How to plan human review for AI agents

Break the task into steps

A task such as responding to a support request includes several decisions: understanding the question, finding the relevant information, drafting an answer, and deciding whether to send it. You can use AI for some of those steps while keeping others with your team. A step with predictable inputs and fixed rules may be better handled by ordinary software.

Write down the steps with someone who does the work. Include the unusual cases and the points where they ask a colleague for help. Those details tell you more about what to build than a broad goal such as automating support.

Check the inputs and the answer

For each step, identify the information the system needs and what a good result looks like. A tool can find a passage in a policy, but that does not establish that the policy is current or applies to this customer.

Decide how a reviewer will verify the result. A draft answer with its source and relevant customer details is easier to check than a confident paragraph with no explanation of where it came from.

• Does the system have the information it needs?
• Can the team describe and check a correct result?
• How will the system handle missing or conflicting information?
• Will reviewing the output take less effort than doing the task manually?

Consider what a mistake would do

The same error can have different consequences at different stages. A wrong figure in a draft can be corrected during review. That figure in a message sent to a customer or a change made to a live account may be harder to fix.

Decide which actions need approval and what the system is allowed to do without it. It might draft a reply but wait for someone to send it, or prepare a change to a record and show the reviewer exactly what would change. Enforce these permissions in the application and its tools; a prompt alone is not an access control.

Make review part of the process

Give the reviewer the output, the source information, and any issue that needs their attention. They should be able to approve, edit, reject, or ask for help in a tool they already use.

Test this part too. If the reviewer has to search several systems to check every answer, the automation may have moved the effort rather than reduced it. Include review time when you measure the result.

Test before expanding the scope

Use examples that reflect the work the system will receive: straightforward requests, missing information, ambiguous wording, and cases that should go to a person. Agree on how to score the outputs and keep notes on the failures.

Passing a small test does not guarantee the same performance on every future request. Start with a limited rollout, monitor what happens, and adjust before adding more tasks or allowing more actions without review.

Before deployment, check the engineering controls described in taking an AI agent from demo to production.

Keep a way to correct problems

Your team needs a clear way to flag a wrong answer and correct the information behind it. If a document or process changes, decide who updates the system and checks that it still works.

Agree on what should happen when the system cannot complete a task or a connected service is unavailable. A person should be able to see the problem and continue the work while it is resolved.

Agree on this ongoing responsibility when you scope and price the project.