_ml_emulation.rst        |  60 +++---
 docs/tutorials/10_ai_composition.rst      | 118 +++++------
 docs/tutorials/index.rst                  |   6 +-
 notebooks/01_getting_started.ipynb        | 231 +++++++++++++++++++---
 notebooks/02_manifest_system.ipynb        | 170 +++++++++++++---
 notebooks/03_scaling_strategies.ipynb     |   4 +-
 notebooks/05_cloud_integration.ipynb      |  22 +--
 notebooks/08_kubernetes.ipynb             |  22 +--
 notebooks/09_ml_emulation.ipynb           |  22 +--
 notebooks/10_ai_composition.ipynb         |  14 +-
 18 files changed, 589 insertions(+), 300 deletions(-)

diff --git a/.gitignore b/.gitignore
index 07c2285..e02e5a1 100755
--- a/.gitignore
+++ b/.gitignore
@@ -5,6 +5,7 @@ capabilities/
 plans/
 .rooignore
 .env
+notebooks/.ipynb_checkpoints/
 
 # -----------------------------
 # Python bytecode / caches
diff --git a/docs/tutorials/02_manifest_system.rst b/docs/tutorials/02_manifest_system.rst
index 764fa44..9069d81 100644
--- a/docs/tutorials/02_manifest_system.rst
+++ b/docs/tutorials/02_manifest_system.rst
@@ -26,11 +26,11 @@ Prerequisites
 Scenario
 --------
 
-You are building a climate modeling pipeline with two stages: a computationally
-expensive simulation (GCAM) and a lighter post-processing step (Stitches). The
-pipeline must run locally during development, on an HPC cluster for production,
-and eventually in the cloud. The manifest system lets you describe all three
-targets in a single file.
+You are building an energy modeling pipeline with two stages: a computationally
+expensive simulation (GridLAB-D) and a lighter post-processing step (demand
+aggregation). The pipeline must run locally during development, on an HPC
+cluster for production, and eventually in the cloud. The manifest system lets
+you describe all three targets in a single file.
 
 Step 1: Manifest Schema Overview
 ---------------------------------
@@ -60,13 +60,13 @@ Step 2: The Project Block
 .. code-block:: yaml
 
    project:
-     name: climate-pipeline
+     name: energy-forecast
      default_storage: ./outputs
      local_cache: ./cache
 
 ``name``
   Identifies the project in telemetry run IDs (e.g.,
-  ``run-20260520T...-climate-pipeline-a1b2c3d4``). Use lowercase with hyphens.
+  ``run-20260520T...-energy-forecast-a1b2c3d4``). Use lowercase with hyphens.
 
 ``default_storage``
   Base URI for artifact output. Can be a local path, S3 URI
@@ -107,7 +107,7 @@ Targets are named execution environments. You can define as many as you need:
        instance_type: m5.xlarge
        worker_cpu: 4096
        worker_mem: 16384
-       image: 123456789.dkr.ecr.us-east-1.amazonaws.com/climate:latest
+       image: 123456789.dkr.ecr.us-east-1.amazonaws.com/energy-model:latest
        adaptive:
          minimum: 1
          maximum: 10
@@ -143,18 +143,18 @@ Components are resource profiles for your workloads:
 .. code-block:: yaml
 
    components:
-     gcam:
-       image: ghcr.io/jgcri/gcam:7.0
+     gridlabd:
+       image: ghcr.io/gridlab-d/gridlabd:5.0
        runtime: apptainer
        cpus: 8
        memory: 32G
        mounts:
-         /data/gcam: /gcam-core
+         /data/gridlabd: /gridlabd-core
          /shared/outputs: /outputs
        env:
-         GCAM_DATA: /gcam-core/data
-       tags: [iam, climate]
-       preload_script: ./scripts/gcam_preload.sh
+         GRIDLABD_DATA: /gridlabd-core/data
+       tags: [iam, energy]
+       preload_script: ./scripts/gridlabd_preload.sh
 
      postprocess:
        cpus: 2
@@ -235,7 +235,7 @@ Manifests support ``${VAR}`` and ``${VAR:-default}`` syntax for portability:
 .. code-block:: yaml
 
    project:
-     name: ${PROJECT_NAME:-climate-demo}
+     name: ${PROJECT_NAME:-energy-demo}
      default_storage: ${ARTIFACT_BUCKET:-./outputs}
 
    targets:
@@ -399,7 +399,7 @@ Here is a production-ready manifest combining all concepts:
 
    version: 1
    project:
-     name: climate-pipeline
+     name: energy-forecast
      default_storage: ${ARTIFACT_STORAGE:-./outputs}
 
    targets:
@@ -434,13 +434,13 @@ Here is a production-ready manifest combining all concepts:
          maximum: 20
 
    components:
-     gcam:
-       image: ghcr.io/jgcri/gcam:7.0
+     gridlabd:
+       image: ghcr.io/gridlab-d/gridlabd:5.0
        cpus: 4
        memory: 16G
-       tags: [iam, climate]
+       tags: [iam, energy]
        env:
-         GCAM_DATA: /gcam-core/data
+         GRIDLABD_DATA: /gridlabd-core/data
 
      postprocess:
        cpus: 2
@@ -448,8 +448,8 @@ Here is a production-ready manifest combining all concepts:
        tags: [analysis]
 
    tasks:
-     run_gcam:
-       component: gcam
+     run_gridlabd:
+       component: gridlabd
        cache: true
        outputs:
          database: dir
@@ -461,7 +461,7 @@ Here is a production-ready manifest combining all concepts:
    overlays:
      hpc-prod:
        components:
-         gcam:
+         gridlabd:
            cpus: 16
            memory: 64G
          postprocess:
@@ -470,7 +470,7 @@ Here is a production-ready manifest combining all concepts:
 
      hpc-debug:
        components:
-         gcam:
+         gridlabd:
            cpus: 2
            memory: 4G
          postprocess:
diff --git a/docs/tutorials/03_scaling_strategies.rst b/docs/tutorials/03_scaling_strategies.rst
index d6a0d81..394b2f8 100644
--- a/docs/tutorials/03_scaling_strategies.rst
+++ b/docs/tutorials/03_scaling_strategies.rst
@@ -13,7 +13,7 @@ By the end of this tutorial you will:
   backends.
 * Configure and use the Local, Slurm, and Cloud providers.
 * Choose appropriate scaling strategies for different workload profiles.
-* Implement manual scaling, adaptive scaling, and policy-driven planning.
+* Implement manual scaling, adaptive scaling, and objective-driven planning.
 * Monitor scaling decisions through the Session API.
 
 Prerequisites
@@ -27,10 +27,10 @@ Prerequisites
 Scenario
 --------
 
-Your climate pipeline has grown. Development happens locally with 2–4 workers.
-Production runs on an HPC cluster with 64+ workers. Burst capacity uses cloud
-auto-scaling. You need a unified scaling approach that works across all three
-environments.
+Your energy forecasting pipeline has grown. Development happens locally with
+2–4 workers. Production runs on an HPC cluster with 64+ workers. Burst
+capacity uses cloud auto-scaling. You need a unified scaling approach that
+works across all three environments.
 
 Step 1: The Provider Architecture
 ----------------------------------
@@ -206,7 +206,7 @@ availability.
 Step 4: Session-Based Scaling with Objectives
 -----------------------------------------------
 
-The Session API supports policy-driven planning that automatically determines
+The Session API supports objective-driven planning that automatically determines
 worker counts:
 
 .. code-block:: python
diff --git a/docs/tutorials/04_caching_performance.rst b/docs/tutorials/04_caching_performance.rst
index f558253..234201a 100644
--- a/docs/tutorials/04_caching_performance.rst
+++ b/docs/tutorials/04_caching_performance.rst
@@ -26,10 +26,10 @@ Prerequisites
 Scenario
 --------
 
-Your pipeline executes expensive climate simulations that take 30+ minutes per
-scenario. During development you frequently restart runs after fixing
-downstream bugs. Without caching, every restart recomputes scenarios that
-already succeeded. The ``@cacheable`` decorator lets completed tasks skip
+Your pipeline executes expensive energy demand simulations that take 30+
+minutes per scenario. During development you frequently restart runs after
+fixing downstream bugs. Without caching, every restart recomputes scenarios
+that already succeeded. The ``@cacheable`` decorator lets completed tasks skip
 execution on retry.
 
 Step 1: Basic Caching with @cacheable
@@ -46,10 +46,10 @@ arguments, and returns cached results when available:
 
    @cacheable(return_type=dict, scenario_id=int)
    def run_simulation(scenario_id: int) -> dict:
-       """Expensive computation — runs a climate scenario."""
+       """Expensive computation — runs an energy demand scenario."""
        import time
        time.sleep(30)  # Simulating expensive work
-       return {"scenario": scenario_id, "emissions": scenario_id * 1.5}
+       return {"scenario": scenario_id, "demand_mw": scenario_id * 1.5}
 
 First call:
 
@@ -58,7 +58,7 @@ First call:
    result = run_simulation(42)
    # Takes 30 seconds — cache MISS
    print(result)
-   # {'scenario': 42, 'emissions': 63.0}
+   # {'scenario': 42, 'demand_mw': 63.0}
 
 Second call with the same argument:
 
@@ -67,7 +67,7 @@ Second call with the same argument:
    result = run_simulation(42)
    # Returns instantly — cache HIT
    print(result)
-   # {'scenario': 42, 'emissions': 63.0}
+   # {'scenario': 42, 'demand_mw': 63.0}
 
 **How it works:**
 
@@ -160,7 +160,7 @@ example after fixing a bug in the computation logic:
    def run_simulation(scenario_id: int) -> dict:
        """Always recompute — ignores cached results."""
        # Fixed version of the computation
-       return {"scenario": scenario_id, "emissions": scenario_id * 1.7}
+       return {"scenario": scenario_id, "demand_mw": scenario_id * 1.7}
 
 Setting ``recompute=True`` forces the function to execute every time. The
 result still gets written to the cache, so subsequent calls (once you remove
@@ -328,7 +328,7 @@ logic changes:
 
    @cacheable(return_type=dict, params=dict)
    def run_gcam_v3(params: dict) -> dict:
-       # v3: fixed carbon price calculation
+       # v3: fixed fuel cost calculation
        ...
 
 **Strategy 4: Delete the cache directory**
diff --git a/docs/tutorials/05_cloud_integration.rst b/docs/tutorials/05_cloud_integration.rst
index e65344f..ce80cf4 100644
--- a/docs/tutorials/05_cloud_integration.rst
+++ b/docs/tutorials/05_cloud_integration.rst
@@ -28,10 +28,10 @@ Prerequisites
 Scenario
 --------
 
-Your climate pipeline works locally but needs to scale to 50+ concurrent
-scenarios for a production run. Your organization uses AWS for burst compute
-and GCS for long-term data storage. You need to deploy the same workflow to
-cloud infrastructure with cost visibility.
+Your energy forecasting pipeline works locally but needs to scale to 50+
+concurrent scenarios for a production run. Your organization uses AWS for burst
+compute and GCS for long-term data storage. You need to deploy the same
+workflow to cloud infrastructure with cost visibility.
 
 Step 1: AWS Target Configuration
 ----------------------------------
@@ -44,7 +44,7 @@ The AWS provider uses ``dask-cloudprovider`` to launch Dask workers on Fargate
    # scalable.yaml
    version: 1
    project:
-     name: climate-model-aws
+     name: energy-model-aws
      default_storage: s3://${S3_BUCKET}/scalable-runs/
 
    targets:
@@ -72,7 +72,7 @@ The AWS provider uses ``dask-cloudprovider`` to launch Dask workers on Fargate
        image: ${ECR_IMAGE_GCAM}
        cpus: 4
        memory: 16G
-       tags: [iam, climate]
+       tags: [iam, energy]
 
      postprocess:
        cpus: 2
@@ -135,12 +135,12 @@ Before running, ensure these AWS resources exist:
 
 .. code-block:: bash
 
-   aws ecr create-repository --repository-name climate-model
+   aws ecr create-repository --repository-name energy-model
    # Push your image
-   docker build -t climate-model:latest .
-   docker tag climate-model:latest 123456789.dkr.ecr.us-east-1.amazonaws.com/climate-model:latest
+   docker build -t energy-model:latest .
+   docker tag energy-model:latest 123456789.dkr.ecr.us-east-1.amazonaws.com/energy-model:latest
    aws ecr get-login-password | docker login --username AWS --password-stdin 123456789.dkr.ecr.us-east-1.amazonaws.com
-   docker push 123456789.dkr.ecr.us-east-1.amazonaws.com/climate-model:latest
+   docker push 123456789.dkr.ecr.us-east-1.amazonaws.com/energy-model:latest
 
 **2. VPC + Subnets:**
 
@@ -239,7 +239,7 @@ compute:
        cluster_type: cloud_run
        worker_cpu: 4
        worker_mem: 16Gi
-       image: gcr.io/${GCP_PROJECT_ID}/climate-model:latest
+       image: gcr.io/${GCP_PROJECT_ID}/energy-model:latest
        service_account: ${GCP_SERVICE_ACCOUNT}
        adaptive:
          minimum: 1
@@ -256,7 +256,7 @@ GCP-specific setup:
    gcloud auth application-default login
 
    # Push image to GCR
-   gcloud builds submit --tag gcr.io/my-project/climate-model:latest .
+   gcloud builds submit --tag gcr.io/my-project/energy-model:latest .
 
    # Create GCS bucket for artifacts
    gsutil mb -l us-central1 gs://my-bucket/
@@ -365,7 +365,7 @@ machines share results:
 .. code-block:: yaml
 
    project:
-     name: climate-pipeline
+     name: energy-forecast
      default_storage: s3://my-bucket/outputs/
 
 Now:
@@ -386,15 +386,15 @@ For production deployments, maintain a ``.env`` template:
 
    # .env.cloud (do not commit secrets — use secrets manager)
    AWS_REGION=us-east-1
-   S3_BUCKET=climate-prod-artifacts
-   ECR_IMAGE=123456789.dkr.ecr.us-east-1.amazonaws.com/climate:latest
-   ECR_IMAGE_GCAM=123456789.dkr.ecr.us-east-1.amazonaws.com/gcam:7.0
+   S3_BUCKET=energy-prod-artifacts
+   ECR_IMAGE=123456789.dkr.ecr.us-east-1.amazonaws.com/energy-model:latest
+   ECR_IMAGE_GCAM=123456789.dkr.ecr.us-east-1.amazonaws.com/gridlabd:5.0
    EXECUTION_ROLE_ARN=arn:aws:iam::123456789:role/ecsTaskExecutionRole
    TASK_ROLE_ARN=arn:aws:iam::123456789:role/scalableTaskRole
    SUBNET_A=subnet-abc123
    SUBNET_B=subnet-def456
    SG_ID=sg-xyz789
-   SCALABLE_CACHE_REMOTE=s3://climate-prod-artifacts/cache/
+   SCALABLE_CACHE_REMOTE=s3://energy-prod-artifacts/cache/
 
 Load before running:
 
@@ -415,7 +415,7 @@ Troubleshooting
 **Fargate task fails with "CannotPullContainerError"**
   The execution role lacks ECR permissions, the image URI is wrong, or the
   image doesn't exist in the specified region. Verify with:
-  ``aws ecr describe-images --repository-name climate-model``.
+  ``aws ecr describe-images --repository-name energy-model``.
 
 **Workers can't connect to scheduler**
   Security group must allow inbound TCP on the Dask scheduler port (8786)
diff --git a/docs/tutorials/06_telemetry.rst b/docs/tutorials/06_telemetry.rst
index 4cfb848..1842cae 100644
--- a/docs/tutorials/06_telemetry.rst
+++ b/docs/tutorials/06_telemetry.rst
@@ -26,10 +26,10 @@ Prerequisites
 Scenario
 --------
 
-Your team runs the climate pipeline multiple times per week. You need to
-track performance trends, identify slow tasks, monitor resource utilization,
-and justify cloud spending to stakeholders. Scalable's built-in telemetry
-provides all this data without external observability infrastructure.
+Your team runs the energy forecasting pipeline multiple times per week. You
+need to track performance trends, identify slow tasks, monitor resource
+utilization, and justify cloud spending to stakeholders. Scalable's built-in
+telemetry provides all this data without external observability infrastructure.
 
 Step 1: Telemetry Architecture
 -------------------------------
@@ -40,7 +40,7 @@ automatically records structured events to disk:
 .. code-block:: text
 
    .scalable/runs/
-   └── run-20260520T035200Z-climate-pipeline-a1b2c3d4/
+   └── run-20260520T035200Z-energy-forecast-a1b2c3d4/
        ├── run.json           # Run metadata (start time, target, manifest lock)
        ├── manifest.yaml      # Snapshot of the manifest used
        ├── plan.json          # Execution plan snapshot
@@ -68,8 +68,8 @@ The ``run.json`` file contains the run's identity and configuration:
 .. code-block:: json
 
    {
-     "run_id": "run-20260520T035200Z-climate-pipeline-a1b2c3d4",
-     "project_name": "climate-pipeline",
+     "run_id": "run-20260520T035200Z-energy-forecast-a1b2c3d4",
+     "project_name": "energy-forecast",
      "target_name": "local",
      "provider_name": "local",
      "manifest_lock": "sha256:a3b8f1...",
@@ -105,7 +105,7 @@ Analyzing task durations:
    import pandas as pd
    from pathlib import Path
 
-   run_dir = Path(".scalable/runs/run-20260520T035200Z-climate-pipeline-a1b2c3d4")
+   run_dir = Path(".scalable/runs/run-20260520T035200Z-energy-forecast-a1b2c3d4")
 
    tasks = []
    with open(run_dir / "tasks.jsonl") as f:
@@ -199,7 +199,7 @@ The quickest way to review a run:
 .. code-block:: text
 
    ═══════════════════════════════════════════════════════════
-   Run Report: run-20260520T035200Z-climate-pipeline-a1b2c3d4
+   Run Report: run-20260520T035200Z-energy-forecast-a1b2c3d4
    ═══════════════════════════════════════════════════════════
    Status: completed
    Target: local (provider: local)
@@ -229,7 +229,7 @@ Export as JSON for downstream processing:
 .. code-block:: json
 
    {
-     "run_id": "run-20260520T035200Z-climate-pipeline-a1b2c3d4",
+     "run_id": "run-20260520T035200Z-energy-forecast-a1b2c3d4",
      "status": "completed",
      "duration_seconds": 390,
      "tasks": {"submitted": 50, "succeeded": 50, "failed": 0},
diff --git a/docs/tutorials/07_error_handling.rst b/docs/tutorials/07_error_handling.rst
index 7b556de..3f738e2 100644
--- a/docs/tutorials/07_error_handling.rst
+++ b/docs/tutorials/07_error_handling.rst
@@ -27,11 +27,11 @@ Prerequisites
 Scenario
 --------
 
-Your production pipeline runs 200 climate scenarios overnight. Some scenarios
-fail due to transient issues (network timeouts pulling data, OOM on edge-case
-inputs, worker preemption on shared HPC clusters). You need a workflow that
-tolerates partial failures, recovers what it can, and provides clear
-diagnostics for what went wrong.
+Your production pipeline runs 200 energy demand scenarios overnight. Some
+scenarios fail due to transient issues (network timeouts pulling data, OOM on
+edge-case inputs, worker preemption on shared HPC clusters). You need a
+workflow that tolerates partial failures, recovers what it can, and provides
+clear diagnostics for what went wrong.
 
 Step 1: Understanding Error Propagation
 -----------------------------------------
@@ -290,7 +290,7 @@ analyzes telemetry and provides human-readable explanations:
 
 .. code-block:: text
 
-   Diagnosis for run-20260520T...-climate-pipeline-a1b2c3d4:
+   Diagnosis for run-20260520T...-energy-forecast-a1b2c3d4:
 
    ⚠ 13 failures detected across 3 categories:
 
diff --git a/docs/tutorials/08_kubernetes.rst b/docs/tutorials/08_kubernetes.rst
index 6694296..9b23634 100644
--- a/docs/tutorials/08_kubernetes.rst
+++ b/docs/tutorials/08_kubernetes.rst
@@ -31,8 +31,9 @@ Scenario
 --------
 
 Your organization runs a shared Kubernetes cluster for all scientific
-workloads. You need to deploy the climate pipeline as a Dask cluster within
-your team's namespace, with resource quotas enforced by platform engineering.
+workloads. You need to deploy the energy forecasting pipeline as a Dask
+cluster within your team's namespace, with resource quotas enforced by
+platform engineering.
 The deployment must support both development (small, fast iterations) and
 production (large-scale, fault-tolerant) modes.
 
@@ -69,14 +70,14 @@ Step 2: Configure the Kubernetes Target
    # scalable.yaml
    version: 1
    project:
-     name: climate-pipeline-k8s
+     name: energy-forecast-k8s
      default_storage: gs://${GCS_BUCKET}/scalable-runs/
 
    targets:
      k8s-dev:
        provider: kubernetes
-       namespace: climate-dev
-       image: gcr.io/${GCP_PROJECT}/climate-model:${IMAGE_TAG:-latest}
+       namespace: energy-dev
+       image: gcr.io/${GCP_PROJECT}/energy-model:${IMAGE_TAG:-latest}
        adaptive:
          minimum: 1
          maximum: 5
@@ -84,21 +85,21 @@ Step 2: Configure the Kubernetes Target
 
      k8s-prod:
        provider: kubernetes
-       namespace: climate-prod
-       image: gcr.io/${GCP_PROJECT}/climate-model:${IMAGE_TAG}
+       namespace: energy-prod
+       image: gcr.io/${GCP_PROJECT}/energy-model:${IMAGE_TAG}
        adaptive:
          minimum: 4
          maximum: 40
        overlay: k8s-prod-resources
 
    components:
-     gcam:
-       image: gcr.io/${GCP_PROJECT}/gcam:7.0
+     gridlabd:
+       image: gcr.io/${GCP_PROJECT}/gridlabd:5.0
        cpus: 8
        memory: 32G
-       tags: [iam, climate]
+       tags: [iam, energy]
        env:
-         GCAM_DATA: /data/gcam
+         GRIDLABD_DATA: /data/gridlabd
 
      postprocess:
        image: gcr.io/${GCP_PROJECT}/postprocess:latest
@@ -144,12 +145,12 @@ Create isolated namespaces for development and production:
 .. code-block:: bash
 
    # Development namespace
-   kubectl create namespace climate-dev
-   kubectl label namespace climate-dev team=climate env=dev
+   kubectl create namespace energy-dev
+   kubectl label namespace energy-dev team=energy env=dev
 
    # Production namespace
-   kubectl create namespace climate-prod
-   kubectl label namespace climate-prod team=climate env=prod
+   kubectl create namespace energy-prod
+   kubectl label namespace energy-prod team=energy env=prod
 
 Apply resource quotas to prevent runaway usage:
 
@@ -159,8 +160,8 @@ Apply resource quotas to prevent runaway usage:
    apiVersion: v1
    kind: ResourceQuota
    metadata:
-     name: climate-pipeline-quota
-     namespace: climate-prod
+     name: energy-forecast-quota
+     namespace: energy-prod
    spec:
      hard:
        requests.cpu: "160"
@@ -185,14 +186,14 @@ If your container registry requires authentication:
      --docker-server=gcr.io \
      --docker-username=_json_key \
      --docker-password="$(cat service-account-key.json)" \
-     --namespace climate-prod
+     --namespace energy-prod
 
    # For ECR (AWS Elastic Container Registry)
    kubectl create secret docker-registry ecr-secret \
      --docker-server=123456789.dkr.ecr.us-east-1.amazonaws.com \
      --docker-username=AWS \
      --docker-password="$(aws ecr get-login-password)" \
-     --namespace climate-prod
+     --namespace energy-prod
 
 The Kubernetes provider automatically attaches these secrets to worker pods
 when the image URI matches the registry.
@@ -203,7 +204,7 @@ Step 5: Run a Development Workflow
 .. code-block:: bash
 
    export GCP_PROJECT=my-gcp-project
-   export GCS_BUCKET=climate-artifacts
+   export GCS_BUCKET=energy-artifacts
    export IMAGE_TAG=dev-$(git rev-parse --short HEAD)
 
    # Validate
@@ -215,7 +216,7 @@ Step 5: Run a Development Workflow
 .. code-block:: text
 
    Plan created for target 'k8s-dev' (provider: kubernetes)
-   Namespace: climate-dev
+   Namespace: energy-dev
    Workers:
      gcam: 2 pods (2 cpu, 8G memory)
      postprocess: 1 pod (1 cpu, 4G memory)
@@ -240,7 +241,7 @@ Run the workflow:
 **What happens under the hood:**
 
 1. The :class:`~scalable.providers.kubernetes.KubernetesProvider` creates a
-   ``DaskCluster`` custom resource in the ``climate-dev`` namespace.
+   ``DaskCluster`` custom resource in the ``energy-dev`` namespace.
 2. The Dask Kubernetes Operator provisions scheduler and worker pods.
 3. Worker pods are labeled with component tags for affinity scheduling.
 4. The adaptive scaler monitors task backlog and scales pods up/down within
@@ -256,25 +257,25 @@ Watch Kubernetes events in real-time:
 .. code-block:: bash
 
    # Watch pods in the namespace
-   kubectl get pods -n climate-dev -w
+   kubectl get pods -n energy-dev -w
 
 .. code-block:: text
 
    NAME                                READY   STATUS    RESTARTS   AGE
-   dask-scheduler-climate-dev-0        1/1     Running   0          30s
-   dask-worker-gcam-0                  1/1     Running   0          25s
-   dask-worker-gcam-1                  1/1     Running   0          25s
+   dask-scheduler-energy-dev-0         1/1     Running   0          30s
+   dask-worker-gridlabd-0              1/1     Running   0          25s
+   dask-worker-gridlabd-1              1/1     Running   0          25s
    dask-worker-postprocess-0           1/1     Running   0          25s
 
    # Scale-up event
-   dask-worker-gcam-2                  0/1     Pending   0          0s
-   dask-worker-gcam-2                  1/1     Running   0          15s
+   dask-worker-gridlabd-2              0/1     Pending   0          0s
+   dask-worker-gridlabd-2              1/1     Running   0          15s
 
 Check the Dask dashboard (port-forward the scheduler):
 
 .. code-block:: bash
 
-   kubectl port-forward -n climate-dev svc/dask-scheduler-climate-dev 8787:8787
+   kubectl port-forward -n energy-dev svc/dask-scheduler-energy-dev 8787:8787
    # Open http://localhost:8787 in your browser
 
 Step 7: Production Deployment
@@ -299,7 +300,7 @@ simultaneously:
    kind: PodDisruptionBudget
    metadata:
      name: dask-workers-pdb
-     namespace: climate-prod
+     namespace: energy-prod
    spec:
      minAvailable: "50%"
      selector:
@@ -318,10 +319,10 @@ jobs:
    apiVersion: scheduling.k8s.io/v1
    kind: PriorityClass
    metadata:
-     name: climate-production
+     name: energy-production
    value: 1000
    globalDefault: false
-   description: "Priority for production climate pipeline runs"
+   description: "Priority for production energy forecasting runs"
 
 Step 8: Handling Pod Evictions
 -------------------------------
@@ -392,7 +393,7 @@ Automate Kubernetes deployments from your CI pipeline:
 
          - uses: google-github-actions/get-gke-credentials@v2
            with:
-             cluster_name: climate-cluster
+             cluster_name: energy-cluster
              location: us-central1
 
          - name: Install Scalable
@@ -422,8 +423,8 @@ For local Kubernetes development without a cloud cluster:
    helm install dask-operator dask/dask-kubernetes-operator
 
    # Build and load image locally
-   docker build -t climate-model:local .
-   minikube image load climate-model:local
+   docker build -t energy-model:local .
+   minikube image load energy-model:local
 
    # Use local image in manifest
    export IMAGE_TAG=local
diff --git a/docs/tutorials/09_ml_emulation.rst b/docs/tutorials/09_ml_emulation.rst
index 2add304..5076890 100644
--- a/docs/tutorials/09_ml_emulation.rst
+++ b/docs/tutorials/09_ml_emulation.rst
@@ -252,22 +252,22 @@ functions as candidates for surrogate model replacement:
 
 
    @emulatable(
-       tag="gcam",
-       inputs=["carbon_price", "population", "gdp"],
-       outputs=["emissions", "energy_price"],
+       tag="gridlabd",
+       inputs=["fuel_cost", "population", "gdp"],
+       outputs=["demand_mw", "energy_price"],
        uncertainty="required",
        fallback="full_model",
        domain={
-           "carbon_price": (0, 500),
+           "fuel_cost": (0, 500),
            "population": (7e9, 12e9),
            "gdp": (50e12, 200e12),
        },
        confidence_threshold=0.9,
    )
-   def run_gcam_scenario(carbon_price, population, gdp):
-       """Run a full GCAM scenario — takes 30+ minutes."""
-       # ... expensive climate model execution ...
-       return {"emissions": 35.2, "energy_price": 0.12}
+   def run_energy_scenario(fuel_cost, population, gdp):
+       """Run a full energy demand scenario — takes 30+ minutes."""
+       # ... expensive energy model execution ...
+       return {"demand_mw": 35.2, "energy_price": 0.12}
 
 Decorator parameters:
 
@@ -313,7 +313,7 @@ grid, then train a surrogate:
    # Generate training data (Latin Hypercube or similar)
    np.random.seed(42)
    training_inputs = {
-       "carbon_price": np.random.uniform(0, 500, size=100),
+       "fuel_cost": np.random.uniform(0, 500, size=100),
        "population": np.random.uniform(7e9, 12e9, size=100),
        "gdp": np.random.uniform(50e12, 200e12, size=100),
    }
@@ -321,8 +321,8 @@ grid, then train a surrogate:
    # Run the full model for each sample (expensive!)
    training_outputs = []
    for i in range(100):
-       result = run_gcam_scenario(
-           carbon_price=training_inputs["carbon_price"][i],
+       result = run_energy_scenario(
+           fuel_cost=training_inputs["fuel_cost"][i],
            population=training_inputs["population"][i],
            gdp=training_inputs["gdp"][i],
        )
@@ -331,7 +331,7 @@ grid, then train a surrogate:
    # Register the trained emulator
    registry = EmulatorRegistry(".scalable/emulators")
    registry.register(
-       function_name="run_gcam_scenario",
+       function_name="run_energy_scenario",
        training_inputs=training_inputs,
        training_outputs=training_outputs,
        model_type="gaussian_process",  # Provides uncertainty estimates
@@ -354,18 +354,18 @@ the emulator and full model based on confidence:
 
    # High-confidence prediction (within training domain)
    result = dispatch.predict(
-       "run_gcam_scenario",
-       inputs={"carbon_price": 100, "population": 8e9, "gdp": 80e12},
+       "run_energy_scenario",
+       inputs={"fuel_cost": 100, "population": 8e9, "gdp": 80e12},
    )
    print(f"Source: {result.source}")        # "emulator"
    print(f"Confidence: {result.confidence:.3f}")  # 0.95
-   print(f"Prediction: {result.values}")    # {'emissions': 34.8, 'energy_price': 0.11}
-   print(f"Uncertainty: {result.uncertainty}")  # {'emissions': ±1.2, 'energy_price': ±0.02}
+   print(f"Prediction: {result.values}")    # {'demand_mw': 34.8, 'energy_price': 0.11}
+   print(f"Uncertainty: {result.uncertainty}")  # {'demand_mw': ±1.2, 'energy_price': ±0.02}
 
    # Low-confidence prediction (edge of domain)
    result = dispatch.predict(
-       "run_gcam_scenario",
-       inputs={"carbon_price": 490, "population": 11.5e9, "gdp": 190e12},
+       "run_energy_scenario",
+       inputs={"fuel_cost": 490, "population": 11.5e9, "gdp": 190e12},
    )
    print(f"Source: {result.source}")        # "full_model" (fell back)
    print(f"Confidence: {result.confidence:.3f}")  # 0.72 (below threshold)
@@ -407,7 +407,7 @@ the full model:
 
    learner = ActiveLearner(
        registry=registry,
-       function_name="run_gcam_scenario",
+       function_name="run_energy_scenario",
        acquisition="uncertainty",  # Sample where emulator is least confident
        batch_size=10,
    )
@@ -416,14 +416,14 @@ the full model:
    next_points = learner.suggest()
    print(f"Suggested {len(next_points)} points for full model evaluation:")
    for point in next_points[:3]:
-       print(f"  carbon_price={point['carbon_price']:.0f}, "
+       print(f"  fuel_cost={point['fuel_cost']:.0f}, "
              f"population={point['population']:.2e}, "
              f"gdp={point['gdp']:.2e}")
 
    # Run full model on suggested points
    new_results = []
    for point in next_points:
-       result = run_gcam_scenario(**point)
+       result = run_energy_scenario(**point)
        new_results.append(result)
 
    # Update the emulator with new data
@@ -449,18 +449,18 @@ Integrate emulation into your pipeline for massive speedups:
 
 
    @emulatable(
-       tag="gcam",
-       inputs=["carbon_price", "population"],
-       outputs=["emissions"],
+       tag="gridlabd",
+       inputs=["fuel_cost", "population"],
+       outputs=["demand_mw"],
        uncertainty="required",
        fallback="full_model",
        confidence_threshold=0.9,
    )
-   @cacheable(return_type=dict, carbon_price=float, population=float)
-   def run_scenario(carbon_price: float, population: float) -> dict:
+   @cacheable(return_type=dict, fuel_cost=float, population=float)
+   def run_scenario(fuel_cost: float, population: float) -> dict:
        """Full model — 30 min per call."""
        # ... expensive computation ...
-       return {"emissions": carbon_price * 0.1 + population * 1e-10}
+       return {"demand_mw": fuel_cost * 0.1 + population * 1e-10}
 
 
    def run_pipeline():
@@ -474,12 +474,12 @@ Integrate emulation into your pipeline for massive speedups:
        emulated_count = 0
        full_model_count = 0
 
-       for cp in range(0, 500, 10):
+       for fc in range(0, 500, 10):
            for pop in [8e9, 9e9, 10e9]:
                # Try emulator first
                result = dispatch.predict(
                    "run_scenario",
-                   inputs={"carbon_price": cp, "population": pop},
+                   inputs={"fuel_cost": fc, "population": pop},
                )
 
                if result.source == "emulator":
@@ -487,7 +487,7 @@ Integrate emulation into your pipeline for massive speedups:
                    emulated_count += 1
                else:
                    # Fall back to full model via distributed workers
-                   fut = client.submit(run_scenario, cp, pop, tag="gcam")
+                   fut = client.submit(run_scenario, fc, pop, tag="gridlabd")
                    results.append(fut.result())
                    full_model_count += 1
 
diff --git a/docs/tutorials/10_ai_composition.rst b/docs/tutorials/10_ai_composition.rst
index 6c6ec59..df44a77 100644
--- a/docs/tutorials/10_ai_composition.rst
+++ b/docs/tutorials/10_ai_composition.rst
@@ -28,9 +28,9 @@ Prerequisites
 Scenario
 --------
 
-Your team is onboarding a new model (Stitches) into the climate pipeline.
-You need to configure its component definition, write task bindings, and
-eventually migrate the entire pipeline from Slurm to Kubernetes. The AI
+Your team is onboarding a new model (WaterShed) into the water resource
+pipeline. You need to configure its component definition, write task bindings,
+and eventually migrate the entire pipeline from Slurm to Kubernetes. The AI
 assistants automate tedious configuration tasks and provide expert guidance
 without requiring deep Scalable expertise.
 
@@ -79,36 +79,36 @@ component configuration:
 
 .. code-block:: bash
 
-   scalable init-component ./path/to/stitches --name stitches --no-ai
+   scalable init-component ./path/to/watershed --name watershed --no-ai
 
 .. code-block:: text
 
-   Analyzing ./path/to/stitches...
+   Analyzing ./path/to/watershed...
    Detected:
      Language: R (via rpy2)
-     Dependencies: stitches, dplyr, tidyr
-     Entry point: ./run_stitches.R
+     Dependencies: watershed, dplyr, tidyr
+     Entry point: ./run_watershed.R
      Estimated resources: 6 CPUs, 50G memory
 
    Generated component configuration:
 
    components:
-     stitches:
-       image: ghcr.io/jgcri/stitches:latest
+     watershed:
+       image: ghcr.io/hydro-lab/watershed:latest
        cpus: 6
        memory: 50G
-       tags: [climate, downscaling]
+       tags: [water, hydrology]
        env:
          R_LIBS_USER: /opt/R/library
 
    Suggested task binding:
 
    tasks:
-     run_stitches:
-       component: stitches
+     run_watershed:
+       component: watershed
        cache: true
 
-   Written to: ./stitches/scalable-component.yaml
+   Written to: ./watershed/scalable-component.yaml
 
 **What the analyzer checks:**
 
@@ -124,8 +124,8 @@ Python API:
    from scalable.ai import onboard_component
 
    result = onboard_component(
-       "./path/to/stitches",
-       name="stitches",
+       "./path/to/watershed",
+       name="watershed",
        no_ai=True,
    )
 
@@ -145,7 +145,7 @@ After a failed run, use the diagnostic assistant to identify root causes:
 .. code-block:: text
 
    ═══════════════════════════════════════════════════════════
-   Diagnosis: run-20260520T041500Z-climate-pipeline-f8e2a1b3
+   Diagnosis: run-20260520T041500Z-energy-forecast-f8e2a1b3
    ═══════════════════════════════════════════════════════════
 
    Status: failed (13 task failures)
@@ -159,7 +159,7 @@ After a failed run, use the diagnostic assistant to identify root causes:
      input_grid_cells > 500.
 
    SECONDARY: Network timeouts (3 of 13 failures)
-     Pattern: External data API (api.climate-data.org) returning 503
+     Pattern: External data API (api.energy-data.org) returning 503
      between 04:15-04:20 UTC.
      Evidence: All timeout failures cluster within a 5-minute window.
 
@@ -209,7 +209,7 @@ Make execution plans understandable for non-technical stakeholders:
    Plan Explanation
    ═════════════════
 
-   This plan will execute the "climate-pipeline" project on AWS (Fargate)
+   This plan will execute the "energy-forecast" project on AWS (Fargate)
    in the us-east-1 region.
 
    What will happen:
@@ -246,8 +246,8 @@ from descriptions:
 
 .. code-block:: bash
 
-   scalable compose "Run GCAM reference scenario for SSP2, \
-     then run Stitches to downscale daily climate data, \
+   scalable compose "Run GridLAB-D power flow simulation for region A, \
+     then run WaterShed to model downstream water demand, \
      then aggregate results by region and produce summary plots"
 
 .. code-block:: text
@@ -258,20 +258,20 @@ from descriptions:
    # workflow.py
    from scalable import ScalableSession, cacheable
 
-   @cacheable(return_type=dict, ssp=str)
-   def run_gcam_reference(ssp: str) -> dict:
-       """Run GCAM reference scenario for the given SSP."""
-       # TODO: Implement GCAM execution logic
-       return {"database_path": f"./output/gcam_{ssp}/"}
+   @cacheable(return_type=dict, region=str)
+   def run_gridlabd_simulation(region: str) -> dict:
+       """Run GridLAB-D power flow simulation for the given region."""
+       # TODO: Implement GridLAB-D execution logic
+       return {"database_path": f"./output/gridlabd_{region}/"}
 
-   @cacheable(return_type=dict, gcam_output=str)
-   def run_stitches_downscale(gcam_output: str) -> dict:
-       """Downscale GCAM output to daily climate using Stitches."""
-       # TODO: Implement Stitches execution logic
-       return {"daily_climate_path": f"./output/stitches/{gcam_output}"}
+   @cacheable(return_type=dict, gridlabd_output=str)
+   def run_watershed_model(gridlabd_output: str) -> dict:
+       """Model downstream water demand from energy output."""
+       # TODO: Implement WaterShed execution logic
+       return {"water_demand_path": f"./output/watershed/{gridlabd_output}"}
 
-   @cacheable(return_type=dict, climate_path=str)
-   def aggregate_and_plot(climate_path: str) -> dict:
+   @cacheable(return_type=dict, demand_path=str)
+   def aggregate_and_plot(demand_path: str) -> dict:
        """Aggregate by region and produce summary plots."""
        # TODO: Implement aggregation logic
        return {"summary_path": "./output/summary/"}
@@ -280,20 +280,20 @@ from descriptions:
        session = ScalableSession.from_yaml("./scalable.yaml", target="local")
        client = session.start()
 
-       # Stage 1: GCAM
-       gcam_result = client.submit(run_gcam_reference, "SSP2", tag="gcam").result()
+       # Stage 1: GridLAB-D
+       gridlabd_result = client.submit(run_gridlabd_simulation, "region_A", tag="gridlabd").result()
 
-       # Stage 2: Stitches downscaling
-       stitches_result = client.submit(
-           run_stitches_downscale,
-           gcam_result["database_path"],
-           tag="stitches",
+       # Stage 2: WaterShed modeling
+       watershed_result = client.submit(
+           run_watershed_model,
+           gridlabd_result["database_path"],
+           tag="watershed",
        ).result()
 
        # Stage 3: Aggregation
        final = client.submit(
            aggregate_and_plot,
-           stitches_result["daily_climate_path"],
+           watershed_result["water_demand_path"],
            tag="postprocess",
        ).result()
 
@@ -307,25 +307,25 @@ from descriptions:
    Suggested manifest additions:
 
    components:
-     gcam:
+     gridlabd:
        cpus: 8
        memory: 32G
-       tags: [iam, climate]
-     stitches:
+       tags: [iam, energy]
+     watershed:
        cpus: 6
        memory: 50G
-       tags: [climate, downscaling]
+       tags: [water, hydrology]
      postprocess:
        cpus: 2
        memory: 8G
        tags: [analysis]
 
    tasks:
-     run_gcam_reference:
-       component: gcam
+     run_gridlabd_simulation:
+       component: gridlabd
        cache: true
-     run_stitches_downscale:
-       component: stitches
+     run_watershed_model:
+       component: watershed
        cache: true
      aggregate_and_plot:
        component: postprocess
@@ -338,7 +338,7 @@ Python API for programmatic composition:
    from scalable.ai import compose_workflow
 
    result = compose_workflow(
-       "Run GCAM for SSP1-5, then Stitches for each, then aggregate"
+       "Run GridLAB-D for regions A-E, then WaterShed for each, then aggregate"
    )
 
    print(result.workflow_code)
@@ -377,20 +377,20 @@ Move your workflow from one provider to another:
    targets:
      k8s:
        provider: kubernetes
-       namespace: climate-prod
-       image: gcr.io/my-project/climate-model:latest
+       namespace: energy-prod
+       image: gcr.io/my-project/energy-model:latest
        adaptive:
          minimum: 2
          maximum: 20
 
    components:
-     gcam:
-       image: gcr.io/my-project/gcam:7.0
+     gridlabd:
+       image: gcr.io/my-project/gridlabd:5.0
        cpus: 8
        memory: 32G
-       tags: [iam, climate]
+       tags: [iam, energy]
        env:
-         GCAM_DATA: /data/gcam
+         GRIDLABD_DATA: /data/gridlabd
 
      postprocess:
        image: gcr.io/my-project/postprocess:latest
@@ -490,9 +490,9 @@ For richer, context-aware responses, enable an LLM backend:
    export OPENAI_API_KEY=sk-...
 
    # Now compose generates more detailed, context-aware workflows
-   scalable compose "Build a multi-model ensemble that runs GCAM, Hector, \
-     and MAGICC in parallel, compares their climate projections, and \
-     produces a weighted average based on historical skill scores"
+   scalable compose "Build a multi-model ensemble that runs GridLAB-D, \
+     WaterShed, and LandUseModel in parallel, compares their resource \
+     projections, and produces a weighted average based on historical skill scores"
 
 LLM-enhanced mode adds:
 
@@ -515,7 +515,7 @@ Always validate AI-generated configurations before running:
    from scalable import ScalableSession
 
    # Generate workflow
-   result = compose_workflow("Run GCAM for all SSPs then aggregate")
+   result = compose_workflow("Run GridLAB-D for all regions then aggregate")
 
    # Write generated manifest additions
    # (merge with your existing scalable.yaml)
diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst
index 07d9831..6fd1f3f 100644
--- a/docs/tutorials/index.rst
+++ b/docs/tutorials/index.rst
@@ -68,7 +68,7 @@ Recommended Learning Path
      - Manifest schema, targets, overlays, validation
    * - 3
      - :ref:`tutorial_scaling_strategies`
-     - Providers, manual/adaptive/policy scaling
+     - Providers, manual/adaptive/objective scaling
    * - 4
      - :ref:`tutorial_caching`
      - @cacheable, FileType/DirType, remote cache
@@ -128,8 +128,8 @@ Throughout these tutorials:
 * All code examples use Python 3.11+ syntax.
 * Shell commands assume a Unix-like environment (macOS/Linux). Windows
   equivalents are noted where they differ.
-* The project name ``climate-pipeline`` and component names ``gcam``,
-  ``stitches``, ``postprocess`` appear consistently across tutorials as a
+* The project name ``energy-forecast`` and component names ``gridlabd``,
+  ``watershed``, ``postprocess`` appear consistently across tutorials as a
   running example.
 * Environment variables use the ``${VAR:-default}`` pattern for portability.
 * Expected output blocks show representative output — exact values (timestamps,
diff --git a/notebooks/01_getting_started.ipynb b/notebooks/01_getting_started.ipynb
index 836289d..e3f1dec 100644
--- a/notebooks/01_getting_started.ipynb
+++ b/notebooks/01_getting_started.ipynb
@@ -33,9 +33,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scalable version: 2.0.0\n"
+     ]
+    }
+   ],
    "source": [
     "# Verify installation\n",
     "import scalable\n",
@@ -58,9 +66,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working directory: /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-tutorial-fz6cyspk\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "import tempfile\n",
@@ -73,9 +89,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Manifest written to scalable.yaml\n",
+      "---\n",
+      "version: 1\n",
+      "project:\n",
+      "  name: hello-scalable\n",
+      "\n",
+      "targets:\n",
+      "  local:\n",
+      "    provider: local\n",
+      "    max_workers: 2\n",
+      "    threads_per_worker: 1\n",
+      "    processes: false\n",
+      "    containers: none\n",
+      "\n",
+      "components:\n",
+      "  analysis:\n",
+      "    cpus: 1\n",
+      "    memory: 1G\n",
+      "\n",
+      "tasks:\n",
+      "  run_analysis:\n",
+      "    component: analysis\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# Write the manifest\n",
     "manifest_content = \"\"\"\\\n",
@@ -137,9 +183,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Manifest is valid (0 errors, 0 warnings)\n"
+     ]
+    }
+   ],
    "source": [
     "from scalable import ScalableSession\n",
     "\n",
@@ -166,9 +220,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Target: local\n",
+      "Provider: local\n",
+      "Manifest lock: 7d378b80bf6861695d621c66c8e523bd3ef735a7918eca547933e4dda7e471d2\n",
+      "Scale plan: ScalePlan(workers_by_tag={'analysis': 1}, resources_by_tag={'analysis': ResourceRequest(cpus=1, memory='1G', walltime=None, gpus=None)})\n"
+     ]
+    }
+   ],
    "source": [
     "plan = session.plan(dry_run=True)\n",
     "\n",
@@ -196,9 +261,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Client connected: <ScalableClient: 'inproc://192.168.1.212/58145/1' processes=1 threads=1, memory=64.00 GiB>\n"
+     ]
+    }
+   ],
    "source": [
     "import time\n",
     "\n",
@@ -216,9 +289,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Submitted 5 tasks\n"
+     ]
+    }
+   ],
    "source": [
     "# Submit tasks tagged to the 'analysis' component\n",
     "futures = []\n",
@@ -231,9 +312,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'scenario': 0, 'result': 0}\n",
+      "{'scenario': 1, 'result': 42}\n",
+      "{'scenario': 2, 'result': 84}\n",
+      "{'scenario': 3, 'result': 126}\n",
+      "{'scenario': 4, 'result': 168}\n"
+     ]
+    }
+   ],
    "source": [
     "# Gather results\n",
     "results = client.gather(futures)\n",
@@ -264,9 +357,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Session closed.\n"
+     ]
+    }
+   ],
    "source": [
     "# Always close the session to finalize telemetry\n",
     "session.close()\n",
@@ -275,9 +376,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Latest run: run-20260520T125819Z-hello-scalable-1143230e\n",
+      "\n",
+      "Contents:\n",
+      "  manifest.lock (65 bytes)\n",
+      "  manifest.yaml (266 bytes)\n",
+      "  plan.json (435 bytes)\n",
+      "  resources.jsonl (2050 bytes)\n",
+      "  run.json (412 bytes)\n",
+      "  summary.json (1153 bytes)\n",
+      "  tasks.jsonl (6354 bytes)\n",
+      "  workers.jsonl (832 bytes)\n"
+     ]
+    }
+   ],
    "source": [
     "# Check telemetry output\n",
     "from pathlib import Path\n",
@@ -298,9 +417,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Run metadata:\n",
+      "  finished_at: 2026-05-20T12:58:53Z\n",
+      "  manifest_lock: 7d378b80bf6861695d621c66c8e523bd3ef735a7918eca547933e4dda7e471d2\n",
+      "  project_name: hello-scalable\n",
+      "  provider_name: local\n",
+      "  run_id: run-20260520T125819Z-hello-scalable-1143230e\n",
+      "  schema_version: 1\n",
+      "  source_manifest_path: scalable.yaml\n",
+      "  started_at: 2026-05-20T12:58:19Z\n",
+      "  status: completed\n",
+      "  target_name: local\n"
+     ]
+    }
+   ],
    "source": [
     "# Read run metadata\n",
     "import json\n",
@@ -326,9 +463,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current settings:\n",
+      "  Cache dir: ./cache\n",
+      "  Seed: 987654321\n",
+      "  Manifest path: ./scalable.yaml\n",
+      "  Runs dir: ./.scalable/runs\n",
+      "  Telemetry enabled: True\n"
+     ]
+    }
+   ],
    "source": [
     "from scalable.common import settings\n",
     "\n",
@@ -362,9 +512,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tutorial workspace cleaned up.\n"
+     ]
+    }
+   ],
    "source": [
     "# Cleanup\n",
     "import shutil\n",
@@ -372,17 +530,32 @@
     "shutil.rmtree(project_dir, ignore_errors=True)\n",
     "print(\"Tutorial workspace cleaned up.\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "py3.13.3_scalable",
    "language": "python",
-   "name": "python3"
+   "name": "py3.13.3_scalable"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.11.0"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/02_manifest_system.ipynb b/notebooks/02_manifest_system.ipynb
index 2582bc4..cfe306d 100644
--- a/notebooks/02_manifest_system.ipynb
+++ b/notebooks/02_manifest_system.ipynb
@@ -23,9 +23,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working directory: /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-manifest-5_vu1zii\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "import tempfile\n",
@@ -46,14 +54,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Multi-target manifest written.\n"
+     ]
+    }
+   ],
    "source": [
     "manifest_content = \"\"\"\\\n",
     "version: 1\n",
     "project:\n",
-    "  name: climate-pipeline\n",
+    "  name: energy-forecast\n",
     "  default_storage: ./outputs\n",
     "\n",
     "targets:\n",
@@ -76,7 +92,7 @@
     "  gcam:\n",
     "    cpus: 4\n",
     "    memory: 16G\n",
-    "    tags: [iam, climate]\n",
+    "    tags: [iam, energy]\n",
     "\n",
     "  postprocess:\n",
     "    cpus: 2\n",
@@ -128,9 +144,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Project name: energy-forecast\n",
+      "Targets: ['local', 'hpc']\n",
+      "Components: ['gcam', 'postprocess']\n",
+      "Tasks: ['run_gcam', 'aggregate']\n"
+     ]
+    }
+   ],
    "source": [
     "from scalable.manifest.parser import load_manifest\n",
     "\n",
@@ -144,9 +171,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Local target provider: local\n",
+      "Local target options: {'max_workers': 4, 'threads_per_worker': 2, 'processes': False, 'containers': 'none'}\n"
+     ]
+    }
+   ],
    "source": [
     "# Inspect a target\n",
     "local_target = manifest.targets[\"local\"]\n",
@@ -156,9 +192,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GCAM cpus: 4\n",
+      "GCAM memory: 16G\n",
+      "GCAM tags: ['iam', 'energy']\n"
+     ]
+    }
+   ],
    "source": [
     "# Inspect a component\n",
     "gcam_component = manifest.components[\"gcam\"]\n",
@@ -178,14 +224,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "${MY_PROJECT} → energy-demo\n",
+      "${MY_PROJECT:-fallback} → energy-demo\n",
+      "${UNSET_VAR:-my-default} → my-default\n",
+      "\n",
+      "Expanded dict: {'project': {'name': 'energy-demo'}, 'path': './data'}\n"
+     ]
+    }
+   ],
    "source": [
     "from scalable.manifest.parser import expand_env_vars\n",
     "\n",
     "# Simulate environment variable expansion\n",
-    "os.environ[\"MY_PROJECT\"] = \"climate-demo\"\n",
+    "os.environ[\"MY_PROJECT\"] = \"energy-demo\"\n",
     "\n",
     "# ${VAR} expansion\n",
     "result = expand_env_vars(\"${MY_PROJECT}\")\n",
@@ -216,9 +274,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Valid: True\n",
+      "Errors: 0\n",
+      "Warnings: 0\n"
+     ]
+    }
+   ],
    "source": [
     "from scalable import ScalableSession\n",
     "\n",
@@ -233,9 +301,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Valid: False\n",
+      "  ERROR [E_UNKNOWN_COMPONENT] tasks.run_task.component: unknown component 'nonexistent_component'; known components: ['worker']\n",
+      "  ERROR [E_UNKNOWN_COMPONENT] tasks.run_task.component: unknown component 'nonexistent_component'; known components: ['worker']\n",
+      "  ERROR [E_BAD_MAX_WORKERS] targets.local.max_workers: max_workers must be a positive integer\n"
+     ]
+    }
+   ],
    "source": [
     "# Now let's create an invalid manifest to see error reporting\n",
     "invalid_manifest = \"\"\"\\\n",
@@ -288,9 +367,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "cannot import name 'apply_overlay' from 'scalable.manifest.overlays' (/Users/d3y010/repos/github/scalable/scalable/manifest/overlays.py)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mImportError\u001b[39m                               Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m scalable.manifest.overlays \u001b[38;5;28;01mimport\u001b[39;00m apply_overlay\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[38;5;66;03m# Show the base component values\u001b[39;00m\n\u001b[32m      4\u001b[39m print(\u001b[33m\"Base components:\"\u001b[39m)\n",
+      "\u001b[31mImportError\u001b[39m: cannot import name 'apply_overlay' from 'scalable.manifest.overlays' (/Users/d3y010/repos/github/scalable/scalable/manifest/overlays.py)"
+     ]
+    }
+   ],
    "source": [
     "from scalable.manifest.overlays import apply_overlay\n",
     "\n",
@@ -384,7 +475,7 @@
    "source": [
     "## Step 8: Planning with Objectives\n",
     "\n",
-    "The Session API supports policy-driven planning."
+    "The Session API supports objective-driven planning."
    ]
   },
   {
@@ -419,7 +510,7 @@
     "4. Overlays for environment-specific resource overrides\n",
     "5. Programmatic validation with error code interpretation\n",
     "6. Target selection and DeploymentSpec\n",
-    "7. Policy-driven planning (cost vs time vs balance)\n",
+    "7. Objective-driven planning (cost vs time vs balance)\n",
     "\n",
     "## Next Steps\n",
     "\n",
@@ -430,9 +521,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cleaned up.\n"
+     ]
+    }
+   ],
    "source": [
     "# Cleanup\n",
     "import shutil\n",
@@ -440,17 +539,32 @@
     "shutil.rmtree(project_dir, ignore_errors=True)\n",
     "print(\"Cleaned up.\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "py3.13.3_scalable",
    "language": "python",
-   "name": "python3"
+   "name": "py3.13.3_scalable"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.11.0"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/03_scaling_strategies.ipynb b/notebooks/03_scaling_strategies.ipynb
index 29b2534..a22c299 100644
--- a/notebooks/03_scaling_strategies.ipynb
+++ b/notebooks/03_scaling_strategies.ipynb
@@ -235,7 +235,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 4: Policy-Driven Planning\n",
+    "## Step 4: Objective-Driven Planning\n",
     "\n",
     "The Session API supports objectives that automatically influence worker allocation."
    ]
@@ -376,7 +376,7 @@
     "1. The provider architecture abstracts execution backends\n",
     "2. LocalProvider supports both threaded and process-based workers\n",
     "3. Heterogeneous pools match resources to task requirements\n",
-    "4. Policy-driven planning automates worker count decisions\n",
+    "4. Objective-driven planning automates worker count decisions\n",
     "5. AdaptiveScaler provides real-time scaling recommendations\n",
     "\n",
     "## Next Steps\n",
diff --git a/notebooks/05_cloud_integration.ipynb b/notebooks/05_cloud_integration.ipynb
index e307704..7be275e 100644
--- a/notebooks/05_cloud_integration.ipynb
+++ b/notebooks/05_cloud_integration.ipynb
@@ -54,7 +54,7 @@
     "aws_manifest = \"\"\"\\\n",
     "version: 1\n",
     "project:\n",
-    "  name: climate-model-aws\n",
+    "  name: energy-model-aws\n",
     "  default_storage: s3://my-bucket/scalable-runs/\n",
     "\n",
     "targets:\n",
@@ -71,7 +71,7 @@
     "    cluster_type: fargate\n",
     "    worker_cpu: 4096\n",
     "    worker_mem: 16384\n",
-    "    image: 123456789.dkr.ecr.us-east-1.amazonaws.com/climate:latest\n",
+    "    image: 123456789.dkr.ecr.us-east-1.amazonaws.com/energy-model:latest\n",
     "    execution_role_arn: arn:aws:iam::123456789:role/ecsTaskExecutionRole\n",
     "    task_role_arn: arn:aws:iam::123456789:role/scalableTaskRole\n",
     "    subnets:\n",
@@ -88,7 +88,7 @@
     "    image: 123456789.dkr.ecr.us-east-1.amazonaws.com/gcam:7.0\n",
     "    cpus: 4\n",
     "    memory: 16G\n",
-    "    tags: [iam, climate]\n",
+    "    tags: [iam, energy]\n",
     "\n",
     "  postprocess:\n",
     "    cpus: 2\n",
@@ -229,7 +229,7 @@
     "# Create a sample output file\n",
     "os.makedirs(\"output\", exist_ok=True)\n",
     "with open(\"output/results.csv\", \"w\") as f:\n",
-    "    f.write(\"scenario,emissions\\n1,35.2\\n2,28.7\\n\")\n",
+    "    f.write(\"scenario,demand_mw\\n1,35.2\\n2,28.7\\n\")\n",
     "\n",
     "# Store an artifact\n",
     "ref = local_store.put(\"output/results.csv\", \"runs/demo/results.csv\")\n",
@@ -289,14 +289,14 @@
     "gcp_manifest = \"\"\"\\\n",
     "version: 1\n",
     "project:\n",
-    "  name: climate-model-gke\n",
+    "  name: energy-model-gke\n",
     "  default_storage: gs://my-bucket/scalable-runs/\n",
     "\n",
     "targets:\n",
     "  gke:\n",
     "    provider: kubernetes\n",
-    "    namespace: climate-prod\n",
-    "    image: gcr.io/my-project/climate-model:latest\n",
+    "    namespace: energy-prod\n",
+    "    image: gcr.io/my-project/energy-model:latest\n",
     "    adaptive:\n",
     "      minimum: 2\n",
     "      maximum: 20\n",
@@ -306,7 +306,7 @@
     "    image: gcr.io/my-project/gcam:7.0\n",
     "    cpus: 8\n",
     "    memory: 32G\n",
-    "    tags: [iam, climate]\n",
+    "    tags: [iam, energy]\n",
     "    env:\n",
     "      GCAM_DATA: /data/gcam\n",
     "\n",
@@ -346,14 +346,14 @@
     "env_template = \"\"\"\\\n",
     "# .env.cloud (do NOT commit secrets)\n",
     "AWS_REGION=us-east-1\n",
-    "S3_BUCKET=climate-prod-artifacts\n",
-    "ECR_IMAGE=123456789.dkr.ecr.us-east-1.amazonaws.com/climate:latest\n",
+    "S3_BUCKET=energy-prod-artifacts\n",
+    "ECR_IMAGE=123456789.dkr.ecr.us-east-1.amazonaws.com/energy-model:latest\n",
     "EXECUTION_ROLE_ARN=arn:aws:iam::123456789:role/ecsTaskExecutionRole\n",
     "TASK_ROLE_ARN=arn:aws:iam::123456789:role/scalableTaskRole\n",
     "SUBNET_A=subnet-abc123\n",
     "SUBNET_B=subnet-def456\n",
     "SG_ID=sg-xyz789\n",
-    "SCALABLE_CACHE_REMOTE=s3://climate-prod-artifacts/cache/\n",
+    "SCALABLE_CACHE_REMOTE=s3://energy-prod-artifacts/cache/\n",
     "\"\"\"\n",
     "\n",
     "print(\"Environment variable template for cloud deployment:\")\n",
diff --git a/notebooks/08_kubernetes.ipynb b/notebooks/08_kubernetes.ipynb
index 73c44b0..4a701ce 100644
--- a/notebooks/08_kubernetes.ipynb
+++ b/notebooks/08_kubernetes.ipynb
@@ -73,8 +73,8 @@
     "k8s_manifest = \"\"\"\\\n",
     "version: 1\n",
     "project:\n",
-    "  name: climate-pipeline-k8s\n",
-    "  default_storage: gs://climate-artifacts/scalable-runs/\n",
+    "  name: energy-forecast-k8s\n",
+    "  default_storage: gs://energy-artifacts/scalable-runs/\n",
     "\n",
     "targets:\n",
     "  local:\n",
@@ -86,8 +86,8 @@
     "\n",
     "  k8s-dev:\n",
     "    provider: kubernetes\n",
-    "    namespace: climate-dev\n",
-    "    image: gcr.io/my-project/climate-model:latest\n",
+    "    namespace: energy-dev\n",
+    "    image: gcr.io/my-project/energy-model:latest\n",
     "    adaptive:\n",
     "      minimum: 1\n",
     "      maximum: 5\n",
@@ -95,8 +95,8 @@
     "\n",
     "  k8s-prod:\n",
     "    provider: kubernetes\n",
-    "    namespace: climate-prod\n",
-    "    image: gcr.io/my-project/climate-model:v2.1.0\n",
+    "    namespace: energy-prod\n",
+    "    image: gcr.io/my-project/energy-model:v2.1.0\n",
     "    adaptive:\n",
     "      minimum: 4\n",
     "      maximum: 40\n",
@@ -107,7 +107,7 @@
     "    image: gcr.io/my-project/gcam:7.0\n",
     "    cpus: 4\n",
     "    memory: 16G\n",
-    "    tags: [iam, climate]\n",
+    "    tags: [iam, energy]\n",
     "    env:\n",
     "      GCAM_DATA: /data/gcam\n",
     "\n",
@@ -299,8 +299,8 @@
     "apiVersion: v1\n",
     "kind: ResourceQuota\n",
     "metadata:\n",
-    "  name: climate-pipeline-quota\n",
-    "  namespace: climate-prod\n",
+    "  name: energy-forecast-quota\n",
+    "  namespace: energy-prod\n",
     "spec:\n",
     "  hard:\n",
     "    requests.cpu: \"{int(total_cpu * 1.25)}\"\n",
@@ -397,7 +397,7 @@
     "          credentials_json: ${{ secrets.GCP_SA_KEY }}\n",
     "      - uses: google-github-actions/get-gke-credentials@v2\n",
     "        with:\n",
-    "          cluster_name: climate-cluster\n",
+    "          cluster_name: energy-cluster\n",
     "          location: us-central1\n",
     "      - name: Run Pipeline\n",
     "        run: |\n",
@@ -431,7 +431,7 @@
     "def run_gcam_local(scenario: int) -> dict:\n",
     "    \"\"\"Simulate GCAM execution.\"\"\"\n",
     "    time.sleep(0.2)\n",
-    "    return {\"scenario\": scenario, \"emissions\": scenario * 1.5}\n",
+    "    return {\"scenario\": scenario, \"demand_mw\": scenario * 1.5}\n",
     "\n",
     "\n",
     "# Run locally — same workflow code works on K8s\n",
diff --git a/notebooks/09_ml_emulation.ipynb b/notebooks/09_ml_emulation.ipynb
index 528f677..188a8a6 100644
--- a/notebooks/09_ml_emulation.ipynb
+++ b/notebooks/09_ml_emulation.ipynb
@@ -88,7 +88,7 @@
     "\n",
     "def simulate_gcam(scenario: int) -> dict:\n",
     "    time.sleep(0.1 + (scenario % 5) * 0.05)\n",
-    "    return {\"scenario\": scenario, \"emissions\": scenario * 1.5}\n",
+    "    return {\"scenario\": scenario, \"demand_mw\": scenario * 1.5}\n",
     "\n",
     "\n",
     "# Generate multiple runs for history\n",
@@ -248,26 +248,26 @@
     "    \n",
     "    @emulatable(\n",
     "        tag=\"gcam\",\n",
-    "        inputs=[\"carbon_price\", \"population\"],\n",
-    "        outputs=[\"emissions\"],\n",
+    "        inputs=[\"fuel_cost\", \"population\"],\n",
+    "        outputs=[\"demand_mw\"],\n",
     "        uncertainty=\"required\",\n",
     "        fallback=\"full_model\",\n",
     "        domain={\n",
-    "            \"carbon_price\": (0, 500),\n",
+    "            \"fuel_cost\": (0, 500),\n",
     "            \"population\": (7e9, 12e9),\n",
     "        },\n",
     "        confidence_threshold=0.9,\n",
     "    )\n",
-    "    def run_gcam_scenario(carbon_price, population):\n",
+    "    def run_energy_scenario(fuel_cost, population):\n",
     "        \"\"\"Full GCAM scenario — expensive computation.\"\"\"\n",
     "        time.sleep(0.5)  # Simulate expensive work\n",
-    "        emissions = carbon_price * 0.07 + population * 3e-9\n",
-    "        return {\"emissions\": emissions}\n",
+    "        demand_mw = fuel_cost * 0.07 + population * 3e-9\n",
+    "        return {\"demand_mw\": demand_mw}\n",
     "    \n",
     "    print(\"Function marked as @emulatable\")\n",
     "    print(f\"\\nRegistered emulatable functions: {list(_EMULATABLE_REGISTRY.keys())}\")\n",
     "    \n",
-    "    spec = _EMULATABLE_REGISTRY[\"run_gcam_scenario\"]\n",
+    "    spec = _EMULATABLE_REGISTRY[\"run_energy_scenario\"]\n",
     "    print(f\"\\nEmulation spec:\")\n",
     "    print(f\"  Tag: {spec.tag}\")\n",
     "    print(f\"  Inputs: {spec.inputs}\")\n",
@@ -297,7 +297,7 @@
     "try:\n",
     "    # The decorated function still works normally\n",
     "    start = time.time()\n",
-    "    result = run_gcam_scenario(100, 8e9)\n",
+    "    result = run_energy_scenario(100, 8e9)\n",
     "    elapsed = time.time() - start\n",
     "    \n",
     "    print(f\"Full model result: {result}\")\n",
@@ -419,8 +419,8 @@
     "for cp in range(0, 500, 10):\n",
     "    for pop in [8e9, 9e9, 10e9]:\n",
     "        result = dispatch.predict(\n",
-    "            \"run_gcam_scenario\",\n",
-    "            inputs={\"carbon_price\": cp, \"population\": pop},\n",
+    "            \"run_energy_scenario\",\n",
+    "            inputs={\"fuel_cost\": cp, \"population\": pop},\n",
     "        )\n",
     "        \n",
     "        if result.source == \"emulator\":\n",
diff --git a/notebooks/10_ai_composition.ipynb b/notebooks/10_ai_composition.ipynb
index 18970db..85b50f6 100644
--- a/notebooks/10_ai_composition.ipynb
+++ b/notebooks/10_ai_composition.ipynb
@@ -121,13 +121,13 @@
     "\n",
     "# Simulate a model with typical files\n",
     "with open(os.path.join(model_dir, \"run_stitches.R\"), \"w\") as f:\n",
-    "    f.write(\"# Stitches climate downscaling model\\n\")\n",
+    "    f.write(\"# Stitches water resource model\\n\")\n",
     "    f.write(\"library(stitches)\\nlibrary(dplyr)\\n\")\n",
     "    f.write(\"result <- run_downscaling(input_path, output_path)\\n\")\n",
     "\n",
     "with open(os.path.join(model_dir, \"DESCRIPTION\"), \"w\") as f:\n",
     "    f.write(\"Package: stitches\\n\")\n",
-    "    f.write(\"Title: Climate Downscaling\\n\")\n",
+    "    f.write(\"Title: Water Resource Modeling\\n\")\n",
     "    f.write(\"Imports: dplyr, tidyr, ggplot2\\n\")\n",
     "\n",
     "with open(os.path.join(model_dir, \"Dockerfile\"), \"w\") as f:\n",
@@ -166,7 +166,7 @@
     "    print(\"      image: (from Dockerfile)\")\n",
     "    print(\"      cpus: 6\")\n",
     "    print(\"      memory: 50G\")\n",
-    "    print(\"      tags: [climate, downscaling]\")"
+    "    print(\"      tags: [water, hydrology]\")"
    ]
   },
   {
@@ -334,7 +334,7 @@
     "try:\n",
     "    result = compose_workflow(\n",
     "        \"Run GCAM reference scenario for SSP2, \"\n",
-    "        \"then run Stitches to downscale daily climate data\"\n",
+    "        \"then run Stitches to downscale downstream water demand\"\n",
     "    )\n",
     "    \n",
     "    print(\"Composed Workflow:\")\n",
@@ -369,7 +369,7 @@
     "slurm_manifest = \"\"\"\\\n",
     "version: 1\n",
     "project:\n",
-    "  name: climate-pipeline\n",
+    "  name: energy-forecast\n",
     "targets:\n",
     "  hpc:\n",
     "    provider: slurm\n",
@@ -381,7 +381,7 @@
     "  gcam:\n",
     "    cpus: 8\n",
     "    memory: 32G\n",
-    "    tags: [climate]\n",
+    "    tags: [energy]\n",
     "tasks:\n",
     "  run_gcam:\n",
     "    component: gcam\n",
@@ -531,7 +531,7 @@
     "  gcam:\n",
     "    cpus: 8\n",
     "    memory: 32G\n",
-    "    tags: [climate]\n",
+    "    tags: [energy]\n",
     "  stitches:\n",
     "    cpus: 6\n",
     "    memory: 50G\n",

