Where should a human stay in the loop for AI agents?
Put human intervention where a person can change a consequential decision before it becomes difficult to reverse. Useful checkpoints include uncertain intent, permission changes, external commitments, and recovery from repeated failure. Review should show the proposed action and evidence, rather than ask for a blind confirmation.
What does the source say?
LangChain documents configurable interruptions for reviewing agent tool calls. This is one implementation pattern; the right policy depends on the action’s consequence and the reviewer’s ability to make an informed choice.
How can you apply this?
List the actions in one agent workflow and sort them by reversibility and potential harm. Select one action that needs a reviewer. Write the exact information shown at that checkpoint: intended change, supporting source, likely impact, and alternatives. Test what happens if the reviewer declines, edits the action, or is unavailable.
What should you watch out for?
Too many low-value approvals can train people to click through. Keep straightforward reversible steps automatic when the permission boundary and failure handling are clear.