add documentation

Author: James N.

README.md

@@ -45,6 +45,15 @@ Welcome to the official repository for the Microsoft AI Agentic Workshop! This r
 - **🖥️ Advanced UI Options:**  
   - **React Frontend:** Real-time streaming visualization with agent internal processes, tool calls, orchestrator planning, and turn-by-turn history tracking
   - **Streamlit Frontend:** Simple, elegant chat interface for quick prototyping and demos
+
+- **🔄 Workflow Orchestration (NEW!):** Enterprise-grade workflow capabilities with [comprehensive orchestration patterns](agentic_ai/workflow/):
+  - **Pregel-style execution engine** for complex multi-agent coordination
+  - **Type-safe messaging** with runtime contract enforcement
+  - **Checkpointing & resume** for long-running workflows
+  - **Human-in-the-loop** approval patterns with RequestInfoExecutor
+  - **Control flow patterns**: Switch/case routing, fan-out/fan-in, conditional edges
+  - **Real-time observability**: OpenTelemetry tracing, event streaming, WebSocket updates
+  - 🎯 **[Featured Demo: Fraud Detection System](agentic_ai/workflow/fraud_detection/)** - Production-ready workflow with React dashboard
 
 - **Configurable LLM Backend:** Use the latest Azure OpenAI GPT models (e.g., GPT-5, GPT-4.1, GPT-4o).  
 - **MCP Server Integration:** Advanced tools to enhance agent orchestration and capabilities with Model Context Protocol.  
@@ -67,48 +76,12 @@ Welcome to the official repository for the Microsoft AI Agentic Workshop! This r
 1. Review the [Setup Instructions](./SETUP.md) for environment prerequisites and step-by-step installation.  
 2. Explore the [Business Scenario and Agent Design](./SCENARIO.md) to understand the workshop challenge.  
 3. Check out the **[Agent Framework Implementation Patterns](agentic_ai/agents/agent_framework/README.md)** to choose the right multi-agent approach (single-agent, Magentic orchestration, or handoff pattern).
-4. Dive into [System Architecture](./ARCHITECTURE.md) before building and customizing your agent solutions.  
-5. Explore **[Workflow Examples & Production Demos](agentic_ai/workflow/)** to learn advanced orchestration patterns.
+4. Try the **[Fraud Detection Workflow Demo](agentic_ai/workflow/fraud_detection/)** to see enterprise orchestration patterns in action.
+5. Dive into [System Architecture](./ARCHITECTURE.md) before building and customizing your agent solutions.  
 6. Utilize the [Support Guide](./SUPPORT.md) for troubleshooting and assistance.  
 
 ---  
 
-## Workflow Orchestration  
-  
-The workshop includes comprehensive **workflow orchestration** capabilities built on the Microsoft Agent Framework's Pregel-style execution engine. Workflows enable complex multi-agent coordination with type-safe messaging, checkpointing, and real-time observability.  
-  
-### 🎯 Featured Demo: Fraud Detection System  
-  
-A production-ready fraud detection workflow showcasing enterprise-grade patterns:  
-  
-- **Real-time React dashboard** with live workflow visualization  
-- **Fan-out/fan-in patterns** for parallel specialist agent analysis  
-- **Human-in-the-loop** analyst review with checkpointing  
-- **MCP tool integration** for customer data access  
-- **WebSocket streaming** for live event updates  
-  
-**[→ Try the Fraud Detection Demo](agentic_ai/workflow/fraud_detection/)**  
-  
-### 📚 Learning Resources  
-  
-| Resource | Description |  
-|----------|-------------|  
-| **[Workflow Architecture Guide](agentic_ai/workflow/README.md)** | Core concepts, execution model, and API reference |  
-| **[Human-in-the-Loop Patterns](agentic_ai/workflow/human-in-the-loop.md)** | Comprehensive guide to approval workflows |  
-| **[Fraud Detection Demo](agentic_ai/workflow/fraud_detection/)** | Production-ready workflow with real-time UI |  
-  
-### Key Capabilities  
-  
-✅ **Control Flow**: Switch/case routing, conditional edges, dynamic branching  
-✅ **Parallelism**: Fan-out/fan-in, concurrent execution, aggregation patterns  
-✅ **Checkpointing**: Pause/resume workflows, long-running processes, crash recovery  
-✅ **Human-in-the-Loop**: External approvals, RequestInfoExecutor, response handling  
-✅ **State Management**: Executor-local state, shared state, persistence  
-✅ **Observability**: OpenTelemetry tracing, event streaming, real-time monitoring  
-✅ **Composition**: Nested workflows, workflow-as-agent, modular design  
-  
----  
-  
 ## Contributing  
 