From 1e7dc305d40c8545f602fe640b8f8c1764bf41eb Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 10:57:52 -0400
Subject: [PATCH 36/47] update systems language

---
 docs/examples/scalable.aws.yaml         |   2 +-
 docs/examples/scalable.gke.yaml         |   2 +-
 docs/tutorials/02_manifest_system.rst   |   4 +-
 docs/tutorials/05_cloud_integration.rst |   4 +-
 docs/tutorials/08_kubernetes.rst        |   2 +-
 docs/tutorials/10_ai_composition.rst    |   4 +-
 notebooks/02_manifest_system.ipynb      | 107 ++++++---
 notebooks/03_scaling_strategies.ipynb   | 116 ++++++++--
 notebooks/04_caching_performance.ipynb  |  30 +--
 notebooks/05_cloud_integration.ipynb    |  12 +-
 notebooks/06_telemetry.ipynb            |   6 +-
 notebooks/07_error_handling.ipynb       |  16 +-
 notebooks/08_kubernetes.ipynb           |  44 ++--
 notebooks/09_ml_emulation.ipynb         | 274 +++++++++++++++++++++---
 notebooks/10_ai_composition.ipynb       | 170 +++++++++++++--
 scalable/caching.py                     |   4 +-
 scalable/providers/local.py             |   2 +-
 scalable/providers/slurm.py             |   2 +-
 scalable/session/session.py             |  12 ++
 19 files changed, 653 insertions(+), 160 deletions(-)

diff --git a/docs/examples/scalable.aws.yaml b/docs/examples/scalable.aws.yaml
index 0337bf3..7da48ce 100644
--- a/docs/examples/scalable.aws.yaml
+++ b/docs/examples/scalable.aws.yaml
@@ -31,7 +31,7 @@ components:
     image: 123456789.dkr.ecr.us-east-1.amazonaws.com/gcam:7.0
     cpus: 4
     memory: 16G
-    tags: [iam, climate]
+    tags: [multi-sector-dynamics, climate]
 
   postprocess:
     cpus: 2
diff --git a/docs/examples/scalable.gke.yaml b/docs/examples/scalable.gke.yaml
index 36b4762..7a80958 100644
--- a/docs/examples/scalable.gke.yaml
+++ b/docs/examples/scalable.gke.yaml
@@ -21,7 +21,7 @@ components:
     image: gcr.io/my-project/gcam:7.0
     cpus: 8
     memory: 32G
-    tags: [iam, climate]
+    tags: [multi-sector-dynamics, climate]
     env:
       GCAM_DATA: /data/gcam
 
diff --git a/docs/tutorials/02_manifest_system.rst b/docs/tutorials/02_manifest_system.rst
index 9069d81..edc5aca 100644
--- a/docs/tutorials/02_manifest_system.rst
+++ b/docs/tutorials/02_manifest_system.rst
@@ -153,7 +153,7 @@ Components are resource profiles for your workloads:
          /shared/outputs: /outputs
        env:
          GRIDLABD_DATA: /gridlabd-core/data
-       tags: [iam, energy]
+       tags: [multi-sector-dynamics, energy]
        preload_script: ./scripts/gridlabd_preload.sh
 
      postprocess:
@@ -438,7 +438,7 @@ Here is a production-ready manifest combining all concepts:
        image: ghcr.io/gridlab-d/gridlabd:5.0
        cpus: 4
        memory: 16G
-       tags: [iam, energy]
+       tags: [multi-sector-dynamics, energy]
        env:
          GRIDLABD_DATA: /gridlabd-core/data
 
diff --git a/docs/tutorials/05_cloud_integration.rst b/docs/tutorials/05_cloud_integration.rst
index ce80cf4..4af977f 100644
--- a/docs/tutorials/05_cloud_integration.rst
+++ b/docs/tutorials/05_cloud_integration.rst
@@ -72,7 +72,7 @@ The AWS provider uses ``dask-cloudprovider`` to launch Dask workers on Fargate
        image: ${ECR_IMAGE_GCAM}
        cpus: 4
        memory: 16G
-       tags: [iam, energy]
+       tags: [multi-sector-dynamics, energy]
 
      postprocess:
        cpus: 2
@@ -286,7 +286,7 @@ storage backends:
    # ArtifactRef(uri='s3://my-bucket/artifacts/runs/run-001/output.csv')
 
    # Retrieve a file
-   local_path = s3_store.get(ref, "./downloads/output.csv")
+   local_path = s3_store.get("runs/run-001/output.csv", "./downloads/output.csv")
 
 The store is protocol-aware via ``fsspec``: it detects the URI scheme and uses
 the appropriate backend (``s3fs`` for S3, ``gcsfs`` for GCS, local filesystem
diff --git a/docs/tutorials/08_kubernetes.rst b/docs/tutorials/08_kubernetes.rst
index 9b23634..af98bc7 100644
--- a/docs/tutorials/08_kubernetes.rst
+++ b/docs/tutorials/08_kubernetes.rst
@@ -97,7 +97,7 @@ Step 2: Configure the Kubernetes Target
        image: gcr.io/${GCP_PROJECT}/gridlabd:5.0
        cpus: 8
        memory: 32G
-       tags: [iam, energy]
+       tags: [multi-sector-dynamics, energy]
        env:
          GRIDLABD_DATA: /data/gridlabd
 
diff --git a/docs/tutorials/10_ai_composition.rst b/docs/tutorials/10_ai_composition.rst
index df44a77..1aa8815 100644
--- a/docs/tutorials/10_ai_composition.rst
+++ b/docs/tutorials/10_ai_composition.rst
@@ -310,7 +310,7 @@ from descriptions:
      gridlabd:
        cpus: 8
        memory: 32G
-       tags: [iam, energy]
+       tags: [multi-sector-dynamics, energy]
      watershed:
        cpus: 6
        memory: 50G
@@ -388,7 +388,7 @@ Move your workflow from one provider to another:
        image: gcr.io/my-project/gridlabd:5.0
        cpus: 8
        memory: 32G
-       tags: [iam, energy]
+       tags: [multi-sector-dynamics, energy]
        env:
          GRIDLABD_DATA: /data/gridlabd
 
diff --git a/notebooks/02_manifest_system.ipynb b/notebooks/02_manifest_system.ipynb
index cfe306d..1c01e30 100644
--- a/notebooks/02_manifest_system.ipynb
+++ b/notebooks/02_manifest_system.ipynb
@@ -23,14 +23,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Working directory: /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-manifest-5_vu1zii\n"
+      "Working directory: /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-manifest-ixeg8jkw\n"
      ]
     }
    ],
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -92,7 +92,7 @@
     "  gcam:\n",
     "    cpus: 4\n",
     "    memory: 16G\n",
-    "    tags: [iam, energy]\n",
+    "    tags: [multi-sector-dynamics, energy]\n",
     "\n",
     "  postprocess:\n",
     "    cpus: 2\n",
@@ -144,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -171,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -192,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -201,7 +201,7 @@
      "text": [
       "GCAM cpus: 4\n",
       "GCAM memory: 16G\n",
-      "GCAM tags: ['iam', 'energy']\n"
+      "GCAM tags: ['multi-sector-dynamics', 'energy']\n"
      ]
     }
    ],
@@ -224,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -274,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -301,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -310,7 +310,6 @@
      "text": [
       "Valid: False\n",
       "  ERROR [E_UNKNOWN_COMPONENT] tasks.run_task.component: unknown component 'nonexistent_component'; known components: ['worker']\n",
-      "  ERROR [E_UNKNOWN_COMPONENT] tasks.run_task.component: unknown component 'nonexistent_component'; known components: ['worker']\n",
       "  ERROR [E_BAD_MAX_WORKERS] targets.local.max_workers: max_workers must be a positive integer\n"
      ]
     }
@@ -367,23 +366,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
-     "ename": "ImportError",
-     "evalue": "cannot import name 'apply_overlay' from 'scalable.manifest.overlays' (/Users/d3y010/repos/github/scalable/scalable/manifest/overlays.py)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mImportError\u001b[39m                               Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m scalable.manifest.overlays \u001b[38;5;28;01mimport\u001b[39;00m apply_overlay\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[38;5;66;03m# Show the base component values\u001b[39;00m\n\u001b[32m      4\u001b[39m print(\u001b[33m\"Base components:\"\u001b[39m)\n",
-      "\u001b[31mImportError\u001b[39m: cannot import name 'apply_overlay' from 'scalable.manifest.overlays' (/Users/d3y010/repos/github/scalable/scalable/manifest/overlays.py)"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Base components:\n",
+      "  gcam: cpus=4, memory=16G\n",
+      "  postprocess: cpus=2, memory=4G\n",
+      "\n",
+      "Overlays defined: []\n"
      ]
     }
    ],
    "source": [
-    "from scalable.manifest.overlays import apply_overlay\n",
+    "from scalable.manifest.overlays import resolve_overlay\n",
     "\n",
     "# Show the base component values\n",
     "print(\"Base components:\")\n",
@@ -396,9 +395,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overlay 'hpc-large' changes:\n",
+      "\n",
+      "After applying 'hpc-large':\n",
+      "  gcam: cpus=16, memory=64G\n",
+      "  postprocess: cpus=8, memory=32G\n"
+     ]
+    }
+   ],
    "source": [
     "# Demonstrate overlay application\n",
     "raw_data = manifest.raw.copy()\n",
@@ -428,9 +439,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Selected target: local\n",
+      "Provider: local\n",
+      "\n",
+      "Env-selected target: local\n"
+     ]
+    }
+   ],
    "source": [
     "# Explicit selection\n",
     "session_local = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
@@ -455,9 +477,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DeploymentSpec:\n",
+      "  target_name: local\n",
+      "  provider_name: local\n",
+      "  components: ['gcam', 'postprocess']\n",
+      "  tasks: ['run_gcam', 'aggregate']\n",
+      "  target options: {'max_workers': 4, 'threads_per_worker': 2, 'processes': False, 'containers': 'none'}\n"
+     ]
+    }
+   ],
    "source": [
     "spec = session_local.spec\n",
     "\n",
@@ -480,9 +515,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Default plan: ScalePlan(workers_by_tag={'gcam': 1, 'postprocess': 1}, resources_by_tag={'gcam': ResourceRequest(cpus=4, memory='16G', walltime=None, gpus=None), 'postprocess': ResourceRequest(cpus=2, memory='4G', walltime=None, gpus=None)})\n",
+      "Cost-optimized plan: ScalePlan(workers_by_tag={'gcam': 1, 'postprocess': 1}, resources_by_tag={'gcam': ResourceRequest(cpus=4, memory='16G', walltime=None, gpus=None), 'postprocess': ResourceRequest(cpus=2, memory='4G', walltime=None, gpus=None)})\n",
+      "Time-optimized plan: ScalePlan(workers_by_tag={'gcam': 3, 'postprocess': 3}, resources_by_tag={'gcam': ResourceRequest(cpus=8, memory='16G', walltime=None, gpus=None), 'postprocess': ResourceRequest(cpus=4, memory='4G', walltime=None, gpus=None)})\n"
+     ]
+    }
+   ],
    "source": [
     "# Default plan\n",
     "plan_default = session_local.plan(dry_run=True)\n",
@@ -521,7 +566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
diff --git a/notebooks/03_scaling_strategies.ipynb b/notebooks/03_scaling_strategies.ipynb
index a22c299..5c7b96f 100644
--- a/notebooks/03_scaling_strategies.ipynb
+++ b/notebooks/03_scaling_strategies.ipynb
@@ -22,9 +22,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working directory: /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-scaling-ev3dj5mx\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "import tempfile\n",
@@ -54,9 +62,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Registered providers:\n"
+     ]
+    }
+   ],
    "source": [
     "# List registered providers\n",
     "from scalable.providers.registry import iter_provider_names\n",
@@ -77,9 +93,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Manifest with two local targets (threads vs processes) written.\n"
+     ]
+    }
+   ],
    "source": [
     "manifest_content = \"\"\"\\\n",
     "version: 1\n",
@@ -98,7 +122,7 @@
     "    provider: local\n",
     "    max_workers: 2\n",
     "    threads_per_worker: 1\n",
-    "    processes: true\n",
+    "    processes: false\n",
     "    containers: none\n",
     "\n",
     "components:\n",
@@ -142,9 +166,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Target: local-threads\n",
+      "Scale plan: ScalePlan(workers_by_tag={'compute': 1, 'io': 1}, resources_by_tag={'compute': ResourceRequest(cpus=2, memory='4G', walltime=None, gpus=None), 'io': ResourceRequest(cpus=1, memory='1G', walltime=None, gpus=None)})\n"
+     ]
+    }
+   ],
    "source": [
     "from scalable import ScalableSession\n",
     "\n",
@@ -167,9 +200,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cluster started with ScalePlan(workers_by_tag={'compute': 1, 'io': 1}, resources_by_tag={'compute': ResourceRequest(cpus=2, memory='4G', walltime=None, gpus=None), 'io': ResourceRequest(cpus=1, memory='1G', walltime=None, gpus=None)})\n"
+     ]
+    }
+   ],
    "source": [
     "def heavy_computation(scenario_id: int) -> dict:\n",
     "    \"\"\"CPU-intensive simulation.\"\"\"\n",
@@ -190,9 +231,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Submitted 8 compute tasks\n",
+      "Compute results: [{'scenario': 0, 'result': 0}, {'scenario': 1, 'result': 1}, {'scenario': 2, 'result': 4}]...\n"
+     ]
+    }
+   ],
    "source": [
     "# Phase 1: Heavy compute tasks go to 'compute' workers\n",
     "compute_futures = [\n",
@@ -208,9 +258,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Aggregation result: {'total': 140, 'count': 8}\n"
+     ]
+    }
+   ],
    "source": [
     "# Phase 2: Light aggregation on 'io' workers\n",
     "agg_future = client.submit(light_aggregation, compute_results, tag=\"io\")\n",
@@ -242,9 +300,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost-optimized: ScalePlan(workers_by_tag={'compute': 1, 'io': 1}, resources_by_tag={'compute': ResourceRequest(cpus=2, memory='4G', walltime=None, gpus=None), 'io': ResourceRequest(cpus=1, memory='1G', walltime=None, gpus=None)})\n",
+      "Time-optimized: ScalePlan(workers_by_tag={'compute': 3, 'io': 3}, resources_by_tag={'compute': ResourceRequest(cpus=4, memory='4G', walltime=None, gpus=None), 'io': ResourceRequest(cpus=2, memory='1G', walltime=None, gpus=None)})\n",
+      "Balanced: ScalePlan(workers_by_tag={'compute': 1, 'io': 1}, resources_by_tag={'compute': ResourceRequest(cpus=2, memory='4G', walltime=None, gpus=None), 'io': ResourceRequest(cpus=1, memory='1G', walltime=None, gpus=None)})\n"
+     ]
+    }
+   ],
    "source": [
     "session.close()\n",
     "\n",
@@ -401,13 +469,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "py3.13.3_scalable",
    "language": "python",
-   "name": "python3"
+   "name": "py3.13.3_scalable"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.11.0"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/04_caching_performance.ipynb b/notebooks/04_caching_performance.ipynb
index 28442ed..13f6ef2 100644
--- a/notebooks/04_caching_performance.ipynb
+++ b/notebooks/04_caching_performance.ipynb
@@ -73,7 +73,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# First call — cache MISS (takes ~1 second)\n",
+    "# First call \u2014 cache MISS (takes ~1 second)\n",
     "start = time.time()\n",
     "result1 = expensive_simulation(42)\n",
     "elapsed1 = time.time() - start\n",
@@ -88,7 +88,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Second call — cache HIT (instant)\n",
+    "# Second call \u2014 cache HIT (instant)\n",
     "start = time.time()\n",
     "result2 = expensive_simulation(42)\n",
     "elapsed2 = time.time() - start\n",
@@ -104,13 +104,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Different argument — cache MISS\n",
+    "# Different argument \u2014 cache MISS\n",
     "start = time.time()\n",
     "result3 = expensive_simulation(99)\n",
     "elapsed3 = time.time() - start\n",
     "\n",
     "print(f\"New argument: {result3}\")\n",
-    "print(f\"Time: {elapsed3:.3f}s (cache MISS — different key)\")"
+    "print(f\"Time: {elapsed3:.3f}s (cache MISS \u2014 different key)\")"
    ]
   },
   {
@@ -194,15 +194,15 @@
     "    return {\"records\": len(lines) - 1, \"file\": data_file}\n",
     "\n",
     "\n",
-    "# First call — hashes file content\n",
+    "# First call \u2014 hashes file content\n",
     "start = time.time()\n",
     "r1 = process_data(\"input_data.csv\")\n",
     "print(f\"First call: {r1} ({time.time()-start:.3f}s)\")\n",
     "\n",
-    "# Second call — same file content = cache hit\n",
+    "# Second call \u2014 same file content = cache hit\n",
     "start = time.time()\n",
     "r2 = process_data(\"input_data.csv\")\n",
-    "print(f\"Second call: {r2} ({time.time()-start:.3f}s) — HIT\")"
+    "print(f\"Second call: {r2} ({time.time()-start:.3f}s) \u2014 HIT\")"
    ]
   },
   {
@@ -211,13 +211,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Modify the file — cache miss (content changed)\n",
+    "# Modify the file \u2014 cache miss (content changed)\n",
     "with open(\"input_data.csv\", \"a\") as f:\n",
     "    f.write(\"3,305,1300\\n\")\n",
     "\n",
     "start = time.time()\n",
     "r3 = process_data(\"input_data.csv\")\n",
-    "print(f\"After modification: {r3} ({time.time()-start:.3f}s) — MISS (content changed)\")"
+    "print(f\"After modification: {r3} ({time.time()-start:.3f}s) \u2014 MISS (content changed)\")"
    ]
   },
   {
@@ -251,7 +251,7 @@
    "source": [
     "@cacheable(return_type=dict, recompute=True, scenario_id=int)\n",
     "def fixed_simulation(scenario_id: int) -> dict:\n",
-    "    \"\"\"Always recomputes — ignores cache.\"\"\"\n",
+    "    \"\"\"Always recomputes \u2014 ignores cache.\"\"\"\n",
     "    return {\"scenario\": scenario_id, \"result\": scenario_id * 1.7}  # Fixed formula\n",
     "\n",
     "\n",
@@ -280,7 +280,7 @@
    "source": [
     "@cacheable\n",
     "def quick_add(x, y):\n",
-    "    \"\"\"Minimal cacheable form — no explicit types.\"\"\"\n",
+    "    \"\"\"Minimal cacheable form \u2014 no explicit types.\"\"\"\n",
     "    time.sleep(0.3)\n",
     "    return x + y\n",
     "\n",
@@ -291,7 +291,7 @@
     "\n",
     "start = time.time()\n",
     "r2 = quick_add(10, 20)\n",
-    "print(f\"Second: {r2} ({time.time()-start:.3f}s) — cache hit\")"
+    "print(f\"Second: {r2} ({time.time()-start:.3f}s) \u2014 cache hit\")"
    ]
   },
   {
@@ -489,9 +489,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "py3.13.3_scalable",
    "language": "python",
-   "name": "python3"
+   "name": "py3.13.3_scalable"
   },
   "language_info": {
    "name": "python",
@@ -500,4 +500,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/notebooks/05_cloud_integration.ipynb b/notebooks/05_cloud_integration.ipynb
index 7be275e..96777f8 100644
--- a/notebooks/05_cloud_integration.ipynb
+++ b/notebooks/05_cloud_integration.ipynb
@@ -88,7 +88,7 @@
     "    image: 123456789.dkr.ecr.us-east-1.amazonaws.com/gcam:7.0\n",
     "    cpus: 4\n",
     "    memory: 16G\n",
-    "    tags: [iam, energy]\n",
+    "    tags: [multi-sector-dynamics, energy]\n",
     "\n",
     "  postprocess:\n",
     "    cpus: 2\n",
@@ -243,7 +243,7 @@
    "outputs": [],
    "source": [
     "# Retrieve the artifact\n",
-    "retrieved_path = local_store.get(ref, \"./downloads/results.csv\")\n",
+    "retrieved_path = local_store.get(\"runs/demo/results.csv\", \"./downloads/results.csv\")\n",
     "print(f\"Retrieved to: {retrieved_path}\")\n",
     "\n",
     "with open(retrieved_path) as f:\n",
@@ -306,7 +306,7 @@
     "    image: gcr.io/my-project/gcam:7.0\n",
     "    cpus: 8\n",
     "    memory: 32G\n",
-    "    tags: [iam, energy]\n",
+    "    tags: [multi-sector-dynamics, energy]\n",
     "    env:\n",
     "      GCAM_DATA: /data/gcam\n",
     "\n",
@@ -431,9 +431,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "py3.13.3_scalable",
    "language": "python",
-   "name": "python3"
+   "name": "py3.13.3_scalable"
   },
   "language_info": {
    "name": "python",
@@ -442,4 +442,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/notebooks/06_telemetry.ipynb b/notebooks/06_telemetry.ipynb
index d14fd7c..1adba92 100644
--- a/notebooks/06_telemetry.ipynb
+++ b/notebooks/06_telemetry.ipynb
@@ -421,9 +421,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "py3.13.3_scalable",
    "language": "python",
-   "name": "python3"
+   "name": "py3.13.3_scalable"
   },
   "language_info": {
    "name": "python",
@@ -432,4 +432,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/notebooks/07_error_handling.ipynb b/notebooks/07_error_handling.ipynb
index 4a61a71..6e173dc 100644
--- a/notebooks/07_error_handling.ipynb
+++ b/notebooks/07_error_handling.ipynb
@@ -158,7 +158,7 @@
     "\n",
     "\n",
     "def sometimes_fails(scenario_id: int) -> dict:\n",
-    "    \"\"\"Transient failure — succeeds on retry with 70% probability.\"\"\"\n",
+    "    \"\"\"Transient failure \u2014 succeeds on retry with 70% probability.\"\"\"\n",
     "    if random.random() < 0.3:\n",
     "        raise ConnectionError(f\"Timeout fetching data for scenario {scenario_id}\")\n",
     "    return {\"scenario\": scenario_id, \"result\": scenario_id * 42}\n",
@@ -247,7 +247,7 @@
     "\n",
     "for err in test_errors:\n",
     "    classification = classify_error(err)\n",
-    "    print(f\"  {type(err).__name__}: '{err}' → {classification}\")"
+    "    print(f\"  {type(err).__name__}: '{err}' \u2192 {classification}\")"
    ]
   },
   {
@@ -272,7 +272,7 @@
     "\n",
     "@cacheable(return_type=dict, scenario_id=int)\n",
     "def cached_simulation(scenario_id: int) -> dict:\n",
-    "    \"\"\"Cached — won't re-run on retry if previously succeeded.\"\"\"\n",
+    "    \"\"\"Cached \u2014 won't re-run on retry if previously succeeded.\"\"\"\n",
     "    time.sleep(0.2)\n",
     "    if scenario_id == 7:\n",
     "        raise RuntimeError(\"Scenario 7 always fails\")\n",
@@ -364,7 +364,7 @@
     "        return []\n",
     "    \n",
     "    finally:\n",
-    "        # ALWAYS close — finalizes telemetry\n",
+    "        # ALWAYS close \u2014 finalizes telemetry\n",
     "        session.close()\n",
     "        print(\"  Session closed (telemetry finalized)\")\n",
     "\n",
@@ -445,7 +445,7 @@
     "    result = future.result(timeout=2)\n",
     "except Exception as e:\n",
     "    print(f\"Timeout handling: {type(e).__name__}\")\n",
-    "    print(f\"  Task exceeded timeout — cancelling\")\n",
+    "    print(f\"  Task exceeded timeout \u2014 cancelling\")\n",
     "    future.cancel()\n",
     "\n",
     "session.close()\n",
@@ -490,9 +490,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "py3.13.3_scalable",
    "language": "python",
-   "name": "python3"
+   "name": "py3.13.3_scalable"
   },
   "language_info": {
    "name": "python",
@@ -501,4 +501,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/notebooks/08_kubernetes.ipynb b/notebooks/08_kubernetes.ipynb
index 4a701ce..9ef1bfc 100644
--- a/notebooks/08_kubernetes.ipynb
+++ b/notebooks/08_kubernetes.ipynb
@@ -45,13 +45,13 @@
     "\n",
     "```\n",
     "ScalableSession\n",
-    "    └── KubernetesProvider\n",
-    "           └── Creates DaskCluster CR (Custom Resource)\n",
-    "                  └── Dask Kubernetes Operator\n",
-    "                         ├── Scheduler Pod\n",
-    "                         ├── Worker Pod (gcam-0)\n",
-    "                         ├── Worker Pod (gcam-1)\n",
-    "                         └── Worker Pod (postprocess-0)\n",
+    "    \u2514\u2500\u2500 KubernetesProvider\n",
+    "           \u2514\u2500\u2500 Creates DaskCluster CR (Custom Resource)\n",
+    "                  \u2514\u2500\u2500 Dask Kubernetes Operator\n",
+    "                         \u251c\u2500\u2500 Scheduler Pod\n",
+    "                         \u251c\u2500\u2500 Worker Pod (gcam-0)\n",
+    "                         \u251c\u2500\u2500 Worker Pod (gcam-1)\n",
+    "                         \u2514\u2500\u2500 Worker Pod (postprocess-0)\n",
     "```\n",
     "\n",
     "The operator manages pod lifecycle, health checks, and scaling."
@@ -107,7 +107,7 @@
     "    image: gcr.io/my-project/gcam:7.0\n",
     "    cpus: 4\n",
     "    memory: 16G\n",
-    "    tags: [iam, energy]\n",
+    "    tags: [multi-sector-dynamics, energy]\n",
     "    env:\n",
     "      GCAM_DATA: /data/gcam\n",
     "\n",
@@ -153,9 +153,9 @@
     "\n",
     "print(\"Kubernetes manifest written with dev/prod overlays.\")\n",
     "print(\"\\nTargets:\")\n",
-    "print(\"  local     → development (no K8s needed)\")\n",
-    "print(\"  k8s-dev   → Kubernetes dev namespace (small pods)\")\n",
-    "print(\"  k8s-prod  → Kubernetes prod namespace (large pods)\")"
+    "print(\"  local     \u2192 development (no K8s needed)\")\n",
+    "print(\"  k8s-dev   \u2192 Kubernetes dev namespace (small pods)\")\n",
+    "print(\"  k8s-prod  \u2192 Kubernetes prod namespace (large pods)\")"
    ]
   },
   {
@@ -201,10 +201,10 @@
    "source": [
     "try:\n",
     "    from scalable.providers.kubernetes import KubernetesProvider\n",
-    "    print(\"✓ KubernetesProvider is available\")\n",
+    "    print(\"\u2713 KubernetesProvider is available\")\n",
     "    print(f\"  Provider name: {KubernetesProvider.name}\")\n",
     "except ImportError:\n",
-    "    print(\"✗ KubernetesProvider not available\")\n",
+    "    print(\"\u2717 KubernetesProvider not available\")\n",
     "    print(\"  Install with: pip install scalable[kubernetes]\")\n",
     "    print(\"  (requires dask-kubernetes and kubernetes packages)\")"
    ]
@@ -228,7 +228,7 @@
     "\n",
     "manifest = load_manifest(\"./scalable.yaml\")\n",
     "\n",
-    "print(\"Component → Pod Resource Mapping:\")\n",
+    "print(\"Component \u2192 Pod Resource Mapping:\")\n",
     "print(\"=\"*50)\n",
     "\n",
     "for name, comp in manifest.components.items():\n",
@@ -280,8 +280,8 @@
     "\n",
     "print(\"Production Resource Quota Planning:\")\n",
     "print(f\"  Max workers: {max_workers}\")\n",
-    "print(f\"  GCAM workers: {gcam_workers} × {gcam_cpus} CPU = {gcam_workers * gcam_cpus} CPU\")\n",
-    "print(f\"  Postprocess workers: {pp_workers} × {pp_cpus} CPU = {pp_workers * pp_cpus} CPU\")\n",
+    "print(f\"  GCAM workers: {gcam_workers} \u00d7 {gcam_cpus} CPU = {gcam_workers * gcam_cpus} CPU\")\n",
+    "print(f\"  Postprocess workers: {pp_workers} \u00d7 {pp_cpus} CPU = {pp_workers * pp_cpus} CPU\")\n",
     "print(f\"  Total CPU needed: {total_cpu}\")\n",
     "print(f\"\\n  Recommended quota (with 25% headroom):\")\n",
     "print(f\"    requests.cpu: {int(total_cpu * 1.25)}\")\n",
@@ -343,7 +343,7 @@
     "        results.append(future.result())\n",
     "    except Exception as e:\n",
     "        if \"KilledWorker\" in type(e).__name__:\n",
-    "            # Pod was evicted — queue for retry\n",
+    "            # Pod was evicted \u2014 queue for retry\n",
     "            evicted.append(future.key)\n",
     "        else:\n",
     "            print(f\"Permanent failure: {e}\")\n",
@@ -416,7 +416,7 @@
    "source": [
     "## Step 9: Local Development with Local Target\n",
     "\n",
-    "The same manifest works locally — just select a different target."
+    "The same manifest works locally \u2014 just select a different target."
    ]
   },
   {
@@ -434,7 +434,7 @@
     "    return {\"scenario\": scenario, \"demand_mw\": scenario * 1.5}\n",
     "\n",
     "\n",
-    "# Run locally — same workflow code works on K8s\n",
+    "# Run locally \u2014 same workflow code works on K8s\n",
     "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
     "client = session.start()\n",
     "\n",
@@ -484,9 +484,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "py3.13.3_scalable",
    "language": "python",
-   "name": "python3"
+   "name": "py3.13.3_scalable"
   },
   "language_info": {
    "name": "python",
@@ -495,4 +495,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/notebooks/09_ml_emulation.ipynb b/notebooks/09_ml_emulation.ipynb
index 188a8a6..3a4291d 100644
--- a/notebooks/09_ml_emulation.ipynb
+++ b/notebooks/09_ml_emulation.ipynb
@@ -24,9 +24,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working directory: /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-ml-b_a0hsay\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "import tempfile\n",
@@ -50,9 +58,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/d3y010/.pyenv/versions/py3.13.3_scalable/lib/python3.13/site-packages/distributed/node.py:188: UserWarning: Port 8787 is already in use.\n",
+      "Perhaps you already have a cluster running?\n",
+      "Hosting the HTTP server on port 58221 instead\n",
+      "  warnings.warn(\n",
+      "/Users/d3y010/.pyenv/versions/py3.13.3_scalable/lib/python3.13/site-packages/distributed/node.py:188: UserWarning: Port 8787 is already in use.\n",
+      "Perhaps you already have a cluster running?\n",
+      "Hosting the HTTP server on port 58224 instead\n",
+      "  warnings.warn(\n",
+      "/Users/d3y010/.pyenv/versions/py3.13.3_scalable/lib/python3.13/site-packages/distributed/node.py:188: UserWarning: Port 8787 is already in use.\n",
+      "Perhaps you already have a cluster running?\n",
+      "Hosting the HTTP server on port 58227 instead\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generated 3 runs of telemetry data\n"
+     ]
+    }
+   ],
    "source": [
     "from scalable import ResourceAdvisor\n",
     "\n",
@@ -105,9 +139,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ResourceAdvisor Recommendation:\n",
+      "  Task: run_gcam\n",
+      "  Target: local\n",
+      "  Confidence: 0.95\n",
+      "  Workers: {'run_gcam': 1}\n",
+      "  Resources: {'run_gcam': {'cpus': 1, 'memory': None, 'walltime': None}}\n",
+      "  Evidence: {'records': 0, 'reason': 'task not found in history'}\n"
+     ]
+    }
+   ],
    "source": [
     "# Now use the ResourceAdvisor\n",
     "try:\n",
@@ -141,9 +189,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LearnedAdvisor: cannot reindex on an axis with duplicate labels\n",
+      "(Needs sufficient history for training)\n"
+     ]
+    }
+   ],
    "source": [
     "try:\n",
     "    from scalable import LearnedAdvisor\n",
@@ -193,9 +250,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AdaptiveScaler Decision:\n",
+      "  Has changes: True\n",
+      "  Add workers: {'gcam': 17}\n",
+      "  Remove workers: {}\n",
+      "  Reasoning: gcam: queue ratio 5.00 > 0.7, adding 17 workers\n",
+      "  Confidence: 0.70\n"
+     ]
+    }
+   ],
    "source": [
     "try:\n",
     "    from scalable import AdaptiveScaler\n",
@@ -238,9 +308,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Function marked as @emulatable\n",
+      "\n",
+      "Registered emulatable functions: ['run_energy_scenario']\n",
+      "\n",
+      "Emulation spec:\n",
+      "  Tag: gcam\n",
+      "  Inputs: ['fuel_cost', 'population']\n",
+      "  Outputs: ['demand_mw']\n",
+      "  Domain: {'fuel_cost': (0, 500), 'population': (7000000000.0, 12000000000.0)}\n",
+      "  Confidence threshold: 0.9\n",
+      "  Fallback: full_model\n"
+     ]
+    }
+   ],
    "source": [
     "try:\n",
     "    from scalable.emulation import emulatable\n",
@@ -290,9 +378,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Full model result: {'demand_mw': 31.0}\n",
+      "Time: 0.505s\n",
+      "\n",
+      "When an emulator is trained and registered, calls can be\n",
+      "routed to the fast surrogate instead of the full model.\n"
+     ]
+    }
+   ],
    "source": [
     "try:\n",
     "    # The decorated function still works normally\n",
@@ -317,9 +417,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Emulator Registry: <scalable.emulation.registry.EmulatorRegistry object at 0x117f36f90>\n",
+      "Registered emulators: []\n",
+      "\n",
+      "EmulatorDispatch configured:\n",
+      "  Confidence threshold: 0.9\n",
+      "  Dispatch logic:\n",
+      "    1. Check if emulator exists for function\n",
+      "    2. Validate inputs are within domain bounds\n",
+      "    3. Get emulator prediction + confidence\n",
+      "    4. If confidence >= 0.9: return emulator result\n",
+      "    5. Else: fall back to full model\n"
+     ]
+    }
+   ],
    "source": [
     "try:\n",
     "    from scalable.emulation import EmulatorRegistry, EmulatorDispatch\n",
@@ -357,9 +475,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Active Learning Acquisition Strategies:\n",
+      "==================================================\n",
+      "\n",
+      "  'uncertainty'\n",
+      "    Sample where prediction uncertainty is highest.\n",
+      "    Best for: Expanding emulator coverage uniformly.\n",
+      "\n",
+      "  'expected_improvement'\n",
+      "    Sample where model is likely wrong.\n",
+      "    Best for: Correcting known weaknesses.\n",
+      "\n",
+      "  'random'\n",
+      "    Uniform random sampling.\n",
+      "    Best for: Baseline comparison.\n",
+      "\n",
+      "Workflow:\n",
+      "  1. Train initial emulator on small sample\n",
+      "  2. Use ActiveLearner.suggest() for next batch\n",
+      "  3. Run full model on suggested points\n",
+      "  4. Update emulator with new data\n",
+      "  5. Repeat until accuracy target is met\n"
+     ]
+    }
+   ],
    "source": [
     "try:\n",
     "    from scalable.emulation import ActiveLearner\n",
@@ -401,9 +547,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Production Emulation Pattern:\n",
+      "\n",
+      "from scalable.emulation import EmulatorDispatch, EmulatorRegistry\n",
+      "\n",
+      "registry = EmulatorRegistry(\".scalable/emulators\")\n",
+      "dispatch = EmulatorDispatch(registry, confidence_threshold=0.9)\n",
+      "\n",
+      "results = []\n",
+      "emulated = 0\n",
+      "full_model = 0\n",
+      "\n",
+      "for cp in range(0, 500, 10):\n",
+      "    for pop in [8e9, 9e9, 10e9]:\n",
+      "        result = dispatch.predict(\n",
+      "            \"run_energy_scenario\",\n",
+      "            inputs={\"fuel_cost\": cp, \"population\": pop},\n",
+      "        )\n",
+      "\n",
+      "        if result.source == \"emulator\":\n",
+      "            emulated += 1\n",
+      "        else:\n",
+      "            full_model += 1\n",
+      "\n",
+      "        results.append(result.values)\n",
+      "\n",
+      "print(f\"Emulated: {emulated} ({emulated*100/(emulated+full_model):.0f}%)\")\n",
+      "print(f\"Full model: {full_model}\")\n",
+      "print(f\"Time saved: ~{emulated * 30} minutes\")\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# Demonstrate the production pattern (conceptual)\n",
     "production_pattern = '''\n",
@@ -448,9 +630,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ML and Emulation Environment Variables:\n",
+      "==================================================\n",
+      "\n",
+      "  SCALABLE_ML=1              Enable ML features\n",
+      "  SCALABLE_ML_CACHE_DIR      ML model cache (.scalable/models)\n",
+      "  SCALABLE_EMULATION=0       Enable emulation (set to 1)\n",
+      "  SCALABLE_EMULATOR_DIR      Emulator registry (.scalable/emulators)\n",
+      "  SCALABLE_EMULATION_CONFIDENCE=0.9  Confidence threshold\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"ML and Emulation Environment Variables:\")\n",
     "print(\"=\"*50)\n",
@@ -488,26 +685,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cleaned up.\n"
+     ]
+    }
+   ],
    "source": [
     "import shutil\n",
     "os.chdir(\"/tmp\")\n",
     "shutil.rmtree(project_dir, ignore_errors=True)\n",
     "print(\"Cleaned up.\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "py3.13.3_scalable",
    "language": "python",
-   "name": "python3"
+   "name": "py3.13.3_scalable"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.11.0"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/10_ai_composition.ipynb b/notebooks/10_ai_composition.ipynb
index 85b50f6..af9350a 100644
--- a/notebooks/10_ai_composition.ipynb
+++ b/notebooks/10_ai_composition.ipynb
@@ -25,9 +25,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working directory: /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-ai-w754gyak\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "import tempfile\n",
@@ -51,9 +59,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AI Configuration:\n",
+      "  Backend: none\n",
+      "  Model: (not set)\n",
+      "\n",
+      "Available backends:\n",
+      "  none   → heuristic only (default, no API needed)\n",
+      "  openai → OpenAI API (requires OPENAI_API_KEY)\n",
+      "  ollama → Local Ollama (requires SCALABLE_AI_ENDPOINT)\n"
+     ]
+    }
+   ],
    "source": [
     "from scalable.common import settings\n",
     "\n",
@@ -79,9 +102,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ AI module available\n",
+      "  Functions: onboard_component, diagnose_run, explain_plan,\n",
+      "             compose_workflow, migrate_manifest\n"
+     ]
+    }
+   ],
    "source": [
     "try:\n",
     "    from scalable.ai import (\n",
@@ -111,9 +144,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created sample model directory: /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-ai-w754gyak/stitches-model\n",
+      "Files: ['Dockerfile', 'DESCRIPTION', 'run_stitches.R']\n"
+     ]
+    }
+   ],
    "source": [
     "# Create a sample model directory to onboard\n",
     "model_dir = os.path.join(project_dir, \"stitches-model\")\n",
@@ -140,9 +182,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Onboarding Result:\n",
+      "  Component YAML:\n",
+      "# Proposed component: stitches\n",
+      "# Detected: r\n",
+      "# Build systems: DESCRIPTION\n",
+      "# Confidence: high\n",
+      "stitches:\n",
+      "  image: '# TODO: build image based on rocker/r-ver:4.3'\n",
+      "  runtime: docker\n",
+      "  cpus: 2\n",
+      "  memory: 8G\n",
+      "  env:\n",
+      "    OMP_NUM_THREADS: '2'\n",
+      "  tags:\n",
+      "  - r-lang\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "try:\n",
     "    result = onboard_component(\n",
@@ -180,9 +245,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/d3y010/.pyenv/versions/py3.13.3_scalable/lib/python3.13/site-packages/distributed/node.py:188: UserWarning: Port 8787 is already in use.\n",
+      "Perhaps you already have a cluster running?\n",
+      "Hosting the HTTP server on port 58292 instead\n",
+      "  warnings.warn(\n",
+      "2026-05-20 10:42:14,958 - distributed.worker - ERROR - Compute Failed\n",
+      "Key:       failing_task-d6e8600e83b44969ad86802e161b6642\n",
+      "State:     executing\n",
+      "Task:  <Task 'failing_task-d6e8600e83b44969ad86802e161b6642' failing_task(...)>\n",
+      "Exception: \"RuntimeError('OOM: task 0 exceeded memory')\"\n",
+      "Traceback: '  File \"/Users/d3y010/repos/github/scalable/scalable/client.py\", line 168, in _wrapped\\n    return func(*wrapped_args, **wrapped_kwargs)\\n  File \"/var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/ipykernel_69642/437167321.py\", line 31, in failing_task\\n    raise RuntimeError(f\"OOM: task {n} exceeded memory\")\\n'\n",
+      "\n",
+      "2026-05-20 10:42:14,988 - distributed.worker - ERROR - Compute Failed\n",
+      "Key:       failing_task-3e0c4670c0df9b22fe82cd8227b12326\n",
+      "State:     executing\n",
+      "Task:  <Task 'failing_task-3e0c4670c0df9b22fe82cd8227b12326' failing_task(...)>\n",
+      "Exception: \"RuntimeError('OOM: task 4 exceeded memory')\"\n",
+      "Traceback: '  File \"/Users/d3y010/repos/github/scalable/scalable/client.py\", line 168, in _wrapped\\n    return func(*wrapped_args, **wrapped_kwargs)\\n  File \"/var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/ipykernel_69642/437167321.py\", line 31, in failing_task\\n    raise RuntimeError(f\"OOM: task {n} exceeded memory\")\\n'\n",
+      "\n",
+      "2026-05-20 10:42:15,045 - distributed.worker - ERROR - Compute Failed\n",
+      "Key:       failing_task-e8d6a90a60115c167ab096a122e1c63c\n",
+      "State:     executing\n",
+      "Task:  <Task 'failing_task-e8d6a90a60115c167ab096a122e1c63c' failing_task(...)>\n",
+      "Exception: \"ConnectionError('Timeout fetching data for task 7')\"\n",
+      "Traceback: '  File \"/Users/d3y010/repos/github/scalable/scalable/client.py\", line 168, in _wrapped\\n    return func(*wrapped_args, **wrapped_kwargs)\\n  File \"/var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/ipykernel_69642/437167321.py\", line 33, in failing_task\\n    raise ConnectionError(f\"Timeout fetching data for task {n}\")\\n'\n",
+      "\n",
+      "2026-05-20 10:42:15,069 - distributed.worker - ERROR - Compute Failed\n",
+      "Key:       failing_task-63c47e14f51517c4a3f442096274a47a\n",
+      "State:     executing\n",
+      "Task:  <Task 'failing_task-63c47e14f51517c4a3f442096274a47a' failing_task(...)>\n",
+      "Exception: \"RuntimeError('OOM: task 8 exceeded memory')\"\n",
+      "Traceback: '  File \"/Users/d3y010/repos/github/scalable/scalable/client.py\", line 168, in _wrapped\\n    return func(*wrapped_args, **wrapped_kwargs)\\n  File \"/var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/ipykernel_69642/437167321.py\", line 31, in failing_task\\n    raise RuntimeError(f\"OOM: task {n} exceeded memory\")\\n'\n",
+      "\n",
+      "2026-05-20 10:42:15,185 - distributed.worker - ERROR - Compute Failed\n",
+      "Key:       failing_task-d5813652378900e40fb923fec812390b\n",
+      "State:     executing\n",
+      "Task:  <Task 'failing_task-d5813652378900e40fb923fec812390b' failing_task(...)>\n",
+      "Exception: \"RuntimeError('OOM: task 12 exceeded memory')\"\n",
+      "Traceback: '  File \"/Users/d3y010/repos/github/scalable/scalable/client.py\", line 168, in _wrapped\\n    return func(*wrapped_args, **wrapped_kwargs)\\n  File \"/var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/ipykernel_69642/437167321.py\", line 31, in failing_task\\n    raise RuntimeError(f\"OOM: task {n} exceeded memory\")\\n'\n",
+      "\n",
+      "2026-05-20 10:42:15,207 - distributed.worker - ERROR - Compute Failed\n",
+      "Key:       failing_task-7f8eb59caca6249b3091e36f4b8a4b5c\n",
+      "State:     executing\n",
+      "Task:  <Task 'failing_task-7f8eb59caca6249b3091e36f4b8a4b5c' failing_task(...)>\n",
+      "Exception: \"ConnectionError('Timeout fetching data for task 14')\"\n",
+      "Traceback: '  File \"/Users/d3y010/repos/github/scalable/scalable/client.py\", line 168, in _wrapped\\n    return func(*wrapped_args, **wrapped_kwargs)\\n  File \"/var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/ipykernel_69642/437167321.py\", line 33, in failing_task\\n    raise ConnectionError(f\"Timeout fetching data for task {n}\")\\n'\n",
+      "\n",
+      "2026-05-20 10:42:15,231 - distributed.worker - ERROR - Compute Failed\n",
+      "Key:       failing_task-7c817c6d78104f0ceee5c9d382483a32\n",
+      "State:     executing\n",
+      "Task:  <Task 'failing_task-7c817c6d78104f0ceee5c9d382483a32' failing_task(...)>\n",
+      "Exception: \"RuntimeError('OOM: task 16 exceeded memory')\"\n",
+      "Traceback: '  File \"/Users/d3y010/repos/github/scalable/scalable/client.py\", line 168, in _wrapped\\n    return func(*wrapped_args, **wrapped_kwargs)\\n  File \"/var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/ipykernel_69642/437167321.py\", line 31, in failing_task\\n    raise RuntimeError(f\"OOM: task {n} exceeded memory\")\\n'\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Run with failures completed.\n"
+     ]
+    }
+   ],
    "source": [
     "import time\n",
     "from scalable import ScalableSession\n",
@@ -599,13 +731,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "py3.13.3_scalable",
    "language": "python",
-   "name": "python3"
+   "name": "py3.13.3_scalable"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.11.0"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/scalable/caching.py b/scalable/caching.py
index b1e5aa5..db778de 100755
--- a/scalable/caching.py
+++ b/scalable/caching.py
@@ -405,7 +405,7 @@ def inner(*args, **kwargs):
                     if return_type is None:
                         new_digest = hash(convert_to_type(stored_value))
                     else:
-                        new_digest = hash(return_type(stored_value))
+                        new_digest = hash(convert_to_type(return_type(stored_value)))
                     if new_digest == stored_digest:
                         ret = stored_value
                     elif not disk.delete(key, retry=True):
@@ -429,7 +429,7 @@ def inner(*args, **kwargs):
                     if return_type is None:
                         new_digest = hash(convert_to_type(ret))
                     else:
-                        new_digest = hash(return_type(ret))
+                        new_digest = hash(convert_to_type(return_type(ret)))
                     if not disk.add(key=key, value=[new_digest, ret], retry=True):
                         logger.warning(
                             "%s could not be added to cache.", func.__name__
diff --git a/scalable/providers/local.py b/scalable/providers/local.py
index cfaa2f4..d49dec1 100644
--- a/scalable/providers/local.py
+++ b/scalable/providers/local.py
@@ -30,7 +30,7 @@ class LocalProvider(DeploymentProvider):
     _ALLOWED_CONTAINER_MODES = {"none", "auto", "docker"}
 
     def validate(self, spec: DeploymentSpec) -> ValidationReport:
-        report = validate_manifest(spec.manifest, known_providers={"local", "slurm"})
+        report = ValidationReport()
         options = spec.target.options
 
         if "max_workers" in options:
diff --git a/scalable/providers/slurm.py b/scalable/providers/slurm.py
index bcf54bb..757d614 100644
--- a/scalable/providers/slurm.py
+++ b/scalable/providers/slurm.py
@@ -32,7 +32,7 @@ class SlurmProvider(DeploymentProvider):
     name = "slurm"
 
     def validate(self, spec: DeploymentSpec) -> ValidationReport:
-        report = validate_manifest(spec.manifest, known_providers={"local", "slurm"})
+        report = ValidationReport()
         options = spec.target.options
 
         _require_type(report, spec.target_name, options, "queue", str)
diff --git a/scalable/session/session.py b/scalable/session/session.py
index 157dbbd..3e53b9a 100644
--- a/scalable/session/session.py
+++ b/scalable/session/session.py
@@ -51,6 +51,18 @@ def validate(self) -> ValidationReport:
         known = set(iter_provider_names(include_entrypoints=True))
         # Keep built-ins discoverable even before first runtime lookup.
         known.update({"local", "slurm"})
+        # Include optional-extra providers so manifests with cloud/k8s targets
+        # pass validation when the extras are installed.
+        try:
+            from scalable.providers.cloud import AWSBatchProvider  # noqa: F401
+            known.update({"aws", "gcp"})
+        except ImportError:
+            pass
+        try:
+            from scalable.providers.kubernetes import KubernetesProvider  # noqa: F401
+            known.add("kubernetes")
+        except ImportError:
+            pass
         report = validate_manifest(self.manifest, known_providers=known)
 
         try:

From d3e0e5beccf61d6cbdd9d54c439307a516da5381 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 11:22:16 -0400
Subject: [PATCH 37/47] extending more ai provider options

---
 .env.example                    |  69 +++++++++++++++---
 pyproject.toml                  |   1 +
 scalable/ai/agents/providers.py | 121 +++++++++++++++++++++++++++-----
 scalable/ai/backend.py          | 114 +++++++++++++++++++++++++-----
 scalable/common.py              |  36 +++++++++-
 tests/conftest.py               |  25 +++++++
 tests/unit/test_ai_agents.py    |   4 +-
 7 files changed, 319 insertions(+), 51 deletions(-)

diff --git a/.env.example b/.env.example
index 74bd51e..387b52f 100644
--- a/.env.example
+++ b/.env.example
@@ -1,13 +1,62 @@
-# OpenAI credentials and model configuration.
-# Required: set your API key.
-OPENAI_API_KEY=your_openai_api_key_here
+# ===========================================================================
+# Scalable AI Provider Configuration
+# ===========================================================================
+# These generic environment variables configure which AI/LLM provider and
+# model Scalable uses. They are provider-agnostic — set them once and switch
+# providers by changing AI_PROVIDER and LLM_MODEL_NAME.
+#
+# Supported providers:
+#   openai     — OpenAI (GPT-5.2, GPT-5.5, etc.)
+#   anthropic  — Anthropic (Claude Opus, Sonnet, Haiku)
+#   google     — Google Gemini (2.0 Flash, 1.5 Pro, etc.)
+#   xai        — xAI (Grok-2, Grok-3)
+#   groq       — Groq (Llama, Mixtral — fast inference)
+#   ollama     — Local Ollama models (Llama3, Mistral, etc.)
+# ===========================================================================
 
-# Embedding model used to vectorize document chunks.
-OPENAI_EMBEDDING_MODEL=text-embedding-3-large
+# Provider name — determines which AI service to use.
+# Options: openai | anthropic | google | xai | groq | ollama
+AI_PROVIDER=openai
 
-# Chat model used by the PydanticAI chatbot.
-OPENAI_CHAT_MODEL=gpt-5.2
+# Universal API key — works for any provider that requires authentication.
+# This single key is used unless a provider-specific key is set (see below).
+AI_API_KEY=your_api_key_here
 
-# Optional OpenAI-compatible base URL.
-# Example: https://api.openai.com/v1
-OPENAI_BASE_URL=your_openai_base_url_here
+# LLM model of choice for generation tasks.
+# Examples by provider:
+#   openai:    gpt-4o, gpt-4o-mini, o1, o1-mini
+#   anthropic: claude-opus-4-20250514, claude-sonnet-4-20250514, claude-haiku-3-20250414
+#   google:    gemini-2.0-flash, gemini-1.5-pro, gemini-1.5-flash
+#   xai:       grok-3, grok-2
+#   groq:      llama-3.1-70b-versatile, mixtral-8x7b-32768
+#   ollama:    llama3, mistral, codellama
+LLM_MODEL_NAME=gpt-5.5
+
+# Optional: Custom API base URL.
+# Required for OpenAI-compatible proxies (e.g., Azure, vLLM, LiteLLM).
+# xAI defaults to https://api.x.ai/v1 automatically.
+# Ollama defaults to http://localhost:11434 automatically.
+# AI_BASE_URL=https://api.openai.com/v1
+
+# ===========================================================================
+# Provider-Specific API Keys (Optional Overrides)
+# ===========================================================================
+# If you use multiple providers, you can set provider-specific keys below.
+# These take priority over AI_API_KEY for their respective provider.
+#
+# OPENAI_API_KEY=sk-...
+# ANTHROPIC_API_KEY=sk-ant-...
+# GOOGLE_API_KEY=AIza...
+# XAI_API_KEY=xai-...
+# GROQ_API_KEY=gsk_...
+
+# ===========================================================================
+# Advanced: SCALABLE_AI_* Overrides
+# ===========================================================================
+# These Scalable-specific variables take priority over the generic ones above.
+# Use them only if you need separate config for Scalable vs. other tools.
+#
+# SCALABLE_AI_BACKEND=openai
+# SCALABLE_AI_MODEL=gpt-4o
+# SCALABLE_AI_ENDPOINT=https://custom-endpoint.example.com/v1
+# SCALABLE_AI_API_KEY=sk-...
diff --git a/pyproject.toml b/pyproject.toml
index 745fef7..5b51ac9 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,6 +26,7 @@ dependencies = [
     "diskcache >= 5.6.3",
     "distributed >= 2022.02.0",
     "joblib >= 1.3.2",
+    "python-dotenv >= 1.0",
     "xxhash >= 3.4.1",
     "numpy >= 1.26.4",
     "pandas >= 2.2.3",
diff --git a/scalable/ai/agents/providers.py b/scalable/ai/agents/providers.py
index fba22a2..36d10e1 100644
--- a/scalable/ai/agents/providers.py
+++ b/scalable/ai/agents/providers.py
@@ -4,14 +4,26 @@
 environment configuration, supporting seamless switching between:
 
 * OpenAI (GPT-4o, GPT-4, etc.)
-* Anthropic (Claude Sonnet, Opus, Haiku)
-* Google Gemini (1.5 Pro, Flash)
+* Anthropic (Claude Opus, Sonnet, Haiku)
+* Google Gemini (1.5 Pro, Flash, 2.0)
+* xAI (Grok-2, Grok-3)
 * Groq (Llama, Mixtral)
 * Ollama (local models)
 * OpenAI-compatible endpoints (vLLM, LiteLLM, etc.)
 
-The provider layer ensures that changing ``SCALABLE_AI_BACKEND`` or
-``SCALABLE_AI_MODEL`` is sufficient to switch models without any code changes.
+The provider layer ensures that changing ``AI_PROVIDER`` (or ``SCALABLE_AI_BACKEND``)
+and ``LLM_MODEL_NAME`` (or ``SCALABLE_AI_MODEL``) is sufficient to switch models
+without any code changes.
+
+Configuration
+-------------
+The following environment variables are used (in priority order):
+
+1. ``SCALABLE_AI_BACKEND`` / ``AI_PROVIDER`` — provider name
+2. ``SCALABLE_AI_MODEL`` / ``LLM_MODEL_NAME`` — model identifier
+3. ``SCALABLE_AI_ENDPOINT`` / ``AI_BASE_URL`` — custom API endpoint
+4. ``SCALABLE_AI_API_KEY`` / ``AI_API_KEY`` — universal API key fallback
+5. Provider-specific keys (``OPENAI_API_KEY``, ``ANTHROPIC_API_KEY``, etc.)
 """
 
 from __future__ import annotations
@@ -62,14 +74,16 @@ def get_pydantic_ai_model(self) -> Any:
         """Construct and return the appropriate PydanticAI model instance.
 
         Returns a model object suitable for passing to ``pydantic_ai.Agent``.
+        Handles OpenAI-compatible providers (including xAI/Grok) via custom
+        endpoints.
 
         Returns
         -------
         Any
             A PydanticAI-compatible model instance or string identifier.
         """
-        if self.endpoint and self.name == "openai":
-            # OpenAI-compatible endpoint (vLLM, LiteLLM, etc.)
+        if self.name in ("openai", "xai") and self.endpoint:
+            # OpenAI-compatible endpoint (vLLM, LiteLLM, xAI/Grok, etc.)
             try:
                 from openai import AsyncOpenAI
                 from pydantic_ai.models.openai import OpenAIModel
@@ -83,6 +97,20 @@ def get_pydantic_ai_model(self) -> Any:
                 # Fall back to string-based resolution
                 return self.model_string
 
+        if self.name == "xai" and not self.endpoint:
+            # xAI always needs its endpoint configured
+            try:
+                from openai import AsyncOpenAI
+                from pydantic_ai.models.openai import OpenAIModel
+
+                client = AsyncOpenAI(
+                    base_url=_DEFAULT_ENDPOINTS["xai"],
+                    api_key=self.api_key or "unused",
+                )
+                return OpenAIModel(self.model, openai_client=client)
+            except ImportError:
+                return self.model_string
+
         if self.endpoint and self.name == "ollama":
             try:
                 from openai import AsyncOpenAI
@@ -103,33 +131,69 @@ def get_pydantic_ai_model(self) -> Any:
     def is_available(self) -> bool:
         """Check whether this provider's dependencies are available.
 
+        Checks for:
+        1. Provider-specific API key env var (e.g. ``OPENAI_API_KEY``)
+        2. Universal ``AI_API_KEY`` / ``SCALABLE_AI_API_KEY`` fallback
+        3. Explicit ``api_key`` on this instance
+
         Returns
         -------
         bool
             True if the necessary packages and credentials are present.
         """
+        universal_key = (
+            os.environ.get("SCALABLE_AI_API_KEY")
+            or os.environ.get("AI_API_KEY")
+        )
+
         if self.name == "openai":
             try:
                 import openai  # noqa: F401
-                return bool(os.environ.get("OPENAI_API_KEY") or self.api_key)
+                return bool(
+                    os.environ.get("OPENAI_API_KEY")
+                    or self.api_key
+                    or universal_key
+                )
             except ImportError:
                 return False
         elif self.name == "anthropic":
             try:
                 import anthropic  # noqa: F401
-                return bool(os.environ.get("ANTHROPIC_API_KEY") or self.api_key)
+                return bool(
+                    os.environ.get("ANTHROPIC_API_KEY")
+                    or self.api_key
+                    or universal_key
+                )
             except ImportError:
                 return False
         elif self.name in ("google", "google-gla"):
             try:
                 import google.generativeai  # noqa: F401
-                return bool(os.environ.get("GOOGLE_API_KEY") or self.api_key)
+                return bool(
+                    os.environ.get("GOOGLE_API_KEY")
+                    or self.api_key
+                    or universal_key
+                )
+            except ImportError:
+                return False
+        elif self.name == "xai":
+            try:
+                import openai  # noqa: F401
+                return bool(
+                    os.environ.get("XAI_API_KEY")
+                    or self.api_key
+                    or universal_key
+                )
             except ImportError:
                 return False
         elif self.name == "groq":
             try:
                 import groq  # noqa: F401
-                return bool(os.environ.get("GROQ_API_KEY") or self.api_key)
+                return bool(
+                    os.environ.get("GROQ_API_KEY")
+                    or self.api_key
+                    or universal_key
+                )
             except ImportError:
                 return False
         elif self.name == "ollama":
@@ -154,21 +218,30 @@ def is_available(self) -> bool:
 _DEFAULT_MODELS: dict[str, str] = {
     "openai": "gpt-4o",
     "anthropic": "claude-sonnet-4-20250514",
-    "google": "gemini-1.5-pro",
-    "google-gla": "gemini-1.5-pro",
+    "google": "gemini-2.0-flash",
+    "google-gla": "gemini-2.0-flash",
+    "xai": "grok-3",
     "groq": "llama-3.1-70b-versatile",
     "ollama": "llama3",
 }
 
-#: Environment variable mapping for API keys
+#: Environment variable mapping for provider-specific API keys.
+#: The universal ``AI_API_KEY`` / ``SCALABLE_AI_API_KEY`` serves as a
+#: fallback when provider-specific keys are not set.
 _API_KEY_ENV_VARS: dict[str, str] = {
     "openai": "OPENAI_API_KEY",
     "anthropic": "ANTHROPIC_API_KEY",
     "google": "GOOGLE_API_KEY",
     "google-gla": "GOOGLE_API_KEY",
+    "xai": "XAI_API_KEY",
     "groq": "GROQ_API_KEY",
 }
 
+#: Default API endpoint overrides for providers that need them.
+_DEFAULT_ENDPOINTS: dict[str, str] = {
+    "xai": "https://api.x.ai/v1",
+}
+
 
 def resolve_model_string(backend: str | None = None, model: str | None = None) -> str | None:
     """Resolve a full PydanticAI model string from backend/model configuration.
@@ -209,9 +282,12 @@ def get_model_provider(
     """Get a configured model provider from settings or explicit params.
 
     Resolves provider configuration from the following sources (in priority order):
-    1. Explicit parameters
-    2. ``SCALABLE_AI_BACKEND`` / ``SCALABLE_AI_MODEL`` / ``SCALABLE_AI_ENDPOINT``
-    3. Returns None if no backend is configured
+
+    1. Explicit parameters passed to this function
+    2. Settings (``SCALABLE_AI_BACKEND`` / ``AI_PROVIDER``, etc.)
+    3. Provider-specific env vars (``OPENAI_API_KEY``, ``ANTHROPIC_API_KEY``, etc.)
+    4. Universal key fallback (``SCALABLE_AI_API_KEY`` / ``AI_API_KEY``)
+    5. Returns None if no backend is configured
 
     Parameters
     ----------
@@ -249,12 +325,23 @@ def get_model_provider(
 
     model_string = f"{provider_name}:{model_name}"
 
-    # Resolve API key from environment
+    # Resolve API key: explicit > provider-specific env var > universal fallback
     resolved_key = api_key
     if not resolved_key:
         env_var = _API_KEY_ENV_VARS.get(provider_name)
         if env_var:
             resolved_key = os.environ.get(env_var)
+    if not resolved_key:
+        # Universal fallback from settings (reads SCALABLE_AI_API_KEY / AI_API_KEY)
+        resolved_key = getattr(settings, "ai_api_key", None)
+
+    # Resolve endpoint: explicit > settings > provider defaults
+    effective_endpoint = effective_endpoint or _DEFAULT_ENDPOINTS.get(provider_name)
+
+    # For xAI, remap the model_string to use openai: prefix since it's
+    # OpenAI-compatible (PydanticAI resolves via model string prefix)
+    if provider_name == "xai":
+        model_string = f"openai:{model_name}"
 
     return ModelProvider(
         name=provider_name,
diff --git a/scalable/ai/backend.py b/scalable/ai/backend.py
index 0e9448a..8420687 100644
--- a/scalable/ai/backend.py
+++ b/scalable/ai/backend.py
@@ -6,10 +6,11 @@
 * ``openai`` — OpenAI-compatible API (requires ``openai`` package)
 * ``anthropic`` — Anthropic Claude models (requires ``anthropic`` package)
 * ``google`` — Google Gemini models (requires ``google-generativeai`` package)
+* ``xai`` — xAI Grok models (OpenAI-compatible, requires ``openai`` package)
 * ``groq`` — Groq inference (requires ``groq`` package)
 * ``ollama`` — local Ollama server (requires running Ollama instance)
 
-Backend selection is controlled by ``SCALABLE_AI_BACKEND`` env var.
+Backend selection is controlled by ``SCALABLE_AI_BACKEND`` or ``AI_PROVIDER`` env var.
 
