When teams design an AI agent, it is tempting to ask how much work the system can perform without a person. A more useful question is where a person’s judgment makes the workflow better—and how the product should bring them in at the right moment.
Human review does not need to mean stopping every task. It can be designed around uncertainty, risk, reversibility, and the information a reviewer needs to make a decision.
Match oversight to the consequence
Suggesting a draft is different from sending it to a customer. Looking up a public document is different from changing an access policy or issuing a payment. Map the actions an agent can take and consider the impact if each action is wrong.
Low-impact, reversible steps may be suitable for automatic execution after testing. High-impact or difficult-to-reverse actions should have an explicit approval point, narrow permissions, and a clear record of the proposed action.
Make review actionable
A reviewer should not have to reconstruct the entire run. Show the request, relevant evidence, proposed tool action, and any uncertainty or policy checks that affected the result. Give them useful choices: approve, edit, reject, ask for more information, or return the task to a person.
Avoid approval screens that simply ask someone to click “OK” without explaining what will happen. A review should support informed judgment, not create the appearance of oversight.
Define when the agent should ask
An agent can pause when required information is missing, retrieved sources conflict, a confidence or policy threshold is not met, a tool fails, or the next action is consequential. These conditions should be explicit and tested. The agent should also have a bounded number of steps and a clear way to hand off when it cannot complete the task.
Measure the handoff
Track how often people approve, edit, reject, or escalate recommendations, and whether the handoff provides enough context. Repeated edits can signal a quality issue; frequent approvals with little review may mean the experience needs to be clearer or the policy needs reconsideration.
The goal is not to maximize automation or maximize approvals. It is to place human attention where it contributes real value. Designed well, the human-in-the-loop experience makes an agent more useful, more understandable, and easier to trust.