Design an approval queue that keeps AI work moving
Make human review effective with clear approval triggers, decision-ready evidence and rules for changes after approval.
Human approval is easy to add to a diagram and harder to operate. If every AI action waits in the same queue, reviewers become a bottleneck. If requests contain too little detail, approval becomes a guess.
Design the review around the decision the person must make.
Define the trigger
Choose actions that require review based on their consequences and reversibility. Drafting an internal outline and sending a customer commitment need different treatment. Write down who can approve each kind of action and what the assistant may do while waiting.
Enforce important approval requirements in the surrounding software or workflow. A sentence in a prompt should not be the only thing preventing an unapproved external action.
Send a complete request
An approval request should show the proposed action, destination, exact content or change, supporting evidence and any unresolved uncertainty. Include the consequence of waiting when timing matters.
For an outgoing email, show the recipient and final message. For a configuration change, show the current value, proposed value and expected effect. The reviewer should not need to reconstruct the task from a chat history.
Keep approval tied to the reviewed version
If the assistant changes a material part of the action after approval, request review again. Record who approved which version and when the action occurred.
Provide clear reject and revise paths. A reviewer who requests a smaller change should not have to restart the entire workflow.
Review the queue regularly for repeated low-value requests and common rejection reasons. Improve the task instructions and authority boundaries using that evidence. The goal is for human attention to reach decisions where judgment matters, with enough context to make that judgment well.