Most AI assistants treat every session as a blank slate, they retain what was said but not what worked. The distinction between episodic memory (conversation logs) and procedural memory (methods crystallized into reusable skills) is well-documented in agent architecture literature. MaxHermes, built on Hermes Agent's open-source framework, compounds completed tasks into permanent skills rather than storing them as searchable chat history.
For those running agents in production: does skill persistence actually deliver stable performance across sessions, or does the cumulative skill layer introduce its own failure modes around skill interference or stale method propagation?
EDIT: For context, I evaluate AI tools for document review workflows. The skill compounding model is architecturally interesting but I have not seen long-running production data on whether it holds under real task diversity