 .. note::
     The PydanticAI-based agent system in :mod:`scalable.ai.agents` is the
@@ -20,9 +21,10 @@
 from __future__ import annotations
 
 import logging
+import os
 from typing import Any, Protocol, runtime_checkable
 
-from scalable.common import settings
+import scalable.common as _common
 
 logger = logging.getLogger(__name__)
 
@@ -92,9 +94,9 @@ def __init__(
         endpoint: str | None = None,
         api_key: str | None = None,
     ) -> None:
-        self._model = model or getattr(settings, "ai_model", None) or "gpt-4o"
-        self._endpoint = endpoint or getattr(settings, "ai_endpoint", None)
-        self._api_key = api_key
+        self._model = model or getattr(_common.settings, "ai_model", None) or "gpt-4o"
+        self._endpoint = endpoint or getattr(_common.settings, "ai_endpoint", None)
+        self._api_key = api_key or getattr(_common.settings, "ai_api_key", None)
 
     def complete(
         self,
@@ -135,7 +137,11 @@ def complete(
     def available(self) -> bool:
         try:
             import openai  # type: ignore[import-untyped] # noqa: F401
-            return True
+            return bool(
+                os.environ.get("OPENAI_API_KEY")
+                or self._api_key
+                or getattr(_common.settings, "ai_api_key", None)
+            )
         except ImportError:
             return False
 
@@ -151,8 +157,8 @@ def __init__(
         model: str | None = None,
         endpoint: str | None = None,
     ) -> None:
-        self._model = model or getattr(settings, "ai_model", None) or "llama3"
-        self._endpoint = endpoint or getattr(settings, "ai_endpoint", None) or "http://localhost:11434"
+        self._model = model or getattr(_common.settings, "ai_model", None) or "llama3"
+        self._endpoint = endpoint or getattr(_common.settings, "ai_endpoint", None) or "http://localhost:11434"
 
     def complete(
         self,
@@ -210,8 +216,8 @@ def __init__(
         model: str | None = None,
         api_key: str | None = None,
     ) -> None:
-        self._model = model or getattr(settings, "ai_model", None) or "claude-sonnet-4-20250514"
-        self._api_key = api_key
+        self._model = model or getattr(_common.settings, "ai_model", None) or "claude-sonnet-4-20250514"
+        self._api_key = api_key or getattr(_common.settings, "ai_api_key", None)
 
     def complete(
         self,
@@ -250,10 +256,12 @@ def complete(
 
     def available(self) -> bool:
         try:
-            import os
-
             import anthropic  # type: ignore[import-untyped]  # noqa: F401
-            return bool(os.environ.get("ANTHROPIC_API_KEY") or self._api_key)
+            return bool(
+                os.environ.get("ANTHROPIC_API_KEY")
+                or self._api_key
+                or getattr(_common.settings, "ai_api_key", None)
+            )
         except ImportError:
             return False
 
@@ -269,8 +277,8 @@ def __init__(
         model: str | None = None,
         api_key: str | None = None,
     ) -> None:
-        self._model = model or getattr(settings, "ai_model", None) or "gemini-1.5-pro"
-        self._api_key = api_key
+        self._model = model or getattr(_common.settings, "ai_model", None) or "gemini-2.0-flash"
+        self._api_key = api_key or getattr(_common.settings, "ai_api_key", None)
 
     def complete(
         self,
@@ -308,10 +316,77 @@ def complete(
 
     def available(self) -> bool:
         try:
-            import os
-
             import google.generativeai  # type: ignore[import-untyped]  # noqa: F401
-            return bool(os.environ.get("GOOGLE_API_KEY") or self._api_key)
+            return bool(
+                os.environ.get("GOOGLE_API_KEY")
+                or self._api_key
+                or getattr(_common.settings, "ai_api_key", None)
+            )
+        except ImportError:
+            return False
+
+
+class XAIBackend:
+    """xAI Grok backend (OpenAI-compatible, requires ``openai`` package)."""
+
+    name: str = "xai"
+
+    _DEFAULT_ENDPOINT: str = "https://api.x.ai/v1"
+
+    def __init__(
+        self,
+        *,
+        model: str | None = None,
+        endpoint: str | None = None,
+        api_key: str | None = None,
+    ) -> None:
+        self._model = model or getattr(_common.settings, "ai_model", None) or "grok-3"
+        self._endpoint = endpoint or getattr(_common.settings, "ai_endpoint", None) or self._DEFAULT_ENDPOINT
+        self._api_key = api_key or getattr(_common.settings, "ai_api_key", None)
+
+    def complete(
+        self,
+        prompt: str,
+        *,
+        system: str | None = None,
+        temperature: float = 0.0,
+        max_tokens: int = 4096,
+    ) -> str:
+        try:
+            import openai  # type: ignore[import-untyped]
+        except ImportError as exc:
+            raise ImportError(
+                "xAI backend requires the 'openai' package (OpenAI-compatible). "
+                "Install with: pip install openai"
+            ) from exc
+
+        kwargs: dict[str, Any] = {"base_url": self._endpoint}
+        api_key = self._api_key or os.environ.get("XAI_API_KEY")
+        if api_key:
+            kwargs["api_key"] = api_key
+
+        client = openai.OpenAI(**kwargs)
+        messages: list[dict[str, str]] = []
+        if system:
+            messages.append({"role": "system", "content": system})
+        messages.append({"role": "user", "content": prompt})
+
+        response = client.chat.completions.create(
+            model=self._model,
+            messages=messages,
+            temperature=temperature,
+            max_tokens=max_tokens,
+        )
+        return response.choices[0].message.content or ""
+
+    def available(self) -> bool:
+        try:
+            import openai  # type: ignore[import-untyped]  # noqa: F401
+            return bool(
+                os.environ.get("XAI_API_KEY")
+                or self._api_key
+                or getattr(_common.settings, "ai_api_key", None)
+            )
         except ImportError:
             return False
 
@@ -321,6 +396,7 @@ def available(self) -> bool:
     "openai": OpenAIBackend,
     "anthropic": AnthropicBackend,
     "google": GoogleBackend,
+    "xai": XAIBackend,
     "ollama": OllamaBackend,
 }
 
@@ -342,7 +418,7 @@ def get_ai_backend(*, force_name: str | None = None) -> AIBackend:
     """
     global _cached_backend
 
-    name = force_name or getattr(settings, "ai_backend", "none") or "none"
+    name = force_name or getattr(_common.settings, "ai_backend", "none") or "none"
 
     if _cached_backend is not None and getattr(_cached_backend, "name", None) == name:
         return _cached_backend
diff --git a/scalable/common.py b/scalable/common.py
index bcbe2ad..55470fe 100755
--- a/scalable/common.py
+++ b/scalable/common.py
@@ -25,9 +25,21 @@
 import logging
 import os
 from dataclasses import dataclass, field
+from pathlib import Path
+
+from dotenv import load_dotenv
 
 __all__ = ["logger", "settings", "Settings", "SEED", "cachedir", "DEFAULT_SEED"]
 
+# ---------------------------------------------------------------------------
+# Load .env file with override=True so that .env values take precedence over
+# any pre-existing system environment variables. This allows users to manage
+# all AI provider configuration in a single .env file.
+# ---------------------------------------------------------------------------
+_dotenv_path = Path.cwd() / ".env"
+if _dotenv_path.is_file():
+    load_dotenv(_dotenv_path, override=True)
+
 DEFAULT_SEED: int = 987654321
 DEFAULT_CACHE_DIR: str = "./cache"
 DEFAULT_MANIFEST_PATH: str = "./scalable.yaml"
@@ -87,14 +99,32 @@ class Settings:
         default_factory=lambda: os.environ.get("SCALABLE_RUNS_DIR_REMOTE")
     )
     # Phase 4 AI additions
+    # Generic env vars (AI_PROVIDER, LLM_MODEL_NAME, AI_BASE_URL, AI_API_KEY)
+    # serve as fallbacks for the SCALABLE_AI_* variants, allowing users to
+    # configure a single set of env vars that work across providers.
     ai_backend: str = field(
-        default_factory=lambda: os.environ.get("SCALABLE_AI_BACKEND", "none")
+        default_factory=lambda: os.environ.get(
+            "SCALABLE_AI_BACKEND",
+            os.environ.get("AI_PROVIDER", "none"),
+        )
     )
     ai_model: str | None = field(
-        default_factory=lambda: os.environ.get("SCALABLE_AI_MODEL")
+        default_factory=lambda: os.environ.get(
+            "SCALABLE_AI_MODEL",
+            os.environ.get("LLM_MODEL_NAME"),
+        )
     )
     ai_endpoint: str | None = field(
-        default_factory=lambda: os.environ.get("SCALABLE_AI_ENDPOINT")
+        default_factory=lambda: os.environ.get(
+            "SCALABLE_AI_ENDPOINT",
+            os.environ.get("AI_BASE_URL"),
+        )
+    )
+    ai_api_key: str | None = field(
+        default_factory=lambda: os.environ.get(
+            "SCALABLE_AI_API_KEY",
+            os.environ.get("AI_API_KEY"),
+        )
     )
     # Phase 5 ML/Emulation additions
     ml_model_cache_dir: str = field(
diff --git a/tests/conftest.py b/tests/conftest.py
index 4338886..6717378 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -21,6 +21,9 @@ def _isolate_scalable_env(tmp_path, monkeypatch):
     and similar paths from the current working directory. To prevent tests
     from polluting each other (or the developer's repo), we ``chdir`` into a
     fresh temporary directory and clear the environment overrides.
+
+    Also clears the generic AI environment variables that may have been loaded
+    from a developer's ``.env`` file via python-dotenv at import time.
     """
     monkeypatch.chdir(tmp_path)
     monkeypatch.delenv("SCALABLE_CACHE_DIR", raising=False)
@@ -30,7 +33,29 @@ def _isolate_scalable_env(tmp_path, monkeypatch):
     monkeypatch.delenv("SCALABLE_TELEMETRY", raising=False)
     monkeypatch.delenv("SCALABLE_TELEMETRY_PARQUET", raising=False)
     monkeypatch.delenv("COMM_PORT", raising=False)
+    # Generic AI provider env vars (loaded from .env via dotenv)
+    monkeypatch.delenv("AI_PROVIDER", raising=False)
+    monkeypatch.delenv("AI_API_KEY", raising=False)
+    monkeypatch.delenv("AI_BASE_URL", raising=False)
+    monkeypatch.delenv("LLM_MODEL_NAME", raising=False)
+    # Scalable-specific AI env vars
+    monkeypatch.delenv("SCALABLE_AI_BACKEND", raising=False)
+    monkeypatch.delenv("SCALABLE_AI_MODEL", raising=False)
+    monkeypatch.delenv("SCALABLE_AI_ENDPOINT", raising=False)
+    monkeypatch.delenv("SCALABLE_AI_API_KEY", raising=False)
+    # Provider-specific API key vars
+    monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+    monkeypatch.delenv("ANTHROPIC_API_KEY", raising=False)
+    monkeypatch.delenv("GOOGLE_API_KEY", raising=False)
+    monkeypatch.delenv("XAI_API_KEY", raising=False)
+    monkeypatch.delenv("GROQ_API_KEY", raising=False)
+
+    # Reset the settings singleton so it picks up the cleaned environment
+    from scalable import common
+    original_settings = common.settings
+    monkeypatch.setattr(common, "settings", common.Settings())
     yield
+    monkeypatch.setattr(common, "settings", original_settings)
 
 
 @pytest.fixture
diff --git a/tests/unit/test_ai_agents.py b/tests/unit/test_ai_agents.py
index 6c13b16..a388870 100644
--- a/tests/unit/test_ai_agents.py
+++ b/tests/unit/test_ai_agents.py
@@ -261,7 +261,7 @@ def test_anthropic_default(self):
         assert resolve_model_string("anthropic") == "anthropic:claude-sonnet-4-20250514"
 
     def test_google_default(self):
-        assert resolve_model_string("google") == "google:gemini-1.5-pro"
+        assert resolve_model_string("google") == "google:gemini-2.0-flash"
 
     def test_full_model_string_passthrough(self):
         assert resolve_model_string("openai:custom-model") == "openai:custom-model"
@@ -940,4 +940,4 @@ def test_anthropic_default_model(self):
     def test_google_default_model(self):
         from scalable.ai.backend import GoogleBackend
         b = GoogleBackend()
-        assert b._model == "gemini-1.5-pro"
+        assert b._model == "gemini-2.0-flash"

From 8254f5be9fe9812a4b3b717e2bbf06671f65f957 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 11:25:28 -0400
Subject: [PATCH 38/47] update docs on ai provider expansion

---
 README.md              | 66 ++++++++++++++++++++++++++++++++++++++----
 docs/ai_assistants.rst | 62 +++++++++++++++++++++++++++++++++++----
 2 files changed, 118 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index 59ec3ab..25862f6 100644
--- a/README.md
+++ b/README.md
@@ -266,7 +266,27 @@ print(result.confidence)
 
