Your agent's memory remembers everything except how to do its job

Most "agent memory" today stores facts and events: what the user said, what the project is, what happened last session. Useful — but watch where an agent actually burns tokens and retries: it re-derives the process every single run. Wrong step order, forgets the migration, retries the exact thing that failed on Tuesday. The context was in memory. The know-how wasn't. Psychology has a name for this split: episodic memory ("I remember going to the dentist") vs procedural memory ("I remember how to ride a bike"). Nearly every memory framework ships the first kind and skips the second — because the second is structurally harder. A workflow isn't a fact you extract once. It's a procedure that must CHANGE when it fails. There's fresh academic backing. A recent paper from Zhejiang University + Alibaba (Memp — arXiv 2508.06433) built procedural memory from agents' own past trajectories, tested on GPT-4o, Claude Sonnet and Qwen. Their strongest mechanism wasn't storing successes — it was reflecting on failures to revise the stored procedure. The failure is the signal. From running this in production, three arguments: Session recall and workflow learning are different problems. Perfect episodic memory still pays the full process tax every run. Procedures need version history, not overwrites. v1 from a session → v2 adds the missing migration step after a failed run → v3 reorders after an env-var race, 11 successes since. An agent loading v3 doesn't repeat the two mistakes that produced it. A procedure that never failed is a procedure you can't trust yet. Success count alone is survivorship bias — you want fail_count, what changed after each failure, recency. That's also your pruning signal. The uncomfortable implication: hand-maintained instruction files (CLAUDE.md, AGENTS.md, rules) are static snapshots of procedural knowledge — they rot because updating them requires a human to notice the failure, remember to edit, and phrase it as an instruction. Nobody does that reliably. Curious what others do for workflow-level memory: hand-rolled? Fine-tuning? Just eating the re-derivation cost every run?