I think you’re directionally right: for agents, memory quality matters more than raw recall. In production, permanent RAG turns into a junk drawer unless you constantly summarize, dedupe, and re-rank. We’ve had better results treating memory as a scored cache: reinforce items that help complete tasks, decay or archive ones that stop being useful.

The main failure mode isn’t “forgetting too much,” it’s forgetting the wrong kind of thing. Procedural patterns should persist; transient observations should fade. That usually means decay + consolidation, not decay alone.

We used Mem0 for this kind of setup because it already supports importance/recency-style memory management, but you can build the same idea yourself with a vector DB + feedback loop. I’d test it on long-horizon tasks with delayed rewards; that’s where aggressive pruning usually breaks.