Keep Automated Agents Proposal-Only Behind a Human Gate

When you point agents at your own knowledge base or project state, resist the urge to let them write directly. The safer pattern is to have the agent derive candidate changes from metadata — session minutes, token counts, activity signals — and emit them as proposals rather than committed edits. Nothing touches the source of truth until a human accepts the ones that are actually real.

This separation matters because inference is noisy. An agent scanning activity logs will confidently surface linkages that look plausible but are false positives. A human gate turns those confident guesses into a review queue, where the cost of a wrong suggestion is a quick “no” instead of a corrupted record you have to hunt down later.

The design generalizes: read broadly, write narrowly. Give agents wide latitude to observe and suggest, but route every mutation through an explicit approval step. You keep the leverage of automation while retaining the judgment that only a human currently has — and you build trust in the system incrementally, one accepted proposal at a time.