A promoted-rule review is the moment between repeated approval and automatic action. It gives operators a way to reduce approval fatigue while preserving consent, auditability, and a fast path back to human review.
Personal AI agents often start by asking before every meaningful action. That is healthy at first, especially when the agent is sending messages, opening browser sessions, booking appointments, or publishing pages. The problem arrives later, when the same approvals repeat so often that users stop reading carefully. A promoted-rule review turns that repetition into a structured decision.
The review should begin with a narrow sentence: when this context appears, the agent may take this action without asking, unless one of these exceptions appears. Anything broader is not a promoted rule; it is hidden delegation.
For many operators, the cleanest surface is a text message AI assistant because approvals, edits, and user corrections already appear in one thread.
Memory can personalize the proposal. Permission decides whether the proposal may execute.
A review without near misses will overpromote rules. Include approvals that looked similar but required a human edit.
Every skipped approval should leave a visible trace showing the rule, context, and outcome.
Use a computer use cache to preserve the state the rule relied on. A payment page, settings panel, or account dashboard can change meaning between sessions.
In AI agent website building, reviews should govern when source links, deployment checks, and final publish steps can be skipped.
Pin the review around evidence before you let the agent skip the queue.
Store the agent proposal, the user response, edits, context, tool state, final action, and later correction. A rule review is only as good as the examples behind it.
Write the narrowest condition that would have covered successful approvals without covering the negative examples. If the rule needs a paragraph of exceptions, keep it in the queue.
Show what the rule would have done for a week, a fixed number of approvals, or a risk-weighted sample. Do not skip approvals until the review can compare predicted actions against human decisions.
Let the rule act only with a receipt and an expiration boundary. When the context changes, the agent should ask again instead of quietly carrying forward stale consent.
The review UI does not need to be complicated. It needs to show the evidence that matters before a rule becomes automatic.
Use this checklist each time a rule graduates from the approval queue.
The examples include ordinary approvals, edited approvals, rejected proposals, and later reversals.
The rule names action, channel, account, risk ceiling, freshness window, and exceptions.
The rule matched human decisions across a meaningful sample before skipping approvals.
Users can inspect automatic uses and revoke or edit the rule without searching logs.
There is no universal count. Look for repeated approvals with similar context, low edits, no later reversal, and low downside if the agent is wrong.
The risk framing aligns with the NIST AI Risk Management Framework and common LLM application risk categories described by the OWASP Top 10 for LLM Applications.
Usually not. Expensive, public, irreversible, regulated, or emotionally sensitive actions should stay in approval unless the promoted rule is extremely narrow and easy to revoke.
Super helps operators design agent workflows where approvals, memory, receipts, and promoted rules each stay visible.