 AI assistants help with onboarding, diagnostics, workflow generation, and
 migration. All features work without an LLM backend via deterministic heuristics;
-LLM enhancement is opt-in via `SCALABLE_AI_BACKEND`.
+LLM enhancement is opt-in via `AI_PROVIDER` (or `SCALABLE_AI_BACKEND`).
+
+Supported AI providers:
+
+| Provider | `AI_PROVIDER` | Example Models |
+|----------|---------------|----------------|
+| OpenAI | `openai` | gpt-4o, gpt-4o-mini, o1 |
+| Anthropic | `anthropic` | claude-opus-4-20250514, claude-sonnet-4-20250514 |
+| Google Gemini | `google` | gemini-2.0-flash, gemini-1.5-pro |
+| xAI (Grok) | `xai` | grok-3, grok-2 |
+| Groq | `groq` | llama-3.1-70b-versatile |
+| Ollama (local) | `ollama` | llama3, mistral |
+
+Configure via `.env` file (loaded automatically with override priority):
+
+```bash
+AI_PROVIDER=openai
+AI_API_KEY=your_api_key_here
+LLM_MODEL_NAME=gpt-4o
+# AI_BASE_URL=https://custom-endpoint.example.com/v1  # optional
+```
 
 ```bash
 # Onboard a new model component
@@ -429,7 +449,34 @@ For reliable behavior, explicitly specify argument and return types whenever pos
 
 ## Environment Variables
 
-Scalable is configured via environment variables for deployment flexibility:
+Scalable is configured via environment variables for deployment flexibility.
+A `.env` file in the project root is loaded automatically with override priority
+(values in `.env` take precedence over system environment variables).
+
+### AI Provider Configuration (Generic)
+
+These provider-agnostic variables are the recommended way to configure AI features:
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `AI_PROVIDER` | `none` | AI provider (`openai`, `anthropic`, `google`, `xai`, `groq`, `ollama`) |
+| `AI_API_KEY` | *(unset)* | Universal API key (works for any provider) |
+| `LLM_MODEL_NAME` | *(unset)* | Model name (e.g. `gpt-4o`, `claude-sonnet-4-20250514`, `grok-3`) |
+| `AI_BASE_URL` | *(unset)* | Custom API endpoint (for proxies, xAI auto-configures) |
+
+### Provider-Specific API Keys (Optional)
+
+Override `AI_API_KEY` for individual providers when using multiple services:
+
+| Variable | Provider |
+|----------|----------|
+| `OPENAI_API_KEY` | OpenAI |
+| `ANTHROPIC_API_KEY` | Anthropic |
+| `GOOGLE_API_KEY` | Google Gemini |
+| `XAI_API_KEY` | xAI (Grok) |
+| `GROQ_API_KEY` | Groq |
+
+### Core Configuration
 
 | Variable | Default | Description |
 |----------|---------|-------------|
@@ -443,15 +490,24 @@ Scalable is configured via environment variables for deployment flexibility:
 | `SCALABLE_TELEMETRY_PARQUET` | `0` | Emit parquet snapshots |
 | `SCALABLE_CACHE_REMOTE` | *(unset)* | Remote cache URI (S3/GCS) |
 | `SCALABLE_DEFAULT_STORAGE` | *(unset)* | Default artifact storage URI |
-| `SCALABLE_AI_BACKEND` | `none` | AI backend (`none`, `openai`, `ollama`) |
-| `SCALABLE_AI_MODEL` | *(unset)* | Model name for AI backend |
-| `SCALABLE_AI_ENDPOINT` | *(unset)* | API endpoint for AI backend |
 | `SCALABLE_ML` | `1` | Enable ML features |
 | `SCALABLE_ML_CACHE_DIR` | `.scalable/models` | ML model cache directory |
 | `SCALABLE_EMULATION` | `0` | Enable model emulation |
 | `SCALABLE_EMULATOR_DIR` | `.scalable/emulators` | Emulator registry directory |
 | `SCALABLE_EMULATION_CONFIDENCE` | `0.9` | Emulation confidence threshold |
 
+### Advanced AI Overrides
+
+These `SCALABLE_AI_*` variables take priority over the generic `AI_*` equivalents.
+Use only when you need Scalable-specific config separate from other tools:
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `SCALABLE_AI_BACKEND` | *(from AI_PROVIDER)* | AI backend override |
+| `SCALABLE_AI_MODEL` | *(from LLM_MODEL_NAME)* | Model name override |
+| `SCALABLE_AI_ENDPOINT` | *(from AI_BASE_URL)* | API endpoint override |
+| `SCALABLE_AI_API_KEY` | *(from AI_API_KEY)* | API key override |
+
 ## How to Contribute
 
 Contributions are welcome.
diff --git a/docs/ai_assistants.rst b/docs/ai_assistants.rst
index e0c2b9e..195e91c 100644
--- a/docs/ai_assistants.rst
+++ b/docs/ai_assistants.rst
@@ -20,11 +20,63 @@ Design Philosophy
 Configuration
 -------------
 
-AI features are controlled via environment variables:
-
-* ``SCALABLE_AI_BACKEND`` — Backend selection (``none``, ``openai``, ``ollama``). Default: ``none``.
-* ``SCALABLE_AI_MODEL`` — Model name for the selected backend.
-* ``SCALABLE_AI_ENDPOINT`` — API endpoint override for the backend.
+AI features are configured via a ``.env`` file in your project root (loaded
+automatically with override priority) or via environment variables.
+
+**Recommended generic variables** (provider-agnostic):
+
+* ``AI_PROVIDER`` — Provider selection. Options: ``openai``, ``anthropic``, ``google``, ``xai``, ``groq``, ``ollama``. Default: ``none``.
+* ``AI_API_KEY`` — Universal API key (works for any provider requiring auth).
+* ``LLM_MODEL_NAME`` — Model identifier for the selected provider.
+* ``AI_BASE_URL`` — Custom API endpoint (required for proxies; xAI and Ollama auto-configure).
+
+**Supported providers and example models:**
+
+.. list-table::
+   :header-rows: 1
+   :widths: 15 15 40
+
+   * - Provider
+     - ``AI_PROVIDER``
+     - Example models
+   * - OpenAI
+     - ``openai``
+     - ``gpt-4o``, ``gpt-4o-mini``, ``o1``, ``o1-mini``
+   * - Anthropic
+     - ``anthropic``
+     - ``claude-opus-4-20250514``, ``claude-sonnet-4-20250514``, ``claude-haiku-3-20250414``
+   * - Google Gemini
+     - ``google``
+     - ``gemini-2.0-flash``, ``gemini-1.5-pro``, ``gemini-1.5-flash``
+   * - xAI (Grok)
+     - ``xai``
+     - ``grok-3``, ``grok-2``
+   * - Groq
+     - ``groq``
+     - ``llama-3.1-70b-versatile``, ``mixtral-8x7b-32768``
+   * - Ollama (local)
+     - ``ollama``
+     - ``llama3``, ``mistral``, ``codellama``
+
+Example ``.env`` file:
+
+.. code-block:: bash
+
+   AI_PROVIDER=openai
+   AI_API_KEY=sk-your-key-here
+   LLM_MODEL_NAME=gpt-4o
+   # AI_BASE_URL=https://custom-endpoint.example.com/v1
+
+**Advanced: SCALABLE_AI_* overrides** (take priority over generic variables):
+
+* ``SCALABLE_AI_BACKEND`` — Backend selection override.
+* ``SCALABLE_AI_MODEL`` — Model name override.
+* ``SCALABLE_AI_ENDPOINT`` — API endpoint override.
+* ``SCALABLE_AI_API_KEY`` — API key override.
+
+**Provider-specific API keys** (optional, override ``AI_API_KEY`` per-provider):
+
+* ``OPENAI_API_KEY``, ``ANTHROPIC_API_KEY``, ``GOOGLE_API_KEY``, ``XAI_API_KEY``, ``GROQ_API_KEY``
 
 Install the AI extra for enhanced output formatting::
 

From be11ab9fab286cea5e0c16fc238165986eb35e5d Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 11:33:50 -0400
Subject: [PATCH 39/47] handle env setup for ai notebook

---
 notebooks/10_ai_composition.ipynb | 87 ++++++++++++++++++++++---------
 notebooks/README.md               | 37 +++++++++++++
 scalable/__init__.py              |  3 +-
 scalable/common.py                | 47 ++++++++++++++++-
 4 files changed, 148 insertions(+), 26 deletions(-)

diff --git a/notebooks/10_ai_composition.ipynb b/notebooks/10_ai_composition.ipynb
index af9350a..64484cf 100644
--- a/notebooks/10_ai_composition.ipynb
+++ b/notebooks/10_ai_composition.ipynb
@@ -23,6 +23,45 @@
     "- (Optional) LLM API key for enhanced mode"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Environment Setup: Loading AI Configuration\n",
+    "\n",
+    "This notebook uses AI/LLM features that require API credentials. Scalable reads\n",
+    "these from a `.env` file. You have two options:\n",
+    "\n",
+    "1. **Place a `.env` file in the `notebooks/` directory** (a template is provided as `.env.example` in the project root)\n",
+    "2. **Pass the path explicitly** using `load_env()` as shown below\n",
+    "\n",
+    "Because this notebook changes the working directory to a temp folder, we load\n",
+    "the `.env` file *before* that happens using an absolute path.\n",
+    "\n",
+    "> **Required variables:** `AI_PROVIDER`, `AI_API_KEY`, `LLM_MODEL_NAME`, and optionally `AI_BASE_URL`.\n",
+    "> See [`.env.example`](../.env.example) for the full template."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from scalable.common import load_env\n",
+    "\n",
+    "# Load .env from the notebooks directory (or pass your own path).\n",
+    "# This must be called BEFORE os.chdir() changes the working directory.\n",
+    "_dotenv_path = Path(\".\").resolve() / \".env\"\n",
+    "\n",
+    "# Alternatively, specify an absolute path to your .env file:\n",
+    "# _dotenv_path = Path(\"/path/to/your/project/.env\")\n",
+    "\n",
+    "load_env(_dotenv_path)\n",
+    "print(f\"Loaded .env from: {_dotenv_path}\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -71,9 +110,9 @@
       "  Model: (not set)\n",
       "\n",
       "Available backends:\n",
-      "  none   → heuristic only (default, no API needed)\n",
-      "  openai → OpenAI API (requires OPENAI_API_KEY)\n",
-      "  ollama → Local Ollama (requires SCALABLE_AI_ENDPOINT)\n"
+      "  none   \u2192 heuristic only (default, no API needed)\n",
+      "  openai \u2192 OpenAI API (requires OPENAI_API_KEY)\n",
+      "  ollama \u2192 Local Ollama (requires SCALABLE_AI_ENDPOINT)\n"
      ]
     }
    ],
@@ -88,9 +127,9 @@
     "print(f\"  Backend: {ai_backend}\")\n",
     "print(f\"  Model: {ai_model}\")\n",
     "print(f\"\\nAvailable backends:\")\n",
-    "print(f\"  none   → heuristic only (default, no API needed)\")\n",
-    "print(f\"  openai → OpenAI API (requires OPENAI_API_KEY)\")\n",
-    "print(f\"  ollama → Local Ollama (requires SCALABLE_AI_ENDPOINT)\")"
+    "print(f\"  none   \u2192 heuristic only (default, no API needed)\")\n",
+    "print(f\"  openai \u2192 OpenAI API (requires OPENAI_API_KEY)\")\n",
+    "print(f\"  ollama \u2192 Local Ollama (requires SCALABLE_AI_ENDPOINT)\")"
    ]
   },
   {
@@ -109,7 +148,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "✓ AI module available\n",
+      "\u2713 AI module available\n",
       "  Functions: onboard_component, diagnose_run, explain_plan,\n",
       "             compose_workflow, migrate_manifest\n"
      ]
@@ -124,11 +163,11 @@
     "        compose_workflow,\n",
     "        migrate_manifest,\n",
     "    )\n",
-    "    print(\"✓ AI module available\")\n",
+    "    print(\"\u2713 AI module available\")\n",
     "    print(\"  Functions: onboard_component, diagnose_run, explain_plan,\")\n",
     "    print(\"             compose_workflow, migrate_manifest\")\n",
     "except ImportError:\n",
-    "    print(\"✗ AI module not available\")\n",
+    "    print(\"\u2717 AI module not available\")\n",
     "    print(\"  Install with: pip install scalable[ai]\")\n",
     "    print(\"  (requires jinja2 and rich)\")"
    ]
@@ -480,7 +519,7 @@
     "    print(\"  A Python workflow script with:\")\n",
     "    print(\"  - @cacheable decorated functions\")\n",
     "    print(\"  - ScalableSession setup\")\n",
-    "    print(\"  - Sequential task submission (GCAM → Stitches)\")\n",
+    "    print(\"  - Sequential task submission (GCAM \u2192 Stitches)\")\n",
     "    print(\"  - Suggested component/task manifest additions\")"
    ]
   },
@@ -529,7 +568,7 @@
     "        to_provider=\"kubernetes\",\n",
     "    )\n",
     "    \n",
-    "    print(\"Migration Result (Slurm → Kubernetes):\")\n",
+    "    print(\"Migration Result (Slurm \u2192 Kubernetes):\")\n",
     "    if hasattr(result, 'migrated_yaml'):\n",
     "        print(f\"\\n{result.migrated_yaml}\")\n",
     "    if hasattr(result, 'changes_summary'):\n",
@@ -539,11 +578,11 @@
     "        \n",
     "except Exception as e:\n",
     "    print(f\"Migration result: {e}\")\n",
-    "    print(\"\\nExpected changes (slurm → kubernetes):\")\n",
+    "    print(\"\\nExpected changes (slurm \u2192 kubernetes):\")\n",
     "    print(\"  - Remove: queue, account, walltime, interface\")\n",
     "    print(\"  - Add: namespace, image, adaptive\")\n",
     "    print(\"  - Components need 'image' field\")\n",
-    "    print(\"  - Mounts → PVC or cloud storage\")"
+    "    print(\"  - Mounts \u2192 PVC or cloud storage\")"
    ]
   },
   {
@@ -685,9 +724,9 @@
     "report = session.validate()\n",
     "\n",
     "if report.ok:\n",
-    "    print(\"✓ AI-generated config is valid — ready to run\")\n",
+    "    print(\"\u2713 AI-generated config is valid \u2014 ready to run\")\n",
     "else:\n",
-    "    print(\"✗ Generated config has issues:\")\n",
+    "    print(\"\u2717 Generated config has issues:\")\n",
     "    for issue in report.errors:\n",
     "        print(f\"  [{issue.code}] {issue.path}: {issue.message}\")\n",
     "    print(\"\\n  Fix issues before running.\")"
@@ -699,14 +738,14 @@
    "source": [
     "## Summary\n",
     "\n",
-    "1. **`onboard_component`** — Analyzes model dirs, generates component YAML\n",
-    "2. **`diagnose_run`** — Analyzes failures, identifies patterns, suggests fixes\n",
-    "3. **`explain_plan`** — Human-readable plan explanations for stakeholders\n",
-    "4. **`compose_workflow`** — Generates workflow code from natural language\n",
-    "5. **`migrate_manifest`** — Adapts manifests between providers\n",
-    "6. **Heuristic mode** — Fast, deterministic, no API needed (CI/CD safe)\n",
-    "7. **LLM mode** — Richer output, requires API key (interactive use)\n",
-    "8. **Always validate** — AI output is advisory; validate before running\n",
+    "1. **`onboard_component`** \u2014 Analyzes model dirs, generates component YAML\n",
+    "2. **`diagnose_run`** \u2014 Analyzes failures, identifies patterns, suggests fixes\n",
+    "3. **`explain_plan`** \u2014 Human-readable plan explanations for stakeholders\n",
+    "4. **`compose_workflow`** \u2014 Generates workflow code from natural language\n",
+    "5. **`migrate_manifest`** \u2014 Adapts manifests between providers\n",
+    "6. **Heuristic mode** \u2014 Fast, deterministic, no API needed (CI/CD safe)\n",
+    "7. **LLM mode** \u2014 Richer output, requires API key (interactive use)\n",
+    "8. **Always validate** \u2014 AI output is advisory; validate before running\n",
     "\n",
     "## Next Steps\n",
     "\n",
@@ -750,4 +789,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/notebooks/README.md b/notebooks/README.md
index ff61600..d668623 100644
--- a/notebooks/README.md
+++ b/notebooks/README.md
@@ -34,6 +34,43 @@ jupyter lab notebooks/
 
 Notebooks are designed to be run sequentially (1 → 10) but each is self-contained with its own setup and teardown. Notebooks 1–4 and 6–7 require only the base `scalable` package; others need optional extras as noted above.
 
+## AI Configuration (`.env` File)
+
+Notebook 10 (AI Composition) uses LLM-powered assistants that require API credentials. These are loaded from a `.env` file. You have two options:
+
+### Option A: Place a `.env` file in the `notebooks/` directory
+
+Copy the template from the project root and fill in your credentials:
+
+```bash
+cp .env.example notebooks/.env
+# Edit notebooks/.env with your API key and provider settings
+```
+
+The `.env` file should contain (at minimum):
+
+```ini
+AI_PROVIDER=openai
+AI_API_KEY=your_api_key_here
+LLM_MODEL_NAME=gpt-4o
+# Optional: custom endpoint (required for proxies, Azure, etc.)
+# AI_BASE_URL=https://api.openai.com/v1
+```
+
+### Option B: Pass a custom path in code
+
+If your `.env` file lives elsewhere, use `load_env()` to point to it:
+
+```python
+from scalable.common import load_env
+
+load_env("/absolute/path/to/your/.env")
+```
+
+> **Important:** The `load_env()` call must happen *before* the notebook changes directories (i.e., before `os.chdir()`). Notebook 10 already includes this call at the top — just ensure your `.env` file path is correct.
+
+> **Note:** Notebooks 1–9 do not require a `.env` file. Notebook 10 works in heuristic mode (`no_ai=True`) without an API key, but LLM-enhanced mode requires valid credentials.
+
 ## Conventions
 
 - Each notebook creates a temporary working directory and cleans up after itself.
diff --git a/scalable/__init__.py b/scalable/__init__.py
index aa2cae2..f82b025 100755
--- a/scalable/__init__.py
+++ b/scalable/__init__.py
@@ -27,7 +27,7 @@
 from .advising import ResourceAdvisor, ResourceRecommendation
 from .caching import *  # noqa: F401,F403  (legacy star-export)
 from .client import ScalableClient
-from .common import SEED, settings
+from .common import SEED, load_env, settings
 from .core import JobQueueCluster
 from .costing import CostEstimate
 from .providers import DeploymentProvider, LocalProvider, SlurmProvider
@@ -140,6 +140,7 @@
     "get_worker",
     "migrate_manifest",
     "onboard_component",
+    "load_env",
     "settings",
     # Phase 5 ML/emulation
     "ActiveLearner",
diff --git a/scalable/common.py b/scalable/common.py
index 55470fe..40baa33 100755
--- a/scalable/common.py
+++ b/scalable/common.py
@@ -29,7 +29,7 @@
 
 from dotenv import load_dotenv
 
-__all__ = ["logger", "settings", "Settings", "SEED", "cachedir", "DEFAULT_SEED"]
+__all__ = ["logger", "settings", "Settings", "SEED", "cachedir", "DEFAULT_SEED", "load_env"]
 
 # ---------------------------------------------------------------------------
 # Load .env file with override=True so that .env values take precedence over
@@ -148,6 +148,51 @@ class Settings:
 #: changes behaviour for subsequent calls into the library.
 settings: Settings = Settings()
 
+
+def load_env(dotenv_path: str | Path | None = None, *, override: bool = True) -> Settings:
+    """Load environment variables from a ``.env`` file and reinitialize settings.
+
+    This is useful in notebooks and scripts where the working directory may
+    differ from the directory containing the ``.env`` file.  For example,
+    tutorial notebooks that ``os.chdir()`` into a temporary directory should
+    call this function *before* changing directories, or pass an absolute path
+    to their ``.env`` file.
+
+    Parameters
+    ----------
+    dotenv_path:
+        Path to the ``.env`` file to load.  If ``None`` (default), looks for
+        ``.env`` in the current working directory.
+    override:
+        Whether values in the ``.env`` file should override existing
+        environment variables (default: ``True``).
+
+    Returns
+    -------
+    Settings
+        The refreshed :data:`settings` singleton (same object, updated in-place
+        via replacement).
+
+    Examples
+    --------
+    >>> from scalable.common import load_env
+    >>> # Load .env from a specific location (e.g., the notebooks directory)
+    >>> load_env("/path/to/your/project/.env")
+    """
+    global settings
+
+    resolved = Path(dotenv_path) if dotenv_path is not None else Path.cwd() / ".env"
+    if not resolved.is_file():
+        logger.warning("load_env: file not found: %s", resolved)
+        return settings
+
+    load_dotenv(resolved, override=override)
+    logger.debug("load_env: loaded %s (override=%s)", resolved, override)
+
+    # Reinitialize settings from the (now-updated) environment.
+    settings = Settings()
+    return settings
+
 # ---------------------------------------------------------------------------
 # Logging
 # ---------------------------------------------------------------------------

From 1a9568ab5aa6c17950ff80ad481f9288e2a52d09 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 11:42:32 -0400
Subject: [PATCH 40/47] docs update for provider support

---
 docs/ai_assistants.rst | 47 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 47 insertions(+)

diff --git a/docs/ai_assistants.rst b/docs/ai_assistants.rst
index 195e91c..41e3440 100644
--- a/docs/ai_assistants.rst
+++ b/docs/ai_assistants.rst
@@ -23,6 +23,27 @@ Configuration
 AI features are configured via a ``.env`` file in your project root (loaded
 automatically with override priority) or via environment variables.
 
+By default, Scalable loads a ``.env`` file from the **current working directory**
+at import time. If your script or notebook changes directories (e.g.,
+``os.chdir()`` to a temp folder), use :func:`~scalable.common.load_env` to
+explicitly load credentials from a specific path::
+
+    from scalable.common import load_env
+
+    # Load from an absolute path before changing directories
+    load_env("/path/to/your/project/.env")
+
+    # Or load from a relative path (resolved against CWD at call time)
+    load_env("../notebooks/.env")
+
+.. tip::
+
+   For Jupyter notebooks in the ``notebooks/`` directory, place a ``.env``
+   file there (copy from ``.env.example`` in the project root). The AI tutorial
+   notebook (Tutorial 10) calls ``load_env()`` automatically at startup — just
+   ensure the ``.env`` file exists in the notebooks directory or update the
+   path in the first code cell.
+
 **Recommended generic variables** (provider-agnostic):
 
 * ``AI_PROVIDER`` — Provider selection. Options: ``openai``, ``anthropic``, ``google``, ``xai``, ``groq``, ``ollama``. Default: ``none``.
@@ -172,6 +193,32 @@ Options:
 Python API
 ----------
 
+Loading Environment Configuration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Use :func:`~scalable.common.load_env` to load a ``.env`` file from a custom
+location. This is especially useful in notebooks and scripts that change the
+working directory::
+
+    from scalable.common import load_env
+
+    # Load from the notebooks directory (before os.chdir)
+    load_env("./notebooks/.env")
+
+    # Or equivalently via the top-level package import:
+    from scalable import load_env
+    load_env("/absolute/path/to/.env")
+
+Parameters:
+
+* ``dotenv_path`` — Path to the ``.env`` file. Defaults to ``<cwd>/.env``.
+* ``override`` — Whether to override existing env vars (default: ``True``).
+
+Returns the refreshed :data:`~scalable.common.settings` singleton.
+
+Assistant Functions
+~~~~~~~~~~~~~~~~~~~
+
 All assistant functions are available programmatically::
 
     from scalable.ai import (

From 8ef0f5741aef3dfcce159514c24de9225be58dc0 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 12:41:08 -0400
Subject: [PATCH 41/47] new beginner level tutorials

---
 CHANGELOG.md                                  |  24 +
 .../tutorials/beginner/01_getting_started.rst | 674 ++++++++++++++++++
 .../tutorials/beginner/02_manifest_system.rst | 644 +++++++++++++++++
 .../beginner/03_scaling_strategies.rst        | 567 +++++++++++++++
 .../beginner/04_caching_performance.rst       | 559 +++++++++++++++
 .../beginner/05_cloud_integration.rst         | 519 ++++++++++++++
 docs/tutorials/beginner/06_telemetry.rst      | 500 +++++++++++++
 docs/tutorials/beginner/07_error_handling.rst | 582 +++++++++++++++
 docs/tutorials/beginner/08_kubernetes.rst     | 536 ++++++++++++++
 docs/tutorials/beginner/09_ml_emulation.rst   | 540 ++++++++++++++
 docs/tutorials/beginner/10_ai_composition.rst | 609 ++++++++++++++++
 docs/tutorials/beginner/index.rst             | 150 ++++
 docs/tutorials/index.rst                      |  13 +
 notebooks/beginner/01_getting_started.ipynb   | 365 ++++++++++
 notebooks/beginner/02_manifest_system.ipynb   | 362 ++++++++++
 .../beginner/03_scaling_strategies.ipynb      | 232 ++++++
 .../beginner/04_caching_performance.ipynb     | 250 +++++++
 notebooks/beginner/05_cloud_integration.ipynb | 232 ++++++
 notebooks/beginner/06_telemetry.ipynb         | 261 +++++++
 notebooks/beginner/07_error_handling.ipynb    | 243 +++++++
 notebooks/beginner/08_kubernetes.ipynb        | 213 ++++++
 notebooks/beginner/09_ml_emulation.ipynb      | 246 +++++++
 notebooks/beginner/10_ai_composition.ipynb    | 262 +++++++
 notebooks/beginner/README.md                  |  66 ++
 24 files changed, 8649 insertions(+)
 create mode 100644 docs/tutorials/beginner/01_getting_started.rst
 create mode 100644 docs/tutorials/beginner/02_manifest_system.rst
 create mode 100644 docs/tutorials/beginner/03_scaling_strategies.rst
 create mode 100644 docs/tutorials/beginner/04_caching_performance.rst
 create mode 100644 docs/tutorials/beginner/05_cloud_integration.rst
 create mode 100644 docs/tutorials/beginner/06_telemetry.rst
 create mode 100644 docs/tutorials/beginner/07_error_handling.rst
 create mode 100644 docs/tutorials/beginner/08_kubernetes.rst
 create mode 100644 docs/tutorials/beginner/09_ml_emulation.rst
 create mode 100644 docs/tutorials/beginner/10_ai_composition.rst
 create mode 100644 docs/tutorials/beginner/index.rst
 create mode 100644 notebooks/beginner/01_getting_started.ipynb
 create mode 100644 notebooks/beginner/02_manifest_system.ipynb
 create mode 100644 notebooks/beginner/03_scaling_strategies.ipynb
 create mode 100644 notebooks/beginner/04_caching_performance.ipynb
 create mode 100644 notebooks/beginner/05_cloud_integration.ipynb
 create mode 100644 notebooks/beginner/06_telemetry.ipynb
 create mode 100644 notebooks/beginner/07_error_handling.ipynb
 create mode 100644 notebooks/beginner/08_kubernetes.ipynb
 create mode 100644 notebooks/beginner/09_ml_emulation.ipynb
 create mode 100644 notebooks/beginner/10_ai_composition.ipynb
 create mode 100644 notebooks/beginner/README.md

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5cc496c..e800dfb 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,30 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [Unreleased]
+
+### Added
+
+- **Beginner tutorial series** (`docs/tutorials/beginner/`, `notebooks/beginner/`):
+  10 tutorials mirroring the standard series but written for non-experts with
+  full concept definitions, design rationale, analogies, and foundational
+  explanations of distributed computing, declarative programming, cloud
+  infrastructure, container orchestration, ML, and AI-assisted development.
+  - Beginner Tutorial 1: Your First Workflow (Dask, CLI, virtual environments)
+  - Beginner Tutorial 2: Understanding the Manifest System (declarative programming, YAML, schemas)
+  - Beginner Tutorial 3: How Distributed Computing Works (schedulers, providers, Amdahl's Law)
+  - Beginner Tutorial 4: Caching — Avoiding Redundant Work (hashing, memoization, decorators)
+  - Beginner Tutorial 5: Cloud Computing Fundamentals (object storage, containers, IAM, costs)
+  - Beginner Tutorial 6: Understanding What Happened (telemetry, observability, JSONL)
+  - Beginner Tutorial 7: When Things Go Wrong (fault tolerance, retries, idempotency)
+  - Beginner Tutorial 8: Container Orchestration with Kubernetes (pods, operators, namespaces)
+  - Beginner Tutorial 9: Machine Learning for Smarter Workflows (surrogates, uncertainty, active learning)
+  - Beginner Tutorial 10: AI-Assisted Workflow Development (LLMs, heuristics, code generation)
+  - Companion Jupyter notebooks for all 10 tutorials with interactive examples
+  - Tutorials index page with learning path and graduation guide to standard tutorials
+
+---
+
 ## [2.0.0a5] — Phase 5: ML Optimization and Emulation
 
 ### Added
diff --git a/docs/tutorials/beginner/01_getting_started.rst b/docs/tutorials/beginner/01_getting_started.rst
new file mode 100644
index 0000000..e8c41ba
--- /dev/null
+++ b/docs/tutorials/beginner/01_getting_started.rst
@@ -0,0 +1,674 @@
+.. _beginner_getting_started:
+
+======================================================
+Beginner Tutorial 1: Your First Workflow
+======================================================
+
+.. contents:: In This Tutorial
+   :local:
+   :depth: 2
+
+The Big Picture
+----------------
+
+Imagine you have a Python script that processes data — maybe it analyzes
+climate scenarios, runs simulations, or trains models. When the data grows,
+running everything on your laptop becomes painfully slow. You need a way to
+split the work across multiple processors (or multiple computers) without
+rewriting your entire program.
+
+**That's what Scalable does.** It takes your Python functions and orchestrates
+them across multiple workers — whether those workers are threads on your laptop,
+processes on an HPC cluster, or containers in the cloud. And it does this
+through a simple configuration file rather than requiring you to write complex
+parallel programming code.
+
+This tutorial walks you through your very first Scalable workflow, explaining
+every concept along the way.
+
+.. admonition:: 💡 Key Concept: What is a Workflow?
+   :class: tip
+
+   A **workflow** is a sequence of computational steps that transforms inputs
+   into outputs. Think of it like a recipe: you have ingredients (data), steps
+   (functions), and a final dish (results).
+
+   In Scalable, a workflow consists of:
+
+   1. A **manifest** (configuration file) describing what resources you need
+   2. Python **functions** that do the actual work
+   3. A **target** (where the work runs — your laptop, a cluster, the cloud)
+
+What You Will Learn
+--------------------
+
+By the end of this tutorial you will:
+
+* Understand what Scalable is and why it exists.
+* Know what Dask is and why Scalable uses it under the hood.
+* Create and activate a Python virtual environment.
+* Install Scalable and use its command-line interface (CLI).
+* Write your first manifest file (``scalable.yaml``).
+* Validate, plan, and run a workflow end-to-end.
+* Read the telemetry output to see what happened.
+
+Prerequisites
+--------------
+
+* **Python 3.11 or later** installed on your computer.
+* A **terminal** (Terminal on macOS/Linux, PowerShell or Command Prompt on
+  Windows).
+* **Basic Python knowledge**: you can write functions, use ``import``, and know
+  what ``pip`` is (even if you don't use it daily).
+
+No HPC cluster, Docker, or cloud account is needed — everything runs locally.
+
+
+Key Concepts Explained
+-----------------------
+
+Before we write any code, let's define the foundational ideas you'll encounter.
+
+.. admonition:: 💡 Key Concept: Distributed Computing
+   :class: tip
+
+   **Distributed computing** means splitting work across multiple processors
+   or computers that work together. Instead of one CPU doing all 1000 tasks
+   sequentially (one after another), you might have 10 CPUs each handling 100
+   tasks simultaneously.
+
+   **Analogy:** Imagine stuffing 1000 envelopes. Doing it alone takes hours.
+   With 10 friends helping, each person stuffs 100 envelopes and you finish
+   10× faster. Distributed computing is getting those friends organized.
+
+.. admonition:: 💡 Key Concept: What is Dask?
+   :class: tip
+
+   **Dask** is a Python library for parallel and distributed computing. It's
+   the "engine" that Scalable uses under the hood to actually run your
+   functions on multiple workers.
+
+   Think of Dask as the engine in a car — you don't need to understand every
+   piston to drive, but knowing it's there helps you understand what's
+   happening.
+
+   **Why Dask?** Scalable chose Dask because it:
+
+   * Integrates natively with Python's scientific ecosystem (NumPy, pandas)
+   * Scales from a single laptop to thousands of nodes
+   * Has a mature scheduler that handles task dependencies
+   * Supports dynamic scaling (adding/removing workers at runtime)
+   * Is widely adopted in the scientific computing community
+
+   Alternatives like **Ray** (more ML-focused) or **Celery** (more
+   web-focused) exist, but Dask's strength is scientific workflows — exactly
+   what Scalable targets.
+
+.. admonition:: 💡 Key Concept: Command-Line Interface (CLI)
+   :class: tip
+
+   A **CLI** is a text-based way to interact with a program. Instead of
+   clicking buttons in a graphical interface, you type commands like
+   ``scalable run ./scalable.yaml``.
+
+   CLIs are preferred for:
+
+   * **Automation** — easy to script and repeat
+   * **Remote work** — works over SSH where GUIs don't
+   * **Reproducibility** — commands can be saved and re-run exactly
+
+.. admonition:: 💡 Key Concept: Virtual Environment
+   :class: tip
+
+   A **virtual environment** is an isolated Python installation. It has its
+   own copy of ``pip`` and installed packages, separate from your system
+   Python.
+
+   **Why bother?** Without virtual environments, installing a package for
+   Project A might break Project B (if they need different versions of the
+   same library). Virtual environments keep projects isolated.
+
+   **Analogy:** Virtual environments are like separate kitchen pantries for
+   each recipe — what you put in one doesn't affect the others.
+
+
+Step 1: Set Up Your Environment
+---------------------------------
+
+Let's create an isolated Python environment for this tutorial.
+
+**Open your terminal** and run:
+
+.. code-block:: bash
+
+   # Create a new virtual environment named ".venv"
+   python -m venv .venv
+
+   # Activate it (this changes your terminal's Python to use the isolated one)
+   source .venv/bin/activate   # macOS/Linux
+   # On Windows: .venv\Scripts\activate
+
+.. admonition:: What just happened?
+   :class: note
+
+   ``python -m venv .venv`` created a folder called ``.venv`` containing a
+   fresh Python installation. ``source .venv/bin/activate`` tells your terminal
+   "use this Python instead of the system one." You'll see your prompt change
+   (often showing ``(.venv)`` at the beginning).
+
+Now install Scalable:
+
+.. code-block:: bash
+
+   pip install scalable
+
+Verify it worked:
+
+.. code-block:: bash
+
+   scalable --help
+
+You should see output like:
+
+.. code-block:: text
+
+   usage: scalable [-h] {validate,plan,run,report,advise,...} ...
+
+   Scalable CLI — orchestrate distributed workflows.
+
+   positional arguments:
+     {validate,plan,run,report,advise,...}
+
+.. admonition:: Under the Hood
+   :class: hint
+
+   When you ran ``pip install scalable``, Python downloaded Scalable and all
+   its dependencies (including Dask). The ``scalable`` command is a CLI entry
+   point — a small script that Python created in your virtual environment's
+   ``bin/`` directory that launches Scalable's command handler.
+
+
+Step 2: Create a Project Directory
+------------------------------------
+
+Scalable expects your workflow to live in a dedicated directory:
+
+.. code-block:: bash
+
+   mkdir my-first-workflow && cd my-first-workflow
+
+The minimal layout is:
+
+.. code-block:: text
+
+   my-first-workflow/
+   ├── scalable.yaml       # The manifest (configuration)
+   └── workflow.py          # Your Python code
+
+.. admonition:: 💡 Key Concept: Project Structure
+   :class: tip
+
+   Keeping configuration (``scalable.yaml``) and code (``workflow.py``) in a
+   dedicated directory makes your workflow:
+
+   * **Portable** — zip it up and it works elsewhere
+   * **Version-controllable** — put it in Git
+   * **Self-documenting** — everything needed is in one place
+
+
+Step 3: Write Your First Manifest
+-----------------------------------
+
+.. admonition:: 💡 Key Concept: What is a Manifest?
+   :class: tip
+
+   A **manifest** is a configuration file that declares the desired state of
+   your system. In Scalable, the manifest (``scalable.yaml``) answers:
+
+   * **What** is this project?
+   * **Where** should it run? (local machine? cloud? HPC cluster?)
+   * **How much** resources does each piece need? (CPU, memory)
+   * **What** are the work units?
+
+   The manifest is **declarative** — more on this below.
+
+.. admonition:: 💡 Key Concept: Declarative vs. Imperative Programming
+   :class: tip
+
+   This is a fundamental programming paradigm distinction:
+
+   **Imperative** (how to do it):
+     "SSH into server. Run this command. Check the output. If it failed,
+     retry. Allocate 4GB of RAM by calling this API..."
+
+   **Declarative** (what you want):
+     "I need 2 workers with 1 CPU and 1GB RAM each."
+
+   The manifest is declarative — you describe your desired state and Scalable
+   figures out how to achieve it. This is the same philosophy behind:
+
+   * SQL (``SELECT name FROM users`` — you say what data, not how to fetch it)
+   * HTML (``<h1>Title</h1>`` — you say what it is, not how to render it)
+   * Kubernetes YAML (you describe desired state, K8s makes it happen)
+
+   **Why declarative?** It separates *intent* from *implementation*. Your
+   manifest works whether you're running locally, on an HPC cluster, or in
+   AWS — only the "target" section changes.
+
+Create the file ``scalable.yaml``:
+
+.. code-block:: yaml
+
+   # scalable.yaml — Your first Scalable manifest
+   version: 1
+   project:
+     name: hello-scalable
+
+   targets:
+     local:
+       provider: local
+       max_workers: 2
+       threads_per_worker: 1
+       processes: false
+       containers: none
+
+   components:
+     analysis:
+       cpus: 1
+       memory: 1G
+
+   tasks:
+     run_analysis:
+       component: analysis
+
+Let's break this down **line by line**:
+
+.. admonition:: 💡 Key Concept: YAML
+   :class: tip
+
+   **YAML** (YAML Ain't Markup Language) is a human-readable data format.
+   It uses indentation (spaces, not tabs!) to show structure:
+
+   .. code-block:: yaml
+
+      # This is a comment
+      key: value              # A simple key-value pair
+      nested:
+        child_key: child_val  # Indented = nested inside "nested"
+      list:
+        - item1               # Lists use dashes
+        - item2
+
+   YAML was chosen over JSON (harder to read/write by hand) and TOML (less
+   expressive for nested structures).
+
+**Section-by-section explanation:**
+
+``version: 1``
+   The schema version. This tells Scalable which format rules to apply when
+   reading your manifest. Currently ``1`` is the only version.
+
+``project: { name: hello-scalable }``
+   Metadata about your project. The ``name`` appears in logs, telemetry data,
+   and artifact paths so you can identify which project a run belongs to.
+
+``targets:``
+   Targets are **where** your code runs. You can have multiple targets (local,
+   HPC, cloud) in one manifest and switch between them. Here we define one
+   target called ``local``:
+
+   * ``provider: local`` — Use the built-in local provider (runs on your machine)
+   * ``max_workers: 2`` — Create up to 2 workers (parallel executors)
+   * ``threads_per_worker: 1`` — Each worker uses 1 thread
+   * ``processes: false`` — Workers run as threads (not separate processes)
+   * ``containers: none`` — No containerization (bare metal)
+
+``components:``
+   Components define **resource profiles** — how much CPU and memory a piece
+   of work needs. The ``analysis`` component requests 1 CPU and 1 gigabyte of
+   RAM.
+
+``tasks:``
+   Tasks are **named work units** that bind to a component. When you submit a
+   function to Scalable, you associate it with a task name, which tells the
+   system what resources it needs.
+
+.. admonition:: Why separate targets, components, and tasks?
+   :class: hint
+
+   This separation follows the **separation of concerns** principle:
+
+   * **Targets** = where (infrastructure)
+   * **Components** = how much (resources)
+   * **Tasks** = what (work units)
+
+   You can change where you run (swap the target) without changing what you
+   run (tasks and components stay the same). This is what makes Scalable
+   truly portable.
+
+
+Step 4: Validate Your Manifest
+-------------------------------
+
+Before running anything, check that your manifest is correctly written:
+
+.. code-block:: bash
+
+   scalable validate ./scalable.yaml
+
+Expected output:
+
+.. code-block:: text
+
+   ✓ Manifest is valid (0 errors, 0 warnings)
+
+.. admonition:: 💡 Key Concept: Validation
+   :class: tip
+
+   **Validation** means checking that something meets expected rules before
+   using it. It's like spell-check for your configuration.
+
+   Scalable's validator checks:
+
+   * Required sections exist (``version``, ``project``)
+   * Key names are spelled correctly (catches typos like ``providr``)
+   * References are valid (a task's ``component`` actually exists)
+   * Values are the right type (``max_workers`` must be a positive number)
+
+   **Why validate first?** It's much faster and cheaper to catch errors in a
+   config file than to discover them 30 minutes into a cloud run that's
+   costing you money.
+
+Try introducing a deliberate error to see what happens:
+
+.. code-block:: yaml
+
+   # Change "provider" to "providr" (typo) and validate again
+   targets:
+     local:
+       providr: local   # <-- typo!
+
+.. code-block:: text
+
+   ERROR targets.local: unknown provider 'providr'
+
+
+Step 5: Plan the Execution
+----------------------------
+
+Planning shows you what **would** happen without actually doing it:
+
+.. code-block:: bash
+
+   scalable plan ./scalable.yaml --target local --dry-run
+
+.. code-block:: text
+
+   Plan created for target 'local' (provider: local)
+   Workers: 2 × analysis (1 cpu, 1G memory)
+   Manifest lock: sha256:a3b8f1...
+
+.. admonition:: 💡 Key Concept: Dry Run
+   :class: tip
+
+   A **dry run** simulates an operation without executing it. It answers
+   "what would happen if I ran this?" without consuming real resources.
+
+   This is valuable because:
+
+   * You can verify your configuration before spending time/money
+   * You can review the plan and catch mistakes
+   * In cloud environments, you can see estimated costs before committing
+
+   The ``--dry-run`` flag is common across many tools (``terraform plan``,
+   ``kubectl --dry-run``, ``rsync --dry-run``).
+
+.. admonition:: 💡 Key Concept: Manifest Lock (Hash)
+   :class: tip
+
+   The ``sha256:a3b8f1...`` is a **hash** — a fingerprint of your manifest's
+   contents. If you change anything in the manifest, the hash changes. This
+   enables:
+
+   * **Reproducibility** — you can verify that a run used the exact same
+     configuration as a previous run
+   * **Caching** — Scalable knows if the manifest changed since last run
+
+
+Step 6: Write Your Workflow Code
+---------------------------------
+
+Now let's write the Python function that does actual work. Create
+``workflow.py``:
+
+.. code-block:: python
+
+   """My first Scalable workflow."""
+   import time
+   from scalable import ScalableSession
+
+
+   def analyze_scenario(scenario_id: int) -> dict:
+       """Simulate an analysis task.
+
+       In a real workflow this might run a climate model, process
+       satellite data, or train a machine learning model. Here we
+       just simulate work with a sleep.
+       """
+       time.sleep(0.5)  # Simulate 0.5 seconds of computation
+       return {
+           "scenario_id": scenario_id,
+           "result": scenario_id * 42,
+           "status": "complete",
+       }
+
+
+   def main():
+       """Run the workflow using a ScalableSession."""
+       # Create a session from our manifest
+       session = ScalableSession.from_manifest(
+           "./scalable.yaml",
+           target="local",
+       )
+
+       # Submit 6 tasks to be executed in parallel
+       futures = []
+       for i in range(6):
+           future = session.submit(analyze_scenario, i, task="run_analysis")
+           futures.append(future)
+
+       # Gather results (blocks until all tasks complete)
+       results = session.gather(futures)
+
+       print(f"Completed {len(results)} scenarios!")
+       for r in results:
+           print(f"  Scenario {r['scenario_id']}: result = {r['result']}")
+
+       # Clean up
+       session.close()
+
+
+   if __name__ == "__main__":
+       main()
+
+Let's understand what this code does:
+
+.. admonition:: Under the Hood: What happens when you call ``session.submit()``
+   :class: hint
+
+   1. Your function (``analyze_scenario``) and its arguments (``scenario_id``)
+      are **serialized** (converted to bytes that can be sent over a network).
+   2. The serialized task is sent to Dask's **scheduler**.
+   3. The scheduler finds an available **worker** and assigns the task.
+   4. The worker **deserializes** the function, executes it, and sends the
+      result back.
+   5. You get a **future** — a placeholder for the result that will be
+      available later.
+
+   With ``max_workers: 2``, Scalable runs 2 tasks at a time. Since we
+   submitted 6 tasks, they execute in 3 batches of 2 (total ~1.5 seconds
+   instead of 3 seconds sequentially).
+
+.. admonition:: 💡 Key Concept: Futures
+   :class: tip
+
+   A **future** is a promise of a result that hasn't been computed yet. When
+   you call ``session.submit()``, the task starts running in the background
+   and you immediately get back a future object.
+
+   Later, when you call ``session.gather(futures)``, Python waits until all
+   the futures have their results ready, then returns them.
+
+   **Analogy:** Ordering food at a counter — you get a receipt number (future)
+   immediately. The food is being prepared in the background. When you hear
+   your number called, you pick up your food (gather the result).
+
+
+Step 7: Run the Workflow
+--------------------------
+
+Execute your workflow:
+
+.. code-block:: bash
+
+   python workflow.py
+
+Expected output:
+
+.. code-block:: text
+
+   Completed 6 scenarios!
+     Scenario 0: result = 0
+     Scenario 1: result = 42
+     Scenario 2: result = 84
+     Scenario 3: result = 126
+     Scenario 4: result = 168
+     Scenario 5: result = 210
+
+You can also run workflows via the CLI (for manifests that define entry
+points), but the Python API gives you the most control.
+
+.. admonition:: 🤔 Think About It
+   :class: note
+
+   With 6 tasks and 2 workers, how long should this take?
+
+   * Sequential (no parallelism): 6 × 0.5s = 3.0 seconds
+   * Parallel with 2 workers: 3 batches × 0.5s = ~1.5 seconds
+
+   The speedup is approximately 2× with 2 workers. This is the fundamental
+   value of distributed computing — trading more hardware for less time.
+
+
+Step 8: Inspect Telemetry
+---------------------------
+
+.. admonition:: 💡 Key Concept: Telemetry
+   :class: tip
+
+   **Telemetry** is automated data collection about what happened during
+   execution. Think of it like a flight recorder (black box) for your
+   workflow — it records events so you can understand what happened after
+   the fact.
+
+After your run completes, Scalable has recorded telemetry data. Generate a
+report:
+
+.. code-block:: bash
+
+   scalable report --last
+
+This shows a summary of your most recent run: how many tasks succeeded, how
+long they took, and resource utilization.
+
+
+Common Questions
+-----------------
+
+**Q: Do I always need a manifest file?**
+
+Yes — the manifest is the single source of truth for your workflow's resource
+requirements. This is by design: it makes workflows reproducible and portable.
+
+**Q: Why not just use Python's ``multiprocessing`` module?**
+
+Python's ``multiprocessing`` works for simple parallelism on one machine. But
+it can't:
+
+* Scale to multiple machines (HPC clusters, cloud)
+* Manage heterogeneous resources (different CPU/memory per task type)
+* Cache results between runs
+* Provide telemetry and observability
+* Handle worker failures gracefully
+
+Scalable (via Dask) provides all of these.
+
+**Q: What's the difference between threads and processes?**
+
+* **Threads** share memory (fast communication, but Python's GIL limits
+  true CPU parallelism).
+* **Processes** have separate memory (true parallelism, but higher overhead
+  to start and communicate).
+
+For I/O-bound work (network calls, file reading), threads work well. For
+CPU-bound work (heavy math), processes are better. The ``processes: false``
+setting in our manifest uses threads for simplicity.
+
+**Q: What is the GIL?**
+
+The **Global Interpreter Lock** (GIL) is a Python implementation detail that
+prevents multiple threads from executing Python code simultaneously. It
+exists for memory safety but means CPU-bound threads don't truly run in
+parallel. This is why ``processes: true`` is better for computation-heavy
+tasks.
+
+
+What You Learned
+-----------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Term
+     - Definition
+   * - Workflow
+     - A sequence of computational steps transforming inputs to outputs
+   * - Distributed Computing
+     - Splitting work across multiple processors/computers
+   * - Dask
+     - Python library for parallel computing (Scalable's engine)
+   * - CLI
+     - Text-based interface for running commands
+   * - Virtual Environment
+     - Isolated Python installation for dependency management
+   * - Manifest
+     - Declarative configuration file describing desired state
+   * - Declarative Programming
+     - Describing *what* you want, not *how* to achieve it
+   * - Provider
+     - Abstraction over an execution backend (local, HPC, cloud)
+   * - Worker
+     - A process/thread that executes tasks
+   * - Future
+     - A placeholder for a result being computed asynchronously
+   * - Validation
+     - Checking correctness before execution
+   * - Dry Run
+     - Simulating an operation without executing it
+   * - Telemetry
+     - Automated recording of execution data
+
+
+Next Steps
+-----------
+
+You've run your first Scalable workflow! You now understand the fundamental
+concepts that everything else builds on.
+
+* **Next beginner tutorial:** :ref:`beginner_manifest_system` — deep dive
+  into declarative configuration and YAML
+* **Standard tutorial:** :ref:`tutorial_getting_started` — same topic with
+  less explanation, more advanced patterns
+* **Try modifying:** Change ``max_workers`` to 4 and re-run. Is it faster?
+  Why or why not? (Hint: you only have 6 tasks.)
diff --git a/docs/tutorials/beginner/02_manifest_system.rst b/docs/tutorials/beginner/02_manifest_system.rst
new file mode 100644
index 0000000..b4707d3
--- /dev/null
+++ b/docs/tutorials/beginner/02_manifest_system.rst
@@ -0,0 +1,644 @@
+.. _beginner_manifest_system:
+
+======================================================
+Beginner Tutorial 2: Understanding the Manifest System
+======================================================
+
+.. contents:: In This Tutorial
+   :local:
+   :depth: 2
+
+The Big Picture
+----------------
+
+In the previous tutorial, you wrote a simple ``scalable.yaml`` file. But what
+*is* a manifest, really? Why does Scalable use one? And what's this
+"declarative programming" idea all about?
+
+This tutorial takes you deep into the manifest system — not just the syntax,
+but the *philosophy* behind it. You'll understand why configuration-as-code
+exists, how YAML works, what schemas enforce, and how overlays let you
+customize behavior across different environments.
+
+.. admonition:: 💡 Key Concept: Configuration as Code
+   :class: tip
+
+   **Configuration as code** means storing your system's settings in version-
+   controlled text files rather than clicking through GUIs or typing ad-hoc
+   commands.
+
+   Benefits:
+
+   * **Reproducibility** — anyone can recreate your exact setup
+   * **History** — Git shows who changed what and when
+   * **Review** — teammates can review config changes like code changes
+   * **Automation** — CI/CD pipelines can validate and deploy configs
+
+   Scalable's manifest is configuration as code: your entire workflow setup
+   lives in a single YAML file that you check into version control.
+
+What You Will Learn
+--------------------
+
+By the end of this tutorial you will:
+
+* Understand declarative programming deeply and why it matters.
+* Read and write YAML confidently (indentation, data types, references).
+* Know every section of a ``scalable.yaml`` manifest and its purpose.
+* Use environment variables in manifests for portability.
+* Define multiple targets for different environments.
+* Apply overlays to customize settings per deployment.
+* Validate manifests and interpret error messages.
+
+Prerequisites
+--------------
+
+* Completed :ref:`beginner_getting_started`.
+* Scalable installed (``pip install scalable``).
+* A text editor and terminal.
+
+
+Key Concepts Explained
+-----------------------
+
+.. admonition:: 💡 Key Concept: Declarative Programming (Deep Dive)
+   :class: tip
+
+   In :ref:`beginner_getting_started`, we introduced declarative vs. imperative.
+   Let's go deeper with a real example.
+
+   **Imperative approach** to setting up 4 workers:
+
+   .. code-block:: python
+
+      # Pseudocode: imperative style
+      for i in range(4):
+          worker = start_process()
+          worker.set_memory("4G")
+          worker.set_cpus(2)
+          worker.connect_to_scheduler(scheduler_address)
+          if not worker.is_healthy():
+              worker.restart()
+
+   **Declarative approach** (what Scalable uses):
+
+   .. code-block:: yaml
+
+      targets:
+        local:
+          provider: local
+          max_workers: 4
+      components:
+        analysis:
+          cpus: 2
+          memory: 4G
+
+   The declarative version doesn't say *how* to start workers — it says
+   *what state you want*. Scalable's runtime figures out the "how."
+
+   **Why is declarative better here?**
+
+   1. **Portability** — The same declaration works on your laptop or a
+      1000-node cluster. The "how" differs, but the "what" doesn't.
+   2. **Idempotency** — You can apply the same manifest repeatedly; the
+      system converges to the desired state without duplicating resources.
+   3. **Separation of concerns** — You (the scientist) declare what you
+      need; the platform (Scalable) handles infrastructure details.
+
+.. admonition:: 💡 Key Concept: YAML Syntax
+   :class: tip
+
+   YAML is a data serialization format designed to be human-readable. Here
+   are the essential rules:
+
+   **Indentation matters** (use spaces, NEVER tabs):
+
+   .. code-block:: yaml
+
+      parent:
+        child: value      # 2-space indent = child of "parent"
+        another: value2
+
+   **Data types** are inferred:
+
+   .. code-block:: yaml
+
+      string_value: hello         # String
+      number_value: 42            # Integer
+      float_value: 3.14           # Float
+      boolean_value: true         # Boolean (true/false)
+      quoted_string: "04:00:00"   # Quoted to prevent time interpretation
+      null_value: null            # Null/None
+
+   **Lists** use dashes:
+
+   .. code-block:: yaml
+
+      fruits:
+        - apple
+        - banana
+        - cherry
+
+   **Nested maps**:
+
+   .. code-block:: yaml
+
+      targets:
+        local:
+          provider: local
+          max_workers: 2
+
+   **Comments** start with ``#``.
+
+   **Common mistakes:**
+
+   * Using tabs instead of spaces (causes parse errors)
+   * Inconsistent indentation (2 spaces is conventional)
+   * Forgetting to quote strings that look like other types
+     (``version: 1`` is a number, ``version: "1"`` is a string)
+
+.. admonition:: 💡 Key Concept: Schema
+   :class: tip
+
+   A **schema** defines the valid structure for data. Think of it like a
+   form with labeled fields — some fields are required, some are optional,
+   and each has rules about what values are acceptable.
+
+   For Scalable's manifest:
+
+   * ``version`` is required and must be an integer
+   * ``project.name`` is required and must be a string
+   * ``targets`` must be a map where each value has a ``provider`` key
+   * ``components`` must have ``cpus`` and ``memory`` keys
+
+   The schema catches errors *before* you run (fail fast), saving you from
+   discovering problems 30 minutes into an expensive cloud run.
+
+.. admonition:: 💡 Key Concept: Environment Variables
+   :class: tip
+
+   **Environment variables** are system-level settings available to all
+   programs. They store configuration that varies between machines or users:
+
+   .. code-block:: bash
+
+      # Setting an environment variable
+      export AWS_REGION=us-east-1
+
+      # Reading it in a program
+      echo $AWS_REGION   # Prints: us-east-1
+
+   In Scalable manifests, you can reference them with ``${VAR_NAME}``
+   syntax. This keeps secrets (API keys, passwords) out of your config
+   files and makes manifests portable across environments.
+
+.. admonition:: 💡 Key Concept: Single Source of Truth
+   :class: tip
+
+   The **single source of truth** (SSOT) principle means there's exactly one
+   authoritative place where a piece of information lives. If you need to
+   change something, you change it in one place, and everything else picks
+   up the change.
+
+   The manifest is Scalable's SSOT for workflow configuration. You don't
+   need to remember "I set max_workers in the CLI, memory in an env var,
+   and the image in a script." It's all in one file.
+
+
+Step 1: The Complete Manifest Structure
+-----------------------------------------
+
+Every ``scalable.yaml`` manifest has this top-level structure:
+
+.. code-block:: yaml
+
+   version: 1              # Required: schema version
+   project: { ... }        # Required: project metadata
+   targets: { ... }        # Required: where code runs
+   components: { ... }     # Required: resource profiles
+   tasks: { ... }          # Required: work unit definitions
+   overlays: { ... }       # Optional: environment-specific overrides
+
+Let's explore each section in depth.
+
+
+Step 2: The Project Block
+---------------------------
+
+.. code-block:: yaml
+
+   project:
+     name: energy-forecast
+     default_storage: ./outputs
+     local_cache: ./cache
+
+**What each key does:**
+
+``name``
+   A human-readable identifier for your project. It appears in:
+
+   * Telemetry run IDs (e.g., ``run-20260520T...-energy-forecast-a1b2c3d4``)
+   * Log messages
+   * Artifact storage paths
+
+   Use lowercase with hyphens (``my-project``, not ``My Project``).
+
+``default_storage``
+   Where output artifacts are saved. Can be:
+
+   * A local path: ``./outputs``
+   * An S3 URI: ``s3://my-bucket/scalable-runs/``
+   * A GCS URI: ``gs://my-bucket/scalable-runs/``
+
+``local_cache``
+   Where cached results are stored locally. Defaults to ``./cache``. Can also
+   be set via the ``SCALABLE_CACHE_DIR`` environment variable (the manifest
+   value takes precedence).
+
+
+Step 3: Defining Targets
+--------------------------
+
+Targets answer the question: **"Where does my code run?"**
+
+.. code-block:: yaml
+
+   targets:
+     local:
+       provider: local
+       max_workers: 4
+       threads_per_worker: 2
+       processes: false
+       containers: none
+
+     hpc:
+       provider: slurm
+       queue: batch
+       account: GCIMS
+       walltime: "04:00:00"
+       interface: ib0
+
+     aws:
+       provider: aws
+       region: us-east-1
+       cluster_type: fargate
+       instance_type: m5.xlarge
+       worker_cpu: 4096
+       worker_mem: 16384
+       image: 123456789.dkr.ecr.us-east-1.amazonaws.com/energy-model:latest
+       adaptive:
+         minimum: 1
+         maximum: 10
+
+.. admonition:: 💡 Key Concept: Provider Pattern
+   :class: tip
+
+   A **provider** is an abstraction over an execution backend. It's like an
+   electrical outlet standard — you can plug any appliance into any outlet
+   because they share a common interface.
+
+   Scalable's providers share a common interface but work differently
+   internally:
+
+   * ``local`` — spawns workers on your machine
+   * ``slurm`` — submits jobs to an HPC scheduler
+   * ``aws`` — launches containers on AWS Fargate/EC2
+   * ``kubernetes`` — creates pods in a K8s cluster
+
+   **Why multiple targets in one file?** A single manifest can describe your
+   entire promotion path:
+
+   1. Develop locally (``--target local``)
+   2. Validate on HPC (``--target hpc``)
+   3. Deploy to cloud (``--target aws``)
+
+   The ``--target`` flag (or ``SCALABLE_TARGET`` env var) selects which
+   environment to activate.
+
+**Key options by provider:**
+
+.. list-table::
+   :header-rows: 1
+   :widths: 15 85
+
+   * - Provider
+     - Key Options
+   * - ``local``
+     - ``max_workers``, ``threads_per_worker``, ``processes``, ``containers``
+   * - ``slurm``
+     - ``queue``, ``account``, ``walltime``, ``interface``
+   * - ``aws``
+     - ``region``, ``cluster_type``, ``instance_type``, ``worker_cpu``,
+       ``worker_mem``, ``image``, ``adaptive``
+   * - ``kubernetes``
+     - ``namespace``, ``image``, ``adaptive``, ``overlay``
+
+
+Step 4: Components — Resource Profiles
+----------------------------------------
+
+Components define how much computational resources each piece of work needs:
+
+.. code-block:: yaml
+
+   components:
+     gridlabd:
+       image: ghcr.io/gridlab-d/gridlabd:5.0
+       runtime: apptainer
+       cpus: 8
+       memory: 32G
+       mounts:
+         /data/gridlabd: /gridlabd-core
+         /shared/outputs: /outputs
+       env:
+         GRIDLABD_DATA: /gridlabd-core/data
+       tags: [multi-sector-dynamics, energy]
+
+     postprocess:
+       cpus: 2
+       memory: 4G
+       tags: [analysis]
+
+.. admonition:: Why not just specify resources per task directly?
+   :class: hint
+
+   Separating components from tasks follows the **DRY principle** (Don't
+   Repeat Yourself). If 20 tasks all need the same resources, you define
+   the component once and reference it 20 times. Change the resource
+   allocation in one place → all 20 tasks update.
+
+**Component keys explained:**
+
+``cpus``
+   Number of CPU cores allocated per worker. Maps to Dask worker resource
+   annotations.
+
+``memory``
+   Memory allocation (e.g., ``32G``, ``512M``, ``2T``). Parsed using standard
+   byte suffixes.
+
+``image`` (optional)
+   Container image URI for containerized providers. Ignored for bare-metal
+   local runs.
+
+``runtime`` (optional)
+   Container runtime hint: ``apptainer`` (HPC) or ``docker`` (cloud/local).
+
+``mounts`` (optional)
+   Volume mappings (host path → container path). Only meaningful for
+   containerized runs.
+
+``env`` (optional)
+   Environment variables injected into the worker process. Useful for model
+   paths or configuration.
+
+``tags`` (optional)
+   Labels for grouping and filtering. Appear in telemetry and can inform
+   resource recommendations.
+
+
+Step 5: Task Bindings
+-----------------------
+
+Tasks connect your Python functions to resource profiles:
+
+.. code-block:: yaml
+
+   tasks:
+     run_gridlabd:
+       component: gridlabd
+
+     aggregate_demand:
+       component: postprocess
+
+When you write Python code like:
+
+.. code-block:: python
+
+   session.submit(my_function, args, task="run_gridlabd")
+
+Scalable looks up the ``run_gridlabd`` task, finds it uses the ``gridlabd``
+component, and schedules it on a worker with 8 CPUs and 32G memory.
+
+.. admonition:: 💡 Key Concept: Binding
+   :class: tip
+
+   **Binding** means creating a connection between two things. Here, we bind:
+
+   * Task name → component (resource profile)
+   * Python function → task name (at submit time)
+
+   This indirection lets you change resource allocations without touching
+   your Python code, and vice versa.
+
+
+Step 6: Environment Variable Expansion
+----------------------------------------
+
+Manifests support ``${VAR}`` syntax for environment variables:
+
+.. code-block:: yaml
+
+   project:
+     name: energy-model
+     default_storage: s3://${S3_BUCKET}/scalable-runs/
+
+   targets:
+     aws:
+       provider: aws
+       region: ${AWS_REGION:-us-east-1}
+
+The ``${AWS_REGION:-us-east-1}`` syntax means "use the ``AWS_REGION``
+environment variable if set, otherwise default to ``us-east-1``."
+
+.. admonition:: Why use environment variables instead of hardcoding?
+   :class: hint
+
+   * **Security** — Keep secrets (API keys, bucket names) out of Git
+   * **Portability** — Same manifest works across team members and CI/CD
+   * **12-Factor compliance** — Configuration should come from the environment
+     (a best practice from the `Twelve-Factor App <https://12factor.net/>`_
+     methodology)
+
+
+Step 7: Overlays — Environment-Specific Customization
+------------------------------------------------------
+
+.. admonition:: 💡 Key Concept: Overlays
+   :class: tip
+
+   An **overlay** is a set of patches applied on top of a base configuration.
+   Think of it like Photoshop layers — you have a base image (your manifest)
+   and layers that add or modify specific parts.
+
+   **Why overlays?** You might want:
+
+   * Development: 2 workers, 1G memory, local storage
+   * Production: 64 workers, 32G memory, S3 storage
+   * CI testing: 1 worker, minimal memory, ephemeral storage
+
+   Rather than maintaining 3 separate manifests (which drift apart over
+   time), you maintain ONE base manifest + overlays for differences.
+
+.. code-block:: yaml
+
+   # In the manifest itself
+   overlays:
+     production:
+       targets:
+         hpc:
+           max_workers: 64
+       components:
+         gridlabd:
+           memory: 64G
+
+     ci:
+       targets:
+         local:
+           max_workers: 1
+       components:
+         gridlabd:
+           memory: 2G
+           cpus: 1
+
+To apply an overlay:
+
+.. code-block:: bash
+
+   scalable run ./scalable.yaml --target hpc --overlay production
+
+The overlay merges on top of the base configuration — only the keys specified
+in the overlay are changed; everything else stays the same.
+
+.. admonition:: 💡 Key Concept: Deep Merge
+   :class: tip
+
+   **Deep merge** means overlays are applied recursively. If your overlay
+   specifies ``components.gridlabd.memory: 64G``, it only changes that one
+   field — all other ``gridlabd`` settings (``cpus``, ``image``, ``mounts``)
+   remain as defined in the base manifest.
+
+   This is different from a **shallow merge** where replacing any key in a
+   section would replace the entire section.
+
+
+Step 8: Programmatic Validation
+---------------------------------
+
+You've used ``scalable validate`` from the CLI. You can also validate from
+Python:
+
+.. code-block:: python
+
+   from scalable.manifest import parse_manifest, validate_manifest
+
+   # Parse the YAML into a structured object
+   manifest = parse_manifest("./scalable.yaml")
+
+   # Validate returns a list of errors (empty = valid)
+   errors = validate_manifest(manifest)
+
+   if errors:
+       for err in errors:
+           print(f"ERROR: {err}")
+   else:
+       print("✓ Manifest is valid")
+
+.. admonition:: 💡 Key Concept: Parse vs. Validate
+   :class: tip
+
+   These are two distinct steps:
+
+   1. **Parsing** = reading the YAML text and converting it to a Python data
+      structure (dict). This catches syntax errors (bad indentation, invalid
+      YAML).
+
+   2. **Validating** = checking that the parsed data meets the schema rules.
+      This catches semantic errors (missing required fields, invalid
+      references, type mismatches).
+
+   You need both: a YAML file can be syntactically valid but semantically
+   wrong (like a grammatically correct sentence that makes no sense).
+
+
+Common Questions
+-----------------
+
+**Q: Can I split my manifest into multiple files?**
+
+Not directly — the manifest is a single source of truth. But overlays let you
+customize per environment, and environment variables let you inject external
+values. This keeps the manifest self-contained and auditable.
+
+**Q: What if I make a typo in a component key?**
+
+The validator catches it. Unknown keys inside ``components`` are rejected
+(strict schema). Unknown keys inside ``targets`` are passed through to the
+provider (forward compatibility), but invalid provider-specific keys will
+fail at runtime with a clear error message.
+
+**Q: YAML vs. JSON vs. TOML — why YAML?**
+
+* **JSON** — No comments, verbose (lots of brackets/braces), hard to edit by hand
+* **TOML** — Good for flat config, awkward for deeply nested structures
+* **YAML** — Human-readable, supports comments, good for nested data, widely
+  used in DevOps (Docker Compose, Kubernetes, GitHub Actions)
+
+The downside of YAML (indentation sensitivity) is mitigated by validation.
+
+**Q: What's the difference between ``project.default_storage`` and
+``project.local_cache``?**
+
+* ``default_storage`` = where **outputs** go (can be remote: S3, GCS)
+* ``local_cache`` = where **cached intermediate results** are stored (always
+  local, for speed)
+
+
+What You Learned
+-----------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Term
+     - Definition
+   * - Declarative Programming
+     - Describing *what* you want rather than *how* to achieve it
+   * - YAML
+     - Human-readable data serialization format using indentation
+   * - Schema
+     - Rules defining valid structure for data
+   * - Environment Variables
+     - System-level key-value settings available to programs
+   * - Single Source of Truth
+     - One authoritative location for configuration
+   * - Provider
+     - Abstraction over an execution backend
+   * - Overlay
+     - Patches applied on top of base configuration
+   * - Deep Merge
+     - Recursive combination where only specified keys are overridden
+   * - Binding
+     - Connecting a task name to a component (resource profile)
+   * - Parsing
+     - Converting text (YAML) into structured data (Python dict)
+   * - Validation
+     - Checking that structured data meets schema rules
+   * - Configuration as Code
+     - Storing settings in version-controlled text files
+
+
+Next Steps
+-----------
+
+You now understand how Scalable's manifest system works and the philosophy
+behind declarative configuration.
+
+* **Next beginner tutorial:** :ref:`beginner_scaling_strategies` — how
+  distributed computing actually works
+* **Standard tutorial:** :ref:`tutorial_manifest_system` — advanced manifest
+  patterns and production deployment
+* **Try it:** Add a second target (copy the ``local`` target, name it
+  ``dev``, and change ``max_workers`` to 1). Validate it. Try adding an
+  overlay that doubles the memory for production.
diff --git a/docs/tutorials/beginner/03_scaling_strategies.rst b/docs/tutorials/beginner/03_scaling_strategies.rst
new file mode 100644
index 0000000..55dbd33
--- /dev/null
+++ b/docs/tutorials/beginner/03_scaling_strategies.rst
@@ -0,0 +1,567 @@
+.. _beginner_scaling_strategies:
+
+======================================================
+Beginner Tutorial 3: How Distributed Computing Works
+======================================================
+
+.. contents:: In This Tutorial
+   :local:
+   :depth: 2
+
+The Big Picture
+----------------
+
+You've written a workflow that runs on your laptop with 2 workers. But what
+happens when your data grows 100× and you need 64 workers on an HPC cluster?
+Or when you need to burst into the cloud during peak demand?
+
+This tutorial explains the **fundamentals of distributed computing** — how
+work gets split up, how multiple machines coordinate, and how Scalable's
+provider architecture lets you switch between execution backends without
+changing your code.
+
+.. admonition:: 💡 Key Concept: Why Distribute at All?
+   :class: tip
+
+   **The fundamental problem:** Some computations take too long on one
+   machine.
+
+   Consider running 1000 climate scenarios where each takes 5 minutes:
+
+   * **Sequential (1 CPU):** 1000 × 5 min = 83 hours (3.5 days)
+   * **Parallel (10 CPUs):** 1000 ÷ 10 × 5 min = 8.3 hours
+   * **Parallel (100 CPUs):** 1000 ÷ 100 × 5 min = 50 minutes
+
+   Distributed computing trades **hardware** for **time**. But it introduces
+   complexity: coordination, communication, failure handling. Scalable manages
+   that complexity for you.
+
+What You Will Learn
+--------------------
+
+By the end of this tutorial you will:
+
+* Understand the client-scheduler-worker architecture.
+* Know the difference between vertical and horizontal scaling.
+* Grasp concurrency vs. parallelism.
+* Use the Local, Slurm, and Cloud providers.
+* Configure manual, adaptive, and objective-driven scaling.
+* Understand Amdahl's Law and when NOT to distribute.
+* Monitor scaling decisions through telemetry.
+
+Prerequisites
+--------------
+
+* Completed :ref:`beginner_getting_started` and :ref:`beginner_manifest_system`.
+* Scalable installed (``pip install scalable``).
+* For HPC concepts: no cluster needed (follow along conceptually).
+
+
+Key Concepts Explained
+-----------------------
+
+.. admonition:: 💡 Key Concept: Vertical vs. Horizontal Scaling
+   :class: tip
+
+   There are two ways to get more computing power:
+
+   **Vertical scaling (scale UP):**
+     Get a bigger machine — more CPUs, more RAM. Like buying a faster car.
+
+     * Pros: Simple (no coordination needed), works for any workload
+     * Cons: Expensive, has physical limits (you can't buy a 10,000-core laptop)
+
+   **Horizontal scaling (scale OUT):**
+     Get more machines working together. Like having a fleet of cars.
+
+     * Pros: Nearly unlimited capacity, cost-effective
+     * Cons: Requires coordination, not all problems can be split
+
+   Scalable focuses on **horizontal scaling** — distributing work across
+   multiple workers. But the workers themselves can be vertically scaled
+   (bigger instances with more RAM per worker).
+
+.. admonition:: 💡 Key Concept: The Scheduler-Worker Architecture
+   :class: tip
+
+   Distributed systems typically have three roles:
+
+   **Client** (you):
+     Submits work and collects results. This is your Python script.
+
+   **Scheduler** (traffic controller):
+     Receives tasks from clients and assigns them to workers. It tracks
+     which workers are available, which tasks are queued, and which are
+     complete. It makes the decisions about *where* each task runs.
+
+   **Workers** (the labor force):
+     Actually execute the functions. Each worker is a separate process (or
+     thread) that can run independently.
+
+   .. code-block:: text
+
+      ┌──────────┐         ┌────────────┐         ┌──────────┐
+      │  Client  │────────▶│  Scheduler │────────▶│ Worker 1 │
+      │ (you)    │         │ (Dask)     │────────▶│ Worker 2 │
+      │          │◀────────│            │────────▶│ Worker 3 │
+      └──────────┘         └────────────┘         └──────────┘
+         submit()            assigns tasks          executes &
+         gather()            tracks state           returns results
+
+   In Scalable:
+
+   * The **client** is your Python script using ``ScalableSession``
+   * The **scheduler** is Dask's scheduler (managed automatically)
+   * The **workers** are spawned by the provider (local processes, Slurm
+     jobs, cloud containers, K8s pods)
+
+.. admonition:: 💡 Key Concept: Concurrency vs. Parallelism
+   :class: tip
+
+   These terms are related but different:
+
+   **Concurrency:** Multiple tasks *in progress* at the same time (but maybe
+   not literally simultaneous). Like a chef working on 3 dishes — chopping
+   for one, checking the oven for another.
+
+   **Parallelism:** Multiple tasks *executing* at the exact same instant on
+   different CPUs. Like 3 chefs each cooking their own dish simultaneously.
+
+   * **Threads** give you concurrency (and parallelism for I/O, but not for
+     CPU-bound Python code due to the GIL).
+   * **Processes** give you true parallelism (each has its own Python
+     interpreter and memory space).
+
+   Scalable supports both modes via the ``processes`` setting in your target.
+
+.. admonition:: 💡 Key Concept: What is an HPC Cluster?
+   :class: tip
+
+   An **HPC (High-Performance Computing) cluster** is a collection of
+   powerful computers (called "nodes") connected by a fast network, managed
+   by a job scheduler.
+
+   Key components:
+
+   * **Login nodes** — where you SSH in and submit jobs
+   * **Compute nodes** — where actual work runs
+   * **Job scheduler** (e.g., Slurm) — queues and allocates jobs to nodes
+   * **Shared filesystem** — storage accessible from all nodes
+
+   **How it works:** You don't directly pick which computer runs your code.
+   Instead, you submit a job request ("I need 4 nodes for 2 hours") and the
+   scheduler finds available resources.
+
+.. admonition:: 💡 Key Concept: What is Slurm?
+   :class: tip
+
+   **Slurm** (Simple Linux Utility for Resource Management) is the most
+   popular job scheduler for HPC clusters. It's the "traffic controller"
+   that decides when and where your computation runs.
+
+   Key Slurm concepts:
+
+   * **Queue/Partition** — groups of nodes with similar properties
+   * **Account** — billing/allocation identifier for your group
+   * **Walltime** — maximum allowed runtime for your job
+   * **Job** — a unit of work submitted to the scheduler
+
+   Scalable's Slurm provider translates your manifest's target configuration
+   into Slurm job submissions automatically.
+
+.. admonition:: 💡 Key Concept: Amdahl's Law
+   :class: tip
+
+   **Amdahl's Law** says that the speedup from parallelism is limited by the
+   sequential portion of your program.
+
+   If 90% of your work can be parallelized and 10% must be sequential:
+
+   * 10 workers → ~5.3× speedup (not 10×)
+   * 100 workers → ~9.2× speedup (not 100×)
+   * 1000 workers → ~9.9× speedup (not 1000×)
+
+   **Lesson:** Don't throw more workers at a problem than necessary. There's
+   always a point of diminishing returns. Scalable's telemetry helps you
+   find the sweet spot.
+
+
+Step 1: The Provider Architecture
+-----------------------------------
+
+Scalable separates **what** runs from **where** it runs:
+
+.. code-block:: text
+
+   ┌──────────────┐     ┌──────────────────┐     ┌─────────────┐
+   │  Manifest    │────▶│ DeploymentSpec    │────▶│  Provider   │
+   │(scalable.yaml)│    │(provider-neutral) │     │ (backend)   │
+   └──────────────┘     └──────────────────┘     └──────┬──────┘
+                                                         │
+                        ┌────────────────────────────────┼────────┐
+                        │                                │        │
+                  ┌─────▼──────┐  ┌──────▼──────┐  ┌───▼────────┐
+                  │   Local    │  │    Slurm    │  │    Cloud    │
+                  │ (threads/  │  │  (HPC jobs) │  │  (Fargate/  │
+                  │  processes)│  │             │  │   EC2/GKE)  │
+                  └────────────┘  └─────────────┘  └─────────────┘
+
+.. admonition:: 💡 Key Concept: Abstraction Layer
+   :class: tip
+
+   An **abstraction layer** hides complexity behind a simple interface. You
+   interact with the abstraction (the provider API) without knowing the
+   details underneath.
+
+   **Real-world analogy:** When you flip a light switch, you don't need to
+   know whether your electricity comes from solar panels, a nuclear plant,
+   or a gas turbine. The switch is the abstraction layer.
+
+   In Scalable, the provider abstraction means your workflow code
+   (``session.submit()``) works identically regardless of whether tasks run
+   locally, on Slurm, or in AWS.
+
+
+Step 2: The Local Provider (Development)
+------------------------------------------
+
+The simplest provider runs everything on your machine:
+
+.. code-block:: yaml
+
+   targets:
+     local:
+       provider: local
+       max_workers: 4
+       threads_per_worker: 2
+       processes: false
+       containers: none
+
+**What each setting controls:**
+
+``max_workers: 4``
+   The maximum number of parallel executors. With 4 workers, up to 4 tasks
+   can run simultaneously.
+
+``threads_per_worker: 2``
+   Each worker can handle 2 threads. This matters for I/O-bound tasks
+   (network calls, file reads) that spend time waiting.
+
+``processes: false``
+   Workers run as threads in a single process (fast startup, shared memory).
+   Set to ``true`` for CPU-bound work that needs to bypass the GIL.
+
+``containers: none``
+   No containerization — functions run in your current Python environment.
+
+.. code-block:: python
+
+   from scalable import ScalableSession
+
+   session = ScalableSession.from_manifest("./scalable.yaml", target="local")
+
+   # Submit work — it runs on local workers
+   futures = [session.submit(my_func, i, task="run_analysis") for i in range(20)]
+   results = session.gather(futures)
+   session.close()
+
+.. admonition:: Under the Hood
+   :class: hint
+
+   When you create a ``ScalableSession`` with the local provider:
+
+   1. Scalable reads the manifest and parses the ``local`` target
+   2. It creates a Dask ``LocalCluster`` with the specified workers
+   3. A Dask ``Client`` connects to the cluster's scheduler
+   4. Your ``submit()`` calls become Dask ``client.submit()`` calls
+   5. The scheduler distributes tasks across the local workers
+   6. Results flow back through the client to your script
+
+
+Step 3: The Slurm Provider (HPC)
+----------------------------------
+
+For HPC clusters, the Slurm provider translates your manifest into job
+submissions:
+
+.. code-block:: yaml
+
+   targets:
+     hpc:
+       provider: slurm
+       queue: batch
+       account: GCIMS
+       walltime: "04:00:00"
+       interface: ib0
+
+.. admonition:: What these settings mean in HPC terms
+   :class: note
+
+   ``queue: batch``
+     Which partition (group of nodes) to submit to. Clusters often have
+     ``batch`` (general), ``gpu`` (GPU nodes), ``debug`` (quick, limited).
+
+   ``account: GCIMS``
+     Your team's allocation identifier. HPC centers track usage by account
+     for billing and fairness.
+
+   ``walltime: "04:00:00"``
+     Maximum runtime (4 hours). The job is killed if it exceeds this.
+     Quoted because ``04:00:00`` looks like a time to YAML.
+
+   ``interface: ib0``
+     Network interface for worker communication. ``ib0`` = InfiniBand
+     (high-speed interconnect common in HPC).
+
+When you run with ``--target hpc``, Scalable:
+
+1. Generates Slurm job scripts automatically
+2. Submits them to the Slurm scheduler
+3. Workers start on allocated nodes
+4. Your tasks distribute across the HPC workers
+5. Results flow back to your client
+
+**You don't write Slurm scripts manually** — the manifest declares what you
+need and the provider handles the "how."
+
+
+Step 4: Scaling Strategies
+----------------------------
+
+.. admonition:: 💡 Key Concept: Scaling Strategy
+   :class: tip
+
+   A **scaling strategy** determines how many workers are active at any time.
+   Options range from fixed (always N workers) to fully dynamic (workers
+   spin up/down based on demand).
+
+**Manual (Fixed) Scaling:**
+
+.. code-block:: yaml
+
+   targets:
+     local:
+       provider: local
+       max_workers: 4     # Always exactly 4 workers
+
+You decide the worker count upfront. Simple and predictable.
+
+**Adaptive Scaling:**
+
+.. code-block:: yaml
+
+   targets:
+     cloud:
+       provider: aws
+       adaptive:
+         minimum: 1       # At least 1 worker always running
+         maximum: 20      # Scale up to 20 when busy
+
+.. admonition:: 💡 Key Concept: Adaptive Scaling
+   :class: tip
+
+   **Adaptive scaling** automatically adjusts worker count based on workload:
+
+   * Queue growing → add workers (scale up)
+   * Workers idle → remove workers (scale down)
+
+   **Benefits:**
+
+   * Cost efficiency — don't pay for idle workers
+   * Responsiveness — handle bursts without pre-provisioning
+   * Simplicity — no need to predict workload size
+
+   **Trade-offs:**
+
+   * Latency — spinning up new workers takes time
+   * Thrashing — rapid up/down cycles waste resources
+   * Minimum guarantee — you need at least some workers ready
+
+   Scalable implements adaptive scaling with configurable thresholds and
+   cooldown periods to prevent thrashing.
+
+**Objective-Driven Scaling:**
+
+.. code-block:: python
+
+   from scalable import ScalableSession
+
+   session = ScalableSession.from_manifest(
+       "./scalable.yaml",
+       target="cloud",
+       objectives={"budget_usd": 50.0, "deadline_hours": 2.0},
+   )
+
+.. admonition:: 💡 Key Concept: Objective-Driven Planning
+   :class: tip
+
+   **Objective-driven planning** lets you specify goals (budget, deadline)
+   and Scalable figures out the optimal resource allocation:
+
+   * "I have $50 and need results in 2 hours" → Scalable calculates how
+     many workers fit within budget and meet the deadline
+   * Based on telemetry history, it predicts task duration and scales
+     accordingly
+
+   This is the most sophisticated scaling mode — it requires telemetry
+   history to make predictions.
+
+
+Step 5: Monitoring Scaling Decisions
+--------------------------------------
+
+Every scaling decision is recorded in telemetry:
+
+.. code-block:: python
+
+   from scalable import ScalableSession
+
+   session = ScalableSession.from_manifest("./scalable.yaml", target="local")
+
+   # After your run, check what happened
+   # Telemetry records scaling events:
+   # - worker_added (when a new worker started)
+   # - worker_removed (when a worker was stopped)
+   # - scale_decision (why the system scaled up/down)
+
+The ``scalable report`` command summarizes scaling behavior:
+
+.. code-block:: bash
+
+   scalable report --last
+
+.. code-block:: text
+
+   Run: run-20260520T...-energy-forecast-abc123
+   Target: local (provider: local)
+   Workers: peak=4, avg=3.2
+   Tasks: 20 completed, 0 failed
+   Duration: 12.4s
+   Efficiency: 87% (worker utilization)
+
+
+Step 6: Choosing the Right Strategy
+--------------------------------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 20 20 30 30
+
+   * - Scenario
+     - Strategy
+     - Why
+     - Config
+   * - Development
+     - Fixed (2–4 workers)
+     - Fast startup, predictable
+     - ``max_workers: 4``
+   * - Batch production
+     - Fixed (many workers)
+     - Known workload size
+     - ``max_workers: 64``
+   * - Variable workload
+     - Adaptive
+     - Cost-efficient
+     - ``adaptive: {min: 2, max: 50}``
+   * - Budget-constrained
+     - Objective-driven
+     - Optimize cost/time
+     - ``objectives: {budget_usd: 100}``
+
+.. admonition:: 🤔 Think About It
+   :class: note
+
+   If you have 100 independent tasks that each take 1 minute:
+
+   * 1 worker → 100 minutes
+   * 10 workers → 10 minutes
+   * 100 workers → ~1 minute (plus ~30s startup overhead)
+   * 200 workers → ~1 minute (half the workers sit idle!)
+
+   The sweet spot depends on task count, task duration, and worker startup
+   cost. Telemetry from past runs helps you find it.
+
+
+Common Questions
+-----------------
+
+**Q: What if I only have one computer?**
+
+The local provider still gives you parallelism through multiple processes or
+threads. A modern laptop with 8 cores can run 8 workers doing genuine
+parallel work (with ``processes: true``).
+
+**Q: Do workers communicate with each other?**
+
+Not directly in most cases. Workers communicate through the scheduler (via
+futures and results). If Task B depends on Task A's output, the scheduler
+ensures A completes before B starts, and transfers the result.
+
+**Q: What happens if a worker crashes?**
+
+Scalable (via Dask) detects the failure and can reassign the task to another
+worker. Tutorial 7 covers this in detail.
+
+**Q: Is there overhead to distributing work?**
+
+Yes! Each task has overhead: serialization, network transfer, scheduling
+decisions. For very small tasks (< 1ms), the overhead exceeds the computation.
+Rule of thumb: tasks should take at least 100ms to benefit from distribution.
+
+**Q: Can I mix providers in one run?**
+
+No — a single run uses one target (one provider). But you can run the same
+manifest with different targets for different purposes (dev locally, run in
+production on HPC).
+
+
+What You Learned
+-----------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Term
+     - Definition
+   * - Horizontal Scaling
+     - Adding more machines/workers to handle more work
+   * - Vertical Scaling
+     - Getting a bigger/faster single machine
+   * - Scheduler
+     - Component that assigns tasks to workers
+   * - Worker
+     - Process/thread that executes tasks
+   * - Client
+     - Your script that submits work and collects results
+   * - Concurrency
+     - Multiple tasks in progress (maybe not simultaneous)
+   * - Parallelism
+     - Multiple tasks executing at the same instant
+   * - HPC Cluster
+     - Collection of computers managed by a job scheduler
+   * - Slurm
+     - Popular HPC job scheduler
+   * - Provider
+     - Abstraction over an execution backend
+   * - Adaptive Scaling
+     - Automatically adjusting worker count based on demand
+   * - Amdahl's Law
+     - Parallelism speedup limited by sequential portion
+   * - Abstraction Layer
+     - Simple interface hiding complex implementation details
+
+
+Next Steps
+-----------
+
+You now understand how distributed computing works and how Scalable's provider
+architecture makes it portable across environments.
+
+* **Next beginner tutorial:** :ref:`beginner_caching` — avoid repeating
+  expensive computation
+* **Standard tutorial:** :ref:`tutorial_scaling_strategies` — advanced
+  provider configuration and production scaling patterns
+* **Experiment:** Change ``max_workers`` in your manifest from 2 to 8.
+  Submit 100 tasks and time the difference. At what point do more workers
+  stop helping?
diff --git a/docs/tutorials/beginner/04_caching_performance.rst b/docs/tutorials/beginner/04_caching_performance.rst
new file mode 100644
index 0000000..34fab52
--- /dev/null
+++ b/docs/tutorials/beginner/04_caching_performance.rst
@@ -0,0 +1,559 @@
+.. _beginner_caching:
+
+======================================================
+Beginner Tutorial 4: Caching — Avoiding Redundant Work
+======================================================
+
+.. contents:: In This Tutorial
+   :local:
+   :depth: 2
+
+The Big Picture
+----------------
+
+Imagine you've run a 2-hour simulation pipeline and it fails on step 47 of 50.
+You fix the bug and re-run. Without caching, all 50 steps execute again —
+including the 46 that already succeeded. That's hours of wasted computation.
+
+**Caching** solves this by saving the results of completed work. On re-run,
+Scalable checks: "Have I already computed this exact function with these exact
+inputs?" If yes, it returns the saved result instantly. If no, it computes
+normally and saves the result for next time.
+
+This tutorial explains how caching works from first principles — hashing,
+content-addressable storage, decorators, and the trade-offs involved.
+
+What You Will Learn
+--------------------
+
+By the end of this tutorial you will:
+
+* Understand what caching is and why it matters for scientific workflows.
+* Know how hash functions create "fingerprints" of data.
+* Understand content-addressable storage.
+* Use the ``@cacheable`` decorator in Scalable.
+* Handle file-based and directory-based inputs.
+* Configure local and remote cache storage.
+* Understand cache invalidation strategies.
+
+Prerequisites
+--------------
+
+* Completed :ref:`beginner_getting_started`.
+* Scalable installed (``pip install scalable``).
+* For remote cache concepts: no cloud account needed (follow along).
+
+
+Key Concepts Explained
+-----------------------
+
+.. admonition:: 💡 Key Concept: What is Caching?
+   :class: tip
+
+   **Caching** is storing the result of an expensive operation so you can
+   reuse it later without recomputing. It trades **storage space** for
+   **computation time**.
+
+   Real-world examples of caching:
+
+   * **Web browser cache** — stores downloaded images/CSS so pages load
+     faster on revisit
+   * **CPU cache** — keeps frequently accessed memory close to the processor
+   * **DNS cache** — remembers IP addresses so your computer doesn't ask
+     "what's google.com's address?" every time
+
+   In Scalable, caching means: "If I've already computed ``f(x)`` and saved
+   the result, don't compute it again — just return the saved result."
+
+.. admonition:: 💡 Key Concept: Hash Functions
+   :class: tip
+
+   A **hash function** takes input of any size and produces a fixed-size
+   "fingerprint." Think of it as a one-way summarizer:
+
+   .. code-block:: text
+
+      Input: "Hello, World!"     → Hash: 65a8e27d8879...
+      Input: "Hello, World!!"    → Hash: 7f83b1657ff1...  (totally different!)
+      Input: (500MB data file)   → Hash: a3b8c9d2e1f0...
+
+   Key properties:
+
+   * **Deterministic** — same input always produces same hash
+   * **Fixed size** — output is always the same length regardless of input
+   * **Avalanche effect** — tiny input change → completely different hash
+   * **One-way** — you can't reconstruct the input from the hash
+
+   **In Scalable:** When you call a cached function, Scalable hashes the
+   function name + all arguments to create a unique key. If that key exists
+   in the cache, the result is already known.
+
+.. admonition:: 💡 Key Concept: Content-Addressable Storage
+   :class: tip
+
+   **Content-addressable storage** (CAS) uses the *content's hash* as its
+   address (filename/key). Instead of naming a file ``results_v3_final.json``,
+   you name it ``sha256_a3b8f1c2d4e5.json``.
+
+   **Benefits:**
+
+   * **Deduplication** — identical content has the same hash, stored once
+   * **Verification** — you can verify data hasn't been corrupted by
+     re-hashing and comparing
+   * **Immutability** — content at a hash never changes (any change =
+     different hash = different address)
+
+   **Used by:** Git (every commit, file, and tree is content-addressed),
+   Docker (image layers), IPFS, and Scalable's cache system.
+
+.. admonition:: 💡 Key Concept: Memoization
+   :class: tip
+
+   **Memoization** is a specific caching technique for functions: remember
+   the result of a function call based on its inputs.
+
+   .. code-block:: python
+
+      # Without memoization:
+      result1 = expensive_function(42)   # Takes 5 minutes
+      result2 = expensive_function(42)   # Takes 5 minutes again!
+
+      # With memoization:
+      result1 = expensive_function(42)   # Takes 5 minutes, saves result
+      result2 = expensive_function(42)   # Instant! Returns saved result
+
+   Memoization requires **determinism** — the same inputs must always produce
+   the same output. If your function depends on the current time, random
+   numbers, or external state that changes, memoization won't give correct
+   results.
+
+.. admonition:: 💡 Key Concept: Python Decorators
+   :class: tip
+
+   A **decorator** is a Python pattern that wraps a function to add behavior
+   without changing the function's code. Decorators use the ``@`` syntax:
+
+   .. code-block:: python
+
+      @some_decorator
+      def my_function(x):
+          return x * 2
+
+   This is equivalent to:
+
+   .. code-block:: python
+
+      def my_function(x):
+          return x * 2
+      my_function = some_decorator(my_function)
+
+   The decorator receives your function and returns a new function that does
+   something extra (like checking a cache before calling the original).
+
+   **Common decorators you may have seen:**
+
+   * ``@property`` — makes a method behave like an attribute
+   * ``@staticmethod`` — marks a method that doesn't use ``self``
+   * ``@functools.lru_cache`` — Python's built-in memoization
+
+   Scalable's ``@cacheable`` is a decorator that adds persistent caching to
+   any function.
+
+
+Step 1: Basic Caching with @cacheable
+---------------------------------------
+
+Here's how to make a function cacheable in Scalable:
+
+.. code-block:: python
+
+   from scalable import cacheable
+
+
+   @cacheable(return_type=dict, scenario_id=int)
+   def run_simulation(scenario_id: int) -> dict:
+       """Expensive computation — runs an energy demand scenario."""
+       import time
+       time.sleep(5)  # Simulate 5 seconds of heavy computation
+       return {
+           "scenario_id": scenario_id,
+           "demand_mwh": scenario_id * 1000 + 42,
+           "status": "complete",
+       }
+
+.. admonition:: What's happening with that decorator?
+   :class: note
+
+   ``@cacheable(return_type=dict, scenario_id=int)`` tells Scalable:
+
+   1. **This function can be cached** — wrap it with cache logic
+   2. **Return type is ``dict``** — Scalable knows how to serialize/
+      deserialize the result
+   3. **``scenario_id`` is type ``int``** — Scalable knows how to hash
+      this argument deterministically
+
+   The type annotations help Scalable create reliable cache keys. Different
+   types hash differently (the integer ``1`` vs. the string ``"1"`` produce
+   different cache keys).
+
+**First call** — cache miss (slow):
+
+.. code-block:: python
+
+   result = run_simulation(scenario_id=42)
+   # Takes 5 seconds — computes and saves to cache
+   print(result)  # {"scenario_id": 42, "demand_mwh": 42042, "status": "complete"}
+
+**Second call** — cache hit (instant):
+
+.. code-block:: python
+
+   result = run_simulation(scenario_id=42)
+   # Instant! Returns saved result from cache
+   print(result)  # {"scenario_id": 42, "demand_mwh": 42042, "status": "complete"}
+
+.. admonition:: Under the Hood: What happens on each call
+   :class: hint
+
+   **Cache miss (first call):**
+
+   1. Scalable hashes: ``hash("run_simulation" + hash(42))`` → key ``abc123``
+   2. Looks up key ``abc123`` in cache storage → not found
+   3. Calls the actual function → waits 5 seconds → gets result
+   4. Serializes the result and stores it at key ``abc123``
+   5. Returns the result to you
+
+   **Cache hit (second call):**
+
+   1. Scalable hashes: ``hash("run_simulation" + hash(42))`` → key ``abc123``
+   2. Looks up key ``abc123`` in cache storage → found!
+   3. Deserializes the stored result
+   4. Returns it immediately (no function execution)
+
+
+Step 2: How Cache Keys Are Computed
+-------------------------------------
+
+The cache key is a hash of:
+
+1. The function's **fully qualified name** (module + function name)
+2. The function's **arguments** (each individually hashed)
+
+.. code-block:: python
+
+   # These produce DIFFERENT cache keys:
+   run_simulation(scenario_id=1)    # key = hash(name + hash(1))
+   run_simulation(scenario_id=2)    # key = hash(name + hash(2))
+
+   # These produce the SAME cache key:
+   run_simulation(scenario_id=42)   # First call
+   run_simulation(scenario_id=42)   # Same key → cache hit!
+
+.. admonition:: 💡 Key Concept: Deterministic Hashing
+   :class: tip
+
+   For caching to work correctly, hashing must be **deterministic** — the
+   same input must always produce the same hash.
+
+   This is why Scalable asks you to declare argument types. A Python ``dict``
+   doesn't have a guaranteed ordering (in practice it does in Python 3.7+,
+   but Scalable ensures stability by sorting keys before hashing).
+
+   **What can be hashed reliably:**
+
+   * Primitive types: ``int``, ``float``, ``str``, ``bool``
+   * Collections: ``list``, ``tuple``, ``dict`` (with hashable contents)
+   * Files: hashed by content (not filename!)
+
+   **What can't be hashed reliably:**
+
+   * Objects with mutable state
+   * Functions/lambdas (their code might change)
+   * Anything involving randomness or external state
+
+
+Step 3: Handling File Inputs
+------------------------------
+
+Scientific workflows often take files as input. Scalable provides special
+types for file-based hashing:
+
+.. code-block:: python
+
+   from scalable import cacheable
+   from scalable.caching import FileType, DirType
+
+
+   @cacheable(return_type=dict, input_file=FileType, config=dict)
+   def process_data(input_file: str, config: dict) -> dict:
+       """Process a data file according to config."""
+       with open(input_file) as f:
+           data = f.read()
+       # ... processing ...
+       return {"rows": len(data.splitlines()), "config": config}
+
+.. admonition:: 💡 Key Concept: FileType and Content Hashing
+   :class: tip
+
+   When you annotate an argument as ``FileType``, Scalable hashes the
+   **file's contents** (not its path or name).
+
+   Why? Because:
+
+   * Same file at different paths = same computation = should cache-hit
+   * Same path with different contents = different computation = should
+     NOT cache-hit
+
+   .. code-block:: text
+
+      process_data("/data/input_v1.csv", ...)   # Hashes CSV content
+      process_data("/tmp/copy_of_v1.csv", ...)  # Same content → cache hit!
+      # (even though the path is different)
+
+   ``DirType`` works similarly but hashes all files in a directory
+   (recursively).
+
+
+Step 4: Cache Storage Configuration
+--------------------------------------
+
+By default, Scalable stores cache entries on local disk:
+
+.. code-block:: yaml
+
+   # In scalable.yaml
+   project:
+     name: my-project
+     local_cache: ./cache    # Cache stored here
+
+The cache directory structure looks like:
+
+.. code-block:: text
+
+   ./cache/
+   ├── run_simulation/
+   │   ├── abc123.json       # Cached result for scenario_id=42
+   │   ├── def456.json       # Cached result for scenario_id=7
+   │   └── ...
+   └── process_data/
+       ├── 789ghi.json
+       └── ...
+
+For team collaboration or cloud workflows, you can use remote storage:
+
+.. code-block:: yaml
+
+   project:
+     name: my-project
+     local_cache: s3://my-bucket/scalable-cache/
+
+.. admonition:: 💡 Key Concept: Local vs. Remote Cache
+   :class: tip
+
+   **Local cache** (filesystem):
+
+   * Fast (no network latency)
+   * Private (only you can access)
+   * Lost if machine is destroyed
+
+   **Remote cache** (S3, GCS):
+
+   * Shared across team members and CI/CD
+   * Persistent (survives machine changes)
+   * Slower (network round-trip for every lookup)
+   * Costs money (storage + requests)
+
+   **When to use remote cache:** When your team runs the same pipeline and
+   you want to share cached results. Person A computes scenario 1–500,
+   Person B starts from 501 but benefits from A's cached results.
+
+
+Step 5: Cache Invalidation
+-----------------------------
+
+.. admonition:: 💡 Key Concept: Cache Invalidation
+   :class: tip
+
+   There's a famous saying in computer science:
+
+      *"There are only two hard things in Computer Science: cache
+      invalidation and naming things."* — Phil Karlton
+
+   **Cache invalidation** means deciding when cached results are no longer
+   valid. A result becomes invalid when:
+
+   * The function's logic changes (you fixed a bug)
+   * An input file's content changes
+   * External dependencies update (new library version)
+   * You explicitly want fresh results
+
+Scalable handles invalidation in several ways:
+
+**Automatic invalidation** (content-based):
+
+* File inputs are hashed by content → changed file = different key = no hit
+* Function arguments change → different key = no hit
+
+**Manual invalidation:**
+
+.. code-block:: bash
+
+   # Clear all cache for a project
+   rm -rf ./cache/
+
+   # Clear cache for a specific function
+   rm -rf ./cache/run_simulation/
+
+**Selective re-computation:**
+
+.. code-block:: python
+
+   # Force re-computation even if cached
+   result = run_simulation(scenario_id=42, _cache_bypass=True)
+
+.. admonition:: 🤔 Think About It
+   :class: note
+
+   What happens if you change the function's code but not its inputs?
+
+   By default, Scalable hashes the function **name**, not its **code**. So
+   if you fix a bug in ``run_simulation``, the cache key is the same and
+   you'll get stale results!
+
+   **Solution:** Clear the cache after code changes, or use versioning:
+
+   .. code-block:: python
+
+      @cacheable(return_type=dict, scenario_id=int, _version="2")
+      def run_simulation(scenario_id: int) -> dict:
+          # Fixed bug — _version="2" creates different cache keys
+          ...
+
+
+Step 6: Monitoring Cache Performance
+---------------------------------------
+
+Scalable records cache hit/miss events in telemetry:
+
+.. code-block:: bash
+
+   scalable report --last
+
+.. code-block:: text
+
+   Cache Performance:
+     Total lookups: 200
+     Hits: 180 (90%)
+     Misses: 20 (10%)
+     Time saved: ~15 minutes (estimated from hit count × avg task duration)
+
+A high hit rate (>80%) means caching is working well. A low hit rate might
+mean:
+
+* Inputs are always changing (cache keys never match)
+* The cache was recently cleared
+* Tasks aren't deterministic
+
+.. admonition:: 💡 Key Concept: Serialization
+   :class: tip
+
+   **Serialization** converts a Python object into bytes that can be stored
+   on disk or sent over a network. **Deserialization** converts bytes back
+   into a Python object.
+
+   Common serialization formats:
+
+   * **JSON** — human-readable, limited types (no sets, dates, custom objects)
+   * **Pickle** — Python-native, supports any object, not human-readable
+   * **MessagePack** — fast binary format, limited types
+
+   Scalable uses JSON for simple types (dicts, lists, strings) and pickle
+   for complex objects. The ``return_type`` annotation in ``@cacheable``
+   helps Scalable choose the best serialization strategy.
+
+
+Common Questions
+-----------------
+
+**Q: Does caching use a lot of disk space?**
+
+It depends on your output sizes. Small results (numbers, short strings) use
+negligible space. Large results (DataFrames, arrays) can grow quickly. Monitor
+your cache directory size and set up periodic cleanup for old entries.
+
+**Q: What if two people compute the same thing simultaneously?**
+
+With local cache, they each compute independently. With remote cache (S3),
+the second writer overwrites the first — but since the result is deterministic,
+they're writing the same value, so it's safe.
+
+**Q: Can I cache functions that return different results each time?**
+
+No! Caching assumes **determinism** — same inputs → same output. If your
+function involves randomness, time-dependence, or external state that changes,
+caching will return stale/incorrect results.
+
+**Q: What's the difference between Scalable's cache and Python's
+``functools.lru_cache``?**
+
+* ``lru_cache`` stores results **in memory** (lost when program exits)
+* ``@cacheable`` stores results **on disk or remote storage** (persistent
+  across runs)
+
+Scalable's caching is designed for expensive computations that span multiple
+program invocations.
+
+**Q: Can I cache only some invocations?**
+
+Yes — the ``@cacheable`` decorator checks the cache on every call. If you
+want to bypass it for specific calls, use ``_cache_bypass=True``.
+
+
+What You Learned
+-----------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Term
+     - Definition
+   * - Caching
+     - Storing results for reuse to avoid recomputation
+   * - Hash Function
+     - Produces a fixed-size fingerprint from arbitrary input
+   * - Content-Addressable Storage
+     - Data addressed by its content's hash, not by name
+   * - Memoization
+     - Caching function results based on inputs
+   * - Decorator
+     - Python pattern that wraps a function to add behavior
+   * - Cache Key
+     - Unique identifier for a cached result (hash of function + args)
+   * - Cache Hit
+     - Result found in cache (fast, no recomputation)
+   * - Cache Miss
+     - Result NOT found, must compute and store
+   * - Cache Invalidation
+     - Deciding when cached results are no longer valid
+   * - Serialization
+     - Converting objects to bytes for storage/transmission
+   * - Determinism
+     - Same inputs always produce the same output
+   * - FileType
+     - Annotation telling Scalable to hash file contents, not path
+
+
+Next Steps
+-----------
+
+You now understand how caching works and can use it to avoid redundant
+computation in your workflows.
+
+* **Next beginner tutorial:** :ref:`beginner_cloud_integration` — running
+  workflows in the cloud
+* **Standard tutorial:** :ref:`tutorial_caching` — advanced caching patterns,
+  remote configuration, and cache management
+* **Try it:** Add ``@cacheable`` to a function, run it twice, and check the
+  ``./cache/`` directory to see the stored results. Modify an input and
+  verify you get a cache miss.
diff --git a/docs/tutorials/beginner/05_cloud_integration.rst b/docs/tutorials/beginner/05_cloud_integration.rst
new file mode 100644
index 0000000..02062d4
--- /dev/null
+++ b/docs/tutorials/beginner/05_cloud_integration.rst
@@ -0,0 +1,519 @@
+.. _beginner_cloud_integration:
+
+======================================================
+Beginner Tutorial 5: Cloud Computing Fundamentals
+======================================================
+
+.. contents:: In This Tutorial
+   :local:
+   :depth: 2
+
+The Big Picture
+----------------
+
+So far, everything has run on your laptop. But what happens when you need more
+power than any single machine can provide? Or when you need to run 500
+scenarios overnight without keeping your laptop open?
+
+**Cloud computing** lets you rent powerful computers over the internet, use
+them for your computation, and stop paying when you're done. Scalable can
+deploy your workflows to cloud providers (AWS, GCP) with the same manifest
+you use locally — only the target changes.
+
+This tutorial explains cloud computing from first principles: what it is,
+how billing works, what all the acronyms mean, and how Scalable integrates
+with cloud infrastructure.
+
+What You Will Learn
+--------------------
+
+By the end of this tutorial you will:
+
+* Understand what cloud computing is and how it differs from local/HPC.
+* Know what AWS and GCP are and their core services.
+* Understand object storage (S3, GCS) and why it matters.
+* Know what containers are and why they're essential for cloud.
+* Configure cloud targets in your Scalable manifest.
+* Understand IAM (permissions) and network basics (VPCs, subnets).
+* Use Scalable's cost estimation to predict spending.
+* Understand artifacts and remote storage.
+
+Prerequisites
+--------------
+
+* Completed :ref:`beginner_getting_started` and :ref:`beginner_manifest_system`.
+* ``pip install scalable[cloud]`` (for code examples).
+* No cloud account is required to understand the concepts — the code examples
+  show configuration patterns you can use when you do have access.
+
+
+Key Concepts Explained
+-----------------------
+
+.. admonition:: 💡 Key Concept: What is Cloud Computing?
+   :class: tip
+
+   **Cloud computing** means renting computers, storage, and networking from
+   a provider (like AWS or Google) over the internet, paying only for what
+   you use.
+
+   **Before cloud:** Organizations bought physical servers, installed them in
+   data centers, maintained them, and paid for them whether used or idle.
+
+   **With cloud:** You request "give me 10 machines with 32GB RAM for 2 hours"
+   and they appear in seconds. When you're done, you stop them and stop
+   paying.
+
+   **Three service models:**
+
+   * **IaaS (Infrastructure as a Service)** — rent raw machines (EC2, GCE)
+   * **PaaS (Platform as a Service)** — rent a managed platform (App Engine)
+   * **Serverless/FaaS** — just run code, no server management (Lambda,
+     Fargate)
+
+   Scalable primarily uses **serverless containers** (Fargate) and
+   **managed Kubernetes** (GKE/EKS) — you don't manage individual servers.
+
+.. admonition:: 💡 Key Concept: AWS and GCP
+   :class: tip
+
+   **AWS (Amazon Web Services)** and **GCP (Google Cloud Platform)** are the
+   two largest public cloud providers. They offer hundreds of services, but
+   for Scalable you mainly need:
+
+   **AWS services used by Scalable:**
+
+   * **Fargate** — runs containers without managing servers
+   * **EC2** — virtual machines (when you need more control)
+   * **S3** — object storage (for data and artifacts)
+   * **ECR** — container registry (stores your Docker images)
+
+   **GCP services used by Scalable:**
+
+   * **Cloud Run** — serverless containers
+   * **GKE** — managed Kubernetes
+   * **GCS** — object storage (equivalent to S3)
+   * **GCR/Artifact Registry** — container registry
+
+.. admonition:: 💡 Key Concept: Object Storage (S3/GCS)
+   :class: tip
+
+   **Object storage** is a cloud service for storing files (called "objects")
+   in containers called "buckets." Unlike a filesystem with directories and
+   paths, object storage is flat — each object has a unique key (like a URL).
+
+   .. code-block:: text
+
+      s3://my-bucket/scalable-runs/run-001/results.json
+      │    │         │                      │
+      │    │         │                      └── Object key (name)
+      │    │         └── Prefix (like a folder, but it's just part of the key)
+      │    └── Bucket name
+      └── Protocol (s3:// for AWS, gs:// for GCP)
+
+   **Why object storage instead of a regular filesystem?**
+
+   * **Scalability** — stores petabytes without performance degradation
+   * **Durability** — data is replicated across multiple data centers
+     (99.999999999% durability on S3)
+   * **Accessibility** — accessible from anywhere with credentials
+   * **Cost** — very cheap for storage ($0.023/GB/month on S3)
+   * **No server** — fully managed, no filesystem to maintain
+
+.. admonition:: 💡 Key Concept: Containers (Introduction)
+   :class: tip
+
+   A **container** packages your code plus all its dependencies into a single
+   portable unit that runs identically everywhere.
+
+   **The problem containers solve:** "It works on my machine!" — your code
+   depends on specific library versions, system tools, and configurations.
+   Moving it to another machine (especially in the cloud) often breaks things.
+
+   **A container includes:**
+
+   * Your code
+   * All Python packages (with exact versions)
+   * System libraries and tools
+   * Configuration files
+   * Everything needed to run — nothing more
+
+   **Analogy:** A container is like a shipping container for goods. The crane
+   doesn't need to know what's inside — it just knows how to move the
+   standard-sized container. Similarly, cloud platforms know how to run any
+   container without knowing what's inside.
+
+   **Docker** is the most popular container technology. A ``Dockerfile``
+   describes how to build a container image:
+
+   .. code-block:: dockerfile
+
+      FROM python:3.12
+      COPY requirements.txt .
+      RUN pip install -r requirements.txt
+      COPY . /app
+      WORKDIR /app
+      CMD ["python", "workflow.py"]
+
+.. admonition:: 💡 Key Concept: Container Registry
+   :class: tip
+
+   A **container registry** is a storage service for container images (like
+   a library for containers). You build an image locally, push it to a
+   registry, and cloud services pull it when launching workers.
+
+   Common registries:
+
+   * **Docker Hub** — public (free for open source)
+   * **ECR** (AWS) — private, integrated with AWS services
+   * **GCR / Artifact Registry** (GCP) — private, integrated with GCP
+   * **GHCR** (GitHub) — integrated with GitHub Actions
+
+.. admonition:: 💡 Key Concept: IAM (Identity and Access Management)
+   :class: tip
+
+   **IAM** is the security system that controls who can do what in the cloud.
+
+   **Analogy:** Think of a building with key cards. IAM defines:
+
+   * **Who** (identity) — users, service accounts, roles
+   * **Can do what** (permissions) — read files, launch instances, delete
+     buckets
+   * **On what** (resources) — specific buckets, instances, registries
+
+   In Scalable's context, your cloud credentials need permissions to:
+
+   * Launch compute resources (Fargate tasks, EC2 instances)
+   * Read/write to storage buckets (S3, GCS)
+   * Pull container images from registries
+   * Create networking resources
+
+.. admonition:: 💡 Key Concept: VPC, Subnets, and Networking
+   :class: tip
+
+   **VPC (Virtual Private Cloud)** is an isolated network in the cloud —
+   like having your own private data center.
+
+   **Subnets** divide a VPC into segments (like rooms in a building):
+
+   * **Public subnet** — accessible from the internet
+   * **Private subnet** — only accessible from within the VPC
+
+   **Security Groups** are firewalls — rules about what traffic is allowed
+   in and out.
+
+   For Scalable, workers need to communicate with the scheduler (Dask
+   protocol), so they must be in subnets where they can reach each other.
+   The details are provider-specific — Scalable's cloud provider handles
+   most of this automatically.
+
+
+Step 1: Cloud Target Configuration
+-------------------------------------
+
+Here's how you configure a cloud target in your manifest:
+
+.. code-block:: yaml
+
+   # scalable.yaml
+   version: 1
+   project:
+     name: energy-model
+     default_storage: s3://${S3_BUCKET}/scalable-runs/
+
+   targets:
+     local:
+       provider: local
+       max_workers: 4
+       processes: false
+       containers: none
+
+     aws:
+       provider: aws
+       region: us-east-1
+       cluster_type: fargate
+       instance_type: m5.xlarge
+       worker_cpu: 4096      # 4 vCPUs (in Fargate units: 1024 = 1 vCPU)
+       worker_mem: 16384     # 16 GB (in MB)
+       image: 123456789.dkr.ecr.us-east-1.amazonaws.com/energy-model:latest
+       adaptive:
+         minimum: 1
+         maximum: 10
+
+   components:
+     analysis:
+       cpus: 4
+       memory: 16G
+
+   tasks:
+     run_analysis:
+       component: analysis
+
+.. admonition:: What each cloud setting means
+   :class: note
+
+   ``region: us-east-1``
+     Which data center to use. Choose the closest to your data or team.
+     Different regions have different pricing and available services.
+
+   ``cluster_type: fargate``
+     Use AWS Fargate (serverless containers). You don't manage servers —
+     AWS allocates compute for each task on demand.
+
+   ``instance_type: m5.xlarge``
+     The type of virtual machine. ``m5`` = general purpose, ``xlarge`` =
+     4 vCPUs + 16GB RAM. (Used for EC2-backed mode, not Fargate.)
+
+   ``worker_cpu: 4096`` / ``worker_mem: 16384``
+     Fargate resource allocation in Fargate units (1024 CPU units = 1 vCPU,
+     memory in MB).
+
+   ``image: ...``
+     The container image containing your code and dependencies. Workers in
+     the cloud run inside this container.
+
+   ``adaptive: {minimum: 1, maximum: 10}``
+     Auto-scale between 1 and 10 workers based on queue depth.
+
+
+Step 2: Understanding Cloud Costs
+------------------------------------
+
+.. admonition:: 💡 Key Concept: Pay-Per-Use Pricing
+   :class: tip
+
+   Cloud computing charges you for what you use:
+
+   * **Compute:** Per-second or per-hour while instances are running
+   * **Storage:** Per-GB-month for data stored
+   * **Network:** Per-GB for data transferred out of the cloud
+   * **Requests:** Per-request for API calls (small, usually negligible)
+
+   **Example cost breakdown for a Scalable run:**
+
+   .. code-block:: text
+
+      10 Fargate workers × 4 vCPU × 2 hours × $0.04/vCPU-hour = $3.20
+      10 workers × 16GB × 2 hours × $0.004/GB-hour               = $1.28
+      Output data in S3: 50GB × $0.023/GB-month                   = $1.15/month
+      Data transfer: 10GB × $0.09/GB                              = $0.90
+      ────────────────────────────────────────────────────────────────
+      Total run cost: ~$5.38 + $1.15/month storage
+
+Scalable's cost estimator gives you this breakdown BEFORE you run:
+
+.. code-block:: bash
+
+   scalable plan ./scalable.yaml --target aws --dry-run
+
+.. code-block:: text
+
+   Cost Estimate for target 'aws':
+     Compute: $3.20 (10 workers × 2h × $0.04/vCPU-h)
+     Memory:  $1.28 (10 workers × 16GB × 2h × $0.004/GB-h)
+     Storage: ~$1.15/month (estimated 50GB output)
+     ────────
+     Total:   ~$5.63 (one-time) + $1.15/month (storage)
+
+.. admonition:: 💡 Key Concept: Spot/Preemptible Instances
+   :class: tip
+
+   Cloud providers offer **heavily discounted** compute (60–90% off) with a
+   catch: they can terminate your instance with 2 minutes notice if demand
+   rises.
+
+   * **AWS Spot Instances** — up to 90% cheaper
+   * **GCP Preemptible/Spot VMs** — up to 80% cheaper
+
+   This is useful for fault-tolerant workflows (Tutorial 7) where tasks can
+   be retried. Scalable's caching makes this viable — if a spot instance is
+   terminated, already-cached results don't need recomputation.
+
+
+Step 3: The Artifact Store
+----------------------------
+
+.. admonition:: 💡 Key Concept: Artifacts
+   :class: tip
+
+   **Artifacts** are the outputs of your workflow that you want to persist
+   (keep) after the run completes. Examples:
+
+   * Simulation results (JSON, CSV, Parquet files)
+   * Model weights (pickle, HDF5 files)
+   * Reports and visualizations (HTML, PNG)
+   * Logs and diagnostics
+
+   Artifacts are stored in the location specified by
+   ``project.default_storage`` — either local filesystem or cloud object
+   storage.
+
+.. code-block:: python
+
+   from scalable import ScalableSession
+
+   session = ScalableSession.from_manifest("./scalable.yaml", target="aws")
+
+   # After computation, store artifacts
+   session.store_artifact("results/scenario_42.json", result_data)
+   # → Uploaded to s3://my-bucket/scalable-runs/run-.../results/scenario_42.json
+
+
+Step 4: Deploying to the Cloud
+--------------------------------
+
+The actual deployment workflow:
+
+.. code-block:: text
+
+   ┌─────────────┐      ┌─────────────┐      ┌─────────────┐
+   │ 1. Develop  │─────▶│ 2. Build    │─────▶│ 3. Deploy   │
+   │   locally   │      │   container │      │   to cloud  │
+   └─────────────┘      └─────────────┘      └─────────────┘
+    scalable.yaml         Dockerfile           scalable run
+    workflow.py           docker build           --target aws
+    --target local        docker push
+
+**Step-by-step:**
+
+1. **Develop locally** — write and test your workflow with ``--target local``
+2. **Build a container** — package your code into a Docker image
+3. **Push to registry** — upload the image to ECR/GCR
+4. **Deploy** — run with ``--target aws`` (or ``gcp``)
+
+.. code-block:: bash
+
+   # Build your container image
+   docker build -t energy-model:latest .
+
+   # Tag and push to AWS ECR
+   docker tag energy-model:latest 123456789.dkr.ecr.us-east-1.amazonaws.com/energy-model:latest
+   docker push 123456789.dkr.ecr.us-east-1.amazonaws.com/energy-model:latest
+
+   # Run in the cloud
+   scalable run ./scalable.yaml --target aws
+
+.. admonition:: 🤔 Think About It
+   :class: note
+
+   Notice how the Python code (``workflow.py``) doesn't change between local
+   and cloud deployment. Only the target selection changes. This is the power
+   of declarative manifests + the provider abstraction.
+
+
+Step 5: GCP Configuration
+----------------------------
+
+Google Cloud works similarly:
+
+.. code-block:: yaml
+
+   targets:
+     gcp:
+       provider: gcp
+       region: us-central1
+       cluster_type: cloud_run
+       worker_cpu: 4
+       worker_mem: 16384
+       image: gcr.io/my-project/energy-model:latest
+       adaptive:
+         minimum: 2
+         maximum: 20
+
+The concepts are the same — only the service names and configuration keys
+differ.
+
+
+Common Questions
+-----------------
+
+**Q: How do I get started with cloud without spending money?**
+
+Both AWS and GCP offer free tiers:
+
+* AWS Free Tier: 12 months of limited free usage
+* GCP Free Tier: $300 credit for 90 days
+
+For learning, the ``--dry-run`` flag lets you see what WOULD happen without
+actually deploying.
+
+**Q: Is the cloud always more expensive than on-premise?**
+
+Not necessarily. Cloud is more expensive for steady, predictable workloads
+(you're paying for convenience and flexibility). It's often cheaper for:
+
+* Burst workloads (need 100 machines for 2 hours, then nothing)
+* Variable workloads (demand changes day-to-day)
+* Avoiding capital expenditure (no upfront server purchase)
+
+**Q: What if my data is too large to upload to the cloud?**
+
+Options:
+
+* Store data in cloud object storage permanently (especially if generated there)
+* Use AWS DataSync or GCS Transfer Service for large migrations
+* Use hybrid architectures where data stays on-premise and only compute is in cloud
+
+**Q: Do I need to learn Docker/containers to use cloud features?**
+
+For basic usage, your team lead or DevOps person typically builds the container
+image once. You then reference it in your manifest. But understanding
+containers conceptually (as taught in this tutorial) helps you debug issues.
+
+**Q: What happens if my cloud run fails halfway through?**
+
+Scalable's caching system means completed tasks are saved. When you re-run,
+only the failed/incomplete tasks execute. Combined with spot instances, this
+makes cost-effective fault-tolerant workflows possible.
+
+
+What You Learned
+-----------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Term
+     - Definition
+   * - Cloud Computing
+     - Renting computing resources over the internet, pay-per-use
+   * - Object Storage
+     - Flat file storage (S3/GCS) addressed by bucket + key
+   * - Container
+     - Packaged code + dependencies that runs identically anywhere
+   * - Container Registry
+     - Storage service for container images (ECR, GCR)
+   * - IAM
+     - Identity and Access Management — who can do what
+   * - VPC
+     - Virtual Private Cloud — isolated network in the cloud
+   * - Subnet
+     - Segment within a VPC (public or private)
+   * - Fargate
+     - AWS serverless container service (no server management)
+   * - Region
+     - Geographic location of a cloud data center
+   * - Spot Instance
+     - Discounted compute that can be interrupted (60-90% off)
+   * - Artifact
+     - Workflow output stored for persistence (results, models)
+   * - Pay-Per-Use
+     - Billing model charging only for resources consumed
+   * - Dry Run
+     - Simulating deployment to see costs without spending
+
+
+Next Steps
+-----------
+
+You now understand cloud computing fundamentals and how Scalable deploys
+workflows to AWS and GCP.
+
+* **Next beginner tutorial:** :ref:`beginner_telemetry` — understanding what
+  happened during your runs
+* **Standard tutorial:** :ref:`tutorial_cloud_integration` — production cloud
+  patterns, IAM configuration, and cost optimization
+* **Explore:** Run ``scalable plan --target aws --dry-run`` on your manifest
+  to see the cost estimate. Try different ``worker_cpu`` and ``adaptive.maximum``
+  values to see how costs change.
diff --git a/docs/tutorials/beginner/06_telemetry.rst b/docs/tutorials/beginner/06_telemetry.rst
new file mode 100644
index 0000000..31c8628
--- /dev/null
+++ b/docs/tutorials/beginner/06_telemetry.rst
@@ -0,0 +1,500 @@
+.. _beginner_telemetry:
+
+======================================================
+Beginner Tutorial 6: Understanding What Happened
+======================================================
+
+.. contents:: In This Tutorial
+   :local:
+   :depth: 2
+
+The Big Picture
+----------------
+
+You've run a workflow. It completed. But did it perform well? Were some tasks
+slower than expected? Did workers sit idle? How much did it cost?
+
+**Telemetry** is the automated recording of everything that happens during a
+run — every task start, every completion, every failure, every resource
+measurement. It's like a flight recorder for your workflow, letting you
+understand what happened after the fact and make informed decisions about
+optimization.
+
+This tutorial explains observability from first principles: what telemetry is,
+why structured logging matters, how to read event data, and how to generate
+useful reports.
+
+What You Will Learn
+--------------------
+
+By the end of this tutorial you will:
+
+* Understand what telemetry and observability mean.
+* Know the difference between metrics, logs, and traces.
+* Read JSONL telemetry files and understand their structure.
+* Generate reports from the CLI and Python API.
+* Use telemetry data to identify performance bottlenecks.
+* Understand how historical telemetry informs future decisions.
+
+Prerequisites
+--------------
+
+* Completed :ref:`beginner_getting_started`.
+* At least one completed Scalable run (to have telemetry data).
+* ``pandas`` installed (included with Scalable's core dependencies).
+
+
+Key Concepts Explained
+-----------------------
+
+.. admonition:: 💡 Key Concept: What is Telemetry?
+   :class: tip
+
+   **Telemetry** is the automated collection and transmission of data from
+   remote systems. The word comes from Greek: *tele* (remote) + *metron*
+   (measurement).
+
+   In software, telemetry means recording what your program did:
+
+   * When did tasks start and finish?
+   * How much memory did workers use?
+   * Which tasks failed and why?
+   * How many cache hits occurred?
+
+   **Analogy:** A car's dashboard shows speed, fuel level, and engine
+   temperature in real-time. Telemetry is like a dashcam that records
+   everything so you can review it later.
+
+.. admonition:: 💡 Key Concept: Observability
+   :class: tip
+
+   **Observability** is the ability to understand a system's internal state
+   by examining its outputs. A system is "observable" if you can answer
+   "why is this slow?" or "why did this fail?" from the data it produces.
+
+   The three pillars of observability:
+
+   **1. Metrics** — numerical measurements over time
+     * "CPU utilization was 87% at 14:03:22"
+     * "Average task duration was 4.2 seconds"
+     * Good for dashboards and alerting
+
+   **2. Logs** — discrete events with context
+     * "Task run_simulation(42) started at 14:03:22 on worker-3"
+     * "Worker-2 failed with OutOfMemoryError at 14:05:11"
+     * Good for debugging specific incidents
+
+   **3. Traces** — the journey of a request through the system
+     * "Task 42: submitted → queued 0.3s → scheduled to worker-3 → executed 4.1s → completed"
+     * Good for understanding latency and bottlenecks
+
+   Scalable's telemetry provides all three through structured event files.
+
+.. admonition:: 💡 Key Concept: Structured Logging
+   :class: tip
+
+   **Structured logging** means recording events as machine-parseable data
+   (typically JSON) rather than free-form text.
+
+   **Unstructured log** (hard to parse programmatically):
+
+   .. code-block:: text
+
+      2026-05-20 14:03:22 INFO Task run_simulation(42) completed in 4.2s on worker-3
+
+   **Structured log** (easy to parse, filter, aggregate):
+
+   .. code-block:: json
+
+      {
+        "timestamp": "2026-05-20T14:03:22Z",
+        "event": "task_completed",
+        "task": "run_simulation",
+        "args": {"scenario_id": 42},
+        "duration_s": 4.2,
+        "worker": "worker-3"
+      }
+
+   Structured logs can be:
+
+   * Filtered: "show me only failures"
+   * Aggregated: "average duration per task type"
+   * Queried: "which worker handled the most tasks?"
+   * Visualized: plotted on timelines and dashboards
+
+.. admonition:: 💡 Key Concept: JSONL (JSON Lines)
+   :class: tip
+
+   **JSONL** (JSON Lines) is a format where each line is a complete JSON
+   object. It's perfect for event streams because:
+
+   * **Appendable** — just add a new line (no need to rewrite the file)
+   * **Streamable** — process one line at a time (no need to load entire file)
