Agent memory flaw: AI knows your rules but ignores them when it matters

A developer identified a subtle but significant failure mode in AI agent memory systems where stored user preferences are never actually applied during relevant actions. The problem stems from how memory retrieval works: similarity search matches queries to stored facts by semantic meaning, so a rule about commit style is never surfaced when the agent processes an unrelated command like 'fix the login bug.' Common workarounds — pinning all rules to every prompt or storing them in static config files — introduce token bloat or go stale over time. The proposed fix introduces a third retrieval method called recall_for_action, which tags preferences with the specific action they govern, such as trigger:git-commit, and fetches them deterministically at the exact moment that action runs. This approach avoids embedding-based search entirely, making retrieval fast and reliable precisely when it is most critical.