AI moves the work forward. You stay in control: an oversight checklist.

What controls should you prepare for AI agent oversight?

Define a clear scope, task-specific access, approvals where needed, useful activity records and a tested human handover. Name the people responsible for reviewing results and approving changes. This checklist helps prepare a trial; it is neither a certification nor evidence of compliance.

Design control into the process

A useful assistant does more than produce an answer. It should also stop when necessary, flag missing information and hand work to a person. These behaviours belong in the process from the first trial.

Oversight helps the people using the assistant understand its role. It makes progress visible, reveals where work gets stuck and supports decisions about improvements. The checklist below provides a starting point to adapt to your team’s context.

1. A scope everyone can explain

Describe the task, its trigger and the expected result. Separate permitted actions, actions requiring approval and work that remains outside the scope.

“Prepare a draft using authorised documents, without sending a message” is a concrete starting point. Add the cases the assistant does not handle and what it should do when a request is unclear. These boundaries should be understandable to the people using the process.

2. Identified sources and access

List the documents and applications required. Check who maintains the information and how versions are recognised. A well-written answer is difficult to use if its supporting source is outdated or cannot be found.

Keep permissions aligned with the intended work. Reading a record, proposing a change and applying that change are different capabilities. The project should make it clear which ones are enabled and why they are needed.

3. Approval that supports a decision

Put approval steps where they are useful. The reviewer needs to see the proposed action, its context and the information required to check it. An “approve” button offers little help if the underlying case is difficult to understand.

Also define what follows a rejection: a correction, a request for more information or a manual handover. Approval is a step in the work, with a clear route forward.

4. A handover that works

Identify the person or team receiving exceptions. Define the context to include, the status of the case and a way to tell whether an action has already happened.

Test missing information, an unavailable tool and a request outside the permitted scope. The aim is to keep work moving without asking a colleague to reconstruct the entire history. Include a way to pause the assistant when needed.

5. Records that help review

Keep the information needed to find operations, results and errors. Align access and retention with your internal rules. These records help people check the process; they do not provide a complete explanation of a model’s internal reasoning.

Area	The practical question
Ownership	Who follows up on results and problems?
Sources	Can the information used be found?
Actions	Can we see what was proposed or carried out?
Recovery	Can we pause and continue manually?
Change	Who approves new rules or a wider scope?

Make review a useful habit

Choose a review frequency that reflects the volume of work and the possible consequences of errors. Look at corrections, exceptions and checking time as well as completed tasks. A process that finishes quickly but needs substantial rework deserves attention.

When a document, rule or connection changes, rerun the relevant examples. Keep the level of autonomy tied to observed results. You can reduce, extend or maintain it according to what the team finds.

This framework creates a clearer way forward: AI contributes its part, while your team keeps an understandable view of the work and its decisions.

Explore our approach to AI agent oversight.

The NIST AI RMF Core sets out explicit responsibilities, measurement and ongoing risk management. Test those controls as you move from prototype to everyday use.