When this matters
- A company wants shared agent memory but needs deletion and expiration rules.
- An engineering team wants temporary incidents to stop influencing future work.
- A reviewer needs clear evidence for why a memory was kept or removed.
agent memory retention policy
Agent Memory Retention Policy Builder
An agent memory retention policy defines what an AI agent may remember, for how long, under which scope, and with what evidence. Teams use it to keep useful continuity while preventing stale, private, or untrusted content from lingering indefinitely.
How to run the audit
- Define memory types: durable project facts, procedures, preferences, warnings, incidents, and blocked content.
- Assign default lifetimes and review owners for each type.
- Require source links or receipts for promoted memories.
- Set deletion and correction paths for wrong or private memories.
- Export a policy that agents can read before writing memory.
Common risks
- No retention policy means temporary notes can live forever.
- Overly strict retention can remove context that prevents repeated mistakes.
- Unclear ownership makes memory correction slow after a bad recall.
How Memory Hygiene Audit connects this to checkout
Memory Hygiene Audit builds retention policy reports from real memory samples and pairs them with ongoing scan metrics.
Teams can preview the score, then use the Team Hygiene annual checkout to generate the full cleanup diff, reviewer notes, and agent-readable JSON policy.