LangGraph 201: Adding Human Oversight to Your Deep Research Agent

Complex AI agents can be made more reliable by incorporating human-in-the-loop patterns, which allow for crucial oversight and course correction during a task. The LangGraph framework enables this by representing workflows as graphs where specific nodes can be designated as checkpoints, pausing the agent's execution to await user input. At these interruption points, a user can review the agent's current state, such as its proposed search queries, and provide feedback or approval before the process continues. This transforms a fully autonomous process into a collaborative one, ensuring the agent's strategy aligns with the user's goals before it performs heavy-lifting tasks.