docs/walkthrough.md

Walkthrough - Fraud Assistant Workflow Pattern

I have successfully transitioned the Fraud Model Explainability Assistant to a structured Workflow Pattern. This change improves determinism, auditability, and reliability by forcing explicit steps for Intent Analysis, Tool Execution, and Response Generation.

Changes

1. New Workflow Engine (workflow.py)

I created a new FraudExplainabilityWorkflow class that orchestrates the conversation:

• State Management: Uses a WorkflowState TypedDict to track input, intent, tool calls, and results.
• Intent Analysis: Explicitly plans which tools to call using an LLM router.
• Tool Execution: Systematically executes tools and catches errors per tool.
• Async Wrapper: Implemented specific async/await methods for all workflow steps to ensure compatibility with uvicorn and uvloop, replacing the initial synchronous design.

2. Application Integration (app.py)

• Replaced the legacy Agent loop with the new FraudExplainabilityWorkflow.
• Added robust dependency handling for confluence-ingestor to allow the app to run in lighter environments.

3. Verification Script (test_workflow.py)

• Created a standalone test script to verify the workflow without needing the full web server.

Verification Results

Test Case: "Why was application APP-78432 flagged as high risk?"

The workflow successfully:

1. Analyzed Intent: Determined it needed to fetch application summary, fraud score explanation, population comparison, and risk indicators.
2. Executed Tools:
   • get_application_summary
   • explain_fraud_score
   • compare_to_population
   • check_fair_lending_flags
   • get_identity_network
3. Generated Response: Synthesized all data into a comprehensive explanation.

Log Output:

INFO | workflow | Intent: Analyze why application APP-78432 was flagged..., Tools: 5
INFO | workflow | Executing get_application_summary with {'application_id': 'APP-78432'}
INFO | workflow | Executing explain_fraud_score with {'application_id': 'APP-78432'}
...
INFO | app | Query completed successfully

Architecture Comparison

Feature	Old Agent	New Workflow
Control Flow	Implicit (LLM decides loop)	Explicit (Code defines steps)
Auditability	Hard (Mixed logs)	Easy (Structured State logs)
Robustness	error-prone tool loops	Per-step error handling
Dependencies	Loose	Managed & Robust

Next Steps

• The app.py is now ready for deployment with the new architecture.
• You can extend the WorkflowState to include user feedback or "Human-in-the-loop" approval steps easily in the future.