Who reviews the work an AI agent produces
When AI starts delivering finished work, you need clear decisions about who reviews it and who approves its use. This article explains how to organise ownership, review and checks without overwhelming your team with checking.
Decide who can sign off on the result
Imagine an AI agent, software that takes steps on its own within an assigned task, delivers a change to a form. The fields look right, the error messages are clear and the existing checks pass. But an exception that matters to your service is missing. Who was supposed to spot it? Once AI produces something you intend to use, someone needs to judge whether it is ready and make that decision. Establish that ownership before work starts, even when the task seems small.
Separate checking whether something works from deciding whether it meets the need. A developer may be able to assess the technical implementation. Someone familiar with the process can judge whether the right exceptions are covered. One person might do both, or the review might need both perspectives. Be explicit about who gives final approval, and give that person enough context and time. Putting a name against a task does not, by itself, make ownership practical.
Reviewing well requires a different way of working
When you build something yourself, you make decisions along the way and remember where you had doubts. Reviewing an agent's work means receiving the result without that shared understanding. You spend more time reading than writing, working out which decisions shaped the solution. Your attention needs a clear purpose: what assumptions does this contain, what else does it affect, and where could a mistake go unnoticed? Reading everything carefully can take considerable effort while still leaving the most consequential issue undiscovered.
Start with the task and the consequences of getting it wrong. For a form change, that might mean checking who can submit it, which information is required and how invalid entries are handled. Then examine how the solution was built. The agent's explanation can help you navigate the work, but you still need to assess it yourself. Allow time for that assessment: faster production creates a greater need for review capacity.
Set clear boundaries for actions and handovers
Your team needs shared rules about what an agent can do independently. Drafting a message requires different permission from sending it. Preparing a change is a different step from making it available to users. Define those boundaries for each type of action, including the point at which a person must review or approve the work. Consider the consequences: does the task involve customer information, access permissions or a process that is difficult to restore? Those handovers need different checks from an internal page edit.
Specify what the reviewer should receive: the original task, an explanation of the changes, the checks performed and any unresolved questions. Make it possible to compare the result with the previous version. A summary alone cannot show everything that changed. Agree on what happens when information is missing or the next step is unclear. The agent should be able to pause and ask for guidance, without broadening its own remit just to produce a finished result.
The agent
- reads the task, boundaries included
- carries it out, stops when unsure
- prepares the diff and the checks
the handover
The human
- reviews where a mistake is expensive
- signs off, with a mandate and time
- checks after it goes live
Build quality into the checks around the work
Choosing a capable model does not automatically give you a reliable result. You need checks that expose mistakes before the work moves forward. In software, automated tests can help establish whether existing features still work and whether agreed scenarios are handled correctly. But tests only cover the expectations you have put into them. Passing them does not establish that the task was fully understood. Review changes to the tests themselves too, especially when the same agent has modified both the implementation and its checks.
Keep tasks small enough for someone to assess the whole result. Store the task, the version used, the changes and the human approval together, so you can trace what happened later. After release, check whether the result behaves as expected and make sure you can undo the change. This gives you several opportunities to catch problems. Mistakes remain possible, but you have a practical way to investigate them and put things right.
Build the process around human responsibility
When AI contributes finished work, review needs to be part of the plan from the start. Give someone the authority to approve it, make changes visible and choose checks that reflect the consequences of a mistake. Before increasing output, consider how much your team can properly assess. People remain responsible for the result, so organise the process around that responsibility.
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