1) Master Python for Production AI
- Build with FastAPI for scalable endpoints.
- Use async programming for efficiency.
- Enforce data validation with Pydantic.
- Skipping these leads to brittle, duct-taped prototypes.
2) Make Agents Stable and Reliable
- Logging is essential for debugging in real-world environments.
- Testing (unit + integration) ensures tools, prompts, and APIs work together.
- Reliability comes from anticipating failure, not ignoring it.
3) Go Deep on Retrieval-Augmented Generation (RAG)
- Without RAG, agents lack memory and factual grounding.
- Core tools: embeddings, vector stores (or Postgres), chunking strategies.
- Frameworks like LangChain + evaluation tools help optimize.
- Most flaky agents fail here due to poor retrieval design.
4) Define Robust Agent Architecture
- Use agent frameworks (e.g., LangGraph) for state and control.
- Apply prompt engineering systematically.
- Persist state with databases (SQLAlchemy + Alembic).
- The goal: agents that track context, manage retries, and adapt.
5) Monitor, Learn, and Improve in Production
Iterative improvement is key—agents evolve through feedback.
Continuous monitoring with tools like Langfuse.
Analyze user interactions to refine prompts and workflows.