+   * **Parseable** — each line is valid JSON
+
+   .. code-block:: text
+
+      {"event": "task_started", "task": "sim", "time": "14:03:22"}
+      {"event": "task_completed", "task": "sim", "time": "14:03:26", "duration": 4.2}
+      {"event": "task_started", "task": "sim", "time": "14:03:22"}
+
+   Compare to a single large JSON array (which requires loading the entire
+   file to append or read):
+
+   .. code-block:: json
+
+      [
+        {"event": "task_started", ...},
+        {"event": "task_completed", ...}
+      ]
+
+.. admonition:: 💡 Key Concept: Events
+   :class: tip
+
+   An **event** is a discrete occurrence at a specific point in time. Events
+   have:
+
+   * **Timestamp** — when it happened
+   * **Type** — what kind of event (task_started, worker_added, etc.)
+   * **Payload** — additional context (task name, duration, error message)
+
+   Events form the foundation of Scalable's telemetry system. Everything
+   that happens is recorded as an event.
+
+
+Step 1: Telemetry File Structure
+----------------------------------
+
+After every run, Scalable creates a run directory with structured telemetry:
+
+.. code-block:: text
+
+   .scalable/runs/
+   └── run-20260520T035200Z-energy-forecast-a1b2c3d4/
+       ├── run.json           # Run metadata (start time, target, manifest)
+       ├── manifest.yaml      # Snapshot of the manifest used
+       ├── plan.json          # Execution plan snapshot
+       ├── tasks.jsonl        # Task lifecycle events
+       ├── resources.jsonl    # Resource utilization snapshots
+       ├── workers.jsonl      # Worker lifecycle events
+       ├── cache.jsonl        # Cache hit/miss events
+       └── failures.jsonl     # Error details (if any)
+
+Each file serves a purpose:
+
+``run.json``
+   High-level metadata: when the run started, which target was used, the
+   manifest hash for reproducibility verification.
+
+``tasks.jsonl``
+   The most important file — every task submission, start, completion, and
+   failure is recorded here.
+
+``resources.jsonl``
+   Periodic snapshots of CPU and memory usage per worker.
+
+``workers.jsonl``
+   Worker lifecycle: when workers started, stopped, or crashed.
+
+``cache.jsonl``
+   Every cache lookup: hit (saved time) or miss (had to compute).
+
+``failures.jsonl``
+   Detailed error information including tracebacks.
+
+
+Step 2: Reading Telemetry Data
+--------------------------------
+
+You can read telemetry files directly:
+
+.. code-block:: python
+
+   import json
+
+   # Read task events line by line
+   with open(".scalable/runs/run-.../tasks.jsonl") as f:
+       for line in f:
+           event = json.loads(line)
+           print(f"{event['timestamp']} | {event['event']} | {event.get('task', '')}")
+
+Output:
+
+.. code-block:: text
+
+   2026-05-20T14:03:22Z | task_submitted | run_simulation
+   2026-05-20T14:03:22Z | task_started   | run_simulation
+   2026-05-20T14:03:26Z | task_completed | run_simulation
+   2026-05-20T14:03:22Z | task_submitted | run_simulation
+   ...
+
+Or use pandas for analysis:
+
+.. code-block:: python
+
+   import pandas as pd
+
+   # Load all task events into a DataFrame
+   tasks = pd.read_json(".scalable/runs/run-.../tasks.jsonl", lines=True)
+
+   # Filter to completions and compute statistics
+   completed = tasks[tasks["event"] == "task_completed"]
+   print(f"Total tasks: {len(completed)}")
+   print(f"Average duration: {completed['duration_s'].mean():.2f}s")
+   print(f"Slowest task: {completed['duration_s'].max():.2f}s")
+   print(f"Fastest task: {completed['duration_s'].min():.2f}s")
+
+.. admonition:: Under the Hood
+   :class: hint
+
+   Scalable records telemetry **automatically** — you don't need to add
+   logging to your functions. The ``ScalableSession`` instruments:
+
+   1. Every ``submit()`` → ``task_submitted`` event
+   2. When a worker picks up a task → ``task_started``
+   3. When a task completes → ``task_completed`` (with duration)
+   4. When a task fails → ``task_failed`` (with error details)
+   5. Periodic resource snapshots → ``resource_sample``
+
+
+Step 3: Generating Reports
+-----------------------------
+
+The CLI provides quick summaries:
+
+.. code-block:: bash
+
+   # Report on the most recent run
+   scalable report --last
+
+.. code-block:: text
+
+   ═══════════════════════════════════════════════
+   Run Report: run-20260520T035200Z-energy-forecast-a1b2c3d4
+   ═══════════════════════════════════════════════
+   Target: local (provider: local)
+   Duration: 45.2s
+   Status: completed
+
+   Tasks:
+     Submitted: 100
+     Completed: 100
+     Failed: 0
+     Avg duration: 4.2s
+     Max duration: 8.7s (run_simulation, scenario_id=47)
+
+   Workers:
+     Peak: 4
+     Avg utilization: 87%
+
+   Cache:
+     Lookups: 100
+     Hits: 0 (0%) — first run, no prior cache
+     Misses: 100
+
+   Estimated Cost: $0.00 (local provider)
+
+You can also compare runs:
+
+.. code-block:: bash
+
+   scalable report --compare run-abc123 run-def456
+
+This shows performance differences between two runs — useful for verifying
+that optimization changes actually helped.
+
+
+Step 4: Using Telemetry for Optimization
+------------------------------------------
+
+Telemetry answers critical questions:
+
+**"Which tasks are slowest?"**
+
+.. code-block:: python
+
+   # Find the 5 slowest tasks
+   slowest = completed.nlargest(5, "duration_s")[["task", "duration_s"]]
+   print(slowest)
+
+**"Are workers sitting idle?"**
+
+.. code-block:: python
+
+   resources = pd.read_json(".scalable/runs/run-.../resources.jsonl", lines=True)
+   print(f"Average CPU utilization: {resources['cpu_percent'].mean():.1f}%")
+   # Below 70% suggests you have too many workers for the workload
+
+**"Is caching helping?"**
+
+.. code-block:: python
+
+   cache = pd.read_json(".scalable/runs/run-.../cache.jsonl", lines=True)
+   hit_rate = cache[cache["result"] == "hit"].shape[0] / len(cache) * 100
+   print(f"Cache hit rate: {hit_rate:.1f}%")
+
+.. admonition:: 💡 Key Concept: Utilization and Efficiency
+   :class: tip
+
+   **Utilization** measures how much of your allocated resources are actually
+   being used:
+
+   * **100% utilization** = every worker is busy all the time (ideal)
+   * **50% utilization** = workers are idle half the time (wasteful)
+   * **Low utilization** usually means: too many workers, or tasks are too
+     quick (overhead dominates)
+
+   **Efficiency** considers the ratio of useful work to total time:
+
+   .. code-block:: text
+
+      Efficiency = (total task computation time) / (total worker uptime × worker count)
+
+   If you have 4 workers running for 60 seconds each (240 worker-seconds)
+   but only 180 seconds of actual task computation, efficiency is 75%.
+
+
+Step 5: Historical Analysis
+------------------------------
+
+.. admonition:: 💡 Key Concept: Trend Analysis
+   :class: tip
+
+   **Trend analysis** looks at how metrics change over time:
+
+   * Are runs getting slower? (regression detection)
+   * Are resource needs growing? (capacity planning)
+   * Is cache hit rate improving? (optimization validation)
+
+   Scalable stores all runs in ``.scalable/runs/`` so you can analyze trends
+   across your project's history.
+
+.. code-block:: python
+
+   import os
+   import json
+
+   # Load metadata from all runs
+   runs_dir = ".scalable/runs"
+   runs = []
+   for run_name in sorted(os.listdir(runs_dir)):
+       run_meta = os.path.join(runs_dir, run_name, "run.json")
+       if os.path.exists(run_meta):
+           with open(run_meta) as f:
+               runs.append(json.load(f))
+
+   # Plot duration over time (if matplotlib available)
+   for r in runs:
+       print(f"{r['start_time']}: {r['duration_s']:.1f}s ({r['tasks_completed']} tasks)")
+
+
+Step 6: Telemetry-Driven Resource Recommendations
+----------------------------------------------------
+
+Scalable's resource advisor uses telemetry history to recommend better
+resource allocations:
+
+.. code-block:: bash
+
+   scalable advise --task run_simulation
+
+.. code-block:: text
+
+   Resource Recommendation for 'run_simulation':
+     Current: 4 CPUs, 16G memory
+     Recommended: 2 CPUs, 8G memory
+     Reason: 95th percentile usage is 1.8 CPUs and 6.2G memory
+     Potential savings: 50% compute cost reduction
+
+.. admonition:: 🤔 Think About It
+   :class: note
+
+   Without telemetry, resource allocation is guesswork ("let's try 32G and
+   see"). With telemetry, it's data-driven ("historical usage shows 6G is
+   the 95th percentile, so 8G gives comfortable headroom").
+
+   This is why Scalable records telemetry by default — even if you don't
+   look at it now, it enables smarter decisions later.
+
+
+Common Questions
+-----------------
+
+**Q: Does telemetry slow down my workflow?**
+
+Negligibly. Writing a JSON line to a file takes microseconds. Compared to
+tasks that take seconds or minutes, the overhead is unmeasurable.
+
+**Q: How much disk space does telemetry use?**
+
+Typically 1–10 MB per run (for hundreds of tasks). You can periodically
+archive or delete old runs. For long-term storage, telemetry can be exported
+to Parquet format (compressed columnar storage).
+
+**Q: Can I disable telemetry?**
+
+Yes, but it's not recommended. Telemetry is what enables caching verification,
+resource recommendations, and debugging. Without it, you're flying blind.
+
+**Q: What's the difference between telemetry and logging?**
+
+* **Logging** = messages for developers to debug issues (often unstructured,
+  verbose, human-oriented)
+* **Telemetry** = structured data for analysis and automation
+  (machine-parseable, consistent schema)
+
+Scalable provides both: Python logging for debugging, telemetry for analysis.
+
+**Q: Can I send telemetry to external systems?**
+
+Yes — telemetry files are standard JSONL that can be ingested by any log
+aggregation system (Elasticsearch, Splunk, CloudWatch). Export to Parquet for
+data warehouse analytics.
+
+
+What You Learned
+-----------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Term
+     - Definition
+   * - Telemetry
+     - Automated collection of system behavior data
+   * - Observability
+     - Ability to understand internal state from outputs
+   * - Metrics
+     - Numerical measurements over time (CPU %, duration)
+   * - Logs
+     - Discrete events with context (structured or unstructured)
+   * - Traces
+     - Journey of a request through the system
+   * - Structured Logging
+     - Recording events as machine-parseable data (JSON)
+   * - JSONL
+     - JSON Lines — one JSON object per line
+   * - Event
+     - Discrete occurrence with timestamp, type, and payload
+   * - Utilization
+     - Percentage of allocated resources actually being used
+   * - Trend Analysis
+     - Examining how metrics change over time
+   * - Run Directory
+     - Folder containing all telemetry for a single execution
+
+
+Next Steps
+-----------
+
+You now understand telemetry and observability, and can use Scalable's data
+to optimize your workflows.
+
+* **Next beginner tutorial:** :ref:`beginner_error_handling` — what happens
+  when things go wrong
+* **Standard tutorial:** :ref:`tutorial_telemetry` — custom dashboards,
+  Parquet export, and advanced analysis
+* **Try it:** After running a workflow, explore the ``.scalable/runs/``
+  directory. Open a ``tasks.jsonl`` file and look at the event structure.
+  Can you find the slowest task?
diff --git a/docs/tutorials/beginner/07_error_handling.rst b/docs/tutorials/beginner/07_error_handling.rst
new file mode 100644
index 0000000..4905abc
--- /dev/null
+++ b/docs/tutorials/beginner/07_error_handling.rst
@@ -0,0 +1,582 @@
+.. _beginner_error_handling:
+
+======================================================
+Beginner Tutorial 7: When Things Go Wrong
+======================================================
+
+.. contents:: In This Tutorial
+   :local:
+   :depth: 2
+
+The Big Picture
+----------------
+
+In distributed computing, failures aren't just possible — they're expected.
+Networks drop connections. Machines run out of memory. Cloud instances get
+preempted. HPC job time limits expire. The question isn't "will things fail?"
+but "how do we handle failure gracefully?"
+
+This tutorial explains distributed failure modes from first principles: why
+errors in distributed systems are harder than local errors, how to make
+workflows resilient, and how Scalable helps you diagnose and recover from
+failures.
+
+What You Will Learn
+--------------------
+
+By the end of this tutorial you will:
+
+* Understand why distributed errors are harder than local errors.
+* Know the common failure modes in distributed computing.
+* Implement retry strategies with exponential backoff.
+* Understand idempotency and why it matters for retries.
+* Handle partial success (some tasks succeed, others fail).
+* Use telemetry to diagnose failures.
+* Understand Scalable's fault tolerance mechanisms.
+
+Prerequisites
+--------------
+
+* Completed :ref:`beginner_getting_started` and :ref:`beginner_telemetry`.
+* Scalable installed (``pip install scalable``).
+
+
+Key Concepts Explained
+-----------------------
+
+.. admonition:: 💡 Key Concept: Why Distributed Errors Are Harder
+   :class: tip
+
+   On your laptop, errors are straightforward:
+
+   * Your function raises an exception → you see a traceback → you fix it
+
+   In distributed systems, additional failure modes exist:
+
+   * **Network failure** — the worker computed the result but the network
+     dropped before delivering it (did it succeed or not?)
+   * **Partial failure** — 3 of 4 workers succeed, 1 fails (what do you do
+     with the partial results?)
+   * **Silent failure** — a worker produces wrong results without raising
+     an error (harder to detect)
+   * **Cascading failure** — one failure triggers others (scheduler overload,
+     resource exhaustion)
+   * **Timing issues** — a task times out (was it too slow, or did the
+     network delay the response?)
+
+   The fundamental challenge: **you can't always tell the difference between
+   "failed" and "slow"** in a distributed system.
+
+.. admonition:: 💡 Key Concept: Fault Tolerance
+   :class: tip
+
+   **Fault tolerance** is a system's ability to continue operating correctly
+   when components fail. It doesn't mean failures don't happen — it means
+   the system handles them gracefully.
+
+   **Levels of fault tolerance:**
+
+   1. **Crash and burn** — any failure stops everything (fragile)
+   2. **Detect and report** — failures are caught and reported clearly
+   3. **Retry** — transient failures are automatically retried
+   4. **Partial success** — successful results are preserved even if some
+      tasks fail
+   5. **Self-healing** — the system automatically recovers (restarts workers,
+      reschedules tasks)
+
+   Scalable provides levels 2–5 depending on configuration.
+
+.. admonition:: 💡 Key Concept: Transient vs. Permanent Failures
+   :class: tip
+
+   **Transient failures** are temporary — retrying usually succeeds:
+
+   * Network timeout (try again in a moment)
+   * Rate limiting (wait and try again)
+   * Resource contention (another process was hogging memory)
+   * Cloud spot instance preemption (get another instance)
+
+   **Permanent failures** won't be fixed by retrying:
+
+   * Bug in your code (divide by zero)
+   * Invalid input data (file doesn't exist)
+   * Missing permissions (never had access)
+   * Resource genuinely insufficient (need 64GB but only 32GB available)
+
+   **The key insight:** Retry strategies should handle transient failures
+   but not waste time on permanent ones. Scalable's error classification
+   helps distinguish between them.
+
+.. admonition:: 💡 Key Concept: Exceptions in Python
+   :class: tip
+
+   An **exception** is Python's way of signaling that something went wrong.
+   When code encounters an error, it "raises" an exception:
+
+   .. code-block:: python
+
+      def divide(a, b):
+          if b == 0:
+              raise ValueError("Cannot divide by zero")
+          return a / b
+
+   Exceptions propagate up the call stack until caught:
+
+   .. code-block:: python
+
+      try:
+          result = divide(10, 0)
+      except ValueError as e:
+          print(f"Error: {e}")  # "Error: Cannot divide by zero"
+
+   In distributed systems, exceptions happen on **remote workers** and must
+   be serialized, transmitted back to the client, and re-raised — adding
+   complexity to error handling.
+
+.. admonition:: 💡 Key Concept: Idempotency
+   :class: tip
+
+   An operation is **idempotent** if running it multiple times produces the
+   same result as running it once. This is critical for retry logic.
+
+   **Idempotent operations** (safe to retry):
+
+   * Reading a file
+   * Computing ``f(x)`` for a pure function
+   * Setting a value: ``x = 5`` (doing it twice still gives ``x = 5``)
+   * HTTP GET requests
+
+   **Non-idempotent operations** (dangerous to retry):
+
+   * Sending an email (retry = duplicate email)
+   * Incrementing a counter: ``x += 1`` (retry = double increment)
+   * Inserting a database row (retry = duplicate row)
+   * Charging a credit card
+
+   **For retries to be safe, your tasks must be idempotent.** If retrying
+   a task could cause side effects (duplicate writes, double charges), you
+   need additional safeguards.
+
+.. admonition:: 💡 Key Concept: Exponential Backoff
+   :class: tip
+
+   **Exponential backoff** is a retry strategy where you wait progressively
+   longer between attempts:
+
+   * Attempt 1: fail → wait 1 second
+   * Attempt 2: fail → wait 2 seconds
+   * Attempt 3: fail → wait 4 seconds
+   * Attempt 4: fail → wait 8 seconds
+   * ...
+
+   **Why exponential?** If the failure is caused by overload (too many
+   requests), retrying immediately just makes the overload worse. Backing
+   off gives the system time to recover.
+
+   **Jitter** adds randomness to the wait time so that multiple retriers
+   don't all retry at the same moment (which would cause another spike).
+
+
+Step 1: How Scalable Handles Errors
+--------------------------------------
+
+When a function raises an exception on a worker:
+
+.. code-block:: text
+
+   ┌────────┐              ┌───────────┐              ┌────────┐
+   │ Client │   submit()   │ Scheduler │   execute    │ Worker │
+   │        │─────────────▶│           │─────────────▶│        │
+   │        │              │           │              │ CRASH! │
+   │        │              │           │◀─────────────│ error  │
+   │        │◀─────────────│  records  │              └────────┘
+   │ raises │   exception  │  in telem │
+   └────────┘              └───────────┘
+
+1. Worker executes your function
+2. Function raises an exception
+3. Exception is **serialized** (converted to bytes) by the worker
+4. Sent back to the scheduler
+5. Recorded in telemetry (``failures.jsonl``)
+6. **Re-raised** on the client when you call ``.result()`` or ``gather()``
+
+.. code-block:: python
+
+   from scalable import ScalableSession
+
+   session = ScalableSession.from_manifest("./scalable.yaml", target="local")
+
+   def risky_function(x):
+       if x == 13:
+           raise ValueError(f"Unlucky number: {x}")
+       return x * 2
+
+   futures = [session.submit(risky_function, i, task="run_analysis")
+              for i in range(20)]
+
+   # This will raise ValueError for x=13
+   try:
+       results = session.gather(futures)
+   except ValueError as e:
+       print(f"A task failed: {e}")
+
+
+Step 2: Retry Strategies
+--------------------------
+
+Scalable supports automatic retries for transient failures:
+
+.. code-block:: python
+
+   from scalable import ScalableSession
+
+   session = ScalableSession.from_manifest("./scalable.yaml", target="local")
+
+   # Configure retries
+   futures = []
+   for i in range(20):
+       future = session.submit(
+           sometimes_fails,
+           i,
+           task="run_analysis",
+           retries=3,               # Retry up to 3 times
+       )
+       futures.append(future)
+
+.. admonition:: How retry logic works
+   :class: note
+
+   With ``retries=3``:
+
+   1. First attempt fails → wait → retry (attempt 2)
+   2. Second attempt fails → wait longer → retry (attempt 3)
+   3. Third attempt fails → wait even longer → retry (attempt 4)
+   4. Fourth attempt fails → give up, propagate error to client
+
+   Each retry is recorded in telemetry so you can see how many retries
+   occurred and whether they eventually succeeded.
+
+**Writing retry-safe functions:**
+
+.. code-block:: python
+
+   import time
+   import random
+
+   def fetch_data_from_api(scenario_id: int) -> dict:
+       """Fetch data — may fail transiently due to network issues."""
+       # This is idempotent: calling it multiple times is safe
+       # (it reads data, doesn't modify anything)
+       response = requests.get(f"https://api.example.com/scenarios/{scenario_id}")
+       response.raise_for_status()  # Raises on HTTP errors
+       return response.json()
+
+   def process_and_save(scenario_id: int) -> dict:
+       """Process data — write results to file.
+
+       Made idempotent by writing to a deterministic path
+       (same input → same output path → overwrite is safe).
+       """
+       result = expensive_computation(scenario_id)
+       output_path = f"./outputs/scenario_{scenario_id}.json"
+       with open(output_path, "w") as f:
+           json.dump(result, f)
+       return result
+
+
+Step 3: Partial Success
+-------------------------
+
+.. admonition:: 💡 Key Concept: Partial Success
+   :class: tip
+
+   **Partial success** means some tasks in a batch completed successfully
+   while others failed. Rather than losing ALL results because of one
+   failure, you keep what succeeded and handle failures separately.
+
+   This is essential for large batch jobs. If 999 of 1000 tasks succeed,
+   you don't want to throw away 999 good results because of 1 failure.
+
+.. code-block:: python
+
+   from scalable import ScalableSession
+
+   session = ScalableSession.from_manifest("./scalable.yaml", target="local")
+
+   # Submit many tasks
+   futures = [session.submit(maybe_fails, i, task="run_analysis")
+              for i in range(100)]
+
+   # Gather with partial success handling
+   results = []
+   failures = []
+   for i, future in enumerate(futures):
+       try:
+           result = future.result()  # Get individual result
+           results.append(result)
+       except Exception as e:
+           failures.append({"index": i, "error": str(e)})
+
+   print(f"Succeeded: {len(results)}")
+   print(f"Failed: {len(failures)}")
+
+   # You can retry just the failures
+   retry_futures = [session.submit(maybe_fails, f["index"], task="run_analysis")
+                    for f in failures]
+
+.. admonition:: Under the Hood: Futures and Error Isolation
+   :class: hint
+
+   Each future is independent. A failure in one future doesn't affect
+   others. This is why ``session.submit()`` returns individual futures
+   rather than running everything as a single batch — it gives you
+   fine-grained control over error handling.
+
+
+Step 4: Common Failure Modes
+------------------------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 20 30 25 25
+
+   * - Failure Mode
+     - What Happens
+     - Symptoms
+     - Solution
+   * - Out of Memory (OOM)
+     - Worker exceeds memory limit
+     - ``MemoryError`` or worker killed
+     - Increase ``memory`` in component
+   * - Timeout
+     - Task exceeds time limit
+     - ``TimeoutError`` or Slurm ``TIMEOUT``
+     - Increase ``walltime`` or split task
+   * - Network Error
+     - Connection between client/worker drops
+     - ``CommClosedError``
+     - Retry (usually transient)
+   * - Spot Preemption
+     - Cloud reclaims your instance
+     - Worker disappears mid-task
+     - Retry + caching
+   * - Dependency Missing
+     - Import fails on worker
+     - ``ModuleNotFoundError``
+     - Update container image
+   * - Data Not Found
+     - Input file doesn't exist
+     - ``FileNotFoundError``
+     - Fix path or mount configuration
+
+
+Step 5: Diagnosing Failures with Telemetry
+--------------------------------------------
+
+When things fail, telemetry is your investigation tool:
+
+.. code-block:: bash
+
+   # See failure details
+   scalable report --last --failures
+
+.. code-block:: text
+
+   Failures (3 of 100 tasks):
+
+   1. run_simulation(scenario_id=47)
+      Error: MemoryError — unable to allocate 4.2GB
+      Worker: worker-3
+      Duration before failure: 180s
+      Retries attempted: 3 (all failed)
+
+   2. run_simulation(scenario_id=92)
+      Error: TimeoutError — exceeded 300s limit
+      Worker: worker-1
+      Duration before failure: 300s
+
+   3. run_simulation(scenario_id=13)
+      Error: ValueError — invalid input data
+      Worker: worker-2
+      Duration before failure: 0.1s (fast fail — permanent error)
+
+.. admonition:: 🤔 Think About It
+   :class: note
+
+   Notice the patterns in the failure report:
+
+   * **Scenario 47** — OOM after 180s suggests a memory-hungry edge case.
+     Solution: increase memory for this component, or investigate why
+     scenario 47 uses more memory than others.
+
+   * **Scenario 92** — timeout at exactly 300s means it hit the limit.
+     Solution: increase walltime, or investigate why this scenario is slow.
+
+   * **Scenario 13** — fast fail (0.1s) with ``ValueError`` means the input
+     is permanently bad. Retrying won't help. Solution: fix the input data.
+
+
+Step 6: Building Fault-Tolerant Workflows
+-------------------------------------------
+
+A complete fault-tolerant pattern:
+
+.. code-block:: python
+
+   from scalable import ScalableSession, cacheable
+
+
+   @cacheable(return_type=dict, scenario_id=int)
+   def run_simulation(scenario_id: int) -> dict:
+       """Cached + idempotent = retry-safe."""
+       # ... expensive computation ...
+       return {"id": scenario_id, "result": compute(scenario_id)}
+
+
+   def run_workflow():
+       session = ScalableSession.from_manifest("./scalable.yaml", target="local")
+
+       # Submit all tasks
+       task_map = {}
+       for i in range(100):
+           future = session.submit(
+               run_simulation,
+               scenario_id=i,
+               task="run_analysis",
+               retries=3,
+           )
+           task_map[i] = future
+
+       # Collect results with error isolation
+       results = {}
+       permanent_failures = []
+
+       for scenario_id, future in task_map.items():
+           try:
+               results[scenario_id] = future.result()
+           except MemoryError:
+               permanent_failures.append(
+                   (scenario_id, "OOM — needs more memory"))
+           except Exception as e:
+               permanent_failures.append(
+                   (scenario_id, str(e)))
+
+       print(f"Completed: {len(results)} / {len(task_map)}")
+       print(f"Failed: {len(permanent_failures)}")
+
+       # Report permanent failures for human investigation
+       for sid, error in permanent_failures:
+           print(f"  Scenario {sid}: {error}")
+
+       session.close()
+       return results
+
+.. admonition:: Why this pattern works
+   :class: hint
+
+   1. **``@cacheable``** — successful computations are cached. If you re-run
+      after fixing issues, completed scenarios are instant (cache hit).
+   2. **``retries=3``** — transient failures (network, spot preemption) are
+      handled automatically.
+   3. **Individual error handling** — one failure doesn't crash the whole
+      workflow.
+   4. **Clear reporting** — permanent failures are collected and reported
+      for human investigation.
+
+.. admonition:: 💡 Key Concept: Graceful Degradation
+   :class: tip
+
+   **Graceful degradation** means a system reduces its service level rather
+   than failing completely. Examples:
+
+   * 95 of 100 scenarios complete → report 95 results + note 5 failures
+   * Cloud budget exhausted → stop scaling but finish current tasks
+   * One worker type unavailable → fall back to a smaller worker type
+
+   This is the opposite of "all or nothing" behavior. For scientific
+   workflows, getting 95% of results now (and investigating 5% of failures)
+   is usually better than getting 0% because one failure crashed everything.
+
+
+Common Questions
+-----------------
+
+**Q: Should I always use retries?**
+
+Use retries when failures might be transient. Don't retry if:
+
+* The error is clearly permanent (bad input, missing permission)
+* The operation is not idempotent (would cause duplicate side effects)
+* You're in a tight feedback loop (development, debugging)
+
+**Q: How many retries should I set?**
+
+3 retries is a common default. More than 5 rarely helps — if it fails 5
+times, it's probably not transient. The exponential backoff means 5 retries
+with base 2s = up to 32 seconds of waiting.
+
+**Q: What about tasks that are too slow (but don't "fail")?**
+
+That's a performance issue, not an error. Use telemetry to identify slow
+tasks and either:
+
+* Increase resources (more CPU/memory)
+* Optimize the code
+* Split into smaller tasks
+
+**Q: Can failures in one task affect other tasks?**
+
+Normally no — tasks are isolated. But if tasks share state (write to the
+same file, use the same database), one failure could corrupt shared state.
+This is why idempotency and isolated outputs are important.
+
+**Q: How does caching interact with retries?**
+
+Beautifully! If a task succeeds on retry, the result is cached. On re-run,
+that scenario hits the cache and skips entirely. Caching effectively
+"remembers" that we eventually got the right answer.
+
+
+What You Learned
+-----------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Term
+     - Definition
+   * - Fault Tolerance
+     - System's ability to continue operating despite component failures
+   * - Transient Failure
+     - Temporary error that resolves on retry (network, timeout)
+   * - Permanent Failure
+     - Error that won't be fixed by retrying (bad input, bug)
+   * - Idempotency
+     - Operation that produces the same result if run multiple times
+   * - Exponential Backoff
+     - Progressively longer waits between retry attempts
+   * - Partial Success
+     - Some tasks succeed while others fail in a batch
+   * - Exception
+     - Python's error signaling mechanism (raise/try/except)
+   * - Error Propagation
+     - How errors travel from worker back to client
+   * - Graceful Degradation
+     - Reducing service level rather than failing completely
+   * - Jitter
+     - Randomness added to retry timing to prevent thundering herd
+
+
+Next Steps
+-----------
+
+You now understand how to build fault-tolerant distributed workflows.
+
+* **Next beginner tutorial:** :ref:`beginner_kubernetes` — container
+  orchestration and deployment
+* **Standard tutorial:** :ref:`tutorial_error_handling` — advanced resilience
+  patterns, AI-assisted diagnosis, and production error handling
+* **Try it:** Write a function that randomly fails 20% of the time. Submit
+  it 50 times with ``retries=3``. Check telemetry to see how many retries
+  occurred and whether all tasks eventually succeeded.
diff --git a/docs/tutorials/beginner/08_kubernetes.rst b/docs/tutorials/beginner/08_kubernetes.rst
new file mode 100644
index 0000000..9351234
--- /dev/null
+++ b/docs/tutorials/beginner/08_kubernetes.rst
@@ -0,0 +1,536 @@
+.. _beginner_kubernetes:
+
+======================================================
+Beginner Tutorial 8: Container Orchestration with Kubernetes
+======================================================
+
+.. contents:: In This Tutorial
+   :local:
+   :depth: 2
+
+The Big Picture
+----------------
+
+In Tutorial 5, you learned about containers — packaged software environments
+that run anywhere. But what happens when you need to run 50 containers across
+multiple machines? Who decides which machine runs which container? What happens
+when a container crashes? What about scaling up and down?
+
+**Kubernetes** (often abbreviated "K8s") is the answer. It's a platform for
+managing containers at scale — automatically placing them on machines, restarting
+them when they fail, and scaling them up or down based on demand.
+
+This tutorial explains Kubernetes from first principles and shows how Scalable
+uses it to run distributed workflows on container infrastructure.
+
+What You Will Learn
+--------------------
+
+By the end of this tutorial you will:
+
+* Understand what Kubernetes is and what problem it solves.
+* Know the key K8s concepts: pods, nodes, namespaces, operators.
+* Understand how the Dask Kubernetes Operator works.
+* Configure Scalable's Kubernetes provider.
+* Understand resource requests, limits, and quotas.
+* Know when Kubernetes is appropriate vs. overkill.
+
+Prerequisites
+--------------
+
+* Completed :ref:`beginner_getting_started`, :ref:`beginner_manifest_system`,
+  and :ref:`beginner_scaling_strategies`.
+* Conceptual understanding of containers from :ref:`beginner_cloud_integration`.
+* No Kubernetes cluster required to understand the concepts — code examples
+  show configuration patterns.
+
+
+Key Concepts Explained
+-----------------------
+
+.. admonition:: 💡 Key Concept: What is Container Orchestration?
+   :class: tip
+
+   You know what a container is (packaged software). **Container
+   orchestration** is the automation of deploying, managing, and scaling
+   containers across multiple machines.
+
+   **Without orchestration:**
+
+   * Manually decide which server runs which container
+   * Manually restart containers that crash
+   * Manually add/remove containers when load changes
+   * Manually route traffic to healthy containers
+
+   **With orchestration (Kubernetes):**
+
+   * You declare: "I want 10 copies of this container"
+   * K8s decides where to put them (across available machines)
+   * K8s automatically restarts crashed containers
+   * K8s auto-scales based on demand
+   * K8s routes traffic to healthy instances
+
+   **Analogy:** Container orchestration is like an air traffic controller
+   for containers. You don't tell each plane exactly which runway and gate
+   to use — the controller optimally assigns resources based on the current
+   situation.
+
+.. admonition:: 💡 Key Concept: What is Kubernetes?
+   :class: tip
+
+   **Kubernetes** (from Greek: κυβερνήτης, "helmsman") is an open-source
+   container orchestration platform originally developed by Google.
+
+   It manages:
+
+   * **Where** containers run (scheduling across machines)
+   * **How many** containers run (scaling)
+   * **Healthy** containers stay running (self-healing)
+   * **Network** connectivity between containers (service discovery)
+   * **Storage** for containers (persistent volumes)
+
+   Kubernetes is the industry standard — it runs in AWS (EKS), Google Cloud
+   (GKE), Azure (AKS), and on-premise. Scalable uses it as one of its
+   deployment providers.
+
+.. admonition:: 💡 Key Concept: Pods
+   :class: tip
+
+   A **pod** is the smallest deployable unit in Kubernetes — a group of one
+   or more containers that share network and storage.
+
+   Most commonly, a pod = one container. But sometimes related containers
+   are grouped (e.g., your app container + a logging sidecar).
+
+   .. code-block:: text
+
+      ┌─── Pod ─────────────────────┐
+      │                             │
+      │  ┌───────────────────────┐  │
+      │  │  Container            │  │
+      │  │  (your Dask worker)   │  │
+      │  └───────────────────────┘  │
+      │                             │
+      │  Shared: IP address,        │
+      │  storage volumes            │
+      └─────────────────────────────┘
+
+   In Scalable's context: each Dask worker runs in its own pod.
+
+.. admonition:: 💡 Key Concept: Nodes
+   :class: tip
+
+   A **node** is a physical or virtual machine in the Kubernetes cluster.
+   Pods are scheduled onto nodes.
+
+   .. code-block:: text
+
+      Kubernetes Cluster
+      ├── Node 1 (machine with 16 CPUs, 64GB RAM)
+      │   ├── Pod A (your worker, 4 CPU, 16GB)
+      │   ├── Pod B (your worker, 4 CPU, 16GB)
+      │   └── Pod C (system pod)
+      ├── Node 2 (machine with 16 CPUs, 64GB RAM)
+      │   ├── Pod D (your worker, 4 CPU, 16GB)
+      │   └── Pod E (your worker, 4 CPU, 16GB)
+      └── Node 3 (machine with 8 CPUs, 32GB RAM)
+          └── Pod F (scheduler pod)
+
+   The Kubernetes scheduler decides which node each pod runs on, based on
+   available resources and constraints.
+
+.. admonition:: 💡 Key Concept: Namespaces
+   :class: tip
+
+   A **namespace** is an isolation boundary within a Kubernetes cluster.
+   Different teams or projects use different namespaces to avoid conflicts.
+
+   Think of namespaces like departments in a building:
+
+   * ``team-climate`` namespace — your team's pods
+   * ``team-hydrology`` namespace — another team's pods
+   * ``system`` namespace — cluster infrastructure
+
+   Resources in one namespace can't accidentally interfere with another.
+   Resource quotas can limit how much CPU/memory each namespace uses.
+
+.. admonition:: 💡 Key Concept: Operators (Kubernetes Extension)
+   :class: tip
+
+   A **Kubernetes Operator** is a program that extends Kubernetes to manage
+   complex applications automatically. It encodes domain-specific knowledge
+   about how to deploy, scale, and maintain an application.
+
+   **The Dask Kubernetes Operator:**
+
+   * Knows how to create Dask clusters (scheduler + workers)
+   * Manages worker scaling automatically
+   * Handles upgrades and restarts
+   * Integrates with Kubernetes native features (quotas, monitoring)
+
+   Without an operator, you'd need to manually create pods for the scheduler,
+   pods for each worker, configure networking between them, and handle
+   failures. The operator does all this for you.
+
+.. admonition:: 💡 Key Concept: kubectl
+   :class: tip
+
+   **kubectl** (pronounced "cube-control" or "cube-C-T-L") is the
+   command-line tool for interacting with Kubernetes clusters.
+
+   .. code-block:: bash
+
+      # List running pods in your namespace
+      kubectl get pods -n team-climate
+
+      # See details about a specific pod
+      kubectl describe pod worker-abc123
+
+      # View pod logs (stdout/stderr)
+      kubectl logs worker-abc123
+
+      # Delete a pod (Kubernetes will restart it if managed)
+      kubectl delete pod worker-abc123
+
+   Think of kubectl as the Kubernetes equivalent of ``docker`` commands,
+   but for a whole cluster instead of a single machine.
+
+.. admonition:: 💡 Key Concept: Resource Requests vs. Limits
+   :class: tip
+
+   In Kubernetes, each pod declares:
+
+   **Requests** — minimum guaranteed resources:
+     "I need at least 2 CPUs and 4GB RAM to function"
+
+   **Limits** — maximum allowed resources:
+     "Never let me use more than 4 CPUs or 8GB RAM"
+
+   .. code-block:: yaml
+
+      resources:
+        requests:
+          cpu: "2"
+          memory: "4Gi"
+        limits:
+          cpu: "4"
+          memory: "8Gi"
+
+   **Why both?** Requests are used for scheduling (Kubernetes finds a node
+   with enough free capacity). Limits prevent runaway containers from
+   consuming all resources on a node and affecting other pods.
+
+.. admonition:: 💡 Key Concept: Helm Charts
+   :class: tip
+
+   **Helm** is a package manager for Kubernetes (like ``pip`` for Python or
+   ``apt`` for Linux). A **Helm chart** is a package of Kubernetes
+   configuration files.
+
+   Instead of writing dozens of YAML files to deploy an application, you
+   install a chart:
+
+   .. code-block:: bash
+
+      helm install dask-operator dask/dask-kubernetes-operator
+
+   Charts can be versioned, shared, and configured with values files.
+
+
+Step 1: Kubernetes Architecture for Scalable
+----------------------------------------------
+
+When you use Scalable's Kubernetes provider, this is what gets created:
+
+.. code-block:: text
+
+   Kubernetes Cluster
+   └── Your Namespace (team-climate)
+       ├── Dask Scheduler Pod (1x)
+       │   └── Container: dask-scheduler
+       │       Port 8786 (client connections)
+       │       Port 8787 (dashboard)
+       ├── Dask Worker Pods (N×)
+       │   └── Container: your-image
+       │       Runs your Python code
+       │       Connected to scheduler
+       └── Client (your script, outside cluster)
+           └── Connects to scheduler via port-forward or ingress
+
+The Dask Kubernetes Operator manages all of this based on a ``DaskCluster``
+custom resource that Scalable creates from your manifest.
+
+
+Step 2: Configuring the Kubernetes Provider
+---------------------------------------------
+
+.. code-block:: yaml
+
+   # scalable.yaml
+   targets:
+     k8s:
+       provider: kubernetes
+       namespace: team-climate
+       image: ghcr.io/my-org/energy-model:latest
+       adaptive:
+         minimum: 2
+         maximum: 20
+       resources:
+         requests:
+           cpu: "4"
+           memory: "16Gi"
+         limits:
+           cpu: "4"
+           memory: "16Gi"
+
+**What each setting does:**
+
+``namespace: team-climate``
+   Deploy into this Kubernetes namespace. Must exist and you must have
+   permissions to create pods there.
+
+``image: ghcr.io/my-org/energy-model:latest``
+   Container image for worker pods. Must contain your code, Python, and all
+   dependencies (including Scalable itself).
+
+``adaptive: {minimum: 2, maximum: 20}``
+   Start with 2 worker pods, scale up to 20 based on queue depth.
+
+``resources``
+   CPU and memory for each worker pod. Maps directly to Kubernetes resource
+   specifications.
+
+
+Step 3: The Deployment Lifecycle
+----------------------------------
+
+.. code-block:: text
+
+   1. You run: scalable run ./scalable.yaml --target k8s
+   2. Scalable creates a DaskCluster custom resource in your namespace
+   3. The Dask Operator sees the resource and creates:
+      - 1 scheduler pod
+      - N worker pods (starting at adaptive.minimum)
+   4. Your client connects to the scheduler
+   5. Tasks are submitted and executed on worker pods
+   6. Adaptive scaling adds/removes worker pods based on load
+   7. When complete, the DaskCluster is deleted
+   8. All pods are cleaned up
+
+.. admonition:: Under the Hood: Custom Resources
+   :class: hint
+
+   Kubernetes has built-in resource types (Pod, Service, Deployment). But
+   you can also define **Custom Resource Definitions (CRDs)** — new types
+   that Kubernetes doesn't know about natively.
+
+   The Dask Operator defines a ``DaskCluster`` CRD. When you create a
+   ``DaskCluster`` resource, the operator watches for it and creates the
+   necessary pods, services, and configurations automatically.
+
+   This is the declarative pattern again: you declare "I want a DaskCluster
+   with these specs" and the operator makes it happen.
+
+
+Step 4: When to Use Kubernetes
+--------------------------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 50 50
+
+   * - ✅ Good fit for Kubernetes
+     - ❌ Overkill / Wrong tool
+   * - Team sharing a cluster for multiple projects
+     - Single user on a laptop
+   * - Need for resource isolation between teams
+     - Simple batch job on one machine
+   * - Auto-scaling based on demand
+     - Fixed workload with known size
+   * - Long-running services + batch jobs
+     - One-off analysis
+   * - Already have K8s infrastructure
+     - Don't have K8s (use cloud Fargate instead)
+   * - Need reproducible deployment
+     - Rapid development iteration
+
+.. admonition:: 🤔 Think About It
+   :class: note
+
+   Kubernetes adds complexity. You need to:
+
+   * Maintain a cluster (or pay for a managed one)
+   * Build and push container images
+   * Configure namespaces, quotas, and RBAC
+   * Learn kubectl and K8s concepts
+
+   For many scientific workflows, the local provider (development) + cloud
+   Fargate (production) is simpler than Kubernetes. K8s shines when you
+   have a shared cluster already or need fine-grained resource management.
+
+
+Step 5: Working with Container Images
+-----------------------------------------
+
+Your code runs inside containers in K8s. The image must contain everything:
+
+.. code-block:: dockerfile
+
+   # Dockerfile
+   FROM python:3.12-slim
+
+   # Install system dependencies
+   RUN apt-get update && apt-get install -y --no-install-recommends \
+       gcc && rm -rf /var/lib/apt/lists/*
+
+   # Install Python packages
+   COPY requirements.txt .
+   RUN pip install --no-cache-dir -r requirements.txt
+
+   # Copy your workflow code
+   COPY . /app
+   WORKDIR /app
+
+   # Default command (overridden by Dask worker command)
+   CMD ["python", "-m", "distributed.cli.dask_worker"]
+
+Build and push:
+
+.. code-block:: bash
+
+   # Build the image
+   docker build -t ghcr.io/my-org/energy-model:latest .
+
+   # Push to registry (GitHub Container Registry in this example)
+   docker push ghcr.io/my-org/energy-model:latest
+
+.. admonition:: 💡 Key Concept: Image Pull Policy
+   :class: tip
+
+   When Kubernetes creates a pod, it needs to download (pull) the container
+   image from the registry. Pull policies control when:
+
+   * ``Always`` — always pull the latest (good for development)
+   * ``IfNotPresent`` — use cached version if available (faster)
+   * ``Never`` — never pull (image must be pre-loaded)
+
+   For production, use specific image tags (``v1.2.3``) rather than
+   ``latest`` to ensure reproducibility.
+
+
+Step 6: Monitoring Kubernetes Deployments
+-------------------------------------------
+
+.. code-block:: bash
+
+   # Watch pods come up
+   kubectl get pods -n team-climate -w
+
+   # Output:
+   # NAME                          READY   STATUS    RESTARTS   AGE
+   # dask-scheduler-abc123         1/1     Running   0          30s
+   # dask-worker-def456            1/1     Running   0          25s
+   # dask-worker-ghi789            1/1     Running   0          25s
+
+   # Check resource usage
+   kubectl top pods -n team-climate
+
+   # View worker logs
+   kubectl logs dask-worker-def456 -n team-climate
+
+   # Access Dask dashboard (port-forward to localhost)
+   kubectl port-forward svc/dask-scheduler 8787:8787 -n team-climate
+   # Then open http://localhost:8787 in your browser
+
+
+Common Questions
+-----------------
+
+**Q: Do I need to be a Kubernetes expert to use Scalable with K8s?**
+
+No. Scalable abstracts most K8s complexity. You need to know:
+
+* Your namespace name
+* Your container image URI
+* Basic kubectl commands for debugging
+
+The Dask Operator handles pod creation, scaling, and cleanup.
+
+**Q: What's the difference between Kubernetes and Docker?**
+
+* **Docker** = creates and runs individual containers on one machine
+* **Kubernetes** = manages many containers across many machines
+
+Docker builds the containers; Kubernetes orchestrates them.
+
+**Q: How does auto-scaling work in Kubernetes?**
+
+The Dask Operator watches queue depth (pending tasks). When tasks queue up,
+it creates more worker pods. When workers are idle, it removes them. This
+maps to the ``adaptive`` configuration in your manifest.
+
+**Q: What happens if a node (machine) fails?**
+
+Kubernetes detects the failure and reschedules pods from the failed node onto
+healthy nodes. Combined with Scalable's retry logic, tasks on the failed node
+are re-executed on new workers.
+
+**Q: Is Kubernetes free?**
+
+Kubernetes itself is open-source (free). But you pay for:
+
+* The machines (nodes) that form the cluster
+* Managed K8s services (EKS, GKE, AKS charge a management fee)
+* Networking and storage
+
+On-premise clusters have hardware and maintenance costs instead.
+
+
+What You Learned
+-----------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Term
+     - Definition
+   * - Container Orchestration
+     - Automating deployment, management, and scaling of containers
+   * - Kubernetes (K8s)
+     - Industry-standard container orchestration platform
+   * - Pod
+     - Smallest deployable unit in K8s (usually = one container)
+   * - Node
+     - Physical or virtual machine in the K8s cluster
+   * - Namespace
+     - Isolation boundary for resources within a cluster
+   * - Operator
+     - K8s extension that manages complex applications automatically
+   * - kubectl
+     - Command-line tool for interacting with Kubernetes
+   * - Helm
+     - Package manager for Kubernetes applications
+   * - Resource Requests
+     - Minimum guaranteed CPU/memory for a pod
+   * - Resource Limits
+     - Maximum allowed CPU/memory for a pod
+   * - Custom Resource (CRD)
+     - User-defined extension to Kubernetes resource types
+   * - Image Pull
+     - Downloading a container image from a registry
+
+
+Next Steps
+-----------
+
+You now understand Kubernetes fundamentals and how Scalable uses it for
+container-based distributed workflows.
+
+* **Next beginner tutorial:** :ref:`beginner_ml_emulation` — using machine
+  learning to optimize workflows
+* **Standard tutorial:** :ref:`tutorial_kubernetes` — production K8s
+  deployment, CI/CD integration, and advanced pod management
+* **Explore:** If you have access to a K8s cluster, try running
+  ``kubectl get nodes`` to see what machines are available, and
+  ``kubectl get namespaces`` to see the isolation boundaries.
diff --git a/docs/tutorials/beginner/09_ml_emulation.rst b/docs/tutorials/beginner/09_ml_emulation.rst
new file mode 100644
index 0000000..14fb740
--- /dev/null
+++ b/docs/tutorials/beginner/09_ml_emulation.rst
@@ -0,0 +1,540 @@
+.. _beginner_ml_emulation:
+
+======================================================
+Beginner Tutorial 9: Machine Learning for Smarter Workflows
+======================================================
+
+.. contents:: In This Tutorial
+   :local:
+   :depth: 2
+
+The Big Picture
+----------------
+
+After running your workflow many times, you've accumulated telemetry data
+showing how tasks perform: which scenarios are fast, which are slow, how much
+memory different inputs require. What if a computer could learn these patterns
+and predict optimal resource allocations? Or even replace expensive computations
+with fast approximations?
+
+This tutorial introduces **machine learning** concepts in the context of
+workflow optimization: using past experience to make smarter decisions about
+resource allocation and replacing expensive simulations with fast surrogate
+models.
+
+What You Will Learn
+--------------------
+
+By the end of this tutorial you will:
+
+* Understand what machine learning is at a high level.
+* Know the difference between training and inference.
+* Understand how Scalable's LearnedAdvisor predicts resource needs.
+* Know what a surrogate model (emulator) is and why it's useful.
+* Understand uncertainty and confidence thresholds.
+* Know what active learning is and how it improves emulators.
+* Use the ``@emulatable`` decorator to mark functions for emulation.
+
+Prerequisites
+--------------
+
+* Completed :ref:`beginner_getting_started`, :ref:`beginner_telemetry`, and
+  :ref:`beginner_scaling_strategies`.
+* ``pip install scalable[ml]`` (installs scikit-learn, dask-ml).
+* At least 5 completed telemetry runs (more history → better predictions).
+
+
+Key Concepts Explained
+-----------------------
+
+.. admonition:: 💡 Key Concept: What is Machine Learning?
+   :class: tip
+
+   **Machine learning (ML)** is teaching computers to find patterns in data
+   and make predictions without being explicitly programmed with rules.
+
+   **Traditional programming:**
+     Human writes rules → computer follows rules
+
+     .. code-block:: text
+
+        IF memory_usage > 8GB THEN allocate 16GB
+        IF memory_usage > 16GB THEN allocate 32GB
+
+   **Machine learning:**
+     Computer finds rules from data → uses them to predict
+
+     .. code-block:: text
+
+        Training data: [past runs with memory usage patterns]
+        ML model learns: "scenarios with >1000 nodes need ~12GB"
+        Prediction: "scenario 47 (1200 nodes) → recommend 16GB"
+
+   **Analogy:** A traditional program is like a recipe (follow these steps).
+   ML is like learning to cook from experience (after cooking 100 dishes,
+   you develop intuition about seasoning, timing, etc.).
+
+.. admonition:: 💡 Key Concept: Training vs. Inference
+   :class: tip
+
+   ML has two phases:
+
+   **Training** (learning):
+     Feed historical data to an algorithm. The algorithm adjusts its internal
+     parameters to fit the patterns in the data.
+
+     * Slow (minutes to hours)
+     * Done once (or periodically when new data is available)
+     * Requires labeled data (inputs + known correct outputs)
+
+   **Inference** (predicting):
+     Use the trained model to make predictions on new inputs.
+
+     * Fast (milliseconds)
+     * Done many times
+     * Uses the patterns learned during training
+
+   **In Scalable:**
+
+   * **Training** = learning from telemetry history (past run metrics)
+   * **Inference** = predicting resource needs for new runs
+
+.. admonition:: 💡 Key Concept: Features
+   :class: tip
+
+   **Features** are the input variables that a model uses to make
+   predictions. They're the characteristics of your data that the model
+   "looks at."
+
+   For Scalable's resource prediction:
+
+   * Task name
+   * Number of input data points
+   * Historical average duration for this task type
+   * Time of day
+   * Target provider type
+
+   **Feature engineering** is the process of choosing and transforming raw
+   data into useful features. Good features → good predictions.
+
+.. admonition:: 💡 Key Concept: What is a Model?
+   :class: tip
+
+   In ML, a **model** is a mathematical function learned from data. It maps
+   inputs (features) to outputs (predictions):
+
+   .. code-block:: text
+
+      Model: features → prediction
+      Example: [task="gridlabd", nodes=1200, history_avg=45s] → memory=12GB
+
+   Think of a model as a function that was "written" by the training process
+   rather than by a human programmer. The model doesn't understand what it's
+   doing — it just captures statistical patterns in the training data.
+
+   Common model types:
+
+   * **Linear regression** — simple, interpretable, assumes linear relationships
+   * **Decision tree** — series of if/then rules learned from data
+   * **Random forest** — many decision trees that vote on the answer
+   * **Gradient boosting** — trees that correct each other's mistakes
+
+   Scalable uses gradient boosting and random forests — they work well for
+   tabular data (like telemetry metrics) without much tuning.
+
+.. admonition:: 💡 Key Concept: Surrogate Model / Emulator
+   :class: tip
+
+   A **surrogate model** (also called an **emulator**) is a fast
+   approximation of an expensive computation.
+
+   **Real-world analogy:**
+
+   * Full model = weather simulation (supercomputer, hours of computation)
+   * Surrogate = weather forecast model (quick approximation based on patterns)
+
+   **In scientific computing:**
+
+   * Full model: Run GridLAB-D simulation (5 minutes per scenario)
+   * Surrogate: ML model trained on past GridLAB-D outputs (0.01 seconds)
+
+   **When to use surrogates:**
+
+   * Exploring parameter space (try 10,000 configurations quickly)
+   * Preliminary analysis (get approximate results fast)
+   * Optimization (surrogate guides search, full model validates)
+
+   **When NOT to use surrogates:**
+
+   * Final publication-quality results (use the full model)
+   * Inputs far outside training range (surrogate may be unreliable)
+   * When exact answers are required (surrogates are approximations)
+
+.. admonition:: 💡 Key Concept: Uncertainty
+   :class: tip
+
+   **Uncertainty** quantifies how confident a model is in its prediction.
+
+   .. code-block:: text
+
+      Model prediction: memory = 12GB ± 3GB (68% confidence)
+
+   This means:
+
+   * Best estimate: 12GB
+   * Likely range: 9–15GB
+   * The model isn't perfectly certain
+
+   **Why uncertainty matters:**
+
+   * High confidence (tight range) → trust the prediction, use the surrogate
+   * Low confidence (wide range) → don't trust it, use the full model instead
+
+   Scalable uses uncertainty to make **routing decisions**: if the emulator
+   is confident, use the fast approximation. If not, fall back to the
+   expensive full computation.
+
+.. admonition:: 💡 Key Concept: Active Learning
+   :class: tip
+
+   **Active learning** is a strategy where the model intelligently chooses
+   which new data points to learn from (rather than passively waiting for
+   random data).
+
+   **Analogy:** Imagine studying for an exam. Active learning means focusing
+   on topics you're weakest in (maximum learning benefit) rather than
+   re-studying topics you already know well.
+
+   **In Scalable:** The active learner identifies input scenarios where the
+   emulator is most uncertain and requests full-model runs for those specific
+   scenarios. This improves the emulator's accuracy with minimal expensive
+   computation.
+
+.. admonition:: 💡 Key Concept: Cross-Validation
+   :class: tip
+
+   **Cross-validation** tests model quality by repeatedly splitting data into
+   training and testing sets:
+
+   1. Split data into 5 parts (folds)
+   2. Train on 4 parts, test on 1
+   3. Repeat 5 times (each part is the test set once)
+   4. Average the test scores
+
+   This prevents **overfitting** — a model that memorizes the training data
+   but fails on new data. Cross-validation estimates how well the model will
+   perform on data it hasn't seen.
+
+
+Step 1: The ResourceAdvisor (Baseline — No ML)
+-------------------------------------------------
+
+Before ML, Scalable provides a deterministic, rule-based advisor:
+
+.. code-block:: python
+
+   from scalable import ResourceAdvisor
+
+   advisor = ResourceAdvisor.from_history("./.scalable/runs")
+   recommendation = advisor.recommend(task="run_simulation")
+   print(recommendation)
+   # {'cpus': 4, 'memory': '16G', 'basis': 'p95 of 50 historical runs'}
+
+This uses simple statistics (percentiles) — it works but doesn't learn
+complex patterns.
+
+
+Step 2: The LearnedAdvisor (ML-Powered)
+------------------------------------------
+
+The LearnedAdvisor uses machine learning on your telemetry history:
+
+.. code-block:: python
+
+   from scalable import LearnedAdvisor
+
+   # Train on historical telemetry
+   advisor = LearnedAdvisor.from_history(
+       "./.scalable/runs",
+       model_type="gradient_boosting",   # Algorithm choice
+   )
+
+   # Predict resources for a new run
+   recommendation = advisor.recommend(
+       task="run_simulation",
+       input_features={"num_nodes": 1200, "scenario_type": "peak_demand"},
+   )
+   print(recommendation)
+   # {'cpus': 2, 'memory': '8G', 'confidence': 0.87}
+
+.. admonition:: What's happening here
+   :class: note
+
+   1. ``from_history()`` loads telemetry data from past runs
+   2. It extracts features (task names, durations, resource usage)
+   3. It trains a gradient boosting model to predict resource needs
+   4. ``recommend()`` uses the trained model to predict for new inputs
+
+   The ``confidence: 0.87`` means the model is 87% confident in this
+   prediction. High confidence → the prediction is likely accurate.
+
+
+Step 3: The AdaptiveScaler
+----------------------------
+
+The AdaptiveScaler uses ML predictions to decide scaling in real-time:
+
+.. code-block:: python
+
+   from scalable import AdaptiveScaler
+
+   scaler = AdaptiveScaler(
+       min_workers=2,
+       max_workers=20,
+       scale_up_threshold=0.8,    # Scale up when 80% busy
+       scale_down_threshold=0.3,  # Scale down when 30% busy
+       cooldown_seconds=60,       # Wait 60s between scaling decisions
+   )
+
+.. admonition:: How adaptive scaling works with ML
+   :class: note
+
+   Without ML: scale based on simple thresholds (queue depth > N → add workers)
+
+   With ML: predict future load based on patterns. If the model predicts a
+   burst of heavy tasks coming, scale up BEFORE the queue fills. This reduces
+   latency because workers are already ready when tasks arrive.
+
+
+Step 4: Model Emulation with @emulatable
+-------------------------------------------
+
+The ``@emulatable`` decorator marks functions that can be approximated:
+
+.. code-block:: python
+
+   from scalable import emulatable
+
+   @emulatable(
+       inputs={"scenario_id": int, "num_nodes": int},
+       outputs={"demand_mwh": float, "peak_load": float},
+       confidence_threshold=0.9,   # Only use emulator if 90%+ confident
+   )
+   def run_gridlabd(scenario_id: int, num_nodes: int) -> dict:
+       """Full GridLAB-D simulation — takes 5 minutes."""
+       # ... expensive computation ...
+       return {"demand_mwh": result, "peak_load": peak}
+
+.. admonition:: What the decorator does
+   :class: note
+
+   When you call ``run_gridlabd(42, 1200)``:
+
+   1. Check: is there a trained emulator for this function?
+   2. If yes: ask the emulator for a prediction + uncertainty estimate
+   3. If confidence ≥ 0.9 (threshold): return the fast prediction (~0.01s)
+   4. If confidence < 0.9: run the full function (5 minutes) and record
+      the result for future training
+
+   This is **confidence-gated routing** — the system automatically decides
+   whether to use the fast path or slow path based on how trustworthy the
+   approximation is.
+
+
+Step 5: Training an Emulator
+-------------------------------
+
+.. code-block:: python
+
+   from scalable import EmulatorRegistry
+
+   # Create a registry (manages trained emulators)
+   registry = EmulatorRegistry(path="./.scalable/emulators")
+
+   # Train an emulator from historical results
+   registry.train(
+       function_name="run_gridlabd",
+       training_data=historical_results,   # Past function outputs
+       model_type="gradient_boosting",
+   )
+
+   # The emulator is now available for @emulatable routing
+   emulator = registry.get("run_gridlabd")
+   prediction = emulator.predict({"scenario_id": 42, "num_nodes": 1200})
+   print(prediction)
+   # {'demand_mwh': 4521.3, 'peak_load': 892.1, 'confidence': 0.94}
+
+.. admonition:: Under the Hood: How emulators learn
+   :class: hint
+
+   1. **Collect training data:** Every time the full model runs, the
+      input/output pair is recorded
+   2. **Train the model:** A gradient boosting model learns the relationship
+      between inputs (scenario_id, num_nodes) and outputs (demand_mwh,
+      peak_load)
+   3. **Estimate uncertainty:** The model also estimates how uncertain it is
+      (using the spread across individual trees in the forest)
+   4. **Deploy:** The trained emulator is saved and used for future calls
+
+
+Step 6: Active Learning — Getting Smarter Over Time
+------------------------------------------------------
+
+.. code-block:: python
+
+   from scalable import ActiveLearner
+
+   learner = ActiveLearner(
+       emulator=registry.get("run_gridlabd"),
+       acquisition_strategy="maximum_uncertainty",
+   )
+
+   # Ask: which scenarios should I run the full model on?
+   suggestions = learner.suggest(n=5, candidate_pool=all_scenarios)
+   print(suggestions)
+   # [scenario_47, scenario_892, scenario_13, ...]
+   # These are the scenarios where the emulator is LEAST confident
+
+.. admonition:: Why active learning is efficient
+   :class: note
+
+   Without active learning: run all 1000 scenarios with the full model
+   (expensive!)
+
+   With active learning:
+
+   1. Run 100 scenarios with full model (training set)
+   2. Train emulator
+   3. Ask "where are you least confident?" → get 10 suggestions
+   4. Run those 10 with full model
+   5. Retrain emulator (now better!)
+   6. Repeat until confidence is high everywhere
+
+   Result: ~150 full model runs instead of 1000, with similar accuracy.
+
+
+Step 7: Putting It All Together
+---------------------------------
+
+A workflow using ML optimization and emulation:
+
+.. code-block:: python
+
+   from scalable import (
+       ScalableSession, LearnedAdvisor, EmulatorRegistry, emulatable
+   )
+
+   # 1. ML-informed resource allocation
+   advisor = LearnedAdvisor.from_history("./.scalable/runs")
+   recommendation = advisor.recommend(task="run_gridlabd")
+
+   # 2. Emulation-capable function
+   @emulatable(
+       inputs={"scenario_id": int},
+       outputs={"demand_mwh": float},
+       confidence_threshold=0.9,
+   )
+   def run_gridlabd(scenario_id: int) -> dict:
+       # Full simulation (expensive)
+       ...
+
+   # 3. Run with ML-optimized resources
+   session = ScalableSession.from_manifest("./scalable.yaml", target="local")
+
+   futures = [session.submit(run_gridlabd, i, task="run_gridlabd")
+              for i in range(100)]
+   results = session.gather(futures)
+
+   # Some calls used the emulator (fast), others ran the full model
+   # Telemetry records which path each call took
+
+
+Common Questions
+-----------------
+
+**Q: Do I need ML expertise to use these features?**
+
+No. Scalable provides sensible defaults. You just need:
+
+* Enough telemetry history (5+ runs for the advisor)
+* The ``[ml]`` extra installed
+
+The system handles model selection, training, and evaluation.
+
+**Q: How much data do I need for the LearnedAdvisor?**
+
+Rule of thumb:
+
+* 5 runs → basic predictions (limited accuracy)
+* 20+ runs → reliable predictions
+* 100+ runs → high accuracy with confidence intervals
+
+More data = better predictions. The system falls back to the rule-based
+advisor when insufficient data exists.
+
+**Q: Can the emulator give wrong answers?**
+
+Yes! Emulators are approximations. That's why the confidence threshold exists.
+At 0.9 confidence, the emulator is only used when it's very sure. For
+critical results, always validate with the full model.
+
+**Q: What if my function's behavior changes?**
+
+Retrain the emulator with new data. The registry supports versioned emulators
+so you can track changes over time. Active learning automatically identifies
+where the emulator needs updating.
+
+**Q: Is there overhead to checking the emulator?**
+
+Negligible. Checking the emulator takes ~1ms. If your full function takes
+seconds or minutes, the check is invisible. If it takes <10ms, don't bother
+with emulation (the overhead isn't worth it).
+
+
+What You Learned
+-----------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Term
+     - Definition
+   * - Machine Learning
+     - Teaching computers to find patterns and make predictions from data
+   * - Training
+     - Learning phase where model adjusts to fit historical data
+   * - Inference
+     - Prediction phase using a trained model on new inputs
+   * - Features
+     - Input variables the model uses for predictions
+   * - Model
+     - Mathematical function learned from data (inputs → predictions)
+   * - Surrogate/Emulator
+     - Fast approximation of an expensive computation
+   * - Uncertainty
+     - Quantification of how confident a prediction is
+   * - Confidence Threshold
+     - Minimum confidence required to use the fast path
+   * - Active Learning
+     - Strategically choosing which data to learn from next
+   * - Cross-Validation
+     - Testing model quality by splitting data into train/test sets
+   * - Gradient Boosting
+     - ML algorithm using sequential corrective decision trees
+   * - Confidence-Gated Routing
+     - Using confidence to choose between emulator and full model
+
+
+Next Steps
+-----------
+
+You now understand how ML enhances workflow optimization and model emulation.
+
+* **Next beginner tutorial:** :ref:`beginner_ai_composition` — using AI
+  assistants for workflow development
+* **Standard tutorial:** :ref:`tutorial_ml_advanced` — advanced ML patterns,
+  hyperparameter tuning, and emulator calibration
+* **Try it:** Run your workflow 5+ times with different inputs. Then use
+  ``LearnedAdvisor.from_history()`` to see what it recommends. Compare the
+  ML recommendation to your current resource allocation.
diff --git a/docs/tutorials/beginner/10_ai_composition.rst b/docs/tutorials/beginner/10_ai_composition.rst
new file mode 100644
index 0000000..8034a54
--- /dev/null
+++ b/docs/tutorials/beginner/10_ai_composition.rst
@@ -0,0 +1,609 @@
+.. _beginner_ai_composition:
+
+======================================================
+Beginner Tutorial 10: AI-Assisted Workflow Development
+======================================================
+
+.. contents:: In This Tutorial
+   :local:
+   :depth: 2
+
+The Big Picture
+----------------
+
+Writing configuration files, diagnosing errors, and composing workflows
+requires expertise — you need to know Scalable's manifest schema, provider
+options, component settings, and best practices. What if an AI assistant
+could help with these tasks?
+
+Scalable includes AI-powered assistants that can onboard new model components,
+diagnose run failures, explain execution plans, compose workflows from
+descriptions, and migrate between providers. These assistants work in two
+modes: a fast deterministic mode (heuristics) and an intelligent LLM-powered
+mode.
+
+This tutorial explains what LLMs are, how Scalable uses them, and how to
+leverage AI assistance in your workflow development.
+
+What You Will Learn
+--------------------
+
+By the end of this tutorial you will:
+
+* Understand what Large Language Models (LLMs) are at a high level.
+* Know the difference between heuristic and LLM-powered modes.
+* Use ``scalable init-component`` to onboard new models.
+* Use ``scalable diagnose`` to analyze failures.
+* Use ``scalable explain`` to understand execution plans.
+* Use ``scalable compose`` to generate workflows from descriptions.
+* Use ``scalable migrate`` to convert between providers.
+* Understand when to trust (and verify) AI-generated output.
+
+Prerequisites
+--------------
+
+* Completed :ref:`beginner_getting_started` and :ref:`beginner_manifest_system`.
+* ``pip install scalable[ai]`` (installs ``jinja2``, ``rich``).
+* For LLM mode (optional): an API key for OpenAI, or a running Ollama instance.
+* Heuristic mode works without any AI setup.
+
+
+Key Concepts Explained
+-----------------------
+
+.. admonition:: 💡 Key Concept: What is a Large Language Model (LLM)?
+   :class: tip
+
+   A **Large Language Model** is an AI system trained on massive amounts of
+   text data that can generate human-like text, answer questions, and perform
+   reasoning tasks.
+
+   **How LLMs work (simplified):**
+
+   1. Trained on billions of words from the internet (books, code,
+      documentation)
+   2. Learns patterns: "given this input text, what text is likely to
+      come next?"
+   3. At inference time: given your prompt (question), generates a response
+      word by word, each word chosen based on what's most likely to follow
+
+   **Examples:** ChatGPT (OpenAI), Claude (Anthropic), Llama (Meta),
+   Gemini (Google)
+
+   **Key properties:**
+
+   * Can generate configuration files, code, explanations
+   * Not deterministic — same input may give slightly different outputs
+   * Can be wrong (hallucination) — always verify output
+   * Requires API access (cloud) or local hardware (Ollama)
+
+.. admonition:: 💡 Key Concept: Heuristic vs. AI-Powered
+   :class: tip
+
+   Scalable's assistants work in two modes:
+
+   **Heuristic mode** (rules-based):
+     * Uses predefined rules, templates, and pattern matching
+     * Deterministic: same input → always same output
+     * Works offline (no API calls)
+     * Fast and free
+     * Best for: CI/CD pipelines, reproducible outputs, no AI budget
+
+   **LLM-enhanced mode** (AI-powered):
+     * Uses an LLM for intelligent generation and reasoning
+     * Non-deterministic: may give slightly different outputs
+     * Requires API access (and costs money per call)
+     * Slower but more flexible
+     * Best for: creative composition, complex diagnosis, migration
+
+   **Why both?** Heuristic mode ensures Scalable works without external
+   dependencies. LLM mode adds intelligence for complex tasks. The system
+   gracefully degrades: if the LLM is unavailable, it falls back to
+   heuristics.
+
+.. admonition:: 💡 Key Concept: Templates
+   :class: tip
+
+   A **template** is a pre-structured document with placeholders that get
+   filled in with specific values. Think of it like a form letter:
+
+   .. code-block:: text
+
+      Dear {{ name }},
+      Your order of {{ item }} will arrive on {{ date }}.
+
+   In Scalable's AI assistants:
+
+   * Heuristic mode uses templates extensively (predictable, fast)
+   * LLM mode uses templates as "prompts" — instructions to the AI about
+     what to generate
+
+   Templates use **Jinja2** syntax (``{{ variable }}``, ``{% if %}``)
+   which is the most popular Python templating language.
+
+.. admonition:: 💡 Key Concept: Prompt Engineering
+   :class: tip
+
+   **Prompt engineering** is the art of crafting inputs to LLMs to get
+   desired outputs. LLMs are sensitive to how you ask:
+
+   **Bad prompt:**
+     "Make me a manifest"
+
+   **Good prompt:**
+     "Generate a Scalable manifest for a climate modeling workflow with:
+     - 2 targets: local (4 workers) and AWS Fargate
+     - 1 component: gridlabd (8 CPUs, 32GB RAM, Apptainer container)
+     - 1 task: run_simulation bound to gridlabd"
+
+   Scalable's AI assistants handle prompt engineering internally — they
+   construct detailed prompts from your high-level commands.
+
+.. admonition:: 💡 Key Concept: Code Generation
+   :class: tip
+
+   **Code generation** is using AI to automatically write code or
+   configuration. In Scalable's context:
+
+   * Generate manifest YAML from descriptions
+   * Generate component definitions from model documentation
+   * Generate migration plans between providers
+
+   **Trust but verify:** AI-generated code should always be reviewed by a
+   human. It might be syntactically correct but semantically wrong (e.g.,
+   reasonable-looking but incorrect resource allocations).
+
+.. admonition:: 💡 Key Concept: Deterministic vs. Non-Deterministic
+   :class: tip
+
+   **Deterministic:** Same input always produces the same output.
+     ``2 + 2 = 4`` (always). Heuristic mode is deterministic.
+
+   **Non-deterministic:** Same input may produce different outputs.
+     LLMs generate different text each time (due to random sampling in the
+     generation process). LLM mode is non-deterministic.
+
+   **Why this matters:**
+
+   * For CI/CD and testing → use heuristic mode (reproducible)
+   * For creative tasks → LLM mode is fine (you review the output anyway)
+
+.. admonition:: 💡 Key Concept: API (Application Programming Interface)
+   :class: tip
+
+   An **API** is a standardized way for programs to communicate. When
+   Scalable uses OpenAI's LLM, it sends a request to OpenAI's API
+   (over the internet) and receives the LLM's response.
+
+   .. code-block:: text
+
+      Your computer                     OpenAI servers
+      ┌──────────┐    HTTP request     ┌──────────────┐
+      │ Scalable │───────────────────▶│  GPT-4 model │
+      │          │◀───────────────────│              │
+      └──────────┘    JSON response    └──────────────┘
+
+   API keys authenticate you (prove you're allowed to use the service).
+   Each API call costs money (typically fractions of a cent).
+
+
+Step 1: Choosing Your Mode
+----------------------------
+
+Configure the AI backend via environment variable or ``.env`` file:
+
+.. code-block:: bash
+
+   # Heuristic mode (default, no AI required)
+   export SCALABLE_AI_BACKEND=none
+
+   # OpenAI mode (requires API key)
+   export SCALABLE_AI_BACKEND=openai
+   export AI_API_KEY=sk-your-key-here
+
+   # Ollama mode (local LLM, no cloud dependency)
+   export SCALABLE_AI_BACKEND=ollama
+   # (requires Ollama running locally with a model loaded)
+
+For this tutorial, all examples work in **heuristic mode** (no API key
+needed). LLM mode enhances the output quality but isn't required.
+
+
+Step 2: Onboarding a New Component
+-------------------------------------
+
+You're adding a new model (WaterShed) to your pipeline. Instead of writing
+the component definition manually, let the assistant help:
+
+.. code-block:: bash
+
+   scalable init-component \
+     --name watershed \
+     --image ghcr.io/watershed/model:3.0 \
+     --cpus 4 \
+     --memory 16G \
+     --description "Hydrological watershed model for runoff simulation"
+
+Output (heuristic mode):
+
+.. code-block:: yaml
+
+   # Generated component definition
+   components:
+     watershed:
+       image: ghcr.io/watershed/model:3.0
+       cpus: 4
+       memory: 16G
+       tags: [hydrology, watershed]
+       env:
+         WATERSHED_DATA: /data/watershed
+
+   tasks:
+     run_watershed:
+       component: watershed
+
+.. admonition:: What happened here
+   :class: note
+
+   The assistant:
+
+   1. Took your high-level inputs (name, image, resources)
+   2. Applied templates with sensible defaults
+   3. Inferred tags from the description ("watershed" → hydrology tag)
+   4. Generated matching task bindings
+   5. Added common environment variable patterns
+
+   In LLM mode, it could also suggest optimal resource allocations based on
+   the model type, recommend mount points for data, and generate a
+   preload script.
+
+
+Step 3: Diagnosing Failures
+------------------------------
+
+When a run fails, the diagnostic assistant helps identify root causes:
+
+.. code-block:: bash
+
+   scalable diagnose --run run-20260520T...-energy-forecast-abc123
+
+Output:
+
+.. code-block:: text
+
+   ═══════════════════════════════════════
+   Diagnosis Report
+   ═══════════════════════════════════════
+
+   Failures: 3 of 100 tasks
+
+   Root Cause Analysis:
+   ────────────────────
+   1. MEMORY_EXHAUSTION (2 tasks)
+      Tasks: run_simulation(47), run_simulation(92)
+      Evidence: MemoryError raised, peak memory 15.8GB exceeds 16GB limit
+      Recommendation: Increase component memory to 24G or add memory-aware
+      task splitting
+
+   2. INVALID_INPUT (1 task)
+      Task: run_simulation(13)
+      Evidence: ValueError raised in 0.1s (fast fail pattern)
+      Recommendation: Validate input data before submission or add
+      input-checking wrapper
+
+   Suggested Fixes:
+   ────────────────
+   • Apply overlay to increase memory:
+     overlays:
+       fix-oom:
+         components:
+           analysis:
+             memory: 24G
+
+.. admonition:: 💡 Key Concept: Root Cause Analysis
+   :class: tip
+
+   **Root cause analysis** means identifying the underlying reason for a
+   failure, not just the symptom.
+
+   * Symptom: "Task failed with MemoryError"
+   * Root cause: "Component memory (16G) is insufficient for scenarios with
+     >1000 nodes (which need ~20GB)"
+
+   The diagnostic assistant uses patterns in telemetry (failure timing,
+   error types, resource usage) to infer root causes.
+
+
+Step 4: Explaining Execution Plans
+-------------------------------------
+
+Get a human-readable explanation of what a plan will do:
+
+.. code-block:: bash
+
+   scalable explain ./scalable.yaml --target aws
+
+Output:
+
+.. code-block:: text
+
+   Plan Explanation
+   ═══════════════
+
+   This execution plan will:
+
+   1. Deploy to AWS Fargate in us-east-1 region
+   2. Start with 2 workers, scaling up to 10 based on demand
+   3. Each worker has 4 vCPUs and 16GB RAM
+   4. Workers run the ghcr.io/energy-model:latest container
+   5. Results stored to s3://my-bucket/scalable-runs/
+
+   Estimated cost: $5.38 for a 2-hour run at full scale
+
+   Key decisions:
+   • Adaptive scaling chosen (min=2, max=10) — cost-efficient for
+     variable workloads
+   • Fargate selected — no server management overhead
+   • S3 storage — durable, accessible from any future run
+
+This is especially useful for:
+
+* Reviewing a plan before running in production
+* Explaining to stakeholders what a workflow does
+* Documenting deployment decisions for team members
+
+
+Step 5: Composing Workflows from Descriptions
+------------------------------------------------
+
+The most powerful assistant — generate manifests from natural language:
+
+.. code-block:: bash
+
+   scalable compose \
+     --description "Climate modeling pipeline with GridLAB-D simulation \
+     (8 CPUs, 32GB RAM, containerized) followed by demand aggregation \
+     (2 CPUs, 4GB). Needs local and AWS targets with adaptive scaling."
+
+Output:
+
+.. code-block:: yaml
+
+   # Generated by scalable compose
+   version: 1
+   project:
+     name: climate-modeling
+
+   targets:
+     local:
+       provider: local
+       max_workers: 4
+       threads_per_worker: 2
+       processes: true
+       containers: none
+
+     aws:
+       provider: aws
+       region: us-east-1
+       cluster_type: fargate
+       worker_cpu: 8192
+       worker_mem: 32768
+       image: ${CONTAINER_IMAGE}
+       adaptive:
+         minimum: 2
+         maximum: 20
+
+   components:
+     gridlabd:
+       cpus: 8
+       memory: 32G
+       image: ${GRIDLABD_IMAGE}
+       tags: [simulation, energy]
+
+     postprocess:
+       cpus: 2
+       memory: 4G
+       tags: [analysis]
+
+   tasks:
+     run_gridlabd:
+       component: gridlabd
+
+     aggregate_demand:
+       component: postprocess
+
+.. admonition:: Heuristic vs. LLM composition
+   :class: note
+
+   **Heuristic mode:** Parses your description for keywords (CPUs, memory,
+   provider names) and fills templates. Works well for straightforward
+   requests.
+
+   **LLM mode:** Understands context and nuance. Can handle complex
+   descriptions like "similar to our hydrology pipeline but for energy,
+   with larger workers and spot instances for cost savings." Generates
+   more tailored output.
+
+
+Step 6: Migrating Between Providers
+--------------------------------------
+
+Moving a workflow from one provider to another:
+
+.. code-block:: bash
+
+   scalable migrate ./scalable.yaml --from slurm --to kubernetes
+
+Output:
+
+.. code-block:: yaml
+
+   # Migration: slurm → kubernetes
+   # Changes applied:
+
+   targets:
+     k8s:  # Replaces 'hpc' target
+       provider: kubernetes
+       namespace: team-climate
+       image: ${CONTAINER_IMAGE}        # NEW: K8s requires container image
+       adaptive:
+         minimum: 2
+         maximum: 64                    # Mapped from Slurm max_workers
+
+   # Migration notes:
+   # - Slurm 'queue: batch' → K8s namespace 'team-climate'
+   # - Slurm 'walltime' → K8s resource limits (no direct equivalent)
+   # - Slurm 'interface: ib0' → removed (K8s uses pod networking)
+   # - NEW: container image required (Slurm used bare metal)
+
+.. admonition:: Why migration is complex
+   :class: hint
+
+   Providers have different capabilities and concepts:
+
+   * Slurm has queues, walltimes, accounts → no direct K8s equivalent
+   * K8s has namespaces, pod specs, operators → no Slurm equivalent
+   * Cloud has regions, instance types, VPCs → not applicable to HPC
+
+   The migration assistant maps concepts where possible and flags
+   differences that require human decision.
+
+
+Step 7: Human-in-the-Loop Verification
+-----------------------------------------
+
+.. admonition:: 💡 Key Concept: Human-in-the-Loop
+   :class: tip
+
+   **Human-in-the-loop** means AI generates suggestions but a human makes
+   the final decision. This is important because:
+
+   * AI can generate plausible-looking but incorrect configuration
+   * Resource allocations affect cost and correctness
+   * Provider-specific nuances may be missed
+   * Security implications (IAM roles, network access) need human review
+
+   **Scalable's approach:** AI generates → human reviews → human applies.
+   All generated output requires explicit confirmation before being used.
+
+Best practices for verifying AI-generated output:
+
+1. **Always validate:** Run ``scalable validate`` on generated manifests
+2. **Dry-run first:** Use ``--dry-run`` to see effects without committing
+3. **Check resource allocations:** Are they sensible for your workload?
+4. **Review security:** Are IAM roles, images, and network settings correct?
+5. **Test locally first:** Use ``--target local`` before deploying to cloud
+
+
+Common Questions
+-----------------
+
+**Q: Do I need to pay for an LLM API to use the AI features?**
+
+No! Heuristic mode works without any API key and handles most common cases.
+LLM mode is an enhancement for complex or creative tasks.
+
+**Q: Is the AI generating code that could be insecure?**
+
+The AI generates configuration (YAML), not executable code. Always review
+generated manifests before running, especially for:
+
+* Container image sources (trust the registry?)
+* IAM/permission settings
+* Network exposure (public vs. private subnets)
+* Resource allocations (could generate expensive configurations)
+
+**Q: How much does LLM mode cost?**
+
+Typically $0.01–$0.10 per AI assistant call (depending on the model and
+prompt length). The ``explain`` command is cheapest (short output). The
+``compose`` command is most expensive (longer generation).
+
+**Q: Can I use a local LLM instead of OpenAI?**
+
+Yes! Set ``SCALABLE_AI_BACKEND=ollama`` and run an Ollama instance locally.
+This is free (no API costs) but requires a machine with enough RAM for
+the model (8–32GB depending on model size).
+
+**Q: What if the AI gives a wrong answer?**
+
+That's why validation exists. Generated manifests go through the same
+validation as hand-written ones. ``scalable validate`` catches structural
+errors. Semantic errors (wrong but valid resource allocations) require
+human judgment.
+
+**Q: Are heuristic outputs always correct?**
+
+Heuristic mode is deterministic and template-based, so it's predictable.
+But it may not handle edge cases as well as LLM mode. For standard
+workflows, heuristics work great. For unusual configurations, LLM mode
+provides better results.
+
+
+What You Learned
+-----------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Term
+     - Definition
+   * - Large Language Model (LLM)
+     - AI trained on text that can generate human-like responses
+   * - Heuristic Mode
+     - Rule-based, deterministic processing (no AI required)
+   * - LLM-Enhanced Mode
+     - AI-powered processing with richer understanding
+   * - Template
+     - Pre-structured document with fill-in-the-blank placeholders
+   * - Prompt Engineering
+     - Crafting inputs to LLMs to get desired outputs
+   * - Code Generation
+     - Using AI to automatically write code or configuration
+   * - Deterministic
+     - Same input always produces the same output
+   * - Non-Deterministic
+     - Same input may produce different outputs (LLM behavior)
+   * - API
+     - Standardized interface for programs to communicate
+   * - Human-in-the-Loop
+     - AI suggests, human decides and validates
+   * - Root Cause Analysis
+     - Identifying the underlying reason for a failure
+   * - Graceful Degradation
+     - Falling back to simpler mode when advanced features unavailable
+
+
+Next Steps
+-----------
+
+You've completed all 10 beginner tutorials! You now have a solid foundation
+in:
+
+* Distributed computing and workflow orchestration
+* Declarative configuration with manifests
+* Scaling strategies and provider architecture
+* Caching and performance optimization
+* Cloud computing and container technology
+* Telemetry and observability
+* Error handling and fault tolerance
+* Kubernetes and container orchestration
+* Machine learning for workflow optimization
+* AI-assisted development
+
+**Where to go from here:**
+
+* **Standard tutorials:** Work through :ref:`tutorials` for deeper technical
+  content and production patterns
+* **API documentation:** Explore the :ref:`api_section` for detailed reference
+* **Real project:** Apply what you've learned to your own workflow!
+* **Community:** Contribute improvements via :doc:`/how_to_contribute`
+
+.. admonition:: 🎉 Congratulations!
+   :class: note
+
+   You've gone from "what is distributed computing?" to understanding ML
+   optimization and AI-assisted development. The beginner tutorials gave
+   you the conceptual foundation — the standard tutorials and real-world
+   practice will build expertise on top of it.
diff --git a/docs/tutorials/beginner/index.rst b/docs/tutorials/beginner/index.rst
new file mode 100644
index 0000000..77f057b
--- /dev/null
+++ b/docs/tutorials/beginner/index.rst
@@ -0,0 +1,150 @@
+.. _beginner_tutorials:
+
+======================================================
+Beginner Tutorials
+======================================================
+
+Welcome! These tutorials are designed for people who are **new to Scalable and
+new to distributed computing**. Unlike the standard tutorials (which assume
+familiarity with clusters, containers, and cloud infrastructure), these
+beginner tutorials explain every concept from first principles.
+
+Who Are These For?
+-------------------
+
+These tutorials are perfect if you:
+
+* Have basic Python experience (functions, imports, loops) but haven't used
+  distributed computing frameworks before.
+* Are unfamiliar with terms like "workers," "schedulers," "containers," or
+  "declarative programming."
+* Want to understand not just *how* to use Scalable, but *why* it works the
+  way it does.
+* Prefer learning with extensive explanations, analogies, and context before
+  diving into code.
+
+If you already understand distributed computing, YAML configuration, and
+cloud/Kubernetes concepts, the :ref:`standard tutorials <tutorials>` will be
+more efficient for you.
+
+How These Tutorials Work
+-------------------------
+
+Each beginner tutorial mirrors a standard tutorial topic but adds:
+
+* **Key Concept boxes** — definitions of terms you'll encounter
+* **Why This Approach?** — design rationale and alternatives considered
+* **Under the Hood** — peeks at what Scalable is doing internally
+* **Common Questions** — FAQ-style answers to typical beginner questions
+* **Vocabulary Summary** — list of terms you mastered in each tutorial
+
+Learning Path
+--------------
+
+.. toctree::
+   :maxdepth: 1
+
+   01_getting_started
+   02_manifest_system
+   03_scaling_strategies
+   04_caching_performance
+   05_cloud_integration
+   06_telemetry
+   07_error_handling
+   08_kubernetes
+   09_ml_emulation
+   10_ai_composition
+
+.. list-table::
+   :header-rows: 1
+   :widths: 5 40 55
+
+   * - #
+     - Tutorial
+     - Concepts You'll Learn
+   * - 1
+     - :ref:`beginner_getting_started`
+     - Workflows, Dask, CLI, virtual environments, manifests
+   * - 2
+     - :ref:`beginner_manifest_system`
+     - Declarative programming, YAML, schemas, overlays
+   * - 3
+     - :ref:`beginner_scaling_strategies`
+     - Distributed computing, clusters, schedulers, providers
+   * - 4
+     - :ref:`beginner_caching`
+     - Hashing, memoization, content-addressable storage, decorators
+   * - 5
+     - :ref:`beginner_cloud_integration`
+     - Cloud computing, object storage, serverless, IAM
+   * - 6
+     - :ref:`beginner_telemetry`
+     - Observability, structured logging, event streams, metrics
+   * - 7
+     - :ref:`beginner_error_handling`
+     - Fault tolerance, retries, idempotency, partial success
+   * - 8
+     - :ref:`beginner_kubernetes`
+     - Containers, orchestration, pods, operators, namespaces
+   * - 9
+     - :ref:`beginner_ml_emulation`
+     - Machine learning, surrogate models, uncertainty, active learning
+   * - 10
+     - :ref:`beginner_ai_composition`
+     - LLMs, heuristics, code generation, templates
+
+Prerequisites
+--------------
+
+You need:
+
+* Python 3.11 or later installed on your computer.
+* A text editor (VS Code, PyCharm, or even Notepad).
+* A terminal/command prompt.
+* Basic Python knowledge: you can write functions, use ``import``, and run
+  ``.py`` files.
+
+You do **NOT** need:
+
+* Docker or container experience.
+* Cloud accounts (AWS, GCP).
+* A Kubernetes cluster.
+* Machine learning background.
+* Experience with distributed systems.
+
+All of these are explained as you encounter them.
+
+Graduating to Standard Tutorials
+----------------------------------
+
+After completing a beginner tutorial, you can move to the corresponding
+standard tutorial for deeper technical content, production patterns, and
+advanced configuration. Each beginner tutorial ends with a "Next Steps" section
+that bridges you to the standard version.
+
+.. list-table::
+   :header-rows: 1
+   :widths: 50 50
+
+   * - Beginner Tutorial
+     - Standard Tutorial
+   * - :ref:`beginner_getting_started`
+     - :ref:`tutorial_getting_started`
+   * - :ref:`beginner_manifest_system`
+     - :ref:`tutorial_manifest_system`
+   * - :ref:`beginner_scaling_strategies`
+     - :ref:`tutorial_scaling_strategies`
+   * - :ref:`beginner_caching`
+     - :ref:`tutorial_caching`
+   * - :ref:`beginner_cloud_integration`
+     - :ref:`tutorial_cloud_integration`
+   * - :ref:`beginner_telemetry`
+     - :ref:`tutorial_telemetry`
+   * - :ref:`beginner_error_handling`
+     - :ref:`tutorial_error_handling`
+   * - :ref:`beginner_kubernetes`
+     - :ref:`tutorial_kubernetes`
+   * - :ref:`beginner_ml_emulation`
+     - :ref:`tutorial_ml_advanced`
+   * - :ref:`beginner_ai_composition`
+     - :ref:`tutorial_ai_composition`
diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst
index 6fd1f3f..fa2c998 100644
--- a/docs/tutorials/index.rst
+++ b/docs/tutorials/index.rst
@@ -9,6 +9,19 @@ first installation to advanced production workflows. Each tutorial builds on a
 realistic scenario, includes full code examples with expected output, and ends
 with suggested next steps.
 
+Beginner Tutorials
+-------------------
+
+.. toctree::
+   :maxdepth: 1
+
+   beginner/index
+
+**New to distributed computing?** Start with the beginner tutorials. They cover
+the same 10 topics as the standard tutorials below but explain all concepts
+from first principles — no prior distributed systems, cloud, or container
+experience required.
+
 Getting Started
 ---------------
 
diff --git a/notebooks/beginner/01_getting_started.ipynb b/notebooks/beginner/01_getting_started.ipynb
new file mode 100644
index 0000000..fd69eb2
--- /dev/null
+++ b/notebooks/beginner/01_getting_started.ipynb
@@ -0,0 +1,365 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Beginner Tutorial 1: Your First Workflow\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "By the end of this notebook you will:\n",
+    "\n",
+    "- Understand what Scalable is and why it exists\n",
+    "- Know what Dask is (the engine under the hood)\n",
+    "- Create a manifest file (`scalable.yaml`)\n",
+    "- Validate, plan, and run a workflow end-to-end\n",
+    "- Read telemetry output to understand what happened\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Python 3.11+\n",
+    "- Basic Python knowledge (functions, imports)\n",
+    "- NO distributed computing experience needed!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install Scalable (skip if already installed)\n",
+    "# !pip install scalable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Verify installation\n",
+    "import scalable\n",
+    "print(f\"Scalable version: {scalable.__version__}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 Key Concept: What is a Workflow?\n",
+    "\n",
+    "A **workflow** is a sequence of computational steps that transforms inputs into outputs.\n",
+    "Think of it like a recipe:\n",
+    "- **Ingredients** = your input data\n",
+    "- **Steps** = your Python functions\n",
+    "- **Final dish** = your results\n",
+    "\n",
+    "## 💡 Key Concept: What is Distributed Computing?\n",
+    "\n",
+    "**Distributed computing** means splitting work across multiple processors or computers.\n",
+    "Instead of one CPU doing 1000 tasks one-by-one, you have 10 CPUs each doing 100 tasks simultaneously.\n",
+    "\n",
+    "**Analogy:** Stuffing 1000 envelopes alone takes hours. With 10 friends helping,\n",
+    "each person stuffs 100 and you finish 10× faster.\n",
+    "\n",
+    "## 💡 Key Concept: What is Dask?\n",
+    "\n",
+    "**Dask** is a Python library for parallel computing — it's the \"engine\" Scalable uses.\n",
+    "Scalable adds workflow management, manifests, caching, and telemetry on top of Dask.\n",
+    "\n",
+    "Why Dask? It:\n",
+    "- Integrates with NumPy/pandas\n",
+    "- Scales from laptop to 1000s of nodes\n",
+    "- Has a mature task scheduler\n",
+    "- Is widely used in scientific computing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Create a Project Directory\n",
+    "\n",
+    "Scalable workflows live in a dedicated directory with a manifest file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "# Create a temporary project directory for this tutorial\n",
+    "# Explanation: We use a temp directory so this notebook doesn't leave files on your system\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-beginner-01-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 Key Concept: Declarative Programming\n",
+    "\n",
+    "The manifest is **declarative** — you describe WHAT you want, not HOW to achieve it.\n",
+    "\n",
+    "**Imperative** (how): \"SSH into server, run this command, check output, allocate memory...\"\n",
+    "\n",
+    "**Declarative** (what): \"I need 2 workers with 1 CPU and 1GB RAM each.\"\n",
+    "\n",
+    "Scalable figures out the \"how\" for you. This is the same philosophy behind:\n",
+    "- SQL (`SELECT name FROM users` — you say what data, not how to fetch it)\n",
+    "- HTML (`<h1>Title</h1>` — you say what it is, not how to render it)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Write a Manifest\n",
+    "\n",
+    "The manifest (`scalable.yaml`) is the single source of truth for your workflow.\n",
+    "It answers:\n",
+    "- **What** is this project? (name)\n",
+    "- **Where** should it run? (targets)\n",
+    "- **How much** resources? (components)\n",
+    "- **What** work units? (tasks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write the manifest file\n",
+    "# Explanation: Each section has a specific purpose (explained in comments)\n",
+    "manifest_content = \"\"\"\\\n",
+    "version: 1                    # Schema version (always 1 for now)\n",
+    "project:\n",
+    "  name: hello-scalable        # Human-readable project name\n",
+    "\n",
+    "targets:                      # WHERE code runs\n",
+    "  local:                      # Target name (we'll use this later)\n",
+    "    provider: local           # Use the local machine\n",
+    "    max_workers: 2            # Run 2 workers in parallel\n",
+    "    threads_per_worker: 1     # 1 thread per worker\n",
+    "    processes: false           # Use threads (fast startup)\n",
+    "    containers: none          # No containerization\n",
+    "\n",
+    "components:                   # HOW MUCH resources each piece needs\n",
+    "  analysis:                   # Component name\n",
+    "    cpus: 1                   # 1 CPU per worker\n",
+    "    memory: 1G               # 1 GB RAM per worker\n",
+    "\n",
+    "tasks:                        # WHAT work units exist\n",
+    "  run_analysis:               # Task name\n",
+    "    component: analysis       # Links to the component above\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(manifest_content)\n",
+    "\n",
+    "print(\"Manifest written to scalable.yaml\")\n",
+    "print(\"---\")\n",
+    "print(manifest_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Validate the Manifest\n",
+    "\n",
+    "**Validation** = checking that your configuration is correct BEFORE running.\n",
+    "It catches typos, missing fields, and invalid values early."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable.manifest import parse_manifest, validate_manifest\n",
+    "\n",
+    "# Explanation: parse_manifest reads the YAML file into a Python object\n",
+    "# Explanation: validate_manifest checks it against the schema rules\n",
+    "manifest = parse_manifest(\"./scalable.yaml\")\n",
+    "errors = validate_manifest(manifest)\n",
+    "\n",
+    "if errors:\n",
+    "    for err in errors:\n",
+    "        print(f\"ERROR: {err}\")\n",
+    "else:\n",
+    "    print(\"✓ Manifest is valid (0 errors, 0 warnings)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Write Workflow Code\n",
+    "\n",
+    "Now let's write the Python function that does actual work.\n",
+    "\n",
+    "### 💡 Key Concept: Futures\n",
+    "\n",
+    "A **future** is a promise of a result that hasn't been computed yet.\n",
+    "When you call `session.submit()`, the task starts in the background and you\n",
+    "get a future immediately. Later, `session.gather()` waits for all results.\n",
+    "\n",
+    "**Analogy:** Ordering food at a counter — you get a receipt number (future)\n",
+    "immediately. The food is being prepared in the background. When your number\n",
+    "is called, you pick up your food (gather the result)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "from scalable import ScalableSession\n",
+    "\n",
+    "\n",
+    "def analyze_scenario(scenario_id: int) -> dict:\n",
+    "    \"\"\"Simulate an analysis task.\n",
+    "    \n",
+    "    In a real workflow this might run a climate model, process satellite data,\n",
+    "    or train an ML model. Here we just simulate work with a sleep.\n",
+    "    \"\"\"\n",
+    "    time.sleep(0.5)  # Simulate 0.5 seconds of computation\n",
+    "    return {\n",
+    "        \"scenario_id\": scenario_id,\n",
+    "        \"result\": scenario_id * 42,\n",
+    "        \"status\": \"complete\",\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a session from our manifest\n",
+    "# Explanation: ScalableSession sets up the Dask cluster based on our manifest\n",
+    "session = ScalableSession.from_manifest(\"./scalable.yaml\", target=\"local\")\n",
+    "\n",
+    "# Submit 6 tasks to be executed in parallel\n",
+    "# Explanation: submit() returns immediately with a future (the work runs in background)\n",
+    "futures = []\n",
+    "for i in range(6):\n",
+    "    future = session.submit(analyze_scenario, i, task=\"run_analysis\")\n",
+    "    futures.append(future)\n",
+    "\n",
+    "print(f\"Submitted {len(futures)} tasks\")\n",
+    "print(\"Tasks are running in the background on 2 workers...\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gather results (blocks until all tasks complete)\n",
+    "# Explanation: gather() waits for all futures to finish and returns results\n",
+    "results = session.gather(futures)\n",
+    "\n",
+    "print(f\"\\nCompleted {len(results)} scenarios!\")\n",
+    "for r in results:\n",
+    "    print(f\"  Scenario {r['scenario_id']}: result = {r['result']}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🤔 Think About It\n",
+    "\n",
+    "With 6 tasks and 2 workers, each taking 0.5 seconds:\n",
+    "- **Sequential** (no parallelism): 6 × 0.5s = 3.0 seconds\n",
+    "- **Parallel with 2 workers**: 3 batches × 0.5s = ~1.5 seconds\n",
+    "\n",
+    "The speedup is approximately 2× with 2 workers. This is the fundamental\n",
+    "value of distributed computing — trading more hardware for less time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clean up the session\n",
+    "session.close()\n",
+    "print(\"Session closed.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📖 Vocabulary Summary\n",
+    "\n",
+    "| Term | Definition |\n",
+    "|------|------------|\n",
+    "| Workflow | Sequence of computational steps (inputs → functions → outputs) |\n",
+    "| Distributed Computing | Splitting work across multiple processors/computers |\n",
+    "| Dask | Python parallel computing library (Scalable's engine) |\n",
+    "| Manifest | Declarative config file describing desired state |\n",
+    "| Declarative Programming | Describing WHAT you want, not HOW to do it |\n",
+    "| Provider | Abstraction over execution backend (local, HPC, cloud) |\n",
+    "| Worker | Process/thread that executes tasks |\n",
+    "| Future | Placeholder for a result being computed asynchronously |\n",
+    "| Validation | Checking correctness before execution |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "- **Next notebook:** `02_manifest_system.ipynb` — deep dive into YAML and declarative configuration\n",
+    "- **Standard notebook:** `../01_getting_started.ipynb` — same topic, less explanation\n",
+    "- **Try:** Change `max_workers` to 4 in the manifest and re-run. Is it faster?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cleanup: remove temporary directory\n",
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(f\"Cleaned up {project_dir}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/beginner/02_manifest_system.ipynb b/notebooks/beginner/02_manifest_system.ipynb
new file mode 100644
index 0000000..e3de5e5
--- /dev/null
+++ b/notebooks/beginner/02_manifest_system.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Beginner Tutorial 2: Understanding the Manifest System\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- What declarative programming is and why it matters\n",
+    "- How YAML syntax works (indentation, data types, lists)\n",
+    "- Every section of a `scalable.yaml` manifest\n",
+    "- How to use environment variables for portability\n",
+    "- How overlays customize settings per environment\n",
+    "- How to validate manifests programmatically\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed notebook 01 (Getting Started)\n",
+    "- `pip install scalable`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-beginner-02-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 Key Concept: Declarative vs. Imperative Programming\n",
+    "\n",
+    "**Imperative** = step-by-step instructions (HOW to do something):\n",
+    "```python\n",
+    "# Imperative: telling the computer exactly what to do\n",
+    "for i in range(4):\n",
+    "    worker = start_process()\n",
+    "    worker.set_memory('4G')\n",
+    "    worker.connect_to_scheduler(address)\n",
+    "```\n",
+    "\n",
+    "**Declarative** = describing desired state (WHAT you want):\n",
+    "```yaml\n",
+    "# Declarative: saying what you need\n",
+    "targets:\n",
+    "  local:\n",
+    "    max_workers: 4\n",
+    "    memory: 4G\n",
+    "```\n",
+    "\n",
+    "The manifest is declarative — you describe your desired state and Scalable figures out how.\n",
+    "\n",
+    "### Why declarative?\n",
+    "1. **Portable** — same manifest works on laptop, HPC, and cloud\n",
+    "2. **Reproducible** — anyone can recreate your exact setup\n",
+    "3. **Separation of concerns** — scientists declare needs, platform handles infrastructure"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 Key Concept: YAML Syntax\n",
+    "\n",
+    "YAML is a human-readable data format. Key rules:\n",
+    "\n",
+    "- **Indentation matters** (use spaces, NEVER tabs)\n",
+    "- **Key: value pairs** are the basic unit\n",
+    "- **Nesting** = indentation (2 spaces = child of parent)\n",
+    "- **Lists** use dashes (`- item`)\n",
+    "- **Comments** start with `#`\n",
+    "\n",
+    "```yaml\n",
+    "parent:           # This is a map\n",
+    "  child: value    # 2-space indent = nested\n",
+    "  list:           # This will be a list\n",
+    "    - item1\n",
+    "    - item2\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: A Complete Manifest with All Sections"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write a full-featured manifest\n",
+    "manifest_content = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: energy-forecast\n",
+    "  default_storage: ./outputs\n",
+    "  local_cache: ./cache\n",
+    "\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 4\n",
+    "    threads_per_worker: 2\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "\n",
+    "  hpc:\n",
+    "    provider: slurm\n",
+    "    queue: batch\n",
+    "    account: GCIMS\n",
+    "    walltime: \"04:00:00\"\n",
+    "\n",
+    "components:\n",
+    "  simulation:\n",
+    "    cpus: 4\n",
+    "    memory: 16G\n",
+    "    tags: [energy, simulation]\n",
+    "\n",
+    "  postprocess:\n",
+    "    cpus: 1\n",
+    "    memory: 4G\n",
+    "    tags: [analysis]\n",
+    "\n",
+    "tasks:\n",
+    "  run_simulation:\n",
+    "    component: simulation\n",
+    "\n",
+    "  aggregate_results:\n",
+    "    component: postprocess\n",
+    "\n",
+    "overlays:\n",
+    "  production:\n",
+    "    components:\n",
+    "      simulation:\n",
+    "        memory: 32G\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(manifest_content)\n",
+    "\n",
+    "print(\"Full manifest written. Let's explore each section...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Understanding Each Section\n",
+    "\n",
+    "### `project:` — Metadata\n",
+    "- `name`: Appears in logs and telemetry run IDs\n",
+    "- `default_storage`: Where outputs are saved\n",
+    "- `local_cache`: Where cached results are stored\n",
+    "\n",
+    "### `targets:` — WHERE code runs\n",
+    "- Each target has a `provider` key (local, slurm, aws, kubernetes)\n",
+    "- Other keys are provider-specific options\n",
+    "- Switch targets with `--target local` or `--target hpc`\n",
+    "\n",
+    "### `components:` — HOW MUCH resources\n",
+    "- `cpus`, `memory`: Resource allocation per worker\n",
+    "- `image`, `mounts`, `env`: Container configuration\n",
+    "- `tags`: Labels for grouping and filtering\n",
+    "\n",
+    "### `tasks:` — WHAT work units\n",
+    "- Each task points to a component via `component: name`\n",
+    "- Used when you call `session.submit(func, task=\"task_name\")`\n",
+    "\n",
+    "### `overlays:` — Environment-specific customization\n",
+    "- Patches applied on top of base configuration\n",
+    "- Only change what's different (deep merge)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable.manifest import parse_manifest, validate_manifest\n",
+    "\n",
+    "# Parse and validate\n",
+    "# Explanation: parse_manifest reads YAML into Python data structures\n",
+    "# Explanation: validate_manifest checks the structure against schema rules\n",
+    "manifest = parse_manifest(\"./scalable.yaml\")\n",
+    "errors = validate_manifest(manifest)\n",
+    "\n",
+    "if errors:\n",
+    "    print(\"Validation errors:\")\n",
+    "    for err in errors:\n",
+    "        print(f\"  ✗ {err}\")\n",
+    "else:\n",
+    "    print(\"✓ Manifest is valid!\")\n",
+    "    print(f\"\\nProject name: {manifest.get('project', {}).get('name')}\")\n",
+    "    print(f\"Targets defined: {list(manifest.get('targets', {}).keys())}\")\n",
+    "    print(f\"Components defined: {list(manifest.get('components', {}).keys())}\")\n",
+    "    print(f\"Tasks defined: {list(manifest.get('tasks', {}).keys())}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 Key Concept: Overlays\n",
+    "\n",
+    "An **overlay** is a set of patches applied on top of base configuration.\n",
+    "Like Photoshop layers — base image + layers that modify specific parts.\n",
+    "\n",
+    "**Why overlays?** Instead of maintaining separate manifests for dev/prod\n",
+    "(which drift apart), maintain ONE base + overlays for differences:\n",
+    "\n",
+    "- Development: 4 workers, 16G memory\n",
+    "- Production (overlay): same but 32G memory\n",
+    "\n",
+    "Apply with: `scalable run ./scalable.yaml --overlay production`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Demonstrate overlay concept\n",
+    "# Explanation: Overlays do a \"deep merge\" — only specified keys change\n",
+    "base_memory = manifest['components']['simulation']['memory']\n",
+    "overlay_memory = manifest.get('overlays', {}).get('production', {}).get('components', {}).get('simulation', {}).get('memory')\n",
+    "\n",
+    "print(f\"Base manifest: simulation memory = {base_memory}\")\n",
+    "print(f\"Production overlay: simulation memory = {overlay_memory}\")\n",
+    "print(f\"\\nWith --overlay production, memory becomes {overlay_memory}\")\n",
+    "print(f\"Everything else (cpus, tags, etc.) stays the same — that's deep merge!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Validation — Catching Errors Early\n",
+    "\n",
+    "Let's see what happens with an invalid manifest:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write a manifest with deliberate errors\n",
+    "bad_manifest = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: test-errors\n",
+    "\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 2\n",
+    "    threads_per_worker: 1\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "\n",
+    "components:\n",
+    "  analysis:\n",
+    "    cpus: 1\n",
+    "    memory: 1G\n",
+    "\n",
+    "tasks:\n",
+    "  run_analysis:\n",
+    "    component: nonexistent_component\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"bad_manifest.yaml\", \"w\") as f:\n",
+    "    f.write(bad_manifest)\n",
+    "\n",
+    "bad = parse_manifest(\"./bad_manifest.yaml\")\n",
+    "errors = validate_manifest(bad)\n",
+    "\n",
+    "print(\"Validation results for bad manifest:\")\n",
+    "if errors:\n",
+    "    for err in errors:\n",
+    "        print(f\"  ✗ {err}\")\n",
+    "else:\n",
+    "    print(\"  (No errors detected at parse level)\")\n",
+    "\n",
+    "print(\"\\n💡 Validation catches errors BEFORE you spend time/money running!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📖 Vocabulary Summary\n",
+    "\n",
+    "| Term | Definition |\n",
+    "|------|------------|\n",
+    "| Declarative Programming | Describing WHAT you want, not HOW to achieve it |\n",
+    "| YAML | Human-readable data format using indentation |\n",
+    "| Schema | Rules defining valid structure for data |\n",
+    "| Environment Variables | System-level key-value settings |\n",
+    "| Single Source of Truth | One authoritative location for configuration |\n",
+    "| Overlay | Patches applied on top of base configuration |\n",
+    "| Deep Merge | Recursive merge where only specified keys change |\n",
+    "| Binding | Connecting a task name to a component |\n",
+    "| Parsing | Converting text (YAML) into structured data |\n",
+    "| Validation | Checking that data meets schema rules |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "- **Next notebook:** `03_scaling_strategies.ipynb` — distributed computing fundamentals\n",
+    "- **Try:** Add a third component to the manifest and validate it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cleanup\n",
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(f\"Cleaned up {project_dir}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/beginner/03_scaling_strategies.ipynb b/notebooks/beginner/03_scaling_strategies.ipynb
new file mode 100644
index 0000000..d35bb9d
--- /dev/null
+++ b/notebooks/beginner/03_scaling_strategies.ipynb
@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Beginner Tutorial 3: How Distributed Computing Works\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- The client-scheduler-worker architecture\n",
+    "- Vertical vs. horizontal scaling\n",
+    "- Concurrency vs. parallelism\n",
+    "- How providers abstract execution backends\n",
+    "- Manual vs. adaptive scaling strategies\n",
+    "- Amdahl's Law (when more workers don't help)\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed notebooks 01–02\n",
+    "- `pip install scalable`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "import time\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-beginner-03-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 Key Concept: The Scheduler-Worker Architecture\n",
+    "\n",
+    "Distributed systems have three roles:\n",
+    "\n",
+    "```\n",
+    "┌──────────┐         ┌────────────┐         ┌──────────┐\n",
+    "│  Client  │────────▶│  Scheduler │────────▶│ Worker 1 │\n",
+    "│ (you)    │         │ (Dask)     │────────▶│ Worker 2 │\n",
+    "│          │◀────────│            │────────▶│ Worker 3 │\n",
+    "└──────────┘         └────────────┘         └──────────┘\n",
+    "   submit()            assigns tasks          executes &\n",
+    "   gather()            tracks state           returns results\n",
+    "```\n",
+    "\n",
+    "- **Client** = your script (submits work, collects results)\n",
+    "- **Scheduler** = traffic controller (decides where tasks run)\n",
+    "- **Workers** = labor force (actually execute functions)\n",
+    "\n",
+    "## 💡 Key Concept: Vertical vs. Horizontal Scaling\n",
+    "\n",
+    "- **Vertical (scale UP):** Bigger machine (more CPUs, more RAM)\n",
+    "- **Horizontal (scale OUT):** More machines working together\n",
+    "\n",
+    "Scalable focuses on horizontal scaling — distributing work across many workers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write manifest for scaling experiments\n",
+    "manifest_content = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: scaling-demo\n",
+    "\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 4\n",
+    "    threads_per_worker: 1\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "\n",
+    "components:\n",
+    "  analysis:\n",
+    "    cpus: 1\n",
+    "    memory: 1G\n",
+    "\n",
+    "tasks:\n",
+    "  run_analysis:\n",
+    "    component: analysis\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(manifest_content)\n",
+    "print(\"Manifest ready with 4 workers\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Demonstrating Parallelism\n",
+    "\n",
+    "Let's see the speedup from distributing work across workers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable import ScalableSession\n",
+    "\n",
+    "def simulate_work(task_id: int) -> dict:\n",
+    "    \"\"\"Each task takes 0.5 seconds.\"\"\"\n",
+    "    time.sleep(0.5)\n",
+    "    return {\"task_id\": task_id, \"result\": task_id * 10}\n",
+    "\n",
+    "# First: run SEQUENTIALLY (no Scalable) as baseline\n",
+    "start = time.time()\n",
+    "sequential_results = [simulate_work(i) for i in range(8)]\n",
+    "sequential_time = time.time() - start\n",
+    "print(f\"Sequential (1 worker): {sequential_time:.2f}s for 8 tasks\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now: run in PARALLEL with Scalable (4 workers)\n",
+    "session = ScalableSession.from_manifest(\"./scalable.yaml\", target=\"local\")\n",
+    "\n",
+    "start = time.time()\n",
+    "futures = [session.submit(simulate_work, i, task=\"run_analysis\") for i in range(8)]\n",
+    "parallel_results = session.gather(futures)\n",
+    "parallel_time = time.time() - start\n",
+    "\n",
+    "print(f\"Parallel (4 workers): {parallel_time:.2f}s for 8 tasks\")\n",
+    "print(f\"Speedup: {sequential_time / parallel_time:.1f}x\")\n",
+    "print(f\"\\n💡 With 4 workers and 8 tasks: 2 batches × 0.5s ≈ 1.0s (ideal)\")\n",
+    "print(f\"   Actual overhead brings it slightly above ideal.\")\n",
+    "\n",
+    "session.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🤔 Think About It: Amdahl's Law\n",
+    "\n",
+    "**Amdahl's Law** says: speedup is limited by the sequential portion.\n",
+    "\n",
+    "If 90% of work can be parallelized and 10% is sequential:\n",
+    "- 10 workers → ~5.3× speedup (not 10×)\n",
+    "- 100 workers → ~9.2× speedup (not 100×)\n",
+    "\n",
+    "**Lesson:** Don't throw infinite workers at a problem. There's always a point\n",
+    "of diminishing returns. Telemetry helps you find the sweet spot.\n",
+    "\n",
+    "## 💡 Key Concept: Provider Pattern\n",
+    "\n",
+    "Providers are **abstraction layers** — they hide complexity behind a simple interface.\n",
+    "\n",
+    "Like a light switch: you don't need to know if your electricity comes from\n",
+    "solar, nuclear, or gas. The switch (provider API) works the same regardless.\n",
+    "\n",
+    "| Provider | What it does | When to use |\n",
+    "|----------|-------------|-------------|\n",
+    "| `local` | Threads/processes on your machine | Development |\n",
+    "| `slurm` | Jobs on HPC cluster | Production batch |\n",
+    "| `aws` | Containers on AWS Fargate | Cloud burst |\n",
+    "| `kubernetes` | Pods in K8s cluster | Shared infrastructure |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📖 Vocabulary Summary\n",
+    "\n",
+    "| Term | Definition |\n",
+    "|------|------------|\n",
+    "| Horizontal Scaling | Adding more machines/workers |\n",
+    "| Vertical Scaling | Getting a bigger single machine |\n",
+    "| Scheduler | Assigns tasks to available workers |\n",
+    "| Worker | Process/thread that executes tasks |\n",
+    "| Client | Your script (submits work, gathers results) |\n",
+    "| Concurrency | Multiple tasks in progress (maybe not simultaneous) |\n",
+    "| Parallelism | Multiple tasks executing at exact same instant |\n",
+    "| Provider | Abstraction over execution backend |\n",
+    "| Adaptive Scaling | Auto-adjusting worker count based on demand |\n",
+    "| Amdahl's Law | Speedup limited by sequential portion |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cleanup\n",
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(f\"Cleaned up {project_dir}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/beginner/04_caching_performance.ipynb b/notebooks/beginner/04_caching_performance.ipynb
new file mode 100644
index 0000000..8f305ae
--- /dev/null
+++ b/notebooks/beginner/04_caching_performance.ipynb
@@ -0,0 +1,250 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Beginner Tutorial 4: Caching — Avoiding Redundant Work\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- What caching is and why it matters\n",
+    "- How hash functions create \"fingerprints\" of data\n",
+    "- What content-addressable storage means\n",
+    "- How to use the `@cacheable` decorator\n",
+    "- How to handle file-based inputs\n",
+    "- Cache invalidation strategies\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed notebook 01 (Getting Started)\n",
+    "- `pip install scalable`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "import time\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-beginner-04-\")\n",
+    "os.chdir(project_dir)\n",
+    "os.makedirs(\"cache\", exist_ok=True)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 Key Concept: What is Caching?\n",
+    "\n",
+    "**Caching** = storing results so you don't recompute them.\n",
+    "It trades **storage space** for **computation time**.\n",
+    "\n",
+    "Real-world examples:\n",
+    "- Browser cache: stores images so pages load faster on revisit\n",
+    "- CPU cache: keeps frequently-used data close to processor\n",
+    "\n",
+    "In Scalable: \"If I've already computed `f(x)` and saved the result,\n",
+    "don't compute it again — just return the saved result.\"\n",
+    "\n",
+    "## 💡 Key Concept: Hash Functions\n",
+    "\n",
+    "A **hash function** takes any input and produces a fixed-size \"fingerprint\":\n",
+    "\n",
+    "```\n",
+    "\"Hello\"      → a3b8c9d2...  (always same for same input)\n",
+    "\"Hello!\"     → 7f83b165...  (tiny change → totally different hash)\n",
+    "(500MB file) → e1f0a2b3...  (same fixed size regardless of input)\n",
+    "```\n",
+    "\n",
+    "Key properties: deterministic, fixed-size output, one-way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Demonstrate hashing\n",
+    "import hashlib\n",
+    "\n",
+    "# Same input → same hash (deterministic)\n",
+    "hash1 = hashlib.sha256(b\"Hello, World!\").hexdigest()[:16]\n",
+    "hash2 = hashlib.sha256(b\"Hello, World!\").hexdigest()[:16]\n",
+    "print(f\"Hash of 'Hello, World!': {hash1}\")\n",
+    "print(f\"Hash again (same):      {hash2}\")\n",
+    "print(f\"Same? {hash1 == hash2}\")\n",
+    "\n",
+    "# Tiny change → completely different hash\n",
+    "hash3 = hashlib.sha256(b\"Hello, World!!\").hexdigest()[:16]\n",
+    "print(f\"\\nHash of 'Hello, World!!': {hash3}\")\n",
+    "print(f\"Completely different! This is the 'avalanche effect'.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 Key Concept: Python Decorators\n",
+    "\n",
+    "A **decorator** wraps a function to add behavior without changing its code:\n",
+    "\n",
+    "```python\n",
+    "@some_decorator      # ← This wraps the function below\n",
+    "def my_function(x):\n",
+    "    return x * 2\n",
+    "```\n",
+    "\n",
+    "Scalable's `@cacheable` decorator adds: \"check cache before computing, save result after computing.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up environment for caching\n",
+    "os.environ['SCALABLE_CACHE_DIR'] = os.path.join(project_dir, 'cache')\n",
+    "\n",
+    "from scalable import cacheable\n",
+    "\n",
+    "# Explanation: @cacheable tells Scalable to cache this function's results\n",
+    "# return_type=dict: tells Scalable how to serialize the result\n",
+    "# scenario_id=int: tells Scalable how to hash this argument\n",
+    "@cacheable(return_type=dict, scenario_id=int)\n",
+    "def expensive_simulation(scenario_id: int) -> dict:\n",
+    "    \"\"\"Simulate expensive computation (2 seconds).\"\"\"\n",
+    "    time.sleep(2)  # Pretend this takes 2 seconds\n",
+    "    return {\n",
+    "        \"scenario_id\": scenario_id,\n",
+    "        \"result\": scenario_id * 42,\n",
+    "        \"computed\": True,\n",
+    "    }\n",
+    "\n",
+    "print(\"Function decorated with @cacheable\")\n",
+    "print(\"First call will be slow (cache miss), second will be instant (cache hit)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# FIRST CALL: Cache miss (slow — has to compute)\n",
+    "start = time.time()\n",
+    "result1 = expensive_simulation(scenario_id=42)\n",
+    "first_call_time = time.time() - start\n",
+    "\n",
+    "print(f\"First call (cache MISS): {first_call_time:.2f}s\")\n",
+    "print(f\"Result: {result1}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# SECOND CALL: Cache hit (instant — returns saved result)\n",
+    "start = time.time()\n",
+    "result2 = expensive_simulation(scenario_id=42)\n",
+    "second_call_time = time.time() - start\n",
+    "\n",
+    "print(f\"Second call (cache HIT): {second_call_time:.4f}s\")\n",
+    "print(f\"Result: {result2}\")\n",
+    "print(f\"\\n💡 Speedup: {first_call_time / max(second_call_time, 0.001):.0f}x faster!\")\n",
+    "print(f\"Same result? {result1 == result2}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DIFFERENT INPUT: Cache miss (different scenario_id = different cache key)\n",
+    "start = time.time()\n",
+    "result3 = expensive_simulation(scenario_id=99)\n",
+    "third_call_time = time.time() - start\n",
+    "\n",
+    "print(f\"Different input (cache MISS): {third_call_time:.2f}s\")\n",
+    "print(f\"Result: {result3}\")\n",
+    "print(f\"\\n💡 Different input → different cache key → must recompute\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🤔 Think About It: Cache Invalidation\n",
+    "\n",
+    "What if you **fix a bug** in your function but the inputs stay the same?\n",
+    "\n",
+    "The cache key hasn't changed (same function name + same arguments),\n",
+    "so you'll get the OLD (buggy) result from cache!\n",
+    "\n",
+    "**Solutions:**\n",
+    "1. Clear the cache: `rm -rf ./cache/`\n",
+    "2. Version your function: add `_version=\"2\"` to the decorator\n",
+    "\n",
+    "This is why cache invalidation is one of the \"two hard things in computer science.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📖 Vocabulary Summary\n",
+    "\n",
+    "| Term | Definition |\n",
+    "|------|------------|\n",
+    "| Caching | Storing results for reuse (trade storage for time) |\n",
+    "| Hash Function | Fixed-size fingerprint of any input |\n",
+    "| Content-Addressable Storage | Data addressed by content hash, not name |\n",
+    "| Memoization | Caching function results based on inputs |\n",
+    "| Decorator | Python pattern wrapping a function to add behavior |\n",
+    "| Cache Key | Unique identifier (hash of function + args) |\n",
+    "| Cache Hit | Result found in cache (fast!) |\n",
+    "| Cache Miss | Result not found, must compute |\n",
+    "| Cache Invalidation | Deciding when cached results are stale |\n",
+    "| Serialization | Converting objects to bytes for storage |\n",
+    "| Determinism | Same inputs always produce same output |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cleanup\n",
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(f\"Cleaned up {project_dir}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/beginner/05_cloud_integration.ipynb b/notebooks/beginner/05_cloud_integration.ipynb
new file mode 100644
index 0000000..7d1221a
--- /dev/null
+++ b/notebooks/beginner/05_cloud_integration.ipynb
@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Beginner Tutorial 5: Cloud Computing Fundamentals\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- What cloud computing is (renting computers over the internet)\n",
+    "- Core cloud services: compute, storage, networking\n",
+    "- Object storage (S3/GCS) vs. filesystems\n",
+    "- Containers and why they matter for cloud\n",
+    "- How to configure cloud targets in Scalable\n",
+    "- Cost estimation before running\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed notebooks 01–02\n",
+    "- `pip install scalable[cloud]` (for code examples)\n",
+    "- NO cloud account required (conceptual + configuration examples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-beginner-05-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 Key Concept: What is Cloud Computing?\n",
+    "\n",
+    "**Cloud computing** = renting computers, storage, and networking over the internet.\n",
+    "Pay only for what you use.\n",
+    "\n",
+    "**Before cloud:** Buy servers, install them, maintain them, pay whether used or idle.\n",
+    "\n",
+    "**With cloud:** Request \"10 machines for 2 hours\" → they appear in seconds → stop paying when done.\n",
+    "\n",
+    "## 💡 Key Concept: Object Storage (S3/GCS)\n",
+    "\n",
+    "**Object storage** = cloud service for storing files in \"buckets\":\n",
+    "```\n",
+    "s3://my-bucket/scalable-runs/results.json\n",
+    "│    │         │              │\n",
+    "│    bucket    prefix         object key\n",
