Correction logs capture the gap between what happened and what should happen next.
A bad alert, a missed event, or a tool mistake becomes useful only when it is written back as a concrete rule.
Personal AI agents need correction logs
A correction log is the difference between an agent that apologizes and an agent that improves. It records the mistake, the expected behavior, and the prompt or policy change that should happen next.
Memory is not enough if the agent cannot learn from corrections.
Personal agents need a record of user corrections that is specific enough to change future behavior across SMS, browser work, summaries, and generated deliverables.
They reduce repeated mistakes.
The log prevents the agent from treating the same correction like a new surprise every week.
They improve prompts.
Each entry should say what went wrong and what should always happen instead.
They preserve context.
Corrections stay attached to the workflow, source, channel, and consequence.
They guide autonomy.
Permissions should expand only after repeated corrections stop recurring.
A correction log should convert every user correction into a specific future rule: trigger, source, expected action, forbidden repeat, and verification step.
The workflow is simple: capture, rewrite, retest.
Do not let corrections live as chat history. Promote them into operational rules the personal agent can apply next time.
Capture the mistake
Store the original message, tool action, source, confidence, and user correction.
Rewrite the rule
Translate the correction into a direct system instruction with replacement behavior.
Retest the pattern
Watch the next similar event and confirm the agent acts differently.
Correction log checklist
- Record the exact agent output or action that was wrong.
- Record the source context the agent used and the context it missed.
- Write the expected behavior in concrete language.
- Mark whether the correction affects SMS, browser work, summaries, or generated deliverables.
- Update the system prompt or policy with the replacement behavior.
- Retest the next similar event and mark whether the correction held.
Where Supers fits
Supers workflows are useful contexts for correction logs because the agent can operate across channels. Start with a text message AI assistant, carry the same correction policy into computer-use cache workflows, and apply correction records when an AI agent builds websites.
Sources
- Microsoft Research on interrupted work for why repeated agent mistakes carry attention cost.
- Nielsen Norman Group on notification design for alert clarity and user-control principles.
- Supers for personal AI agent workflows across SMS, browser work, and web tasks.
FAQ
Is a correction log the same as memory?
No. Memory may store preference. A correction log stores a failure, the replacement behavior, and a verification step.
What should be logged first?
Start with repeated false positives, missed urgent events, and tool actions the user had to undo.
Who reviews the log?
The owner or operator should review it weekly until the recurring failures flatten out.
How does it change autonomy?
Autonomy should expand where corrections hold and shrink where the same failure repeats.
What teams notice after adding correction logs.
The agent becomes easier to trust because its failures are no longer vague. They become specific, reviewed, and testable.
"The correction log helped us stop repeating the same preference conversation with the agent."
"The useful part was the replacement behavior. We could finally tell whether the prompt change worked."
"Autonomy became less emotional. We expanded it only where corrections stopped recurring."
Make every correction improve the next agent decision.
Supers can help teams test personal agents that text, browse, and build while keeping correction records tied to real workflow outcomes.