agentic-ai-coding-standard

To standardize Python agentic AI coding practices, enforce strict typing with TypedDict and annotated message lists for reliable state management. Define naming conventions, import order, and guarded routing to prevent errors and ensure maintainability across services.

LangGraph state management breaks due to inconsistent typing and unguarded routing. Resolve this by enforcing strict TypedDict state definitions and annotated message lists, applying guarded routing to prevent unsafe behavior and ensure reliable execution.

The best way to structure FastAPI services for agentic AI is to enforce consistent configuration patterns, strict import order, and typed state management. This reduces drift across services and ensures reliable, maintainable agentic workflows.

Implement observability and prevent unsafe behavior in LangChain agents by applying guarded routing and strict typing with TypedDict. Use provided anti-pattern guidance and reference examples to guide review, maintenance, and safe implementation.

Yes, you can and should use TypedDict and annotated message lists for LangChain state management. This enforces strict typing, ensures reliable message handling, and reduces brittle code in agentic AI deployments.

Common anti-patterns in Python agentic AI services include inconsistent state management, unguarded graph routing, and drifted configuration patterns. Address these by following strict typing rules and reference examples to guide safe implementation and review.