 Please review our [Code of Conduct](./CODE_OF_CONDUCT.md) and [Security Guidelines](./SECURITY.md) before contributing.  

agentic_ai/workflow/.env.sample

@@ -5,4 +5,4 @@ AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com/
 AZURE_OPENAI_API_VERSION=2024-10-01-preview
 
 # MCP Server Configuration
-MCP_SERVER_URI=http://localhost:8000
+MCP_SERVER_URI=http://localhost:8000/mcp

agentic_ai/workflow/fraud_detection/README.md

@@ -122,7 +122,7 @@ This example demonstrates a comprehensive fraud detection system using the Agent
 
 2. **MCP Server**:
    ```bash
-   export MCP_SERVER_URI="http://localhost:8000"
+   export MCP_SERVER_URI="http://localhost:8000/mcp"
    ```
 
 3. **Start MCP Server** (in separate terminal):
@@ -131,7 +131,7 @@ This example demonstrates a comprehensive fraud detection system using the Agent
    python mcp_service.py
    ```
 
-### Running the Workflow
+### Running the Workflow with command line
 
 ```bash
 cd agentic_ai/workflow/examples/fraud_detection
@@ -267,52 +267,6 @@ instructions=(
 8. **Type-Safe Messaging**: Pydantic models for all messages
 9. **Event Streaming**: Real-time workflow event monitoring
 
-## Recent Improvements
-
-### ✅ Fixed Issues (Latest)
-
-1. **Analyst Review Data Rendering**: Fixed dataclass serialization in `_serialize_analyst_request()` to properly display Risk Score, Recommended Action, Alert ID, and other assessment details in the UI.
-
-2. **Event Ordering**: Corrected event broadcast sequence so Analyst Review completion appears before Fraud Action execution, maintaining proper workflow visualization order.
-
-These fixes ensure the UI accurately reflects the workflow state and displays all relevant fraud assessment information during analyst review.
-
-## Troubleshooting
-
-### MCP Connection Fails
-
-Ensure MCP server is running:
-```bash
-curl http://localhost:8000/health
-```
-
-### Missing Environment Variables
-
-Check all required variables are set:
-```bash
-echo $AZURE_OPENAI_API_KEY
-echo $AZURE_OPENAI_CHAT_DEPLOYMENT
-echo $AZURE_OPENAI_ENDPOINT
-```
-
-### Analyst Review Panel Not Showing Data
-
-This should now be fixed. If you still see issues:
-- Restart the backend server to load the updated code
-- Check browser console for WebSocket errors
-- Verify the MCP server is responding
-
-### Workflow Hangs
-
-Check logs for executor errors. Each executor logs its progress.
-
-### Checkpoint Restoration
-
-To restore from checkpoint:
-```python
-checkpoint_id = workflow.get_latest_checkpoint_id()
-await workflow.restore_from_checkpoint(checkpoint_id)
-```
 
 ## Real-Time Workflow Visualizer UI
 
@@ -431,24 +385,6 @@ http://localhost:3000
 - Check WebSocket connection in DevTools
 - Verify events are being sent from backend
 
-## Next Steps
-
-- ✅ ~~Implement real-time UI for analyst review~~ (Completed)
-- Add database persistence for checkpoints (Redis, Cosmos DB)
-- Add metrics and monitoring (OpenTelemetry)
-- Integrate with actual fraud detection systems
-- Add multiple workflow instances for parallel alert processing
-- Implement escalation chains for critical cases
-- Add workflow history/replay to UI
-- Export workflow results to PDF
-- Add custom alert creation in UI
-
-## Related Examples
-
-- `human-in-the-loop/guessing_game_with_human_input.py` - Basic human-in-the-loop pattern
-- `agents/workflow_as_agent_human_in_the_loop_azure.py` - Azure-integrated human input
-- `control-flow/` - Switch/case and conditional routing examples
-- `parallelism/` - Fan-out and concurrent execution patterns
 
 ## License
 

agentic_ai/workflow/fraud_detection/fraud_detection_workflow.py

@@ -916,7 +916,7 @@ async def main() -> None:
 
     # Load configuration
     azure_openai_key = os.getenv("AZURE_OPENAI_API_KEY")
-    azure_deployment = os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME")
+    azure_deployment = os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT")
     azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
     mcp_server_uri = os.getenv("MCP_SERVER_URI", "http://localhost:8000/mcp")
     if not all([azure_openai_key, azure_deployment, azure_endpoint]):