One thing I've been wrestling with building persistent agents is memory quality. Most frameworks treat memory as a vector store — everything goes in, nothing gets resolved. Over time the agent is recalling contradictory facts with equal confidence.

The architecture we landed on: ingest goes through a certainty scoring layer before storage. Contradictions get flagged rather than silently stacked. Memories that get recalled frequently get promoted; stale ones fade.

It's early but the difference in agent coherence over long sessions is noticeable. Happy to share more if anyone's going down this path.

Interesting. I’ve been playing with something similar, at the coding agent harness message sequence level (memory, I guess). I’m looking at human driven UX for compaction and resolving/pruning dead ends