Do not treat LangGraph as a longer chain: define state, interrupts, and recovery first

The easiest way to misunderstand LangGraph is to see it as “LangChain, but with more steps.”

That misses the point.

LangGraph becomes useful when an agent is no longer a single prompt or a simple chain. It becomes useful when the workflow has state, branches, tool calls, human approval, checkpointing, and recovery behavior that must be inspected before the agent is trusted inside a real AI host.

I used the Doramagic LangGraph manual as the source-backed reading layer for this note:

https://doramagic.ai/en/projects/langgraph/manual/