Everyone building agents obsesses over recall: vector stores, embeddings, RAG pipelines, bigger context windows. But after running a few long-lived agents in production, the failure mode that actually bit me wasn't "it forgot something." It was the opposite — it remembered too much, and the junk drowned the signal.
An agent that never forgets isn't wise. It's a hoarder. Every stale decision, every superseded fact, every one-off detail from three weeks ago sits in its memory with equal weight, and the quality of its answers quietly degrades. The interesting engineering problem isn't storage. It's forgetting on purpose.
Here's the policy I converged on. No framework — just plain Markdown files and a few rules the agent applies to itself.
Why "remember everything" rots
Say you log everything an agent learns into one growing store. Two things happen:
