Key takeaways
- Trigger approval by action risk and uncertainty, not by a generic confidence score alone.
- Show the requested outcome, exact change, evidence, affected records, downside, and recovery plan.
- Bind approval to a versioned proposal and expire it when data, policy, or scope changes.
- Measure overrides, reviewer disagreement, wait time, and post-approval failures to improve the policy.
01
Put the checkpoint before the side effect
An approval that appears after a message was sent or a record was overwritten is an audit acknowledgment, not control. Pause the run before the privileged tool call, persist the proposed action and current state, and resume only after a valid decision.
OpenAI’s agent guidance recommends human intervention for repeated failure and high-risk actions. The practical work is defining “high risk” for this business: money, deletion, publication, external communication, access changes, regulated decisions, sensitive data, or a large affected audience.
02
Give the reviewer enough to disagree
The review surface should show the user’s request, the proposed action, exact arguments, important source evidence, policy checks, affected people or records, and what happens next. Highlight uncertainty and conflicting evidence. Do not bury the meaningful change inside a transcript dump.
Offer approve, edit, reject, and escalate where the workflow needs them. Require a reason for high-impact overrides, but keep the interface quick enough that reviewers do not learn to approve blindly just to clear a queue.
Swipe to compare every column
| Review question | What the interface should show |
|---|---|
| What will happen? | Exact tool, arguments, records, and side effects |
| Why this action? | Request, evidence, source, and policy result |
| What could go wrong? | Uncertainty, conflicts, scale, and sensitive fields |
| Can we recover? | Expiry, rollback or compensation, and owner |
03
Make approval specific and short-lived
Bind a decision to the proposal hash, arguments, record version, policy version, approver, and timestamp. If the agent changes the recipient list or price after approval, it needs a new decision. Set expiry based on how quickly the underlying state can change.
The reviewer’s own permissions still apply. Approval should not elevate a person beyond the authority they already have in the underlying system.
04
Study the queue, not just the model
Track approval volume, response time, edits, rejections, escalation, disagreement between reviewers, actions abandoned while waiting, and incidents after approval. A queue that grows without bound is a process design failure, even if it technically keeps a human in the loop.
Move low-risk patterns toward constrained automation only after review evidence supports it. Keep rare, sensitive, and irreversible actions visible. The objective is well-placed judgment, not the absence of people.
Primary sources and further reading
Use the source material to validate details against your own context and current platform configuration.
This field note follows the XenGrowth editorial policy: primary sources where available, visible limitations, material review dates, and no invented first-hand experience.
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