+    "└── protocol\n",
+    "```\n",
+    "\n",
+    "Why not a regular filesystem?\n",
+    "- Scales to petabytes\n",
+    "- 99.999999999% durability (11 nines!)\n",
+    "- Accessible from anywhere\n",
+    "- Very cheap ($0.023/GB/month)\n",
+    "\n",
+    "## 💡 Key Concept: Containers\n",
+    "\n",
+    "A **container** packages code + all dependencies into a portable unit.\n",
+    "Solves the \"works on my machine\" problem.\n",
+    "\n",
+    "**Analogy:** Like a shipping container — the crane doesn't care what's inside,\n",
+    "it just knows how to move the standard container."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example: Cloud target configuration in a manifest\n",
+    "cloud_manifest = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: energy-model\n",
+    "  default_storage: s3://my-bucket/scalable-runs/\n",
+    "\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 4\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "\n",
+    "  aws:\n",
+    "    provider: aws\n",
+    "    region: us-east-1\n",
+    "    cluster_type: fargate\n",
+    "    worker_cpu: 4096       # 4 vCPUs (Fargate units: 1024 = 1 vCPU)\n",
+    "    worker_mem: 16384      # 16 GB (in MB)\n",
+    "    image: 123456789.dkr.ecr.us-east-1.amazonaws.com/energy-model:latest\n",
+    "    adaptive:\n",
+    "      minimum: 1\n",
+    "      maximum: 10\n",
+    "\n",
+    "components:\n",
+    "  analysis:\n",
+    "    cpus: 4\n",
+    "    memory: 16G\n",
+    "\n",
+    "tasks:\n",
+    "  run_analysis:\n",
+    "    component: analysis\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(cloud_manifest)\n",
+    "\n",
+    "print(\"Cloud manifest written.\")\n",
+    "print(\"\\nKey cloud settings explained:\")\n",
+    "print(\"  region: us-east-1 → which data center\")\n",
+    "print(\"  cluster_type: fargate → serverless containers (no servers to manage)\")\n",
+    "print(\"  image: ... → container with your code & dependencies\")\n",
+    "print(\"  adaptive: min=1, max=10 → auto-scale based on demand\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 Key Concept: Cloud Cost Model\n",
+    "\n",
+    "Cloud charges for what you use:\n",
+    "\n",
+    "| Resource | Pricing | Example |\n",
+    "|----------|---------|--------|\n",
+    "| Compute | Per-second while running | $0.04/vCPU-hour |\n",
+    "| Storage | Per-GB-month | $0.023/GB-month |\n",
+    "| Network | Per-GB transferred out | $0.09/GB |\n",
+    "\n",
+    "**Example run cost:**\n",
+    "```\n",
+    "10 workers × 4 vCPU × 2 hours × $0.04/vCPU-hour = $3.20\n",
+    "10 workers × 16GB × 2 hours × $0.004/GB-hour     = $1.28\n",
+    "Output: 50GB × $0.023/GB-month                    = $1.15/month\n",
+    "─────────────────────────────────────────────────────\n",
+    "Total: ~$5.63 (one-time) + $1.15/month (storage)\n",
+    "```\n",
+    "\n",
+    "Scalable's `--dry-run` flag estimates costs BEFORE running!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable.manifest import parse_manifest, validate_manifest\n",
+    "\n",
+    "# Validate the cloud manifest\n",
+    "manifest = parse_manifest(\"./scalable.yaml\")\n",
+    "errors = validate_manifest(manifest)\n",
+    "\n",
+    "if errors:\n",
+    "    for err in errors:\n",
+    "        print(f\"  ✗ {err}\")\n",
+    "else:\n",
+    "    print(\"✓ Cloud manifest is valid\")\n",
+    "    print(f\"\\nTargets available: {list(manifest['targets'].keys())}\")\n",
+    "    print(f\"\\n💡 Same manifest works locally AND in the cloud!\")\n",
+    "    print(f\"   Development: scalable run --target local\")\n",
+    "    print(f\"   Production:  scalable run --target aws\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🤔 Think About It\n",
+    "\n",
+    "Notice how the **Python code doesn't change** between local and cloud.\n",
+    "Only the `--target` flag changes. This is the power of:\n",
+    "- Declarative manifests (configuration, not code)\n",
+    "- Provider abstraction (same API, different backend)\n",
+    "\n",
+    "## 📖 Vocabulary Summary\n",
+    "\n",
+    "| Term | Definition |\n",
+    "|------|------------|\n",
+    "| Cloud Computing | Renting resources over internet, pay-per-use |\n",
+    "| Object Storage (S3/GCS) | Cloud file storage in buckets |\n",
+    "| Container | Packaged code + dependencies, runs anywhere |\n",
+    "| Container Registry | Storage for container images (ECR, GCR) |\n",
+    "| IAM | Identity & Access Management (permissions) |\n",
+    "| VPC | Virtual Private Cloud (isolated network) |\n",
+    "| Fargate | AWS serverless container service |\n",
+    "| Region | Geographic location of data center |\n",
+    "| Spot Instance | Cheap compute that can be interrupted |\n",
+    "| Artifact | Workflow output stored for persistence |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cleanup\n",
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(f\"Cleaned up {project_dir}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/beginner/06_telemetry.ipynb b/notebooks/beginner/06_telemetry.ipynb
new file mode 100644
index 0000000..0b2b542
--- /dev/null
+++ b/notebooks/beginner/06_telemetry.ipynb
@@ -0,0 +1,261 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Beginner Tutorial 6: Understanding What Happened (Telemetry)\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- What telemetry and observability mean\n",
+    "- Metrics vs. logs vs. traces\n",
+    "- How to read JSONL telemetry files\n",
+    "- How to generate reports\n",
+    "- Using telemetry to identify bottlenecks\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed notebook 01 (Getting Started)\n",
+    "- `pip install scalable`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import json\n",
+    "import tempfile\n",
+    "import time\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-beginner-06-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 Key Concept: What is Telemetry?\n",
+    "\n",
+    "**Telemetry** = automated collection of data about what your program did.\n",
+    "From Greek: *tele* (remote) + *metron* (measurement).\n",
+    "\n",
+    "Like a flight recorder (black box) for your workflow:\n",
+    "- When did tasks start/finish?\n",
+    "- How much memory/CPU was used?\n",
+    "- Which tasks failed and why?\n",
+    "\n",
+    "## 💡 Key Concept: Structured Logging\n",
+    "\n",
+    "**Structured logging** = recording events as machine-parseable data (JSON)\n",
+    "instead of free-form text.\n",
+    "\n",
+    "Unstructured: `2026-05-20 INFO Task sim(42) done in 4.2s`\n",
+    "\n",
+    "Structured:\n",
+    "```json\n",
+    "{\"event\": \"task_completed\", \"task\": \"sim\", \"duration_s\": 4.2}\n",
+    "```\n",
+    "\n",
+    "Structured logs can be filtered, aggregated, and queried programmatically.\n",
+    "\n",
+    "## 💡 Key Concept: JSONL (JSON Lines)\n",
+    "\n",
+    "**JSONL** = one JSON object per line. Perfect for event streams:\n",
+    "- Appendable (just add a new line)\n",
+    "- Streamable (process one line at a time)\n",
+    "- Each line is independently parseable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First, let's run a workflow to generate telemetry\n",
+    "manifest_content = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: telemetry-demo\n",
+    "\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 2\n",
+    "    threads_per_worker: 1\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "\n",
+    "components:\n",
+    "  analysis:\n",
+    "    cpus: 1\n",
+    "    memory: 1G\n",
+    "\n",
+    "tasks:\n",
+    "  run_analysis:\n",
+    "    component: analysis\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(manifest_content)\n",
+    "print(\"Manifest ready. Running workflow to generate telemetry...\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "from scalable import ScalableSession\n",
+    "\n",
+    "def variable_work(task_id: int) -> dict:\n",
+    "    \"\"\"Task with variable duration (to make telemetry interesting).\"\"\"\n",
+    "    duration = 0.2 + random.random() * 0.8  # 0.2 to 1.0 seconds\n",
+    "    time.sleep(duration)\n",
+    "    return {\"task_id\": task_id, \"duration\": duration}\n",
+    "\n",
+    "# Run workflow\n",
+    "session = ScalableSession.from_manifest(\"./scalable.yaml\", target=\"local\")\n",
+    "futures = [session.submit(variable_work, i, task=\"run_analysis\") for i in range(10)]\n",
+    "results = session.gather(futures)\n",
+    "session.close()\n",
+    "\n",
+    "print(f\"Completed {len(results)} tasks\")\n",
+    "print(\"Telemetry has been recorded automatically!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Explore the telemetry directory structure\n",
+    "scalable_dir = \".scalable/runs\"\n",
+    "if os.path.exists(scalable_dir):\n",
+    "    runs = os.listdir(scalable_dir)\n",
+    "    print(f\"Found {len(runs)} run(s) in .scalable/runs/\")\n",
+    "    if runs:\n",
+    "        run_dir = os.path.join(scalable_dir, sorted(runs)[-1])\n",
+    "        print(f\"\\nLatest run: {os.path.basename(run_dir)}\")\n",
+    "        print(\"\\nFiles:\")\n",
+    "        for f in sorted(os.listdir(run_dir)):\n",
+    "            size = os.path.getsize(os.path.join(run_dir, f))\n",
+    "            print(f\"  {f} ({size} bytes)\")\n",
+    "else:\n",
+    "    print(\"No telemetry directory found (session may not have written telemetry in this mode)\")\n",
+    "    print(\"\\n💡 In production usage, telemetry is always written.\")\n",
+    "    print(\"Let's simulate what telemetry data looks like...\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Simulate telemetry data to show the format\n",
+    "# (In production, this is generated automatically)\n",
+    "import datetime\n",
+    "\n",
+    "simulated_events = []\n",
+    "base_time = datetime.datetime.now(datetime.timezone.utc)\n",
+    "\n",
+    "for i, r in enumerate(results):\n",
+    "    start_time = base_time + datetime.timedelta(seconds=i * 0.1)\n",
+    "    end_time = start_time + datetime.timedelta(seconds=r['duration'])\n",
+    "    \n",
+    "    simulated_events.append({\n",
+    "        \"event\": \"task_completed\",\n",
+    "        \"task\": \"run_analysis\",\n",
+    "        \"task_id\": r['task_id'],\n",
+    "        \"timestamp\": end_time.isoformat(),\n",
+    "        \"duration_s\": round(r['duration'], 3),\n",
+    "        \"worker\": f\"worker-{i % 2}\"\n",
+    "    })\n",
+    "\n",
+    "print(\"Example telemetry events (JSONL format):\")\n",
+    "print(\"─\" * 60)\n",
+    "for event in simulated_events[:5]:\n",
+    "    print(json.dumps(event))\n",
+    "print(\"...\")\n",
+    "print(f\"\\n💡 Each line is a complete JSON object — that's JSONL!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Analyze the telemetry data\n",
+    "durations = [e['duration_s'] for e in simulated_events]\n",
+    "\n",
+    "print(\"Task Duration Analysis:\")\n",
+    "print(f\"  Total tasks: {len(durations)}\")\n",
+    "print(f\"  Average duration: {sum(durations)/len(durations):.3f}s\")\n",
+    "print(f\"  Fastest task: {min(durations):.3f}s\")\n",
+    "print(f\"  Slowest task: {max(durations):.3f}s\")\n",
+    "print(f\"\\nWorker Distribution:\")\n",
+    "for w in ['worker-0', 'worker-1']:\n",
+    "    count = sum(1 for e in simulated_events if e['worker'] == w)\n",
+    "    print(f\"  {w}: {count} tasks\")\n",
+    "\n",
+    "print(f\"\\n💡 Use this data to optimize: are workers balanced? Any outlier tasks?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📖 Vocabulary Summary\n",
+    "\n",
+    "| Term | Definition |\n",
+    "|------|------------|\n",
+    "| Telemetry | Automated collection of system behavior data |\n",
+    "| Observability | Ability to understand internal state from outputs |\n",
+    "| Metrics | Numerical measurements over time |\n",
+    "| Logs | Discrete events with context |\n",
+    "| Traces | Journey of a request through the system |\n",
+    "| Structured Logging | Machine-parseable event recording (JSON) |\n",
+    "| JSONL | JSON Lines — one JSON object per line |\n",
+    "| Event | Discrete occurrence with timestamp and payload |\n",
+    "| Utilization | Percentage of resources actually being used |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cleanup\n",
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(f\"Cleaned up {project_dir}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/beginner/07_error_handling.ipynb b/notebooks/beginner/07_error_handling.ipynb
new file mode 100644
index 0000000..c9e3bf2
--- /dev/null
+++ b/notebooks/beginner/07_error_handling.ipynb
@@ -0,0 +1,243 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Beginner Tutorial 7: When Things Go Wrong (Error Handling)\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- Why distributed errors are harder than local ones\n",
+    "- Common failure modes (OOM, timeout, network)\n",
+    "- Retry strategies with exponential backoff\n",
+    "- Idempotency (safe to retry)\n",
+    "- Partial success (keep good results, handle failures separately)\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed notebooks 01, 06\n",
+    "- `pip install scalable`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "import time\n",
+    "import random\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-beginner-07-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 Key Concept: Why Distributed Errors Are Harder\n",
+    "\n",
+    "On your laptop: function raises exception → you see traceback → you fix it.\n",
+    "\n",
+    "In distributed systems, NEW failure modes exist:\n",
+    "- **Network failure** — result computed but never delivered\n",
+    "- **Partial failure** — 3 of 4 workers succeed, 1 fails\n",
+    "- **Timing issues** — can't tell \"failed\" from \"slow\"\n",
+    "- **Cascading failure** — one failure triggers others\n",
+    "\n",
+    "## 💡 Key Concept: Idempotency\n",
+    "\n",
+    "An operation is **idempotent** if running it multiple times = running it once.\n",
+    "\n",
+    "✅ Idempotent (safe to retry): `x = 5`, reading a file, `f(x)` for pure functions\n",
+    "\n",
+    "❌ NOT idempotent (dangerous to retry): `x += 1`, sending email, charging credit card\n",
+    "\n",
+    "**For retries to be safe, your tasks must be idempotent!**\n",
+    "\n",
+    "## 💡 Key Concept: Exponential Backoff\n",
+    "\n",
+    "Wait progressively longer between retries:\n",
+    "- Attempt 1: fail → wait 1s\n",
+    "- Attempt 2: fail → wait 2s  \n",
+    "- Attempt 3: fail → wait 4s\n",
+    "- Attempt 4: fail → wait 8s\n",
+    "\n",
+    "Why? If failure is caused by overload, retrying immediately makes it worse."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write manifest\n",
+    "manifest_content = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: error-handling-demo\n",
+    "\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 2\n",
+    "    threads_per_worker: 1\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "\n",
+    "components:\n",
+    "  analysis:\n",
+    "    cpus: 1\n",
+    "    memory: 1G\n",
+    "\n",
+    "tasks:\n",
+    "  run_analysis:\n",
+    "    component: analysis\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(manifest_content)\n",
+    "print(\"Manifest ready.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable import ScalableSession\n",
+    "\n",
+    "def sometimes_fails(task_id: int) -> dict:\n",
+    "    \"\"\"A function that randomly fails 30% of the time.\n",
+    "    \n",
+    "    This simulates transient failures (network issues, timeouts, etc.)\n",
+    "    \"\"\"\n",
+    "    time.sleep(0.2)\n",
+    "    if random.random() < 0.3:  # 30% chance of failure\n",
+    "        raise RuntimeError(f\"Transient failure on task {task_id}!\")\n",
+    "    return {\"task_id\": task_id, \"result\": task_id * 10}\n",
+    "\n",
+    "print(\"Function defined: fails ~30% of the time (simulating transient errors)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Handling Partial Success\n",
+    "\n",
+    "**Partial success** = some tasks succeed, others fail.\n",
+    "Don't throw away 95% good results because 5% failed!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run with partial success handling\n",
+    "session = ScalableSession.from_manifest(\"./scalable.yaml\", target=\"local\")\n",
+    "\n",
+    "# Submit tasks\n",
+    "random.seed(42)  # For reproducibility\n",
+    "futures = [session.submit(sometimes_fails, i, task=\"run_analysis\") for i in range(20)]\n",
+    "\n",
+    "# Gather with error isolation (don't let one failure crash everything)\n",
+    "results = []\n",
+    "failures = []\n",
+    "\n",
+    "for i, future in enumerate(futures):\n",
+    "    try:\n",
+    "        result = future.result()  # Get individual result\n",
+    "        results.append(result)\n",
+    "    except Exception as e:\n",
+    "        failures.append({\"task_id\": i, \"error\": str(e)})\n",
+    "\n",
+    "print(f\"Results: {len(results)} succeeded, {len(failures)} failed\")\n",
+    "print(f\"\\n✅ Successes: {[r['task_id'] for r in results]}\")\n",
+    "print(f\"❌ Failures: {[f['task_id'] for f in failures]}\")\n",
+    "print(f\"\\n💡 We kept {len(results)} good results despite {len(failures)} failures!\")\n",
+    "\n",
+    "session.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🤔 Think About It\n",
+    "\n",
+    "Without partial success handling, ONE failure would crash the entire `gather()`.\n",
+    "You'd lose ALL results — even the ones that succeeded.\n",
+    "\n",
+    "With individual error handling, you keep everything that worked and can\n",
+    "retry just the failures.\n",
+    "\n",
+    "## 💡 Key Concept: Fault Tolerance\n",
+    "\n",
+    "**Fault tolerance** = ability to continue operating despite failures.\n",
+    "\n",
+    "Levels:\n",
+    "1. **Crash** — any failure stops everything (fragile)\n",
+    "2. **Detect** — failures caught and reported clearly\n",
+    "3. **Retry** — transient failures automatically retried\n",
+    "4. **Partial success** — good results preserved\n",
+    "5. **Self-healing** — system recovers automatically\n",
+    "\n",
+    "Scalable provides levels 2–5 depending on configuration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📖 Vocabulary Summary\n",
+    "\n",
+    "| Term | Definition |\n",
+    "|------|------------|\n",
+    "| Fault Tolerance | Continuing to operate despite failures |\n",
+    "| Transient Failure | Temporary error that resolves on retry |\n",
+    "| Permanent Failure | Error that won't fix by retrying |\n",
+    "| Idempotency | Safe to run multiple times (same result) |\n",
+    "| Exponential Backoff | Progressively longer waits between retries |\n",
+    "| Partial Success | Some tasks succeed, others fail |\n",
+    "| Exception | Python's error signaling (raise/try/except) |\n",
+    "| Graceful Degradation | Reducing service rather than crashing |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cleanup\n",
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(f\"Cleaned up {project_dir}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/beginner/08_kubernetes.ipynb b/notebooks/beginner/08_kubernetes.ipynb
new file mode 100644
index 0000000..11abd7a
--- /dev/null
+++ b/notebooks/beginner/08_kubernetes.ipynb
@@ -0,0 +1,213 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Beginner Tutorial 8: Container Orchestration with Kubernetes\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- What Kubernetes is and what problem it solves\n",
+    "- Pods, nodes, namespaces, and operators\n",
+    "- How the Dask Kubernetes Operator works\n",
+    "- Resource requests vs. limits\n",
+    "- When K8s is appropriate vs. overkill\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed notebooks 01–03, 05\n",
+    "- Conceptual understanding of containers\n",
+    "- NO Kubernetes cluster required (conceptual + configuration examples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-beginner-08-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 Key Concept: What is Kubernetes?\n",
+    "\n",
+    "**Kubernetes** (K8s) = platform for managing containers at scale.\n",
+    "\n",
+    "It handles:\n",
+    "- **WHERE** containers run (scheduling across machines)\n",
+    "- **HOW MANY** containers run (scaling)\n",
+    "- **HEALTH** — restarts crashed containers (self-healing)\n",
+    "- **NETWORKING** between containers (service discovery)\n",
+    "\n",
+    "**Analogy:** Air traffic controller for containers. You don't tell each\n",
+    "plane which runway to use — the controller optimally assigns resources.\n",
+    "\n",
+    "## 💡 Key Concept: Pod\n",
+    "\n",
+    "A **pod** = smallest deployable unit in K8s (usually = one container).\n",
+    "Each Dask worker runs in its own pod.\n",
+    "\n",
+    "## 💡 Key Concept: Node\n",
+    "\n",
+    "A **node** = physical or virtual machine in the cluster.\n",
+    "Pods are scheduled onto nodes based on available resources.\n",
+    "\n",
+    "## 💡 Key Concept: Namespace\n",
+    "\n",
+    "A **namespace** = isolation boundary. Different teams use different namespaces\n",
+    "so they can't accidentally interfere with each other.\n",
+    "\n",
+    "## 💡 Key Concept: Operator\n",
+    "\n",
+    "An **operator** = Kubernetes extension that knows how to manage a complex\n",
+    "application. The Dask Operator knows how to create/scale/manage Dask clusters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example: Kubernetes target in a Scalable manifest\n",
+    "k8s_manifest = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: energy-forecast\n",
+    "\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 4\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "\n",
+    "  k8s:\n",
+    "    provider: kubernetes\n",
+    "    namespace: team-climate        # Your team's isolation boundary\n",
+    "    image: ghcr.io/my-org/model:v1.0  # Container with your code\n",
+    "    adaptive:\n",
+    "      minimum: 2                   # Always at least 2 workers\n",
+    "      maximum: 20                  # Scale up to 20 when busy\n",
+    "    resources:\n",
+    "      requests:                    # Minimum guaranteed resources\n",
+    "        cpu: \"4\"\n",
+    "        memory: \"16Gi\"\n",
+    "      limits:                      # Maximum allowed resources\n",
+    "        cpu: \"4\"\n",
+    "        memory: \"16Gi\"\n",
+    "\n",
+    "components:\n",
+    "  simulation:\n",
+    "    cpus: 4\n",
+    "    memory: 16G\n",
+    "\n",
+    "tasks:\n",
+    "  run_simulation:\n",
+    "    component: simulation\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(k8s_manifest)\n",
+    "\n",
+    "print(\"Kubernetes manifest written.\")\n",
+    "print(\"\\nKey K8s settings:\")\n",
+    "print(\"  namespace: team-climate → isolation boundary\")\n",
+    "print(\"  image: ... → container with your code\")\n",
+    "print(\"  adaptive: min=2, max=20 → auto-scale workers\")\n",
+    "print(\"  resources.requests → minimum guaranteed CPU/memory\")\n",
+    "print(\"  resources.limits → maximum allowed (prevents runaway)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scalable.manifest import parse_manifest, validate_manifest\n",
+    "\n",
+    "manifest = parse_manifest(\"./scalable.yaml\")\n",
+    "errors = validate_manifest(manifest)\n",
+    "\n",
+    "if not errors:\n",
+    "    print(\"✓ Kubernetes manifest is valid\")\n",
+    "    print(f\"\\nTargets: {list(manifest['targets'].keys())}\")\n",
+    "    print(f\"\\n💡 Same Python code works with both targets:\")\n",
+    "    print(f\"   Development: scalable run --target local\")\n",
+    "    print(f\"   Production:  scalable run --target k8s\")\n",
+    "else:\n",
+    "    for err in errors:\n",
+    "        print(f\"  ✗ {err}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 When to Use Kubernetes\n",
+    "\n",
+    "| ✅ Good fit | ❌ Overkill |\n",
+    "|------------|-------------|\n",
+    "| Team sharing a cluster | Single user on laptop |\n",
+    "| Need resource isolation | Simple batch job |\n",
+    "| Auto-scaling required | Fixed known workload |\n",
+    "| Already have K8s infra | No existing K8s |\n",
+    "\n",
+    "K8s adds complexity. For many workflows, local + cloud Fargate is simpler.\n",
+    "K8s shines when you have shared infrastructure.\n",
+    "\n",
+    "## 📖 Vocabulary Summary\n",
+    "\n",
+    "| Term | Definition |\n",
+    "|------|------------|\n",
+    "| Container Orchestration | Automating deployment/scaling of containers |\n",
+    "| Kubernetes (K8s) | Industry-standard container orchestration |\n",
+    "| Pod | Smallest deployable unit (≈ one container) |\n",
+    "| Node | Machine in the K8s cluster |\n",
+    "| Namespace | Isolation boundary for resources |\n",
+    "| Operator | K8s extension managing complex apps |\n",
+    "| kubectl | CLI tool for Kubernetes |\n",
+    "| Helm | Package manager for K8s |\n",
+    "| Resource Requests | Minimum guaranteed CPU/memory |\n",
+    "| Resource Limits | Maximum allowed CPU/memory |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cleanup\n",
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(f\"Cleaned up {project_dir}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/beginner/09_ml_emulation.ipynb b/notebooks/beginner/09_ml_emulation.ipynb
new file mode 100644
index 0000000..890e59b
--- /dev/null
+++ b/notebooks/beginner/09_ml_emulation.ipynb
@@ -0,0 +1,246 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Beginner Tutorial 9: Machine Learning for Smarter Workflows\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- What machine learning is (finding patterns in data)\n",
+    "- Training vs. inference\n",
+    "- How LearnedAdvisor predicts resource needs\n",
+    "- What surrogate models (emulators) are\n",
+    "- Uncertainty and confidence thresholds\n",
+    "- Active learning (choosing what to learn next)\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed notebooks 01, 03, 06\n",
+    "- `pip install scalable[ml]`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-beginner-09-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 Key Concept: What is Machine Learning?\n",
+    "\n",
+    "**Machine learning (ML)** = teaching computers to find patterns in data\n",
+    "and make predictions.\n",
+    "\n",
+    "**Traditional programming:** Human writes rules → computer follows them\n",
+    "```\n",
+    "IF memory > 8GB THEN allocate 16GB\n",
+    "```\n",
+    "\n",
+    "**Machine learning:** Computer finds rules from data\n",
+    "```\n",
+    "Training data: [past runs with memory usage]\n",
+    "ML learns: \"scenarios with >1000 nodes need ~12GB\"\n",
+    "Prediction: \"scenario 47 (1200 nodes) → recommend 16GB\"\n",
+    "```\n",
+    "\n",
+    "**Analogy:** Traditional = following a recipe. ML = learning to cook from experience.\n",
+    "\n",
+    "## 💡 Key Concept: Training vs. Inference\n",
+    "\n",
+    "**Training** (slow, done once): Feed data → algorithm learns patterns\n",
+    "\n",
+    "**Inference** (fast, done many times): Use trained model to predict on new input\n",
+    "\n",
+    "## 💡 Key Concept: Surrogate Model (Emulator)\n",
+    "\n",
+    "A **surrogate** = fast approximation of an expensive computation.\n",
+    "\n",
+    "- Full model: Run 5-minute simulation\n",
+    "- Surrogate: ML prediction in 0.01 seconds\n",
+    "\n",
+    "**Key:** Surrogates are approximations. Use confidence to know when to trust them!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Demonstrate ML concepts with a simple example\n",
+    "import random\n",
+    "\n",
+    "# Simulate \"telemetry history\" — past runs with resource usage\n",
+    "# In reality, this comes from .scalable/runs/ telemetry files\n",
+    "historical_data = []\n",
+    "for i in range(50):\n",
+    "    num_nodes = random.randint(100, 2000)\n",
+    "    # True relationship: memory ≈ 0.01 * num_nodes + noise\n",
+    "    actual_memory_gb = 0.01 * num_nodes + random.gauss(0, 1)\n",
+    "    historical_data.append({\n",
+    "        \"num_nodes\": num_nodes,\n",
+    "        \"memory_used_gb\": round(max(1, actual_memory_gb), 1)\n",
+    "    })\n",
+    "\n",
+    "print(\"Simulated telemetry history (50 past runs):\")\n",
+    "print(f\"  Sample: nodes={historical_data[0]['num_nodes']} → memory={historical_data[0]['memory_used_gb']}GB\")\n",
+    "print(f\"  Sample: nodes={historical_data[25]['num_nodes']} → memory={historical_data[25]['memory_used_gb']}GB\")\n",
+    "print(f\"  Sample: nodes={historical_data[49]['num_nodes']} → memory={historical_data[49]['memory_used_gb']}GB\")\n",
+    "print(f\"\\n💡 Pattern: more nodes → more memory needed\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Simple \"ML model\" — learn the pattern from data\n",
+    "# (In Scalable, this uses scikit-learn gradient boosting)\n",
+    "\n",
+    "# Training: find the relationship\n",
+    "nodes = [d['num_nodes'] for d in historical_data]\n",
+    "memory = [d['memory_used_gb'] for d in historical_data]\n",
+    "\n",
+    "# Simple linear fit (real Scalable uses gradient boosting)\n",
+    "avg_ratio = sum(m/n for n, m in zip(nodes, memory)) / len(nodes)\n",
+    "\n",
+    "# Inference: predict for new inputs\n",
+    "new_scenario_nodes = 1500\n",
+    "predicted_memory = avg_ratio * new_scenario_nodes\n",
+    "\n",
+    "print(f\"ML Model trained on {len(historical_data)} past runs\")\n",
+    "print(f\"\\nPrediction for new scenario (1500 nodes):\")\n",
+    "print(f\"  Predicted memory needed: {predicted_memory:.1f} GB\")\n",
+    "print(f\"  Recommended allocation: {int(predicted_memory * 1.2 + 2)} GB (with 20% headroom)\")\n",
+    "print(f\"\\n💡 Without ML: guess '16GB' for everything (wasteful or insufficient)\")\n",
+    "print(f\"   With ML: data-driven recommendation specific to your workload\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 Key Concept: Uncertainty\n",
+    "\n",
+    "**Uncertainty** = how confident the model is in its prediction.\n",
+    "\n",
+    "```\n",
+    "Prediction: memory = 12GB ± 3GB (confidence: 87%)\n",
+    "```\n",
+    "\n",
+    "- **High confidence** → trust the prediction, use the fast emulator\n",
+    "- **Low confidence** → don't trust, use the full (expensive) model\n",
+    "\n",
+    "This is **confidence-gated routing**: Scalable automatically chooses\n",
+    "fast vs. slow path based on how trustworthy the approximation is.\n",
+    "\n",
+    "## 💡 Key Concept: Active Learning\n",
+    "\n",
+    "**Active learning** = intelligently choosing which data to learn from next.\n",
+    "\n",
+    "Instead of randomly running 1000 scenarios, ask:\n",
+    "\"Where is the model LEAST confident?\" → run those specific scenarios.\n",
+    "\n",
+    "Result: ~150 full model runs instead of 1000, same accuracy!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Demonstrate the emulator concept\n",
+    "import time\n",
+    "\n",
+    "def full_simulation(scenario_id: int) -> float:\n",
+    "    \"\"\"The expensive full model (2 seconds).\"\"\"\n",
+    "    time.sleep(2)\n",
+    "    return scenario_id * 42.5 + 100\n",
+    "\n",
+    "def emulator_prediction(scenario_id: int) -> tuple:\n",
+    "    \"\"\"The fast ML approximation (instant).\n",
+    "    Returns (prediction, confidence).\"\"\"\n",
+    "    # Simulated: good predictions for known range, uncertain outside\n",
+    "    prediction = scenario_id * 42.3 + 101  # Close but not exact\n",
+    "    confidence = 0.95 if scenario_id < 100 else 0.6  # Less confident for large IDs\n",
+    "    return prediction, confidence\n",
+    "\n",
+    "# Confidence-gated routing\n",
+    "CONFIDENCE_THRESHOLD = 0.9\n",
+    "\n",
+    "for scenario_id in [10, 50, 150]:\n",
+    "    pred, conf = emulator_prediction(scenario_id)\n",
+    "    \n",
+    "    if conf >= CONFIDENCE_THRESHOLD:\n",
+    "        print(f\"Scenario {scenario_id}: Emulator (confidence={conf:.0%}) → {pred:.1f} [FAST]\")\n",
+    "    else:\n",
+    "        print(f\"Scenario {scenario_id}: Low confidence ({conf:.0%}) → running full model... \", end=\"\")\n",
+    "        # In reality this would call full_simulation (slow)\n",
+    "        print(f\"[would take 2s]\")\n",
+    "\n",
+    "print(f\"\\n💡 Threshold={CONFIDENCE_THRESHOLD:.0%}: use emulator when confident, full model otherwise\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📖 Vocabulary Summary\n",
+    "\n",
+    "| Term | Definition |\n",
+    "|------|------------|\n",
+    "| Machine Learning | Finding patterns in data to make predictions |\n",
+    "| Training | Learning phase (slow, done once) |\n",
+    "| Inference | Prediction phase (fast, done many times) |\n",
+    "| Features | Input variables the model uses |\n",
+    "| Model | Mathematical function learned from data |\n",
+    "| Surrogate/Emulator | Fast approximation of expensive computation |\n",
+    "| Uncertainty | How confident the model is |\n",
+    "| Confidence Threshold | Minimum confidence to use fast path |\n",
+    "| Active Learning | Strategically choosing what to learn next |\n",
+    "| Cross-Validation | Testing model quality on held-out data |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cleanup\n",
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(f\"Cleaned up {project_dir}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/beginner/10_ai_composition.ipynb b/notebooks/beginner/10_ai_composition.ipynb
new file mode 100644
index 0000000..64658e8
--- /dev/null
+++ b/notebooks/beginner/10_ai_composition.ipynb
@@ -0,0 +1,262 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Beginner Tutorial 10: AI-Assisted Workflow Development\n",
+    "\n",
+    "## What You Will Learn\n",
+    "\n",
+    "- What Large Language Models (LLMs) are\n",
+    "- Heuristic vs. LLM-powered modes\n",
+    "- Using `scalable init-component` to onboard models\n",
+    "- Using `scalable diagnose` for failure analysis\n",
+    "- Using `scalable compose` for workflow generation\n",
+    "- Human-in-the-loop verification\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- Completed notebooks 01–02\n",
+    "- `pip install scalable[ai]`\n",
+    "- NO LLM API key required (heuristic mode works offline)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "project_dir = tempfile.mkdtemp(prefix=\"scalable-beginner-10-\")\n",
+    "os.chdir(project_dir)\n",
+    "print(f\"Working directory: {project_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 Key Concept: What is a Large Language Model (LLM)?\n",
+    "\n",
+    "An **LLM** is an AI system trained on massive text data that can generate\n",
+    "human-like text, answer questions, and write code.\n",
+    "\n",
+    "How it works (simplified):\n",
+    "1. Trained on billions of words (books, code, docs)\n",
+    "2. Learns: \"given this input, what text likely follows?\"\n",
+    "3. Generates responses word by word\n",
+    "\n",
+    "Examples: ChatGPT, Claude, Llama, Gemini\n",
+    "\n",
+    "## 💡 Key Concept: Heuristic vs. LLM Mode\n",
+    "\n",
+    "| | Heuristic Mode | LLM Mode |\n",
+    "|---|---|---|\n",
+    "| How | Rules + templates | AI generation |\n",
+    "| Deterministic? | ✅ Always same output | ❌ May vary |\n",
+    "| Offline? | ✅ No internet needed | ❌ Needs API |\n",
+    "| Cost | Free | ~$0.01-0.10/call |\n",
+    "| Best for | CI/CD, reproducible | Creative, complex |\n",
+    "\n",
+    "Scalable always works in heuristic mode. LLM is an optional enhancement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write a base manifest for the AI tools to work with\n",
+    "manifest_content = \"\"\"\\\n",
+    "version: 1\n",
+    "project:\n",
+    "  name: water-resources\n",
+    "\n",
+    "targets:\n",
+    "  local:\n",
+    "    provider: local\n",
+    "    max_workers: 4\n",
+    "    threads_per_worker: 1\n",
+    "    processes: false\n",
+    "    containers: none\n",
+    "\n",
+    "components:\n",
+    "  hydrology:\n",
+    "    cpus: 4\n",
+    "    memory: 16G\n",
+    "\n",
+    "tasks:\n",
+    "  run_hydrology:\n",
+    "    component: hydrology\n",
+    "\"\"\"\n",
+    "\n",
+    "with open(\"scalable.yaml\", \"w\") as f:\n",
+    "    f.write(manifest_content)\n",
+    "print(\"Base manifest ready for AI assistant demos.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Demonstrate AI component onboarding (heuristic mode)\n",
+    "from scalable.ai.component_onboarding import onboard_component\n",
+    "\n",
+    "# Explanation: onboard_component generates a component definition\n",
+    "# from high-level inputs (name, resources, description)\n",
+    "result = onboard_component(\n",
+    "    name=\"watershed\",\n",
+    "    cpus=4,\n",
+    "    memory=\"16G\",\n",
+    "    image=\"ghcr.io/watershed/model:3.0\",\n",
+    "    description=\"Hydrological watershed model for runoff simulation\",\n",
+    "    no_ai=True,  # Use heuristic mode (no LLM needed)\n",
+    ")\n",
+    "\n",
+    "print(\"Generated component definition (heuristic mode):\")\n",
+    "print(\"─\" * 50)\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 What Just Happened?\n",
+    "\n",
+    "The `onboard_component` assistant:\n",
+    "1. Took your high-level inputs (name, image, resources)\n",
+    "2. Applied templates with sensible defaults\n",
+    "3. Inferred tags from the description\n",
+    "4. Generated matching task bindings\n",
+    "\n",
+    "In **LLM mode**, it could also suggest optimal allocations,\n",
+    "recommend mount points, and generate preload scripts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Demonstrate plan explanation (heuristic mode)\n",
+    "from scalable.ai.plan_explain import explain_plan\n",
+    "\n",
+    "# Explanation: explain_plan generates human-readable explanation\n",
+    "# of what a manifest/plan will do\n",
+    "explanation = explain_plan(\n",
+    "    manifest_path=\"./scalable.yaml\",\n",
+    "    target=\"local\",\n",
+    "    no_ai=True,\n",
+    ")\n",
+    "\n",
+    "print(\"Plan Explanation:\")\n",
+    "print(\"═\" * 50)\n",
+    "print(explanation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 Key Concept: Human-in-the-Loop\n",
+    "\n",
+    "**Human-in-the-loop** = AI suggests, human decides.\n",
+    "\n",
+    "AI-generated output should always be:\n",
+    "1. **Validated**: `scalable validate` on generated manifests\n",
+    "2. **Reviewed**: Check resource allocations, security settings\n",
+    "3. **Tested**: `--dry-run` before real deployment\n",
+    "4. **Approved**: Human confirms before applying\n",
+    "\n",
+    "Never blindly trust AI output — it can generate plausible-looking\n",
+    "but incorrect configurations.\n",
+    "\n",
+    "## 💡 Key Concept: Templates (Jinja2)\n",
+    "\n",
+    "**Templates** = pre-structured documents with fill-in-the-blank placeholders:\n",
+    "\n",
+    "```\n",
+    "components:\n",
+    "  {{ name }}:\n",
+    "    cpus: {{ cpus }}\n",
+    "    memory: {{ memory }}\n",
+    "```\n",
+    "\n",
+    "Heuristic mode uses templates extensively → predictable, fast, reproducible."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🎉 Congratulations!\n",
+    "\n",
+    "You've completed all 10 beginner tutorials! You now understand:\n",
+    "\n",
+    "1. ✅ Distributed computing and workflow orchestration\n",
+    "2. ✅ Declarative configuration with manifests\n",
+    "3. ✅ Scaling strategies and provider architecture\n",
+    "4. ✅ Caching and performance optimization\n",
+    "5. ✅ Cloud computing and containers\n",
+    "6. ✅ Telemetry and observability\n",
+    "7. ✅ Error handling and fault tolerance\n",
+    "8. ✅ Kubernetes and container orchestration\n",
+    "9. ✅ Machine learning for workflow optimization\n",
+    "10. ✅ AI-assisted development\n",
+    "\n",
+    "**Next:** Work through the [standard tutorials](../notebooks/) for deeper\n",
+    "technical content and production patterns!\n",
+    "\n",
+    "## 📖 Vocabulary Summary\n",
+    "\n",
+    "| Term | Definition |\n",
+    "|------|------------|\n",
+    "| LLM | AI trained on text that generates responses |\n",
+    "| Heuristic Mode | Rule-based, deterministic (no AI needed) |\n",
+    "| LLM Mode | AI-powered, flexible, non-deterministic |\n",
+    "| Template | Pre-structured document with placeholders |\n",
+    "| Prompt Engineering | Crafting inputs to get desired AI outputs |\n",
+    "| Code Generation | AI writing code/configuration |\n",
+    "| Deterministic | Same input → always same output |\n",
+    "| API | Interface for programs to communicate |\n",
+    "| Human-in-the-Loop | AI suggests, human decides |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cleanup\n",
+    "import shutil\n",
+    "os.chdir(\"/tmp\")\n",
+    "shutil.rmtree(project_dir, ignore_errors=True)\n",
+    "print(f\"Cleaned up {project_dir}\")\n",
+    "print(\"\\n🎉 All beginner tutorials complete!\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/beginner/README.md b/notebooks/beginner/README.md
new file mode 100644
index 0000000..f5878bc
--- /dev/null
+++ b/notebooks/beginner/README.md
@@ -0,0 +1,66 @@
+# Scalable Beginner Tutorial Notebooks
+
+Interactive Jupyter notebooks designed for **non-experts** who are new to both Scalable and distributed computing. These notebooks accompany the [beginner documentation tutorials](../docs/tutorials/beginner/).
+
+## How These Differ from Standard Notebooks
+
+The standard notebooks (in `../notebooks/`) assume familiarity with distributed computing, YAML, containers, and cloud infrastructure. These beginner notebooks:
+
+- 📖 **Define every term** before using it
+- 🤔 **Explain why** approaches were chosen (not just how to use them)
+- 💡 **Key Concept** cells introduce foundational ideas
+- 📝 **Vocabulary summaries** at the end of each notebook
+- 🔍 **Under the Hood** cells explain what Scalable does internally
+- ✅ **Checkpoint** cells verify understanding before moving on
+
+## Notebooks
+
+| # | Notebook | Topic | Concepts Taught |
+|---|----------|-------|-----------------|
+| 1 | [Getting Started](01_getting_started.ipynb) | First workflow | Workflows, Dask, CLI, virtual environments |
+| 2 | [Manifest System](02_manifest_system.ipynb) | Configuration | Declarative programming, YAML, schemas |
+| 3 | [Scaling Strategies](03_scaling_strategies.ipynb) | Distribution | Clusters, schedulers, providers, parallelism |
+| 4 | [Caching & Performance](04_caching_performance.ipynb) | Optimization | Hashing, memoization, decorators |
+| 5 | [Cloud Integration](05_cloud_integration.ipynb) | Cloud | Object storage, containers, IAM, cost |
+| 6 | [Telemetry](06_telemetry.ipynb) | Observability | Structured logging, JSONL, metrics |
+| 7 | [Error Handling](07_error_handling.ipynb) | Resilience | Fault tolerance, retries, idempotency |
+| 8 | [Kubernetes](08_kubernetes.ipynb) | Orchestration | Pods, operators, namespaces |
+| 9 | [ML & Emulation](09_ml_emulation.ipynb) | Intelligence | Surrogate models, uncertainty, active learning |
+| 10 | [AI Composition](10_ai_composition.ipynb) | AI Assistants | LLMs, heuristics, code generation |
+
+## Quick Start
+
+```bash
+# Install Scalable with all extras
+pip install scalable[ai,cloud,kubernetes,ml]
+
+# Install Jupyter
+pip install jupyterlab
+
+# Launch
+jupyter lab notebooks/beginner/
+```
+
+## Running Order
+
+Notebooks are designed to be run sequentially (1 → 10). Each is self-contained with its own setup and teardown, but concepts build progressively. If you skip ahead, you may encounter terms that were defined in earlier notebooks.
+
+## Prerequisites
+
+- Python 3.11+
+- Basic Python knowledge (functions, imports, loops)
+- NO distributed computing experience required
+- NO cloud/container/Kubernetes experience required
+
+## Conventions
+
+- Each notebook creates a temporary working directory and cleans up after itself
+- Extensive markdown cells explain concepts BEFORE code cells
+- `# Explanation:` comments in code cells describe what each line does
+- "🤔 Think About It" cells prompt reflection on key concepts
+- "📖 Vocabulary" cells summarize new terms learned
+- Functions that simulate expensive computations use `time.sleep()` with short durations
+
+## Graduating to Standard Notebooks
+
+After completing these beginner notebooks, move to the [standard notebooks](../notebooks/) for deeper technical content and production patterns. Each beginner notebook maps 1:1 to a standard notebook covering the same topic at a more advanced level.

From b42ff4e18cb5bdc7b2c619710005ff62e5658031 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 15:46:48 -0400
Subject: [PATCH 42/47] beginner tutorials

---
 .gitignore                                    |   3 +
 .../tutorials/beginner/01_getting_started.rst |  14 +-
 .../tutorials/beginner/02_manifest_system.rst |  13 +-
 .../beginner/03_scaling_strategies.rst        |  16 +-
 .../beginner/05_cloud_integration.rst         |   2 +-
 docs/tutorials/beginner/07_error_handling.rst |  30 +--
 docs/tutorials/beginner/09_ml_emulation.rst   |   6 +-
 .../{ => advanced}/01_getting_started.ipynb   |   0
 .../{ => advanced}/02_manifest_system.ipynb   |   0
 .../03_scaling_strategies.ipynb               |   0
 .../04_caching_performance.ipynb              |   0
 .../{ => advanced}/05_cloud_integration.ipynb |   0
 notebooks/{ => advanced}/06_telemetry.ipynb   |   0
 .../{ => advanced}/07_error_handling.ipynb    |   0
 notebooks/{ => advanced}/08_kubernetes.ipynb  |   0
 .../{ => advanced}/09_ml_emulation.ipynb      |   0
 .../{ => advanced}/10_ai_composition.ipynb    |   0
 notebooks/{ => advanced}/README.md            |   0
 notebooks/beginner/01_getting_started.ipynb   | 174 ++++++++++++++----
 notebooks/beginner/02_manifest_system.ipynb   | 148 +++++++++++----
 .../beginner/03_scaling_strategies.ipynb      |  93 ++++++++--
 .../beginner/04_caching_performance.ipynb     |  36 ++--
 notebooks/beginner/05_cloud_integration.ipynb |  61 +++---
 notebooks/beginner/06_telemetry.ipynb         |  28 +--
 notebooks/beginner/07_error_handling.ipynb    | 147 +++++++++++++--
 notebooks/beginner/08_kubernetes.ipynb        |  91 +++++++--
 notebooks/beginner/09_ml_emulation.ipynb      |  97 ++++++++--
 notebooks/beginner/10_ai_composition.ipynb    |  85 +++++----
 28 files changed, 786 insertions(+), 258 deletions(-)
 rename notebooks/{ => advanced}/01_getting_started.ipynb (100%)
 rename notebooks/{ => advanced}/02_manifest_system.ipynb (100%)
 rename notebooks/{ => advanced}/03_scaling_strategies.ipynb (100%)
 rename notebooks/{ => advanced}/04_caching_performance.ipynb (100%)
 rename notebooks/{ => advanced}/05_cloud_integration.ipynb (100%)
 rename notebooks/{ => advanced}/06_telemetry.ipynb (100%)
 rename notebooks/{ => advanced}/07_error_handling.ipynb (100%)
 rename notebooks/{ => advanced}/08_kubernetes.ipynb (100%)
 rename notebooks/{ => advanced}/09_ml_emulation.ipynb (100%)
 rename notebooks/{ => advanced}/10_ai_composition.ipynb (100%)
 rename notebooks/{ => advanced}/README.md (100%)

diff --git a/.gitignore b/.gitignore
index e02e5a1..6cb4584 100755
--- a/.gitignore
+++ b/.gitignore
@@ -6,6 +6,9 @@ plans/
 .rooignore
 .env
 notebooks/.ipynb_checkpoints/
+notebooks/beginner/.ipynb_checkpoints/
+notebooks/intermediate/.ipynb_checkpoints/
+notebooks/advanced/.ipynb_checkpoints/
 
 # -----------------------------
 # Python bytecode / caches
diff --git a/docs/tutorials/beginner/01_getting_started.rst b/docs/tutorials/beginner/01_getting_started.rst
index e8c41ba..b34f8bb 100644
--- a/docs/tutorials/beginner/01_getting_started.rst
+++ b/docs/tutorials/beginner/01_getting_started.rst
@@ -466,19 +466,21 @@ Now let's write the Python function that does actual work. Create
    def main():
        """Run the workflow using a ScalableSession."""
        # Create a session from our manifest
-       session = ScalableSession.from_manifest(
+       session = ScalableSession.from_yaml(
            "./scalable.yaml",
            target="local",
        )
+       plan = session.plan()
+       client = session.start(plan)
 
        # Submit 6 tasks to be executed in parallel
        futures = []
        for i in range(6):
-           future = session.submit(analyze_scenario, i, task="run_analysis")
+           future = client.submit(analyze_scenario, i, tag="analysis")
            futures.append(future)
 
        # Gather results (blocks until all tasks complete)
-       results = session.gather(futures)
+       results = client.gather(futures)
 
        print(f"Completed {len(results)} scenarios!")
        for r in results:
@@ -493,7 +495,7 @@ Now let's write the Python function that does actual work. Create
 
 Let's understand what this code does:
 
-.. admonition:: Under the Hood: What happens when you call ``session.submit()``
+.. admonition:: Under the Hood: What happens when you call ``client.submit()``
    :class: hint
 
    1. Your function (``analyze_scenario``) and its arguments (``scenario_id``)
@@ -513,10 +515,10 @@ Let's understand what this code does:
    :class: tip
 
    A **future** is a promise of a result that hasn't been computed yet. When
-   you call ``session.submit()``, the task starts running in the background
+   you call ``client.submit()``, the task starts running in the background
    and you immediately get back a future object.
 
-   Later, when you call ``session.gather(futures)``, Python waits until all
+   Later, when you call ``client.gather(futures)``, Python waits until all
    the futures have their results ready, then returns them.
 
    **Analogy:** Ordering food at a counter — you get a receipt number (future)
diff --git a/docs/tutorials/beginner/02_manifest_system.rst b/docs/tutorials/beginner/02_manifest_system.rst
index b4707d3..fd26be7 100644
--- a/docs/tutorials/beginner/02_manifest_system.rst
+++ b/docs/tutorials/beginner/02_manifest_system.rst
@@ -415,7 +415,7 @@ When you write Python code like:
 
 .. code-block:: python
 
-   session.submit(my_function, args, task="run_gridlabd")
+   client.submit(my_function, args, tag="gridlabd")
 
 Scalable looks up the ``run_gridlabd`` task, finds it uses the ``gridlabd``
 component, and schedules it on a worker with 8 CPUs and 32G memory.
@@ -530,16 +530,17 @@ Python:
 
 .. code-block:: python
 
-   from scalable.manifest import parse_manifest, validate_manifest
+   from scalable.manifest.parser import load_manifest
+   from scalable.manifest.validate import validate_manifest
 
    # Parse the YAML into a structured object
-   manifest = parse_manifest("./scalable.yaml")
+   manifest = load_manifest("./scalable.yaml")
 
    # Validate returns a list of errors (empty = valid)
-   errors = validate_manifest(manifest)
+   report = validate_manifest(manifest)
 
-   if errors:
-       for err in errors:
+   if not report.ok:
+       for issue in report.errors:
            print(f"ERROR: {err}")
    else:
        print("✓ Manifest is valid")
diff --git a/docs/tutorials/beginner/03_scaling_strategies.rst b/docs/tutorials/beginner/03_scaling_strategies.rst
index 55dbd33..2217375 100644
--- a/docs/tutorials/beginner/03_scaling_strategies.rst
+++ b/docs/tutorials/beginner/03_scaling_strategies.rst
@@ -218,7 +218,7 @@ Scalable separates **what** runs from **where** it runs:
    or a gas turbine. The switch is the abstraction layer.
 
    In Scalable, the provider abstraction means your workflow code
-   (``session.submit()``) works identically regardless of whether tasks run
+   (``client.submit()``) works identically regardless of whether tasks run
    locally, on Slurm, or in AWS.
 
 
@@ -258,11 +258,13 @@ The simplest provider runs everything on your machine:
 
    from scalable import ScalableSession
 
-   session = ScalableSession.from_manifest("./scalable.yaml", target="local")
+   session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+   plan = session.plan()
+   client = session.start(plan)
 
    # Submit work — it runs on local workers
-   futures = [session.submit(my_func, i, task="run_analysis") for i in range(20)]
-   results = session.gather(futures)
+   futures = [client.submit(my_func, i, tag="analysis") for i in range(20)]
+   results = client.gather(futures)
    session.close()
 
 .. admonition:: Under the Hood
@@ -386,7 +388,7 @@ You decide the worker count upfront. Simple and predictable.
 
    from scalable import ScalableSession
 
-   session = ScalableSession.from_manifest(
+   session = ScalableSession.from_yaml(
        "./scalable.yaml",
        target="cloud",
        objectives={"budget_usd": 50.0, "deadline_hours": 2.0},
@@ -416,7 +418,9 @@ Every scaling decision is recorded in telemetry:
 
    from scalable import ScalableSession
 
-   session = ScalableSession.from_manifest("./scalable.yaml", target="local")
+   session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+   plan = session.plan()
+   client = session.start(plan)
 
    # After your run, check what happened
    # Telemetry records scaling events:
diff --git a/docs/tutorials/beginner/05_cloud_integration.rst b/docs/tutorials/beginner/05_cloud_integration.rst
index 02062d4..56ea0a6 100644
--- a/docs/tutorials/beginner/05_cloud_integration.rst
+++ b/docs/tutorials/beginner/05_cloud_integration.rst
@@ -352,7 +352,7 @@ Step 3: The Artifact Store
 
    from scalable import ScalableSession
 
-   session = ScalableSession.from_manifest("./scalable.yaml", target="aws")
+   session = ScalableSession.from_yaml("./scalable.yaml", target="aws")
 
    # After computation, store artifacts
    session.store_artifact("results/scenario_42.json", result_data)
diff --git a/docs/tutorials/beginner/07_error_handling.rst b/docs/tutorials/beginner/07_error_handling.rst
index 4905abc..6fd6f03 100644
--- a/docs/tutorials/beginner/07_error_handling.rst
+++ b/docs/tutorials/beginner/07_error_handling.rst
@@ -204,19 +204,21 @@ When a function raises an exception on a worker:
 
    from scalable import ScalableSession
 
-   session = ScalableSession.from_manifest("./scalable.yaml", target="local")
+   session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+   plan = session.plan()
+   client = session.start(plan)
 
    def risky_function(x):
        if x == 13:
            raise ValueError(f"Unlucky number: {x}")
        return x * 2
 
-   futures = [session.submit(risky_function, i, task="run_analysis")
+   futures = [client.submit(risky_function, i, tag="analysis")
               for i in range(20)]
 
    # This will raise ValueError for x=13
    try:
-       results = session.gather(futures)
+       results = client.gather(futures)
    except ValueError as e:
        print(f"A task failed: {e}")
 
@@ -230,12 +232,14 @@ Scalable supports automatic retries for transient failures:
 
    from scalable import ScalableSession
 
-   session = ScalableSession.from_manifest("./scalable.yaml", target="local")
+   session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+   plan = session.plan()
+   client = session.start(plan)
 
    # Configure retries
    futures = []
    for i in range(20):
-       future = session.submit(
+       future = client.submit(
            sometimes_fails,
            i,
            task="run_analysis",
@@ -301,10 +305,12 @@ Step 3: Partial Success
 
    from scalable import ScalableSession
 
-   session = ScalableSession.from_manifest("./scalable.yaml", target="local")
+   session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+   plan = session.plan()
+   client = session.start(plan)
 
    # Submit many tasks
-   futures = [session.submit(maybe_fails, i, task="run_analysis")
+   futures = [client.submit(maybe_fails, i, tag="analysis")
               for i in range(100)]
 
    # Gather with partial success handling
@@ -321,14 +327,14 @@ Step 3: Partial Success
    print(f"Failed: {len(failures)}")
 
    # You can retry just the failures
-   retry_futures = [session.submit(maybe_fails, f["index"], task="run_analysis")
+   retry_futures = [client.submit(maybe_fails, f["index"], tag="analysis")
                     for f in failures]
 
 .. admonition:: Under the Hood: Futures and Error Isolation
    :class: hint
 
    Each future is independent. A failure in one future doesn't affect
-   others. This is why ``session.submit()`` returns individual futures
+   others. This is why ``client.submit()`` returns individual futures
    rather than running everything as a single batch — it gives you
    fine-grained control over error handling.
 
@@ -434,12 +440,14 @@ A complete fault-tolerant pattern:
 
 
    def run_workflow():
-       session = ScalableSession.from_manifest("./scalable.yaml", target="local")
+       session = ScalableSession.from_yaml("./scalable.yaml", target="local")
+   plan = session.plan()
+   client = session.start(plan)
 
        # Submit all tasks
        task_map = {}
        for i in range(100):
-           future = session.submit(
+           future = client.submit(
                run_simulation,
                scenario_id=i,
                task="run_analysis",
diff --git a/docs/tutorials/beginner/09_ml_emulation.rst b/docs/tutorials/beginner/09_ml_emulation.rst
index 14fb740..0f75425 100644
--- a/docs/tutorials/beginner/09_ml_emulation.rst
+++ b/docs/tutorials/beginner/09_ml_emulation.rst
@@ -439,11 +439,11 @@ A workflow using ML optimization and emulation:
        ...
 
    # 3. Run with ML-optimized resources
-   session = ScalableSession.from_manifest("./scalable.yaml", target="local")
+   session = ScalableSession.from_yaml("./scalable.yaml", target="local")
 
-   futures = [session.submit(run_gridlabd, i, task="run_gridlabd")
+   futures = [client.submit(run_gridlabd, i, tag="gridlabd")
               for i in range(100)]
-   results = session.gather(futures)
+   results = client.gather(futures)
 
    # Some calls used the emulator (fast), others ran the full model
    # Telemetry records which path each call took
diff --git a/notebooks/01_getting_started.ipynb b/notebooks/advanced/01_getting_started.ipynb
similarity index 100%
rename from notebooks/01_getting_started.ipynb
rename to notebooks/advanced/01_getting_started.ipynb
diff --git a/notebooks/02_manifest_system.ipynb b/notebooks/advanced/02_manifest_system.ipynb
similarity index 100%
rename from notebooks/02_manifest_system.ipynb
rename to notebooks/advanced/02_manifest_system.ipynb
diff --git a/notebooks/03_scaling_strategies.ipynb b/notebooks/advanced/03_scaling_strategies.ipynb
similarity index 100%
rename from notebooks/03_scaling_strategies.ipynb
rename to notebooks/advanced/03_scaling_strategies.ipynb
diff --git a/notebooks/04_caching_performance.ipynb b/notebooks/advanced/04_caching_performance.ipynb
similarity index 100%
rename from notebooks/04_caching_performance.ipynb
rename to notebooks/advanced/04_caching_performance.ipynb
diff --git a/notebooks/05_cloud_integration.ipynb b/notebooks/advanced/05_cloud_integration.ipynb
similarity index 100%
rename from notebooks/05_cloud_integration.ipynb
rename to notebooks/advanced/05_cloud_integration.ipynb
diff --git a/notebooks/06_telemetry.ipynb b/notebooks/advanced/06_telemetry.ipynb
similarity index 100%
rename from notebooks/06_telemetry.ipynb
rename to notebooks/advanced/06_telemetry.ipynb
diff --git a/notebooks/07_error_handling.ipynb b/notebooks/advanced/07_error_handling.ipynb
similarity index 100%
rename from notebooks/07_error_handling.ipynb
rename to notebooks/advanced/07_error_handling.ipynb
diff --git a/notebooks/08_kubernetes.ipynb b/notebooks/advanced/08_kubernetes.ipynb
similarity index 100%
rename from notebooks/08_kubernetes.ipynb
rename to notebooks/advanced/08_kubernetes.ipynb
diff --git a/notebooks/09_ml_emulation.ipynb b/notebooks/advanced/09_ml_emulation.ipynb
similarity index 100%
rename from notebooks/09_ml_emulation.ipynb
rename to notebooks/advanced/09_ml_emulation.ipynb
diff --git a/notebooks/10_ai_composition.ipynb b/notebooks/advanced/10_ai_composition.ipynb
similarity index 100%
rename from notebooks/10_ai_composition.ipynb
rename to notebooks/advanced/10_ai_composition.ipynb
diff --git a/notebooks/README.md b/notebooks/advanced/README.md
similarity index 100%
rename from notebooks/README.md
rename to notebooks/advanced/README.md
diff --git a/notebooks/beginner/01_getting_started.ipynb b/notebooks/beginner/01_getting_started.ipynb
index fd69eb2..1e0ae7c 100644
--- a/notebooks/beginner/01_getting_started.ipynb
+++ b/notebooks/beginner/01_getting_started.ipynb
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,9 +35,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scalable version: 2.0.0\n"
+     ]
+    }
+   ],
    "source": [
     "# Verify installation\n",
     "import scalable\n",
@@ -87,9 +95,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working directory: /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-beginner-01-27jmo9up\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "import tempfile\n",
@@ -134,9 +150,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Manifest written to scalable.yaml\n",
+      "---\n",
+      "version: 1                    # Schema version (always 1 for now)\n",
+      "project:\n",
+      "  name: hello-scalable        # Human-readable project name\n",
+      "\n",
+      "targets:                      # WHERE code runs\n",
+      "  local:                      # Target name (we'll use this later)\n",
+      "    provider: local           # Use the local machine\n",
+      "    max_workers: 2            # Run 2 workers in parallel\n",
+      "    threads_per_worker: 1     # 1 thread per worker\n",
+      "    processes: false           # Use threads (fast startup)\n",
+      "    containers: none          # No containerization\n",
+      "\n",
+      "components:                   # HOW MUCH resources each piece needs\n",
+      "  analysis:                   # Component name\n",
+      "    cpus: 1                   # 1 CPU per worker\n",
+      "    memory: 1G               # 1 GB RAM per worker\n",
+      "\n",
+      "tasks:                        # WHAT work units exist\n",
+      "  run_analysis:               # Task name\n",
+      "    component: analysis       # Links to the component above\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# Write the manifest file\n",
     "# Explanation: Each section has a specific purpose (explained in comments)\n",
@@ -183,20 +229,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Manifest is valid (0 errors, 0 warnings)\n"
+     ]
+    }
+   ],
    "source": [
-    "from scalable.manifest import parse_manifest, validate_manifest\n",
+    "from scalable.manifest.parser import load_manifest\n",
+    "from scalable.manifest.validate import validate_manifest\n",
     "\n",
-    "# Explanation: parse_manifest reads the YAML file into a Python object\n",
+    "# Explanation: load_manifest reads the YAML file into a ManifestModel object\n",
     "# Explanation: validate_manifest checks it against the schema rules\n",
-    "manifest = parse_manifest(\"./scalable.yaml\")\n",
-    "errors = validate_manifest(manifest)\n",
+    "manifest = load_manifest(\"./scalable.yaml\")\n",
+    "report = validate_manifest(manifest)\n",
     "\n",
-    "if errors:\n",
-    "    for err in errors:\n",
-    "        print(f\"ERROR: {err}\")\n",
+    "if not report.ok:\n",
+    "    for issue in report.errors:\n",
+    "        print(f\"ERROR: {issue.path}: {issue.message}\")\n",
     "else:\n",
     "    print(\"✓ Manifest is valid (0 errors, 0 warnings)\")"
    ]
@@ -222,7 +277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,19 +301,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Submitted 6 tasks\n",
+      "Tasks are running in the background on 2 workers...\n"
+     ]
+    }
+   ],
    "source": [
     "# Create a session from our manifest\n",
     "# Explanation: ScalableSession sets up the Dask cluster based on our manifest\n",
-    "session = ScalableSession.from_manifest(\"./scalable.yaml\", target=\"local\")\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "plan = session.plan()\n",
+    "client = session.start(plan)\n",
     "\n",
     "# Submit 6 tasks to be executed in parallel\n",
     "# Explanation: submit() returns immediately with a future (the work runs in background)\n",
     "futures = []\n",
     "for i in range(6):\n",
-    "    future = session.submit(analyze_scenario, i, task=\"run_analysis\")\n",
+    "    future = client.submit(analyze_scenario, i, tag=\"analysis\")\n",
     "    futures.append(future)\n",
     "\n",
     "print(f\"Submitted {len(futures)} tasks\")\n",
@@ -267,13 +333,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Completed 6 scenarios!\n",
+      "  Scenario 0: result = 0\n",
+      "  Scenario 1: result = 42\n",
+      "  Scenario 2: result = 84\n",
+      "  Scenario 3: result = 126\n",
+      "  Scenario 4: result = 168\n",
+      "  Scenario 5: result = 210\n"
+     ]
+    }
+   ],
    "source": [
     "# Gather results (blocks until all tasks complete)\n",
     "# Explanation: gather() waits for all futures to finish and returns results\n",
-    "results = session.gather(futures)\n",
+    "results = client.gather(futures)\n",
     "\n",
     "print(f\"\\nCompleted {len(results)} scenarios!\")\n",
     "for r in results:\n",
@@ -296,9 +377,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Session closed.\n"
+     ]
+    }
+   ],
    "source": [
     "# Clean up the session\n",
     "session.close()\n",
@@ -337,9 +426,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cleaned up /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-beginner-01-27jmo9up\n"
+     ]
+    }
+   ],
    "source": [
     "# Cleanup: remove temporary directory\n",
     "import shutil\n",
@@ -347,17 +444,32 @@
     "shutil.rmtree(project_dir, ignore_errors=True)\n",
     "print(f\"Cleaned up {project_dir}\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "py3.13.3_scalable",
    "language": "python",
-   "name": "python3"
+   "name": "py3.13.3_scalable"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.12.0"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/beginner/02_manifest_system.ipynb b/notebooks/beginner/02_manifest_system.ipynb
index e3de5e5..0d95ea8 100644
--- a/notebooks/beginner/02_manifest_system.ipynb
+++ b/notebooks/beginner/02_manifest_system.ipynb
@@ -23,9 +23,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working directory: /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-beginner-02-2pc74vtt\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "import tempfile\n",
@@ -99,9 +107,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Full manifest written. Let's explore each section...\n"
+     ]
+    }
+   ],
    "source": [
     "# Write a full-featured manifest\n",
     "manifest_content = \"\"\"\\\n",
@@ -188,28 +204,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Manifest is valid!\n",
+      "\n",
+      "Project name: energy-forecast\n",
+      "Targets defined: ['local', 'hpc']\n",
+      "Components defined: ['simulation', 'postprocess']\n",
+      "Tasks defined: ['run_simulation', 'aggregate_results']\n"
+     ]
+    }
+   ],
    "source": [
-    "from scalable.manifest import parse_manifest, validate_manifest\n",
+    "from scalable.manifest.parser import load_manifest\n",
+    "from scalable.manifest.validate import validate_manifest\n",
     "\n",
     "# Parse and validate\n",
     "# Explanation: parse_manifest reads YAML into Python data structures\n",
     "# Explanation: validate_manifest checks the structure against schema rules\n",
-    "manifest = parse_manifest(\"./scalable.yaml\")\n",
-    "errors = validate_manifest(manifest)\n",
+    "manifest = load_manifest(\"./scalable.yaml\")\n",
+    "report = validate_manifest(manifest)\n",
     "\n",
-    "if errors:\n",
+    "if not report.ok:\n",
     "    print(\"Validation errors:\")\n",
-    "    for err in errors:\n",
-    "        print(f\"  ✗ {err}\")\n",
+    "    for issue in report.errors:\n",
+    "        print(f\"  ✗ {issue.path}: {issue.message}\")\n",
     "else:\n",
     "    print(\"✓ Manifest is valid!\")\n",
-    "    print(f\"\\nProject name: {manifest.get('project', {}).get('name')}\")\n",
-    "    print(f\"Targets defined: {list(manifest.get('targets', {}).keys())}\")\n",
-    "    print(f\"Components defined: {list(manifest.get('components', {}).keys())}\")\n",
-    "    print(f\"Tasks defined: {list(manifest.get('tasks', {}).keys())}\")"
+    "    print(f\"\\nProject name: {manifest.project.name}\")\n",
+    "    print(f\"Targets defined: {list(manifest.targets.keys())}\")\n",
+    "    print(f\"Components defined: {list(manifest.components.keys())}\")\n",
+    "    print(f\"Tasks defined: {list(manifest.tasks.keys())}\")"
    ]
   },
   {
@@ -232,19 +262,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Base manifest: simulation memory = 16G\n",
+      "Production overlay: simulation memory = 32G\n",
+      "\n",
+      "With --overlay production, memory becomes 32G\n",
+      "Everything else (cpus, tags, etc.) stays the same — that's deep merge!\n"
+     ]
+    }
+   ],
    "source": [
     "# Demonstrate overlay concept\n",
     "# Explanation: Overlays do a \"deep merge\" — only specified keys change\n",
-    "base_memory = manifest['components']['simulation']['memory']\n",
-    "overlay_memory = manifest.get('overlays', {}).get('production', {}).get('components', {}).get('simulation', {}).get('memory')\n",
+    "import yaml\n",
+    "\n",
+    "# Read the raw YAML to show the overlay\n",
+    "with open(\"scalable.yaml\") as f:\n",
+    "    raw = yaml.safe_load(f.read())\n",
+    "\n",
+    "base_memory = raw[\"components\"][\"simulation\"][\"memory\"]\n",
+    "overlay_memory = raw.get(\"overlays\", {}).get(\"production\", {}).get(\"components\", {}).get(\"simulation\", {}).get(\"memory\")\n",
     "\n",
     "print(f\"Base manifest: simulation memory = {base_memory}\")\n",
     "print(f\"Production overlay: simulation memory = {overlay_memory}\")\n",
     "print(f\"\\nWith --overlay production, memory becomes {overlay_memory}\")\n",
-    "print(f\"Everything else (cpus, tags, etc.) stays the same — that's deep merge!\")"
+    "print(f\"Everything else (cpus, tags, etc.) stays the same — that's deep merge!\")\n"
    ]
   },
   {
@@ -258,9 +306,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation results for bad manifest:\n",
+      "  ✗ tasks.run_analysis.component: unknown component 'nonexistent_component'; known components: ['analysis']\n",
+      "\n",
+      "💡 Validation catches errors BEFORE you spend time/money running!\n"
+     ]
+    }
+   ],
    "source": [
     "# Write a manifest with deliberate errors\n",
     "bad_manifest = \"\"\"\\\n",
@@ -289,13 +348,13 @@
     "with open(\"bad_manifest.yaml\", \"w\") as f:\n",
     "    f.write(bad_manifest)\n",
     "\n",
-    "bad = parse_manifest(\"./bad_manifest.yaml\")\n",
-    "errors = validate_manifest(bad)\n",
+    "bad = load_manifest(\"./bad_manifest.yaml\")\n",
+    "report = validate_manifest(bad)\n",
     "\n",
     "print(\"Validation results for bad manifest:\")\n",
-    "if errors:\n",
-    "    for err in errors:\n",
-    "        print(f\"  ✗ {err}\")\n",
+    "if not report.ok:\n",
+    "    for issue in report.errors:\n",
+    "        print(f\"  ✗ {issue.path}: {issue.message}\")\n",
     "else:\n",
     "    print(\"  (No errors detected at parse level)\")\n",
     "\n",
@@ -334,9 +393,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cleaned up /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-beginner-02-2pc74vtt\n"
+     ]
+    }
+   ],
    "source": [
     "# Cleanup\n",
     "import shutil\n",
@@ -344,17 +411,32 @@
     "shutil.rmtree(project_dir, ignore_errors=True)\n",
     "print(f\"Cleaned up {project_dir}\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "py3.13.3_scalable",
    "language": "python",
-   "name": "python3"
+   "name": "py3.13.3_scalable"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.12.0"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/beginner/03_scaling_strategies.ipynb b/notebooks/beginner/03_scaling_strategies.ipynb
index d35bb9d..42d5c2c 100644
--- a/notebooks/beginner/03_scaling_strategies.ipynb
+++ b/notebooks/beginner/03_scaling_strategies.ipynb
@@ -23,9 +23,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working directory: /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-beginner-03-_y1nrkpq\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "import tempfile\n",
@@ -68,9 +76,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Manifest ready with 4 workers\n"
+     ]
+    }
+   ],
    "source": [
     "# Write manifest for scaling experiments\n",
     "manifest_content = \"\"\"\\\n",
@@ -112,9 +128,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sequential (1 worker): 4.03s for 8 tasks\n"
+     ]
+    }
+   ],
    "source": [
     "from scalable import ScalableSession\n",
     "\n",
@@ -132,16 +156,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parallel (4 workers): 4.06s for 8 tasks\n",
+      "Speedup: 1.0x\n",
+      "\n",
+      "💡 With 4 workers and 8 tasks: 2 batches × 0.5s ≈ 1.0s (ideal)\n",
+      "   Actual overhead brings it slightly above ideal.\n"
+     ]
+    }
+   ],
    "source": [
     "# Now: run in PARALLEL with Scalable (4 workers)\n",
-    "session = ScalableSession.from_manifest(\"./scalable.yaml\", target=\"local\")\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "plan = session.plan()\n",
+    "client = session.start(plan)\n",
     "\n",
     "start = time.time()\n",
-    "futures = [session.submit(simulate_work, i, task=\"run_analysis\") for i in range(8)]\n",
-    "parallel_results = session.gather(futures)\n",
+    "futures = [client.submit(simulate_work, i, tag=\"analysis\") for i in range(8)]\n",
+    "parallel_results = client.gather(futures)\n",
     "parallel_time = time.time() - start\n",
     "\n",
     "print(f\"Parallel (4 workers): {parallel_time:.2f}s for 8 tasks\")\n",
@@ -204,9 +242,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cleaned up /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-beginner-03-_y1nrkpq\n"
+     ]
+    }
+   ],
    "source": [
     "# Cleanup\n",
     "import shutil\n",
@@ -214,17 +260,32 @@
     "shutil.rmtree(project_dir, ignore_errors=True)\n",
     "print(f\"Cleaned up {project_dir}\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "py3.13.3_scalable",
    "language": "python",
-   "name": "python3"
+   "name": "py3.13.3_scalable"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.12.0"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/beginner/04_caching_performance.ipynb b/notebooks/beginner/04_caching_performance.ipynb
index 8f305ae..409e449 100644
--- a/notebooks/beginner/04_caching_performance.ipynb
+++ b/notebooks/beginner/04_caching_performance.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Beginner Tutorial 4: Caching — Avoiding Redundant Work\n",
+    "# Beginner Tutorial 4: Caching \u2014 Avoiding Redundant Work\n",
     "\n",
     "## What You Will Learn\n",
     "\n",
@@ -41,7 +41,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 💡 Key Concept: What is Caching?\n",
+    "## \ud83d\udca1 Key Concept: What is Caching?\n",
     "\n",
     "**Caching** = storing results so you don't recompute them.\n",
     "It trades **storage space** for **computation time**.\n",
@@ -51,16 +51,16 @@
     "- CPU cache: keeps frequently-used data close to processor\n",
     "\n",
     "In Scalable: \"If I've already computed `f(x)` and saved the result,\n",
-    "don't compute it again — just return the saved result.\"\n",
+    "don't compute it again \u2014 just return the saved result.\"\n",
     "\n",
-    "## 💡 Key Concept: Hash Functions\n",
+    "## \ud83d\udca1 Key Concept: Hash Functions\n",
     "\n",
     "A **hash function** takes any input and produces a fixed-size \"fingerprint\":\n",
     "\n",
     "```\n",
-    "\"Hello\"      → a3b8c9d2...  (always same for same input)\n",
-    "\"Hello!\"     → 7f83b165...  (tiny change → totally different hash)\n",
-    "(500MB file) → e1f0a2b3...  (same fixed size regardless of input)\n",
+    "\"Hello\"      \u2192 a3b8c9d2...  (always same for same input)\n",
+    "\"Hello!\"     \u2192 7f83b165...  (tiny change \u2192 totally different hash)\n",
+    "(500MB file) \u2192 e1f0a2b3...  (same fixed size regardless of input)\n",
     "```\n",
     "\n",
     "Key properties: deterministic, fixed-size output, one-way."
@@ -75,14 +75,14 @@
     "# Demonstrate hashing\n",
     "import hashlib\n",
     "\n",
-    "# Same input → same hash (deterministic)\n",
+    "# Same input \u2192 same hash (deterministic)\n",
     "hash1 = hashlib.sha256(b\"Hello, World!\").hexdigest()[:16]\n",
     "hash2 = hashlib.sha256(b\"Hello, World!\").hexdigest()[:16]\n",
     "print(f\"Hash of 'Hello, World!': {hash1}\")\n",
     "print(f\"Hash again (same):      {hash2}\")\n",
     "print(f\"Same? {hash1 == hash2}\")\n",
     "\n",
-    "# Tiny change → completely different hash\n",
+    "# Tiny change \u2192 completely different hash\n",
     "hash3 = hashlib.sha256(b\"Hello, World!!\").hexdigest()[:16]\n",
     "print(f\"\\nHash of 'Hello, World!!': {hash3}\")\n",
     "print(f\"Completely different! This is the 'avalanche effect'.\")"
@@ -92,12 +92,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 💡 Key Concept: Python Decorators\n",
+    "## \ud83d\udca1 Key Concept: Python Decorators\n",
     "\n",
     "A **decorator** wraps a function to add behavior without changing its code:\n",
     "\n",
     "```python\n",
-    "@some_decorator      # ← This wraps the function below\n",
+    "@some_decorator      # \u2190 This wraps the function below\n",
     "def my_function(x):\n",
     "    return x * 2\n",
     "```\n",
@@ -139,7 +139,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# FIRST CALL: Cache miss (slow — has to compute)\n",
+    "# FIRST CALL: Cache miss (slow \u2014 has to compute)\n",
     "start = time.time()\n",
     "result1 = expensive_simulation(scenario_id=42)\n",
     "first_call_time = time.time() - start\n",
@@ -154,14 +154,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# SECOND CALL: Cache hit (instant — returns saved result)\n",
+    "# SECOND CALL: Cache hit (instant \u2014 returns saved result)\n",
     "start = time.time()\n",
     "result2 = expensive_simulation(scenario_id=42)\n",
     "second_call_time = time.time() - start\n",
     "\n",
     "print(f\"Second call (cache HIT): {second_call_time:.4f}s\")\n",
     "print(f\"Result: {result2}\")\n",
-    "print(f\"\\n💡 Speedup: {first_call_time / max(second_call_time, 0.001):.0f}x faster!\")\n",
+    "print(f\"\\n\ud83d\udca1 Speedup: {first_call_time / max(second_call_time, 0.001):.0f}x faster!\")\n",
     "print(f\"Same result? {result1 == result2}\")"
    ]
   },
@@ -178,14 +178,14 @@
     "\n",
     "print(f\"Different input (cache MISS): {third_call_time:.2f}s\")\n",
     "print(f\"Result: {result3}\")\n",
-    "print(f\"\\n💡 Different input → different cache key → must recompute\")"
+    "print(f\"\\n\ud83d\udca1 Different input \u2192 different cache key \u2192 must recompute\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 🤔 Think About It: Cache Invalidation\n",
+    "## \ud83e\udd14 Think About It: Cache Invalidation\n",
     "\n",
     "What if you **fix a bug** in your function but the inputs stay the same?\n",
     "\n",
@@ -203,7 +203,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 📖 Vocabulary Summary\n",
+    "## \ud83d\udcd6 Vocabulary Summary\n",
     "\n",
     "| Term | Definition |\n",
     "|------|------------|\n",
@@ -247,4 +247,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/notebooks/beginner/05_cloud_integration.ipynb b/notebooks/beginner/05_cloud_integration.ipynb
index 7d1221a..25c27fa 100644
--- a/notebooks/beginner/05_cloud_integration.ipynb
+++ b/notebooks/beginner/05_cloud_integration.ipynb
@@ -17,7 +17,7 @@
     "\n",
     "## Prerequisites\n",
     "\n",
-    "- Completed notebooks 01–02\n",
+    "- Completed notebooks 01\u201302\n",
     "- `pip install scalable[cloud]` (for code examples)\n",
     "- NO cloud account required (conceptual + configuration examples)"
    ]
@@ -40,23 +40,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 💡 Key Concept: What is Cloud Computing?\n",
+    "## \ud83d\udca1 Key Concept: What is Cloud Computing?\n",
     "\n",
     "**Cloud computing** = renting computers, storage, and networking over the internet.\n",
     "Pay only for what you use.\n",
     "\n",
     "**Before cloud:** Buy servers, install them, maintain them, pay whether used or idle.\n",
     "\n",
-    "**With cloud:** Request \"10 machines for 2 hours\" → they appear in seconds → stop paying when done.\n",
+    "**With cloud:** Request \"10 machines for 2 hours\" \u2192 they appear in seconds \u2192 stop paying when done.\n",
     "\n",
-    "## 💡 Key Concept: Object Storage (S3/GCS)\n",
+    "## \ud83d\udca1 Key Concept: Object Storage (S3/GCS)\n",
     "\n",
     "**Object storage** = cloud service for storing files in \"buckets\":\n",
     "```\n",
     "s3://my-bucket/scalable-runs/results.json\n",
-    "│    │         │              │\n",
-    "│    bucket    prefix         object key\n",
-    "└── protocol\n",
+    "\u2502    \u2502         \u2502              \u2502\n",
+    "\u2502    bucket    prefix         object key\n",
+    "\u2514\u2500\u2500 protocol\n",
     "```\n",
     "\n",
     "Why not a regular filesystem?\n",
@@ -65,12 +65,12 @@
     "- Accessible from anywhere\n",
     "- Very cheap ($0.023/GB/month)\n",
     "\n",
-    "## 💡 Key Concept: Containers\n",
+    "## \ud83d\udca1 Key Concept: Containers\n",
     "\n",
     "A **container** packages code + all dependencies into a portable unit.\n",
     "Solves the \"works on my machine\" problem.\n",
     "\n",
-    "**Analogy:** Like a shipping container — the crane doesn't care what's inside,\n",
+    "**Analogy:** Like a shipping container \u2014 the crane doesn't care what's inside,\n",
     "it just knows how to move the standard container."
    ]
   },
@@ -120,17 +120,17 @@
     "\n",
     "print(\"Cloud manifest written.\")\n",
     "print(\"\\nKey cloud settings explained:\")\n",
-    "print(\"  region: us-east-1 → which data center\")\n",
-    "print(\"  cluster_type: fargate → serverless containers (no servers to manage)\")\n",
-    "print(\"  image: ... → container with your code & dependencies\")\n",
-    "print(\"  adaptive: min=1, max=10 → auto-scale based on demand\")"
+    "print(\"  region: us-east-1 \u2192 which data center\")\n",
+    "print(\"  cluster_type: fargate \u2192 serverless containers (no servers to manage)\")\n",
+    "print(\"  image: ... \u2192 container with your code & dependencies\")\n",
+    "print(\"  adaptive: min=1, max=10 \u2192 auto-scale based on demand\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 💡 Key Concept: Cloud Cost Model\n",
+    "## \ud83d\udca1 Key Concept: Cloud Cost Model\n",
     "\n",
     "Cloud charges for what you use:\n",
     "\n",
@@ -142,10 +142,10 @@
     "\n",
     "**Example run cost:**\n",
     "```\n",
-    "10 workers × 4 vCPU × 2 hours × $0.04/vCPU-hour = $3.20\n",
-    "10 workers × 16GB × 2 hours × $0.004/GB-hour     = $1.28\n",
-    "Output: 50GB × $0.023/GB-month                    = $1.15/month\n",
-    "─────────────────────────────────────────────────────\n",
+    "10 workers \u00d7 4 vCPU \u00d7 2 hours \u00d7 $0.04/vCPU-hour = $3.20\n",
+    "10 workers \u00d7 16GB \u00d7 2 hours \u00d7 $0.004/GB-hour     = $1.28\n",
+    "Output: 50GB \u00d7 $0.023/GB-month                    = $1.15/month\n",
+    "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
     "Total: ~$5.63 (one-time) + $1.15/month (storage)\n",
     "```\n",
     "\n",
@@ -158,19 +158,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from scalable.manifest import parse_manifest, validate_manifest\n",
+    "from scalable.manifest.parser import load_manifest\n",
+    "from scalable.manifest.validate import validate_manifest\n",
     "\n",
     "# Validate the cloud manifest\n",
-    "manifest = parse_manifest(\"./scalable.yaml\")\n",
-    "errors = validate_manifest(manifest)\n",
+    "manifest = load_manifest(\"./scalable.yaml\")\n",
+    "report = validate_manifest(manifest)\n",
     "\n",
-    "if errors:\n",
-    "    for err in errors:\n",
-    "        print(f\"  ✗ {err}\")\n",
+    "if not report.ok:\n",
+    "    for issue in report.errors:\n",
+    "        print(f\"  \u2717 {issue.path}: {issue.message}\")\n",
     "else:\n",
-    "    print(\"✓ Cloud manifest is valid\")\n",
-    "    print(f\"\\nTargets available: {list(manifest['targets'].keys())}\")\n",
-    "    print(f\"\\n💡 Same manifest works locally AND in the cloud!\")\n",
+    "    print(\"\u2713 Cloud manifest is valid\")\n",
+    "    print(f\"\\nTargets available: {list(manifest.targets.keys())}\")\n",
+    "    print(f\"\\n\ud83d\udca1 Same manifest works locally AND in the cloud!\")\n",
     "    print(f\"   Development: scalable run --target local\")\n",
     "    print(f\"   Production:  scalable run --target aws\")"
    ]
@@ -179,14 +180,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 🤔 Think About It\n",
+    "## \ud83e\udd14 Think About It\n",
     "\n",
     "Notice how the **Python code doesn't change** between local and cloud.\n",
     "Only the `--target` flag changes. This is the power of:\n",
     "- Declarative manifests (configuration, not code)\n",
     "- Provider abstraction (same API, different backend)\n",
     "\n",
-    "## 📖 Vocabulary Summary\n",
+    "## \ud83d\udcd6 Vocabulary Summary\n",
     "\n",
     "| Term | Definition |\n",
     "|------|------------|\n",
@@ -229,4 +230,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/notebooks/beginner/06_telemetry.ipynb b/notebooks/beginner/06_telemetry.ipynb
index 0b2b542..e6a9e77 100644
--- a/notebooks/beginner/06_telemetry.ipynb
+++ b/notebooks/beginner/06_telemetry.ipynb
@@ -40,7 +40,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 💡 Key Concept: What is Telemetry?\n",
+    "## \ud83d\udca1 Key Concept: What is Telemetry?\n",
     "\n",
     "**Telemetry** = automated collection of data about what your program did.\n",
     "From Greek: *tele* (remote) + *metron* (measurement).\n",
@@ -50,7 +50,7 @@
     "- How much memory/CPU was used?\n",
     "- Which tasks failed and why?\n",
     "\n",
-    "## 💡 Key Concept: Structured Logging\n",
+    "## \ud83d\udca1 Key Concept: Structured Logging\n",
     "\n",
     "**Structured logging** = recording events as machine-parseable data (JSON)\n",
     "instead of free-form text.\n",
@@ -64,7 +64,7 @@
     "\n",
     "Structured logs can be filtered, aggregated, and queried programmatically.\n",
     "\n",
-    "## 💡 Key Concept: JSONL (JSON Lines)\n",
+    "## \ud83d\udca1 Key Concept: JSONL (JSON Lines)\n",
     "\n",
     "**JSONL** = one JSON object per line. Perfect for event streams:\n",
     "- Appendable (just add a new line)\n",
@@ -123,9 +123,11 @@
     "    return {\"task_id\": task_id, \"duration\": duration}\n",
     "\n",
     "# Run workflow\n",
-    "session = ScalableSession.from_manifest(\"./scalable.yaml\", target=\"local\")\n",
-    "futures = [session.submit(variable_work, i, task=\"run_analysis\") for i in range(10)]\n",
-    "results = session.gather(futures)\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "plan = session.plan()\n",
+    "client = session.start(plan)\n",
+    "futures = [client.submit(variable_work, i, tag=\"analysis\") for i in range(10)]\n",
+    "results = client.gather(futures)\n",
     "session.close()\n",
     "\n",
     "print(f\"Completed {len(results)} tasks\")\n",
@@ -152,7 +154,7 @@
     "            print(f\"  {f} ({size} bytes)\")\n",
     "else:\n",
     "    print(\"No telemetry directory found (session may not have written telemetry in this mode)\")\n",
-    "    print(\"\\n💡 In production usage, telemetry is always written.\")\n",
+    "    print(\"\\n\ud83d\udca1 In production usage, telemetry is always written.\")\n",
     "    print(\"Let's simulate what telemetry data looks like...\")"
    ]
   },
@@ -183,11 +185,11 @@
     "    })\n",
     "\n",
     "print(\"Example telemetry events (JSONL format):\")\n",
-    "print(\"─\" * 60)\n",
+    "print(\"\u2500\" * 60)\n",
     "for event in simulated_events[:5]:\n",
     "    print(json.dumps(event))\n",
     "print(\"...\")\n",
-    "print(f\"\\n💡 Each line is a complete JSON object — that's JSONL!\")"
+    "print(f\"\\n\ud83d\udca1 Each line is a complete JSON object \u2014 that's JSONL!\")"
    ]
   },
   {
@@ -209,14 +211,14 @@
     "    count = sum(1 for e in simulated_events if e['worker'] == w)\n",
     "    print(f\"  {w}: {count} tasks\")\n",
     "\n",
-    "print(f\"\\n💡 Use this data to optimize: are workers balanced? Any outlier tasks?\")"
+    "print(f\"\\n\ud83d\udca1 Use this data to optimize: are workers balanced? Any outlier tasks?\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 📖 Vocabulary Summary\n",
+    "## \ud83d\udcd6 Vocabulary Summary\n",
     "\n",
     "| Term | Definition |\n",
     "|------|------------|\n",
@@ -226,7 +228,7 @@
     "| Logs | Discrete events with context |\n",
     "| Traces | Journey of a request through the system |\n",
     "| Structured Logging | Machine-parseable event recording (JSON) |\n",
-    "| JSONL | JSON Lines — one JSON object per line |\n",
+    "| JSONL | JSON Lines \u2014 one JSON object per line |\n",
     "| Event | Discrete occurrence with timestamp and payload |\n",
     "| Utilization | Percentage of resources actually being used |"
    ]
@@ -258,4 +260,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/notebooks/beginner/07_error_handling.ipynb b/notebooks/beginner/07_error_handling.ipynb
index c9e3bf2..e3af357 100644
--- a/notebooks/beginner/07_error_handling.ipynb
+++ b/notebooks/beginner/07_error_handling.ipynb
@@ -22,9 +22,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working directory: /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-beginner-07-2xlocai8\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "import tempfile\n",
@@ -73,9 +81,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Manifest ready.\n"
+     ]
+    }
+   ],
    "source": [
     "# Write manifest\n",
     "manifest_content = \"\"\"\\\n",
@@ -108,9 +124,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Function defined: fails ~30% of the time (simulating transient errors)\n"
+     ]
+    }
+   ],
    "source": [
     "from scalable import ScalableSession\n",
     "\n",
@@ -139,16 +163,86 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-05-20 15:37:38,868 - distributed.worker - ERROR - Compute Failed\n",
+      "Key:       sometimes_fails-6068594bd143dc209109d7f0928e0d37\n",
+      "State:     executing\n",
+      "Task:  <Task 'sometimes_fails-6068594bd143dc209109d7f0928e0d37' sometimes_fails(...)>\n",
+      "Exception: \"RuntimeError('Transient failure on task 6!')\"\n",
+      "Traceback: '  File \"/Users/d3y010/repos/github/scalable/scalable/client.py\", line 168, in _wrapped\\n    return func(*wrapped_args, **wrapped_kwargs)\\n  File \"/var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/ipykernel_55442/3726870185.py\", line 10, in sometimes_fails\\n    raise RuntimeError(f\"Transient failure on task {task_id}!\")\\n'\n",
+      "\n",
+      "2026-05-20 15:37:39,080 - distributed.worker - ERROR - Compute Failed\n",
+      "Key:       sometimes_fails-1628e30922a2f2bce6a340cf1952bc00\n",
+      "State:     executing\n",
+      "Task:  <Task 'sometimes_fails-1628e30922a2f2bce6a340cf1952bc00' sometimes_fails(...)>\n",
+      "Exception: \"RuntimeError('Transient failure on task 5!')\"\n",
+      "Traceback: '  File \"/Users/d3y010/repos/github/scalable/scalable/client.py\", line 168, in _wrapped\\n    return func(*wrapped_args, **wrapped_kwargs)\\n  File \"/var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/ipykernel_55442/3726870185.py\", line 10, in sometimes_fails\\n    raise RuntimeError(f\"Transient failure on task {task_id}!\")\\n'\n",
+      "\n",
+      "2026-05-20 15:37:39,290 - distributed.worker - ERROR - Compute Failed\n",
+      "Key:       sometimes_fails-8185eda66aa3425c1f78c6ec368423ff\n",
+      "State:     executing\n",
+      "Task:  <Task 'sometimes_fails-8185eda66aa3425c1f78c6ec368423ff' sometimes_fails(...)>\n",
+      "Exception: \"RuntimeError('Transient failure on task 4!')\"\n",
+      "Traceback: '  File \"/Users/d3y010/repos/github/scalable/scalable/client.py\", line 168, in _wrapped\\n    return func(*wrapped_args, **wrapped_kwargs)\\n  File \"/var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/ipykernel_55442/3726870185.py\", line 10, in sometimes_fails\\n    raise RuntimeError(f\"Transient failure on task {task_id}!\")\\n'\n",
+      "\n",
+      "2026-05-20 15:37:40,112 - distributed.worker - ERROR - Compute Failed\n",
+      "Key:       sometimes_fails-c4a2a8ec31cc6001826bf3fa5aa4f000\n",
+      "State:     executing\n",
+      "Task:  <Task 'sometimes_fails-c4a2a8ec31cc6001826bf3fa5aa4f000' sometimes_fails(...)>\n",
+      "Exception: \"RuntimeError('Transient failure on task 15!')\"\n",
+      "Traceback: '  File \"/Users/d3y010/repos/github/scalable/scalable/client.py\", line 168, in _wrapped\\n    return func(*wrapped_args, **wrapped_kwargs)\\n  File \"/var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/ipykernel_55442/3726870185.py\", line 10, in sometimes_fails\\n    raise RuntimeError(f\"Transient failure on task {task_id}!\")\\n'\n",
+      "\n",
+      "2026-05-20 15:37:41,552 - distributed.worker - ERROR - Compute Failed\n",
+      "Key:       sometimes_fails-2ba34d0e8d60b9937952faa1d434eed5\n",
+      "State:     executing\n",
+      "Task:  <Task 'sometimes_fails-2ba34d0e8d60b9937952faa1d434eed5' sometimes_fails(...)>\n",
+      "Exception: \"RuntimeError('Transient failure on task 8!')\"\n",
+      "Traceback: '  File \"/Users/d3y010/repos/github/scalable/scalable/client.py\", line 168, in _wrapped\\n    return func(*wrapped_args, **wrapped_kwargs)\\n  File \"/var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/ipykernel_55442/3726870185.py\", line 10, in sometimes_fails\\n    raise RuntimeError(f\"Transient failure on task {task_id}!\")\\n'\n",
+      "\n",
+      "2026-05-20 15:37:41,761 - distributed.worker - ERROR - Compute Failed\n",
+      "Key:       sometimes_fails-3201a67087dc66573c02819e78ffaca8\n",
+      "State:     executing\n",
+      "Task:  <Task 'sometimes_fails-3201a67087dc66573c02819e78ffaca8' sometimes_fails(...)>\n",
+      "Exception: \"RuntimeError('Transient failure on task 19!')\"\n",
+      "Traceback: '  File \"/Users/d3y010/repos/github/scalable/scalable/client.py\", line 168, in _wrapped\\n    return func(*wrapped_args, **wrapped_kwargs)\\n  File \"/var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/ipykernel_55442/3726870185.py\", line 10, in sometimes_fails\\n    raise RuntimeError(f\"Transient failure on task {task_id}!\")\\n'\n",
+      "\n",
+      "2026-05-20 15:37:42,173 - distributed.worker - ERROR - Compute Failed\n",
+      "Key:       sometimes_fails-149cdf824027f26a97006021677fe072\n",
+      "State:     executing\n",
+      "Task:  <Task 'sometimes_fails-149cdf824027f26a97006021677fe072' sometimes_fails(...)>\n",
+      "Exception: \"RuntimeError('Transient failure on task 17!')\"\n",
+      "Traceback: '  File \"/Users/d3y010/repos/github/scalable/scalable/client.py\", line 168, in _wrapped\\n    return func(*wrapped_args, **wrapped_kwargs)\\n  File \"/var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/ipykernel_55442/3726870185.py\", line 10, in sometimes_fails\\n    raise RuntimeError(f\"Transient failure on task {task_id}!\")\\n'\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Results: 13 succeeded, 7 failed\n",
+      "\n",
+      "✅ Successes: [0, 1, 2, 3, 7, 9, 10, 11, 12, 13, 14, 16, 18]\n",
+      "❌ Failures: [4, 5, 6, 8, 15, 17, 19]\n",
+      "\n",
+      "💡 We kept 13 good results despite 7 failures!\n"
+     ]
+    }
+   ],
    "source": [
     "# Run with partial success handling\n",
-    "session = ScalableSession.from_manifest(\"./scalable.yaml\", target=\"local\")\n",
+    "session = ScalableSession.from_yaml(\"./scalable.yaml\", target=\"local\")\n",
+    "plan = session.plan()\n",
+    "client = session.start(plan)\n",
     "\n",
     "# Submit tasks\n",
     "random.seed(42)  # For reproducibility\n",
-    "futures = [session.submit(sometimes_fails, i, task=\"run_analysis\") for i in range(20)]\n",
+    "futures = [client.submit(sometimes_fails, i, tag=\"analysis\") for i in range(20)]\n",
     "\n",
     "# Gather with error isolation (don't let one failure crash everything)\n",
     "results = []\n",
@@ -215,9 +309,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cleaned up /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-beginner-07-2xlocai8\n"
+     ]
+    }
+   ],
    "source": [
     "# Cleanup\n",
     "import shutil\n",
@@ -225,17 +327,32 @@
     "shutil.rmtree(project_dir, ignore_errors=True)\n",
     "print(f\"Cleaned up {project_dir}\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "py3.13.3_scalable",
    "language": "python",
-   "name": "python3"
+   "name": "py3.13.3_scalable"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.12.0"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/beginner/08_kubernetes.ipynb b/notebooks/beginner/08_kubernetes.ipynb
index 11abd7a..c40fdf2 100644
--- a/notebooks/beginner/08_kubernetes.ipynb
+++ b/notebooks/beginner/08_kubernetes.ipynb
@@ -23,9 +23,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working directory: /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-beginner-08-oo0g4xm3\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "import tempfile\n",
@@ -75,9 +83,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Kubernetes manifest written.\n",
+      "\n",
+      "Key K8s settings:\n",
+      "  namespace: team-climate → isolation boundary\n",
+      "  image: ... → container with your code\n",
+      "  adaptive: min=2, max=20 → auto-scale workers\n",
+      "  resources.requests → minimum guaranteed CPU/memory\n",
+      "  resources.limits → maximum allowed (prevents runaway)\n"
+     ]
+    }
+   ],
    "source": [
     "# Example: Kubernetes target in a Scalable manifest\n",
     "k8s_manifest = \"\"\"\\\n",
@@ -131,24 +154,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  ✗ targets.k8s.provider: unknown provider 'kubernetes'; known providers: ['local', 'slurm']\n"
+     ]
+    }
+   ],
    "source": [
-    "from scalable.manifest import parse_manifest, validate_manifest\n",
+    "from scalable.manifest.parser import load_manifest\n",
+    "from scalable.manifest.validate import validate_manifest\n",
     "\n",
-    "manifest = parse_manifest(\"./scalable.yaml\")\n",
-    "errors = validate_manifest(manifest)\n",
+    "manifest = load_manifest(\"./scalable.yaml\")\n",
+    "report = validate_manifest(manifest)\n",
     "\n",
-    "if not errors:\n",
+    "if report.ok:\n",
     "    print(\"✓ Kubernetes manifest is valid\")\n",
-    "    print(f\"\\nTargets: {list(manifest['targets'].keys())}\")\n",
+    "    print(f\"\\nTargets: {list(manifest.targets.keys())}\")\n",
     "    print(f\"\\n💡 Same Python code works with both targets:\")\n",
     "    print(f\"   Development: scalable run --target local\")\n",
     "    print(f\"   Production:  scalable run --target k8s\")\n",
     "else:\n",
-    "    for err in errors:\n",
-    "        print(f\"  ✗ {err}\")"
+    "    for issue in report.errors:\n",
+    "        print(f\"  ✗ {issue.path}: {issue.message}\")"
    ]
   },
   {
@@ -185,9 +217,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cleaned up /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-beginner-08-oo0g4xm3\n"
+     ]
+    }
+   ],
    "source": [
     "# Cleanup\n",
     "import shutil\n",
@@ -195,17 +235,32 @@
     "shutil.rmtree(project_dir, ignore_errors=True)\n",
     "print(f\"Cleaned up {project_dir}\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "py3.13.3_scalable",
    "language": "python",
-   "name": "python3"
+   "name": "py3.13.3_scalable"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.12.0"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/beginner/09_ml_emulation.ipynb b/notebooks/beginner/09_ml_emulation.ipynb
index 890e59b..a33616c 100644
--- a/notebooks/beginner/09_ml_emulation.ipynb
+++ b/notebooks/beginner/09_ml_emulation.ipynb
@@ -23,9 +23,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working directory: /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-beginner-09-rhfzmo8w\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "import tempfile\n",
@@ -76,9 +84,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Simulated telemetry history (50 past runs):\n",
+      "  Sample: nodes=1688 → memory=16.5GB\n",
+      "  Sample: nodes=270 → memory=3.6GB\n",
+      "  Sample: nodes=988 → memory=8.5GB\n",
+      "\n",
+      "💡 Pattern: more nodes → more memory needed\n"
+     ]
+    }
+   ],
    "source": [
     "# Demonstrate ML concepts with a simple example\n",
     "import random\n",
@@ -104,9 +125,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ML Model trained on 50 past runs\n",
+      "\n",
+      "Prediction for new scenario (1500 nodes):\n",
+      "  Predicted memory needed: 14.9 GB\n",
+      "  Recommended allocation: 19 GB (with 20% headroom)\n",
+      "\n",
+      "💡 Without ML: guess '16GB' for everything (wasteful or insufficient)\n",
+      "   With ML: data-driven recommendation specific to your workload\n"
+     ]
+    }
+   ],
    "source": [
     "# Simple \"ML model\" — learn the pattern from data\n",
     "# (In Scalable, this uses scikit-learn gradient boosting)\n",
@@ -160,9 +196,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scenario 10: Emulator (confidence=95%) → 524.0 [FAST]\n",
+      "Scenario 50: Emulator (confidence=95%) → 2216.0 [FAST]\n",
+      "Scenario 150: Low confidence (60%) → running full model... [would take 2s]\n",
+      "\n",
+      "💡 Threshold=90%: use emulator when confident, full model otherwise\n"
+     ]
+    }
+   ],
    "source": [
     "# Demonstrate the emulator concept\n",
     "import time\n",
@@ -218,9 +266,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cleaned up /var/folders/8c/787g_pzs7wq7hljnnx3prdzr0000gn/T/scalable-beginner-09-rhfzmo8w\n"
+     ]
+    }
+   ],
    "source": [
     "# Cleanup\n",
     "import shutil\n",
@@ -228,17 +284,32 @@
     "shutil.rmtree(project_dir, ignore_errors=True)\n",
     "print(f\"Cleaned up {project_dir}\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "py3.13.3_scalable",
    "language": "python",
-   "name": "python3"
+   "name": "py3.13.3_scalable"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.12.0"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/beginner/10_ai_composition.ipynb b/notebooks/beginner/10_ai_composition.ipynb
index 64658e8..6603040 100644
--- a/notebooks/beginner/10_ai_composition.ipynb
+++ b/notebooks/beginner/10_ai_composition.ipynb
@@ -17,7 +17,7 @@
     "\n",
     "## Prerequisites\n",
     "\n",
-    "- Completed notebooks 01–02\n",
+    "- Completed notebooks 01\u201302\n",
     "- `pip install scalable[ai]`\n",
     "- NO LLM API key required (heuristic mode works offline)"
    ]
@@ -40,7 +40,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 💡 Key Concept: What is a Large Language Model (LLM)?\n",
+    "## \ud83d\udca1 Key Concept: What is a Large Language Model (LLM)?\n",
     "\n",
     "An **LLM** is an AI system trained on massive text data that can generate\n",
     "human-like text, answer questions, and write code.\n",
@@ -52,13 +52,13 @@
     "\n",
     "Examples: ChatGPT, Claude, Llama, Gemini\n",
     "\n",
-    "## 💡 Key Concept: Heuristic vs. LLM Mode\n",
+    "## \ud83d\udca1 Key Concept: Heuristic vs. LLM Mode\n",
     "\n",
     "| | Heuristic Mode | LLM Mode |\n",
     "|---|---|---|\n",
     "| How | Rules + templates | AI generation |\n",
-    "| Deterministic? | ✅ Always same output | ❌ May vary |\n",
-    "| Offline? | ✅ No internet needed | ❌ Needs API |\n",
+    "| Deterministic? | \u2705 Always same output | \u274c May vary |\n",
+    "| Offline? | \u2705 No internet needed | \u274c Needs API |\n",
     "| Cost | Free | ~$0.01-0.10/call |\n",
     "| Best for | CI/CD, reproducible | Creative, complex |\n",
     "\n",
@@ -107,29 +107,32 @@
    "outputs": [],
    "source": [
     "# Demonstrate AI component onboarding (heuristic mode)\n",
+    "import os\n",
     "from scalable.ai.component_onboarding import onboard_component\n",
     "\n",
-    "# Explanation: onboard_component generates a component definition\n",
-    "# from high-level inputs (name, resources, description)\n",
+    "# Create a mock model directory for onboarding\n",
+    "os.makedirs(\"watershed_model\", exist_ok=True)\n",
+    "with open(\"watershed_model/README.md\", \"w\") as f:\n",
+    "    f.write(\"# WaterShed Model\\nHydrological watershed model for runoff simulation\\n\")\n",
+    "\n",
+    "# Explanation: onboard_component analyzes a directory and generates\n",
+    "# a component definition from what it finds\n",
     "result = onboard_component(\n",
+    "    \"./watershed_model\",\n",
     "    name=\"watershed\",\n",
-    "    cpus=4,\n",
-    "    memory=\"16G\",\n",
-    "    image=\"ghcr.io/watershed/model:3.0\",\n",
-    "    description=\"Hydrological watershed model for runoff simulation\",\n",
     "    no_ai=True,  # Use heuristic mode (no LLM needed)\n",
     ")\n",
     "\n",
     "print(\"Generated component definition (heuristic mode):\")\n",
-    "print(\"─\" * 50)\n",
-    "print(result)"
+    "print(\"\u2500\" * 50)\n",
+    "print(result.component_yaml)\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 💡 What Just Happened?\n",
+    "## \ud83d\udca1 What Just Happened?\n",
     "\n",
     "The `onboard_component` assistant:\n",
     "1. Took your high-level inputs (name, image, resources)\n",
@@ -150,24 +153,30 @@
     "# Demonstrate plan explanation (heuristic mode)\n",
     "from scalable.ai.plan_explain import explain_plan\n",
     "\n",
+    "# Create a minimal plan dict for explanation\n",
+    "plan_data = {\n",
+    "    \"target_name\": \"local\",\n",
+    "    \"provider\": \"local\",\n",
+    "    \"manifest_lock\": \"sha256:abc123\",\n",
+    "    \"scale_plan\": {\"hydrology\": {\"workers\": 4, \"cpus\": 4, \"memory\": \"16G\"}},\n",
+    "}\n",
+    "\n",
     "# Explanation: explain_plan generates human-readable explanation\n",
-    "# of what a manifest/plan will do\n",
     "explanation = explain_plan(\n",
-    "    manifest_path=\"./scalable.yaml\",\n",
-    "    target=\"local\",\n",
+    "    plan_data=plan_data,\n",
     "    no_ai=True,\n",
     ")\n",
     "\n",
     "print(\"Plan Explanation:\")\n",
-    "print(\"═\" * 50)\n",
-    "print(explanation)"
+    "print(\"\u2550\" * 50)\n",
+    "print(explanation.render_text())\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 💡 Key Concept: Human-in-the-Loop\n",
+    "## \ud83d\udca1 Key Concept: Human-in-the-Loop\n",
     "\n",
     "**Human-in-the-loop** = AI suggests, human decides.\n",
     "\n",
@@ -177,10 +186,10 @@
     "3. **Tested**: `--dry-run` before real deployment\n",
     "4. **Approved**: Human confirms before applying\n",
     "\n",
-    "Never blindly trust AI output — it can generate plausible-looking\n",
+    "Never blindly trust AI output \u2014 it can generate plausible-looking\n",
     "but incorrect configurations.\n",
     "\n",
-    "## 💡 Key Concept: Templates (Jinja2)\n",
+    "## \ud83d\udca1 Key Concept: Templates (Jinja2)\n",
     "\n",
     "**Templates** = pre-structured documents with fill-in-the-blank placeholders:\n",
     "\n",
@@ -191,32 +200,32 @@
     "    memory: {{ memory }}\n",
     "```\n",
     "\n",
-    "Heuristic mode uses templates extensively → predictable, fast, reproducible."
+    "Heuristic mode uses templates extensively \u2192 predictable, fast, reproducible."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 🎉 Congratulations!\n",
+    "## \ud83c\udf89 Congratulations!\n",
     "\n",
     "You've completed all 10 beginner tutorials! You now understand:\n",
     "\n",
-    "1. ✅ Distributed computing and workflow orchestration\n",
-    "2. ✅ Declarative configuration with manifests\n",
-    "3. ✅ Scaling strategies and provider architecture\n",
-    "4. ✅ Caching and performance optimization\n",
-    "5. ✅ Cloud computing and containers\n",
-    "6. ✅ Telemetry and observability\n",
-    "7. ✅ Error handling and fault tolerance\n",
-    "8. ✅ Kubernetes and container orchestration\n",
-    "9. ✅ Machine learning for workflow optimization\n",
-    "10. ✅ AI-assisted development\n",
+    "1. \u2705 Distributed computing and workflow orchestration\n",
+    "2. \u2705 Declarative configuration with manifests\n",
+    "3. \u2705 Scaling strategies and provider architecture\n",
+    "4. \u2705 Caching and performance optimization\n",
+    "5. \u2705 Cloud computing and containers\n",
+    "6. \u2705 Telemetry and observability\n",
+    "7. \u2705 Error handling and fault tolerance\n",
+    "8. \u2705 Kubernetes and container orchestration\n",
+    "9. \u2705 Machine learning for workflow optimization\n",
+    "10. \u2705 AI-assisted development\n",
     "\n",
     "**Next:** Work through the [standard tutorials](../notebooks/) for deeper\n",
     "technical content and production patterns!\n",
     "\n",
-    "## 📖 Vocabulary Summary\n",
+    "## \ud83d\udcd6 Vocabulary Summary\n",
     "\n",
     "| Term | Definition |\n",
     "|------|------------|\n",
@@ -226,7 +235,7 @@
     "| Template | Pre-structured document with placeholders |\n",
     "| Prompt Engineering | Crafting inputs to get desired AI outputs |\n",
     "| Code Generation | AI writing code/configuration |\n",
-    "| Deterministic | Same input → always same output |\n",
+    "| Deterministic | Same input \u2192 always same output |\n",
     "| API | Interface for programs to communicate |\n",
     "| Human-in-the-Loop | AI suggests, human decides |"
    ]
@@ -242,7 +251,7 @@
     "os.chdir(\"/tmp\")\n",
     "shutil.rmtree(project_dir, ignore_errors=True)\n",
     "print(f\"Cleaned up {project_dir}\")\n",
-    "print(\"\\n🎉 All beginner tutorials complete!\")"
+    "print(\"\\n\ud83c\udf89 All beginner tutorials complete!\")"
    ]
   }
  ],
@@ -259,4 +268,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file

From bf7c7ada87406dd24f1b935fdc53fb36c93b1862 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 15:52:16 -0400
Subject: [PATCH 43/47] break out tutorials into beginner and advanced

---
 CHANGELOG.md                 |  9 ++++++++-
 README.md                    |  9 +++++++++
 docs/getting_started.rst     |  2 ++
 docs/tutorials/index.rst     | 19 ++++++++++++++----
 notebooks/README.md          | 37 ++++++++++++++++++++++++++++++++++++
 notebooks/beginner/README.md |  4 ++--
 6 files changed, 73 insertions(+), 7 deletions(-)
 create mode 100644 notebooks/README.md

diff --git a/CHANGELOG.md b/CHANGELOG.md
index e800dfb..9d46419 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -25,7 +25,14 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   - Beginner Tutorial 9: Machine Learning for Smarter Workflows (surrogates, uncertainty, active learning)
   - Beginner Tutorial 10: AI-Assisted Workflow Development (LLMs, heuristics, code generation)
   - Companion Jupyter notebooks for all 10 tutorials with interactive examples
-  - Tutorials index page with learning path and graduation guide to standard tutorials
+  - Tutorials index page with learning path and graduation guide to advanced tutorials
+- **Reorganized notebook directory structure**:
+  - Original notebooks moved to `notebooks/advanced/`
+  - Beginner notebooks in `notebooks/beginner/`
+  - Top-level `notebooks/README.md` directs users to appropriate track
+- **Documentation recommends beginner path**: `README.md`, `docs/getting_started.rst`,
+  and `docs/tutorials/index.rst` now recommend starting with beginner tutorials for
+  users unfamiliar with distributed computing concepts.
 
 ---
 
diff --git a/README.md b/README.md
index 25862f6..4af6927 100644
--- a/README.md
+++ b/README.md
@@ -35,6 +35,15 @@ Scalable is a Python framework for orchestrating containerized, distributed work
 
 Full documentation is available at [jgcri.github.io/scalable](https://jgcri.github.io/scalable/).
 
+### Tutorials
+
+Scalable includes two sets of tutorials:
+
+- **[Beginner Tutorials](notebooks/beginner/)** — Start here if you are new to Scalable or unfamiliar with distributed computing, containers, cloud infrastructure, or declarative programming. These tutorials explain every concept from first principles with analogies and definitions.
+- **[Advanced Tutorials](notebooks/advanced/)** — Production-focused tutorials for users already comfortable with distributed systems concepts.
+
+Both are available as [interactive Jupyter notebooks](notebooks/) and as [comprehensive RST documentation](docs/tutorials/).
+
 ## Installation
 
 Install from PyPI:
diff --git a/docs/getting_started.rst b/docs/getting_started.rst
index bc3faa6..ad6fc25 100644
--- a/docs/getting_started.rst
+++ b/docs/getting_started.rst
@@ -138,6 +138,8 @@ Next Steps
 
 After setup:
 
+* **New to distributed computing?** Start with the :ref:`beginner_tutorials`
+  for a guided introduction that explains all concepts from first principles.
 * For declarative workflows, start with :doc:`manifest` and :doc:`providers`.
 * Use manifest overlays for environment-specific overrides: :doc:`overlays`.
 * Review run telemetry in :doc:`telemetry`.
diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst
index fa2c998..41db4c0 100644
--- a/docs/tutorials/index.rst
+++ b/docs/tutorials/index.rst
@@ -17,10 +17,21 @@ Beginner Tutorials
 
    beginner/index
 
-**New to distributed computing?** Start with the beginner tutorials. They cover
-the same 10 topics as the standard tutorials below but explain all concepts
-from first principles — no prior distributed systems, cloud, or container
-experience required.
+.. tip::
+
+   **New to Scalable or distributed computing?** Start with the beginner
+   tutorials above. They cover the same 10 topics as the advanced tutorials
+   below but explain every concept from first principles — no prior distributed
+   systems, cloud, or container experience required. Once you're comfortable
+   with the concepts, graduate to the advanced tutorials for production patterns.
+
+Advanced Tutorials
+===================
+
+The following advanced tutorials assume familiarity with distributed computing
+concepts. If terms like "workers," "schedulers," "containers," or "declarative
+programming" are unfamiliar, please start with the :ref:`beginner_tutorials`
+above.
 
 Getting Started
 ---------------
diff --git a/notebooks/README.md b/notebooks/README.md
new file mode 100644
index 0000000..514c077
--- /dev/null
+++ b/notebooks/README.md
@@ -0,0 +1,37 @@
+# Scalable Tutorial Notebooks
+
+Interactive Jupyter notebooks for learning Scalable.
+
+## 📚 Start Here: Beginner Tutorials
+
+If you are **new to Scalable or distributed computing**, start with the beginner tutorials:
+
+→ **[`beginner/`](beginner/)** — 10 notebooks that explain every concept from first principles. No prior distributed computing, cloud, or container experience required.
+
+## 🚀 Advanced Tutorials
+
+Once you're comfortable with the concepts, the advanced tutorials cover production patterns and deeper technical details:
+
+→ **[`advanced/`](advanced/)** — 10 notebooks covering the same topics with less explanation and more advanced patterns for production use.
+
+## Recommended Path
+
+1. Work through `beginner/01` → `beginner/10` sequentially
+2. Graduate to `advanced/01` → `advanced/10` for production patterns
+3. Use the [RST documentation](../docs/tutorials/) for full architectural context
+
+## Quick Start
+
+```bash
+# Install Scalable with all extras
+pip install scalable[ai,cloud,kubernetes,ml]
+
+# Install Jupyter
+pip install jupyterlab
+
+# Launch beginner tutorials
+jupyter lab notebooks/beginner/
+
+# Or advanced tutorials
+jupyter lab notebooks/advanced/
+```
diff --git a/notebooks/beginner/README.md b/notebooks/beginner/README.md
index f5878bc..8fa12ea 100644
--- a/notebooks/beginner/README.md
+++ b/notebooks/beginner/README.md
@@ -4,7 +4,7 @@ Interactive Jupyter notebooks designed for **non-experts** who are new to both S
 
 ## How These Differ from Standard Notebooks
 
-The standard notebooks (in `../notebooks/`) assume familiarity with distributed computing, YAML, containers, and cloud infrastructure. These beginner notebooks:
+The advanced notebooks (in `../advanced/`) assume familiarity with distributed computing, YAML, containers, and cloud infrastructure. These beginner notebooks:
 
 - 📖 **Define every term** before using it
 - 🤔 **Explain why** approaches were chosen (not just how to use them)
@@ -63,4 +63,4 @@ Notebooks are designed to be run sequentially (1 → 10). Each is self-contained
 
 ## Graduating to Standard Notebooks
 
-After completing these beginner notebooks, move to the [standard notebooks](../notebooks/) for deeper technical content and production patterns. Each beginner notebook maps 1:1 to a standard notebook covering the same topic at a more advanced level.
+After completing these beginner notebooks, move to the [advanced notebooks](../advanced/) for deeper technical content and production patterns. Each beginner notebook maps 1:1 to a standard notebook covering the same topic at a more advanced level.

From eff3b5d4b7de3ced35830486dc85181b294ac6d2 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 16:18:55 -0400
Subject: [PATCH 44/47] reorder docs

---
 README.md                |  83 +++++++++++++++++++++
 docs/getting_started.rst | 152 +++++++++++++++++++++++++++++++++++++++
 2 files changed, 235 insertions(+)

diff --git a/README.md b/README.md
index 4af6927..91cf8a8 100644
--- a/README.md
+++ b/README.md
@@ -16,6 +16,7 @@ Scalable is a Python framework for orchestrating containerized, distributed work
 - [Installation](#installation)
 - [System Requirements](#system-requirements)
 - [Quick Start](#quick-start)
+- [Configuration (`.env` File)](#configuration-env-file)
 - [Usage](#usage)
   - [Manifest-Driven Workflows](#manifest-driven-workflows)
   - [Session API](#session-api)
@@ -118,6 +119,88 @@ python3 <filename>.py
 
 Bootstrap performs multiple SSH operations. For best reliability and usability, configure key-based passwordless SSH authentication in advance.
 
+## Configuration (`.env` File)
+
+Scalable uses a **`.env` file** in your project's working directory to centralize
+runtime configuration — particularly AI provider credentials, cache paths, and
+telemetry settings.
+
+### How It Works
+
+When any part of the Scalable library is imported (or any CLI command is run),
+the module [`scalable.common`](scalable/common.py:39) automatically loads a
+`.env` file from **the current working directory** (`$CWD/.env`) using
+[python-dotenv](https://pypi.org/project/python-dotenv/) with `override=True`.
+This means values in `.env` take precedence over pre-existing system environment
+variables.
+
+### Setup Steps
+
+1. **Copy the example file** from the repository root into your project directory:
+
+   ```bash
+   cp .env.example .env
+   ```
+
+2. **Edit `.env`** and fill in your values (at minimum, set `AI_PROVIDER` and
+   `AI_API_KEY` if you want AI features):
+
+   ```bash
+   AI_PROVIDER=openai
+   AI_API_KEY=sk-your-key-here
+   LLM_MODEL_NAME=gpt-4o
+   ```
+
+3. **Run Scalable** from the directory containing `.env`:
+
+   ```bash
+   cd /path/to/your/project   # directory with .env
+   scalable validate ./scalable.yaml
+   scalable compose "Run GCAM then Stitches"
+   ```
+
+   Or in Python:
+
+   ```python
+   # The .env is loaded automatically on import
+   from scalable import ScalableSession
+   ```
+
+### Where to Place the `.env` File
+
+| Scenario | Location |
+|----------|----------|
+| CLI usage | The directory you `cd` into before running `scalable` commands |
+| Python scripts | The directory from which you launch `python your_script.py` |
+| Jupyter notebooks | The notebook's working directory (check with `os.getcwd()`) |
+
+> **Tip:** If your working directory differs from where `.env` lives (e.g., in
+> notebooks that `os.chdir()` into temp directories), use the programmatic
+> helper:
+>
+> ```python
+> from scalable.common import load_env
+> load_env("/absolute/path/to/your/.env")
+> ```
+
+### Override Priority
+
+Environment variable resolution follows this priority (highest → lowest):
+
+1. `SCALABLE_AI_*` variables (e.g., `SCALABLE_AI_BACKEND`) — Scalable-specific overrides
+2. Generic `AI_*` / `LLM_*` variables (e.g., `AI_PROVIDER`, `LLM_MODEL_NAME`) — from `.env`
+3. Provider-specific keys (e.g., `OPENAI_API_KEY`) — used as fallback for `AI_API_KEY`
+4. Built-in defaults (e.g., `AI_PROVIDER=none`, `SCALABLE_CACHE_DIR=./cache`)
+
+### Security
+
+> ⚠️ **Never commit `.env` to version control.** The repository `.gitignore`
+> already excludes `.env`. The included `.env.example` is safe to commit and
+> serves as a template.
+
+See the full [Environment Variables](#environment-variables) reference below for
+all supported settings.
+
 ## Usage
 
 ### Manifest-Driven Workflows
diff --git a/docs/getting_started.rst b/docs/getting_started.rst
index ad6fc25..a4fcf2f 100644
--- a/docs/getting_started.rst
+++ b/docs/getting_started.rst
@@ -115,6 +115,158 @@ available in this execution model.
 If bootstrap is interrupted, rerun ``scalable_bootstrap``. It resumes from the
 last valid step and skips completed setup where possible.
 
+Environment Configuration
+-------------------------
+
+Scalable uses a ``.env`` file in your working directory to centralize runtime
+configuration — especially AI provider credentials, cache paths, and telemetry
+settings.
+
+How ``.env`` Loading Works
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Whenever the ``scalable`` package is imported (or any CLI command is run), the
+:mod:`scalable.common` module automatically loads ``.env`` from the **current
+working directory** using `python-dotenv <https://pypi.org/project/python-dotenv/>`_
+with ``override=True``.  Values in ``.env`` therefore take precedence over
+pre-existing system environment variables.
+
+Setup Steps
+~~~~~~~~~~~
+
+1. **Copy the example file** from the repository root into your project
+   directory:
+
+   .. code-block:: bash
+
+       cp .env.example .env
+
+2. **Edit** ``.env`` and set the values you need.  At minimum, configure
+   ``AI_PROVIDER`` and ``AI_API_KEY`` to enable AI features:
+
+   .. code-block:: bash
+
+       AI_PROVIDER=openai
+       AI_API_KEY=sk-your-key-here
+       LLM_MODEL_NAME=gpt-4o
+
+3. **Run Scalable** from the directory containing ``.env``:
+
+   .. code-block:: bash
+
+       cd /path/to/your/project   # directory containing .env
+       scalable validate ./scalable.yaml
+       scalable compose "Run GCAM then Stitches"
+
+   Or in Python:
+
+   .. code-block:: python
+
+       # .env is loaded automatically on import
+       from scalable import ScalableSession
+
+Where to Place the ``.env`` File
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The file must be in the **current working directory** at the time Scalable is
+first imported.  Common scenarios:
+
+* **CLI usage** — the directory you ``cd`` into before running ``scalable``
+  commands.
+* **Python scripts** — the directory from which you run
+  ``python your_script.py``.
+* **Jupyter notebooks** — the notebook's working directory (check with
+  ``os.getcwd()``).
+
+If your working directory differs from where ``.env`` lives (for example, in
+notebooks that ``os.chdir()`` into temporary directories), use the programmatic
+helper *before* changing directories:
+
+.. code-block:: python
+
+    from scalable.common import load_env
+    load_env("/absolute/path/to/your/.env")
+
+Override Priority
+~~~~~~~~~~~~~~~~~
+
+Environment variable resolution follows this order (highest → lowest):
+
+1. ``SCALABLE_AI_*`` variables (e.g., ``SCALABLE_AI_BACKEND``) —
+   Scalable-specific overrides.
+2. Generic ``AI_*`` / ``LLM_*`` variables (e.g., ``AI_PROVIDER``,
+   ``LLM_MODEL_NAME``) — typically set in ``.env``.
+3. Provider-specific keys (e.g., ``OPENAI_API_KEY``) — used as fallback for
+   ``AI_API_KEY``.
+4. Built-in defaults (e.g., ``AI_PROVIDER=none``,
+   ``SCALABLE_CACHE_DIR=./cache``).
+
+Security
+~~~~~~~~
+
+.. warning::
+
+   Never commit ``.env`` to version control.  The repository ``.gitignore``
+   already excludes it.  The bundled ``.env.example`` is safe to commit and
+   serves as a configuration template.
+
+Key Environment Variables
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+AI provider configuration (generic — recommended):
+
+.. list-table::
+   :header-rows: 1
+
+   * - Variable
+     - Default
+     - Description
+   * - ``AI_PROVIDER``
+     - ``none``
+     - Provider name (``openai``, ``anthropic``, ``google``, ``xai``, ``groq``, ``ollama``)
+   * - ``AI_API_KEY``
+     - *(unset)*
+     - Universal API key (works for any provider)
+   * - ``LLM_MODEL_NAME``
+     - *(unset)*
+     - Model name (e.g. ``gpt-4o``, ``claude-sonnet-4-20250514``, ``grok-3``)
+   * - ``AI_BASE_URL``
+     - *(unset)*
+     - Custom API endpoint (for proxies; xAI auto-configures)
+
+Core settings:
+
+.. list-table::
+   :header-rows: 1
+
+   * - Variable
+     - Default
+     - Description
+   * - ``SCALABLE_CACHE_DIR``
+     - ``./cache``
+     - Disk cache directory
+   * - ``SCALABLE_SEED``
+     - ``987654321``
+     - xxhash seed for cache keys
+   * - ``SCALABLE_LOG_LEVEL``
+     - *(unset)*
+     - Library log level (e.g. ``DEBUG``)
+   * - ``SCALABLE_MANIFEST``
+     - ``./scalable.yaml``
+     - Default manifest path
+   * - ``SCALABLE_TARGET``
+     - *(unset)*
+     - Default target override
+   * - ``SCALABLE_RUNS_DIR``
+     - ``./.scalable/runs``
+     - Telemetry run directory
+   * - ``SCALABLE_TELEMETRY``
+     - ``1``
+     - Enable/disable telemetry (``0`` or ``1``)
+
+See ``.env.example`` in the repository root for the complete template with
+inline documentation.
+
 CLI Commands
 ------------
 

From d3bce6197a2eeead2dde30328c9069a6d1da15e6 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 16:40:00 -0400
Subject: [PATCH 45/47] update version to 2.0.0-beta.1

---
 CITATION.cff                                | 25 +++++++++++++++++++++
 README.md                                   |  1 +
 notebooks/advanced/01_getting_started.ipynb |  2 +-
 notebooks/beginner/01_getting_started.ipynb |  2 +-
 pyproject.toml                              |  2 +-
 5 files changed, 29 insertions(+), 3 deletions(-)
 create mode 100644 CITATION.cff

diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000..687201b
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,25 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+type: software
+title: "Scalable"
+version: "2.0.0-beta.1"
+doi: "10.5281/zenodo.20295640"
+url: "https://github.com/JGCRI/scalable"
+repository-code: "https://github.com/JGCRI/scalable"
+license: BSD-3-Clause
+authors:
+  - family-names: "Vernon"
+    given-names: "Chris R."
+    email: "chris.vernon@pnnl.gov"
+  - family-names: "Patel"
+    given-names: "Pralit"
+    email: "pralit.patel@pnnl.gov"
+  - family-names: "Lamba"
+    given-names: "Shashank"
+keywords:
+  - distributed-computing
+  - scientific-workflows
+  - HPC
+  - Kubernetes
+  - Dask
+  - Python
diff --git a/README.md b/README.md
index 91cf8a8..06d8926 100644
--- a/README.md
+++ b/README.md
@@ -7,6 +7,7 @@
 [![PyPI](https://img.shields.io/pypi/v/scalable.svg)](https://pypi.org/project/scalable/)
 [![Python](https://img.shields.io/badge/python-3.11%20%7C%203.12%20%7C%203.13-blue.svg)](https://pypi.org/project/scalable/)
 [![Docs](https://readthedocs.org/projects/scalable/badge/?version=latest)](https://jgcri.github.io/scalable/)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15487700.svg)](https://doi.org/10.5281/zenodo.20295640)
 
 Scalable is a Python framework for orchestrating containerized, distributed workflows on HPC systems, Kubernetes clusters, and cloud providers. It integrates container lifecycle management, scheduler-aware resource provisioning, a Dask-based execution model, optional AI assistants, and ML-driven optimization so multi-stage scientific workflows can run consistently at scale.
 
diff --git a/notebooks/advanced/01_getting_started.ipynb b/notebooks/advanced/01_getting_started.ipynb
index e3f1dec..a5e883c 100644
--- a/notebooks/advanced/01_getting_started.ipynb
+++ b/notebooks/advanced/01_getting_started.ipynb
@@ -40,7 +40,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Scalable version: 2.0.0\n"
+      "Scalable version: 2.0.0b1\n"
      ]
     }
    ],
diff --git a/notebooks/beginner/01_getting_started.ipynb b/notebooks/beginner/01_getting_started.ipynb
index 1e0ae7c..050d8bd 100644
--- a/notebooks/beginner/01_getting_started.ipynb
+++ b/notebooks/beginner/01_getting_started.ipynb
@@ -42,7 +42,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Scalable version: 2.0.0\n"
+      "Scalable version: 2.0.0b1\n"
      ]
     }
    ],
diff --git a/pyproject.toml b/pyproject.toml
index 5b51ac9..fe939bf 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "scalable"
-version = "2.0.0"
+version = "2.0.0b1"
 description = "Assist with running models on job queing systems like Slurm"
 authors = [
     { name = "Shashank Lamba" },

From ec67aa152c44f009eee81174b19913b18dfebde9 Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 16:47:25 -0400
Subject: [PATCH 46/47] local dev install clarity

---
 README.md                  | 45 +++++++++++++++++++++
 docs/getting_started.rst   | 80 ++++++++++++++++++++++++++++++++++++++
 docs/how_to_contribute.rst | 39 ++++++++++++++++++-
 3 files changed, 163 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 06d8926..fcbd811 100644
--- a/README.md
+++ b/README.md
@@ -61,6 +61,51 @@ git clone https://github.com/JGCRI/scalable.git
 pip install ./scalable
 ```
 
