Decide what needs approval
A human approval queue is a pause between an AI workflow’s proposed action and the action itself. The workflow can draft a reply, suggest a record change, or prepare a file for publication, but it cannot complete that step until a person reviews it. This is useful when a mistake would be awkward, difficult to reverse, or costly to correct.
Start by naming the actions that require review. A draft internal summary may be safe to save automatically, while sending that summary to a customer may need approval. Set the boundary at the action, not at the entire workflow: routine preparation can continue automatically, and only the consequential step needs to wait. Write down what the reviewer is authorizing, such as “send this message to this recipient” rather than the vague “approve output.”
Make each queue item reviewable
A reviewer should not have to reconstruct the workflow to understand a request. Each item needs the proposed action, its destination, the exact content or change being proposed, and enough context to judge it. For a drafted customer reply, that might mean the incoming request, the proposed reply, and any relevant account details the reviewer is permitted to see. For a record update, show the current value beside the proposed value.
Give every request a stable identifier and a clear status, such as pending, approved, rejected, or completed. Record when it entered the queue and which workflow produced it. Keep the proposed action separate from explanatory text generated by the model. An explanation can help a reviewer, but it should not conceal the actual message, recipient, or field change that approval would release.
Keep the display focused. A long transcript may bury the detail that matters most, while too little context encourages reflexive approval. A concise summary with an option to inspect the original input and full proposed output offers a workable middle ground.
Offer decisions that match the work
Approval and rejection are the minimum useful choices. Some workflows also benefit from “edit and approve” and “send back for revision.” Editing works well when a reviewer can correct a small issue directly. Revision is better when the proposal rests on a misunderstanding and needs to be generated again. Make it clear whether an edited version replaces the original proposal and retain both versions in the record.
Avoid treating a model’s confidence statement as permission to skip review. It may be useful as a prompt to inspect a case more closely, but a fluent or confident-sounding answer is not proof that an action is appropriate. The reviewer’s job is to check the substance: Is the destination right? Is the proposed change supported by the available input? Are there sensitive details that should not be sent?
Assign ownership of the queue before turning the workflow on. If several people can review, decide who is responsible for unclaimed items and how reviewers will avoid working on the same request at once. A queue that collects proposals without a clear owner becomes a delay, not a safeguard.
Connect approval to the action safely
The action step should accept only an approved request, not merely a message that says “approved.” Have it check the request identifier, current status, authorized reviewer decision, and exact version being approved. If the proposal changes after approval, send it back for review. Otherwise, a reviewer may approve one draft while the automation executes another.
Plan for the time between proposal and execution. A recipient, record, or source document may change while an item waits. Before acting, check any details that could have gone stale; if a material detail has changed, return the request to the queue rather than guessing. Mark an item as completed only when the downstream action succeeds, and make retries visible so a failed connection does not turn one approval into two sends or edits.
Keep a record of the proposal, decision, reviewer, approved version, and final outcome. This helps the team answer a practical question later: what was authorized, and what actually happened?
Test the queue as a workflow, not just a screen
Run a few harmless examples from input through final action before using the queue for real work. Test an approval, a rejection, an edited approval, and an item left pending. Confirm that rejected and pending items cannot trigger the action. Then try approving an older proposal after its underlying record changes, and verify that the workflow pauses rather than acting on stale information.
Set a simple policy for requests that sit too long. They might expire, return to their originator, or move to a named reviewer. Avoid silently approving them to clear the queue. As the team uses the workflow, note which proposals repeatedly require the same correction. Those patterns can reveal a prompt or process worth improving, while the approval boundary remains in place for actions that still need a person’s judgment.
