Separate preparation from authority
A model can extract an invoice, draft a reply, or suggest a product match. Those are different responsibilities from authorizing payment, committing to a refund, or releasing a shipment. Map which steps prepare work and which steps exercise business authority.
Use policy and permissions to make that boundary real. A model should not acquire payment authority merely because it can call a tool with a convincing argument.
Give the reviewer usable evidence
An approval screen should show the proposed action, original source, important discrepancies, and what will change. A reviewer should not have to reconstruct the whole workflow to understand the decision.
Prioritize the fields that could cause harm or rework. A low-risk wording correction and a supplier bank-account change deserve different review procedures.
Make the approval specific and durable
Record the reviewer, exact action, relevant record version, timestamp, and any expiry. An approval is permission for a particular action against a particular state. It is not indefinite permission to do something similar later.
If the invoice, ticket, order, or policy changes after approval, revalidate it before execution. Material changes should invalidate the old approval and return the work to review.
Design the unhappy path
Plan what happens when the reviewer is away, the API times out, two people act, or a retry arrives after the first action succeeded. Give each action an idempotency key, bound retries, and reconcile with the destination system.
A failed write must remain visibly unconfirmed. The system should never infer success merely because a request was sent or a model said the task was complete.
Expand autonomy only with evidence
Begin with offline evaluation and operator-assisted work. Use real corrections to improve the process. If a category proves sufficiently reliable and low risk, agree a tightly scoped automatic path with the business owner.
Keep a route back to manual operation and review the decision periodically. Human approval is one control within a broader system of data boundaries, permissions, monitoring, and accountability.
Ready to make this concrete? Explore the capacity calculator or prepare a workflow brief.
Further reading
This article offers Tern & Loom’s practical framework. The primary references below provide supporting technical and market context.
NIST: AI Risk Management Framework ↗