Agent memory is not just RAG over user facts

I keep seeing agent memory implemented as: 1. Extract facts/preferences from conversation 2. Store them 3. Retrieve top-k before each response 4. Inject them into the prompt This works for demos, but it breaks in production because memory becomes policy once it enters the prompt. A stale preference can be true and still wrong for the current task. A follow-up question can omit the original task keyword. An edited memory can keep a stale embedding. A selector failure can accidentally lead to broad prompt injection. The pattern I’m arguing for: \- layered memory: evidence / scene / stable profile \- Active Memory selection before injection \- deterministic fallback, never full injection \- memory\_usage telemetry \- governance: edit, deprecate, merge, supersede \- janitor cleanup for memories that repeatedly pollute context \- scenario replay tests based on real traces Curious how others are handling “memory that is true but should not influence this turn.” I’ll put the full write-up in a comment to respect the subreddit rule about links.