Designing the human review step for agentic work

Where to insert human-in-the-loop checkpoints

Human-in-the-loop (HITL) for AI agents means building specific points where the agent pauses and waits for a person before continuing, rather than running end-to-end on its own.

Common checkpoint patterns:

• Pre-run review: Before the agent starts, to catch misspecified goals, over-broad tool access, or wrong scope.
• Mid-run approval gate: Right before a high-stakes action (e.g., sending external communications, modifying financial records).
• Post-run audit: After completion, on a sample or every run for high-risk agents, to detect drift and near-misses.

Use HITL when:

• An error would be costly or hard to reverse.
• The AI is operating on ambiguous or incomplete information.
• The output will be seen outside the organization.
• A decision requires context the AI doesn’t have.

What the reviewer needs to see

Speed and quality of human review depend on the “evidence pack” the agent presents.

Design the review surface so a person can decide in 10–30 seconds:

• Show the relevant inputs, the agent’s proposed action, and a confidence or risk score.
• Provide one-click approve, reject, or edit options; allow “approve with edits” so the reviewer doesn’t restart the whole process.
• Route requests automatically to the right role, and use exception-only review (auto-approve unless flagged) for mature workflows.
• Stage outputs in a draft folder or pending-approval queue instead of writing directly to the system of record.
• Log every gate, including approvals that fired and those that didn’t, to calibrate thresholds over time.

Well-designed HITL turns human review from a bottleneck into a control layer: the agent handles volume, humans handle judgment, and the system learns where to tighten or relax gates.