An approval screen is the point in an AI agent approval workflow where a person reviews what the agent is about to do and decides to approve it, change it or stop it. As enterprise agents move from drafting to acting, sending customer replies, updating CRM records and queuing payments, this one screen carries most of the weight of human oversight.
Article 14 of the EU AI Act sets human-oversight requirements for high-risk AI systems, not for enterprise agents as a whole. It calls for “appropriate human-machine interface tools” and for people to be able to “override or reverse the output” or halt the system through “a ‘stop’ button or a similar procedure.” Even when an agent is not classified as high-risk, those principles make a useful brief for an approval screen.
The screen has to answer five questions before anyone clicks Approve. What exactly will the agent do? What will it touch? What evidence supports it? Why is approval required, and who may grant it? How can the action be reversed, compensated for or stopped?
What should an AI agent approval screen show?
An approval screen should lead with effects, not reasoning. An operator approving dozens of actions a day will not translate a paragraph of agent reasoning into consequences each time, so the screen has to do that work for them.
That means a diff view: the records that will change, the current and proposed values side by side, and every person who will receive a message. It also means showing the blast radius, how many records, accounts or customers one approval touches. Unverified inputs and any uncertainty that could change the decision belong beside the proposed changes, never behind a click. Detailed sources and the agent’s full rationale can sit one click away.
How do you keep a proposal from looking like a result?
On data interfaces, a projection should never look as certain as a measurement, and the same rule applies to agent actions. Proposed changes need a distinct visual state, outlined or labeled “proposed” in plain text, until a person approves them. When proposed and completed records look alike, operators start reading the plan as finished work, and within weeks the review becomes something people click through.
Which actions need human approval?
Not every action should wait for a person, because people who approve long runs of routine steps stop reading them. The EU AI Act names this risk as automation bias, “the possible tendency of automatically relying or over-relying on the output” of an AI system.
Where the line sits depends on context, not on action type alone. A small refund is routine in one business and sensitive in another, and a customer email is low risk as a standard notification but high risk when it makes a commitment. Four factors usually decide it: the impact if the action is wrong, whether it can be reversed, its scope, and whether it exposes sensitive data or commits the company to something.
The screen should state which rule caused a hold, so the operator knows what they are being asked to judge. The rules themselves are a product and engineering decision. The interface can make the line visible and auditable, but it cannot fix a policy that labels a risky action as routine.
How should edit, undo and stop work?
Approve and Reject are not enough. Edit lets the operator correct one value and approve the rest, and after an edit the agent should re-check every step that depends on the changed value before it continues. A corrected amount in step two can make step four wrong.
Reversal is where approval screens can overpromise. A record update or status change may be reversible if the system preserves the previous state and nothing downstream has already acted on it. A sent email cannot be unsent, so the approval view should say “cannot be reversed” before the operator approves, and afterward offer the compensating action, such as a correction message or a reversal entry.
Stopping has to work mid-run too. A multi-step agent needs a visible stop control while it runs, a record of which steps finished and a rollback for those steps where one exists. After any stop, the screen should show what state the agent left things in without anyone opening a log file.
What has to exist behind the screen?
An approval only means something if the system enforces it. The approval should be bound to the exact action that was shown: its target, its parameters and its scope. If any of those change after approval, because the agent re-plans or another process edits the request, the approval should be invalidated and a new one requested.
Just before execution, the system should also confirm that the preconditions still hold, such as the recipient, the account balance or the record state the approval depended on. These checks belong in the execution path, not only in the interface.
These controls begin with clear boundaries established before launch. Teams building custom AI agents, such as CorLab Tech, need to define which actions an agent can take, which require human approval and what happens when execution fails. Every proposal, approval, edit and stop should be recorded with who acted and when, creating a readable timeline for operators and auditors. A handoff should carry the original request, completed steps and the point where the agent got stuck.
How to test the approval screen you already have
A quick diagnostic: pull the last ten actions an agent proposed and ask an operator to explain, from the screen alone, what each would change, who it would reach and how it would be reversed. Wherever they hesitate or open another tool, the screen is missing information.
For a firmer answer, seed known errors into test proposals and see whether reviewers catch them, and compare review times and outcomes across risk tiers. Fast approvals on high-impact actions deserve investigation, particularly when reviewers miss seeded errors or cannot explain what they approved. The screens around this one are covered in a guide to UI patterns for AI agents. The approval step is where operators decide how much work to hand over.
