Human-in-the-Loop Testing
Human-in-the-loop testing validates the checkpoints where an AI system pauses to ask a person for approval, correction, or a decision before continuing — confirming those interruptions actually happen when they should, present the right context to the reviewer, and correctly resume based on the human's response.
The point of a human-in-the-loop checkpoint is usually risk control — pausing before a destructive, expensive, or hard-to-reverse action — so the highest-priority test is a negative one: confirm the agent genuinely cannot proceed past the checkpoint without approval, rather than trusting that it will ask.
The second common failure is a checkpoint that technically fires but gives the human nothing useful to decide from — a generic "approve this action?" prompt with no summary of what the action actually does. Testing should check that the information shown at the checkpoint is sufficient for someone to make an informed call, not just that the checkpoint exists.
Resume logic deserves its own tests too: after a human edits or rejects a proposed action, does the agent correctly incorporate that feedback going forward, or does it revert to its original plan on the next step?