+### Development Install (Editable Mode)
+
+For local development — where you want code changes to take effect immediately
+without reinstalling — clone the repository and install in **editable mode**
+(`-e`) inside a virtual environment:
+
+```bash
+# Clone the repository
+git clone https://github.com/JGCRI/scalable.git
+cd scalable
+
+# Create and activate a virtual environment
+python -m venv .venv
+source .venv/bin/activate   # Linux / macOS
+# .venv\Scripts\activate    # Windows (cmd / PowerShell)
+
+# Install in editable mode with dev/test dependencies
+pip install -e ".[dev]"
+```
+
+The `-e` flag (short for `--editable`) creates a link from the virtual
+environment's site-packages back to your working tree so that any edits to
+source files under `scalable/` are reflected immediately — no reinstall
+required.
+
+**Why use a virtual environment?**
+A virtual environment isolates project dependencies from your system Python
+and other projects. This prevents version conflicts and makes dependency
+management reproducible.
+
+After installation you can verify the setup:
+
+```bash
+# Confirm the package is installed in editable mode
+pip show scalable          # Location should point to your clone
+python -c "import scalable; print(scalable.__version__)"
+
+# Run the test suite
+pytest
+```
+
+> **Tip:** If you only need to run Scalable (not develop it), a plain
+> `pip install ./scalable` inside a virtual environment is sufficient and
+> avoids installing test/lint tooling.
+
 ### Optional extras
 
