An autonomy gate rehearsal is a dry run for a promoted rule. It lets operators test what the agent would have done, compare it with human approvals, and decide whether the rule deserves to skip the queue.
The rehearsal makes promoted rules visible before approvals disappear.
A personal AI agent should not move from repeated approval to silent execution in one step. The safer pattern is rehearsal: run the proposed autonomy gate against recent history, see where it would have acted, inspect the misses, and only then promote a narrow rule.
Start with the evidence that the operator would otherwise have to hunt for across chat history and logs. The packet should contain enough context to recreate the decision without asking the user to remember what happened.
A text message AI assistant captures approvals, corrections, and escalation language in one place.
Use a computer use cache when the rule depends on page state or session context.
For AI agent website building, rehearse source checks and publish gates before skipping review.
Without near misses, the gate will look safer than it is.
The output should be a concise recommendation: promote, narrow, keep in queue, or reject. It should include the sample size, misses, exceptions, and the first receipt preview.
Most failures come from overbroad rules, stale context, or treating preference memory as permission. Rehearsal catches those problems before they become automatic behavior.
Run it across recent approvals and record every action it would have skipped.
Mark exact matches, harmless misses, risky misses, and cases that require new exception language.
Reduce scope until the rule covers only the repeated decision pattern it actually proved.
Show the operator the proof that will remain after each automatic action.
Choose the mode based on risk and available history.
Run the candidate rule across past approvals to find obvious mismatches.
Let the rule make predictions while humans still approve the real action.
Promote the rule for a small scope, short time window, and low-risk action set.
Run this checklist when the rehearsal recommends promotion.
The rehearsal includes approvals, edits, rejections, reversals, and ambiguous cases.
Action, scope, channel, account, risk ceiling, freshness, and exceptions are explicit.
A normal operator can understand why the agent acted without opening raw logs.
When the rule does not fit, the agent returns to approval immediately.
Run it until the rule has enough repeated approvals and enough near misses to prove its boundary. For higher-risk actions, use a longer shadow period.
The risk framing aligns with the NIST AI Risk Management Framework and common LLM application risks described by the OWASP Top 10 for LLM Applications.
Yes. Rejection is a successful outcome when the evidence shows the pattern is too broad, too stale, too risky, or too dependent on hidden context.
Super helps operators build personal AI workflows where approvals, rehearsals, promoted rules, and receipts stay visible.