An agent that drafts a reply and an agent that sends it are two different products. The distinction is one button, but that button changes the responsibility of the system. A useful starting point is to write down exactly what the agent may read, suggest, and change.
Define the action boundary
Consider a customer support workflow. An agent might search approved documentation, gather order details, and prepare a response. Sending that response, issuing a refund, and changing an account each deserve their own decision. Do not hide those permissions inside a broad instruction to handle the customer request.
Make the boundary specific enough to test. Name the allowed tools and records, the situations that require approval, and the person or queue that receives the handoff. Start with a narrower scope if the cost of a mistaken action is unclear.
Make review a usable screen
A reviewer needs more than an Approve button. Show the proposed action, the information it is based on, and the records that would change. Preserve links to source material where possible. Let the reviewer edit, reject, or request more information without restarting the whole task.
- Separate the agent's suggestion from verified source information.
- Explain what approval will do before asking for it.
- Show the current owner and status of the task.
- Keep a record of the review decision and resulting action.
Plan the awkward states
A document may be unavailable, a request may time out, or the same event may arrive twice. Decide what the user sees in each case. A task should not appear completed simply because the model finished writing. Completion means the intended workflow reached its defined end state.
Create a small collection of representative tasks, including ambiguous and unsuccessful ones, and review them when the workflow changes. Look at the quality of the proposed action, the usefulness of the evidence, and the effort required from the reviewer. Record where the agent gets stuck instead of concealing those moments behind optimistic copy.
Human review works best when the product makes judgment easier.
The goal is a collaboration people can understand. Clear permissions, useful evidence, and visible handoffs give a team a practical basis for deciding what to delegate next.
Where should human review sit in an AI workflow?
Put review before actions that carry consequences your team is not comfortable delegating. The boundary depends on the task, the data, and the effect of a mistake. Define it explicitly and reassess it using observed results.
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