Human-in-the-loop planning lets people inspect and steer an AI agent’s plan before it executes, instead of only accepting or rejecting the final output. In Megagon Labs’ AMBIPOM system, plans are shown as editable graphs of subtasks and data dependencies. Users can give natural-language feedback, edit the structure directly, or apply higher-level operations such as merge and split.
