Where human review belongs in an AI workflow

Human review belongs before an AI workflow makes a consequential change, while a person can still stop or correct it. Place the review around the proposed action and its evidence. Asking someone to approve a fluent paragraph is weak control if the system executes different parameters afterward.

The practical question is where judgment changes the outcome enough to justify the interruption.

Use consequence and reversibility to place the gate

Separate drafts from actions. A suggested internal label that can be corrected cheaply may suit sampled review. Sending a customer message or changing a shared operational record deserves a different assessment because another person may act on it before a correction arrives.

OWASP’s prompt injection guidance recommends approval before consequential tool actions and verification of the exact approved action at execution. Human review complements server authorization; it cannot grant permissions the reviewer does not hold.

Proposed behavior	Reasonable starting review policy
Private draft with no side effects	User reviews during editing
Reversible internal classification	Sample review plus an exception queue
External message	Review recipient, content, and attachments before sending
Destructive or hard-to-reverse operation	Explicit authorized approval or keep it outside automation

These are design starting points, not universal risk categories. The same field update can be harmless in a sandbox and consequential in a dispatch system.

Give the reviewer something worth checking

Show the source evidence beside the proposed change. Identify missing information, conflicting values, the affected record, and what will happen after approval. Use a readable difference view when modifying existing data.

Allow correction, rejection, and escalation. A screen with only an approve button pressures the reviewer to accept work they cannot repair. Record the reason for corrections so the team can distinguish extraction errors from unclear business rules.

Do not use the model’s stated confidence as the sole routing rule. Route missing required evidence and unresolved contradictions to review even when the answer sounds certain. Calibrate any numerical threshold against labeled cases from the actual task.

A hypothetical customer reply

Suppose an assistant drafts a reply about a delayed delivery. The reviewer sees the intended recipient, order reference, source status, and message. They approve that exact version.

Before dispatch, another process updates the delivery status. The system must decide whether that invalidates the approval. For this example, the design invalidates it because the reply’s central fact changed. It returns the revised message to review instead of sending the old promise.

Bind approval to the proposal version and relevant source versions. Recheck current authorization at execution, and make the send operation idempotent. An approval record should not become a reusable permission slip for future messages.

Budget the queue, including absence

A review gate creates work. Assign a role, expected response window, and substitute owner. Show queue age and let the workflow remain safely pending when nobody is available. A timeout should not silently turn missing approval into approval.

Track corrections and escaped mistakes as well as approval volume. If reviewers accept everything, investigate whether the suggestions are good or the interface makes checking impractical.

Choose one consequential action and prototype its evidence, correction, and expired-approval states with UX/UI design. Ask the intended reviewer to reject a deliberately flawed proposal. Check whether they can find the problem and explain their decision from the evidence on screen.