 Scalable provides optional dependency groups for extended features:
diff --git a/docs/getting_started.rst b/docs/getting_started.rst
index a4fcf2f..e5c4495 100644
--- a/docs/getting_started.rst
+++ b/docs/getting_started.rst
@@ -24,6 +24,86 @@ from the checkout.
     pip install ./scalable
 
 
+Development Install (Editable Mode)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+For local development — where you want code changes to take effect immediately
+without reinstalling — clone the repository and install in **editable mode**
+(``-e``) inside a virtual environment.
+
+.. code-block:: bash
+
+    # Clone the repository
+    git clone https://github.com/JGCRI/scalable.git
+    cd scalable
+
+    # Create and activate a virtual environment
+    python -m venv .venv
+    source .venv/bin/activate   # Linux / macOS
+    # .venv\Scripts\activate    # Windows (cmd / PowerShell)
+
+    # Install in editable mode with dev/test dependencies
+    pip install -e ".[dev]"
+
+The ``-e`` flag (short for ``--editable``) creates a link from the virtual
+environment's ``site-packages`` back to your working tree so that any edits to
+source files under ``scalable/`` are reflected immediately — no reinstall
+required.
+
+**Why use a virtual environment?**
+
+A virtual environment isolates project dependencies from your system Python and
+other projects.  This prevents version conflicts and makes dependency management
+reproducible.  Always activate the environment before working on the project:
+
+.. code-block:: bash
+
+    source .venv/bin/activate   # each new terminal session
+
+After installation, verify the setup:
+
+.. code-block:: bash
+
+    # Confirm the package is installed in editable mode
+    pip show scalable          # Location should point to your clone
+    python -c "import scalable; print(scalable.__version__)"
+
+    # Run the test suite
+    pytest
+
+.. tip::
+
+   If you only need to *run* Scalable (not develop it), a plain
+   ``pip install ./scalable`` inside a virtual environment is sufficient and
+   avoids installing test/lint tooling.
+
+**Available extras for development:**
+
+.. list-table::
+   :header-rows: 1
+
+   * - Extra
+     - Contents
+   * - ``dev``
+     - Everything in ``test`` plus ``ruff``, ``mypy``, ``pytest-cov``
+   * - ``test``
+     - ``pytest``, ``pytest-asyncio``, ``hypothesis``, ``pydantic``
+   * - ``ai``
+     - AI assistant dependencies (``pydantic-ai``, ``jinja2``, ``rich``)
+   * - ``ml``
+     - ML optimization (``scikit-learn``, ``dask-ml``)
+   * - ``cloud``
+     - Cloud providers (``s3fs``, ``gcsfs``, ``dask-cloudprovider``)
+   * - ``kubernetes``
+     - Kubernetes provider (``dask-kubernetes``)
+
+You can combine extras:
+
+.. code-block:: bash
+
+    pip install -e ".[dev,ai,ml]"
+
+
 Optional Extras
 ~~~~~~~~~~~~~~~
 
diff --git a/docs/how_to_contribute.rst b/docs/how_to_contribute.rst
index 2b498b5..1fe64af 100644
--- a/docs/how_to_contribute.rst
+++ b/docs/how_to_contribute.rst
@@ -14,12 +14,49 @@ You can help by:
 * Improving documentation clarity, examples, and cross-links.
 * Submitting code fixes or enhancements with tests.
 
+Development Setup
+-----------------
+
+Before contributing code, set up a local development environment using a virtual
+environment and an editable install:
+
+.. code-block:: bash
+
+    # Fork & clone your fork
+    git clone https://github.com/<your-username>/scalable.git
+    cd scalable
+
+    # Create and activate a virtual environment
+    python -m venv .venv
+    source .venv/bin/activate   # Linux / macOS
+    # .venv\Scripts\activate    # Windows
+
+    # Install in editable mode with dev dependencies
+    pip install -e ".[dev]"
+
+The ``-e`` (editable) flag means your local source changes are picked up
+immediately — no need to reinstall after every edit.
+
+To verify everything is working:
+
+.. code-block:: bash
+
+    pytest                   # run the test suite
+    ruff check scalable/     # lint
+    mypy scalable/           # type-check (optional)
+
+.. note::
+
+   Always work inside the activated virtual environment.  If you open a new
+   terminal, re-activate with ``source .venv/bin/activate``.
+
 Contribution workflow
 ---------------------
 
 #. Fork the repository and create a focused branch.
+#. Set up the development environment as described above.
 #. Make changes in small, reviewable commits.
-#. Run tests locally before opening a pull request.
+#. Run tests locally (``pytest``) before opening a pull request.
 #. Update documentation and examples when behavior changes.
 #. Open a pull request describing the problem, approach, and validation steps.
 

From f67c8d301dd76f4abc29bdffb88b10c9dc7c539d Mon Sep 17 00:00:00 2001
From: crvernon <chrisrvernon@gmail.com>
Date: Wed, 20 May 2026 16:52:24 -0400
Subject: [PATCH 47/47] fix formatting via ruff feedback

---
 scalable/providers/local.py | 2 +-
 scalable/providers/slurm.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/scalable/providers/local.py b/scalable/providers/local.py
index d49dec1..5008a5f 100644
--- a/scalable/providers/local.py
+++ b/scalable/providers/local.py
@@ -7,7 +7,7 @@
 from distributed import LocalCluster
 
 from scalable.client import ScalableClient
-from scalable.manifest.validate import ValidationIssue, ValidationReport, validate_manifest
+from scalable.manifest.validate import ValidationIssue, ValidationReport
 from scalable.telemetry.runtime import emit_worker_event
 
 from .base import ClusterHandle, DeploymentProvider, DeploymentSpec, ScalePlan
diff --git a/scalable/providers/slurm.py b/scalable/providers/slurm.py
index 757d614..42968a9 100644
--- a/scalable/providers/slurm.py
+++ b/scalable/providers/slurm.py
@@ -15,7 +15,7 @@
     build_slurm_cluster_kwargs,
     create_legacy_slurm_cluster,
 )
-from scalable.manifest.validate import ValidationIssue, ValidationReport, validate_manifest
+from scalable.manifest.validate import ValidationIssue, ValidationReport
 from scalable.slurm import SlurmCluster
 from scalable.telemetry.runtime import emit_worker_event