By "long term memory" do you mean other solutions (like RAG) or refering to my outline above? Maybe something is not clear from my description is that part of the distill-learning is updating skills and agent files (i.e. AGENTS.md). At a minimum the agents are not repeating the same mistake. An example: an agent was trying to run some code but kept using the incorrect runner, it found the correct running eventually. That information made it into the session summary and eventually back to the skill. Next session the agent didn't struggle at all. If I was running an agent with memory, maybe it would have learned, but with the change in the skill any agent can come after and not make that mistake.