The LangGraph Trap: Why Your Agentic Python App Will Fail in Production

Everyone is building agentic AI apps in Python. Most of them won't survive contact with production.LangGraph promises elegant, stateful multi-agent workflows — and it delivers, until it doesn't. In this talk, we'll walk through the real failure modes that emerge when your beautifully designed agent graph meets actual users, live data, and unpredictable LLM behavior.Drawing from hands-on experience shipping a multi-agent RAG system for financial data pipelines, you'll learn:
Why graph state accumulation silently corrupts long-running agents
How tool-calling loops burn your token budget without warning
Why naive RAG retrieval produces confident, wrong answers — and how to fix it with re-ranking and hybrid search
How to instrument your agents with Python observability hooks before you're debugging blind in production
Whether you're just starting with LangGraph or already neck-deep in agent hell, you'll leave with concrete Python patterns to make your agentic apps actually work

Presented by

Kamal Singh Bisht