How human approval should work in an AI workflow
Design an approval step with evidence, explicit scope, rejection and a traceable result.
Show the decision the reviewer is making.
An approval request should describe the proposed action, its target and its likely effect. Include the evidence and any unresolved uncertainty. A reviewer needs to know whether they are approving a draft, a system update or an external commitment. Avoid a generic approve button with an unclear scope.
Bind approval to a specific action.
The approved action and the executed action should match. If the amount, recipient, destination or source evidence changes materially, request review again. Define how long approval remains valid and who can grant it. Model confidence should never substitute for the reviewer’s authority.
Make rejection and timeout useful.
A rejected or unanswered request should lead to a defined state. That may mean returning the item to its owner, requesting missing information or leaving the underlying system untouched. Do not silently turn a timeout into permission to proceed.
Record enough to explain what happened.
Keep an appropriate record of the evidence, proposed action, reviewer decision and execution result. Apply the organization’s data retention and access rules to that record. During evaluation, test rejection, expired approval, changed inputs and failed execution as deliberately as the successful path.
Put the next decision to work.
ANY builds permission and approval boundaries into deployment, then reviews them during managed operations.
Explore ANY Scan