Building LangChain Agents That Handle Errors and Failures
Most LangChain demos feel like toy examples — a prompt here, a tool call there, and suddenly you’ve got a chatbot that can tell you the weather. It’s clean, it’s tidy, and it barely scratches the surface of what real-world AI applications actually need to do. The truth is, building agents that work outside of a controlled notebook is messy. Tools fail. Models hallucinate. Ambiguity creeps in at every turn — whether it’s a vague user request, an incomplete API response, or a file system that doesn’t behave like you expect. LangChain gives you the primitives to handle that, but most tutorials skip straight to the happy path. Deep Agents changes that. It’s not another abstraction layer for the sake of it — it’s a opinionated set of patterns baked into LangChain for the stuff you actually wrestle with: planning when the goal isn’t clear, delegating to subagents when one model can’t do it all, and safely poking around in a file system without bringing down your whole app. If you’ve...