Improvement Loop
- Memorize What Matters
- Retrieve Past Decisions
- Turn a Method into a Skill
- Reuse the Skill
- Improve It with Each Use
20. Improvement Loop — Hermes’s Closed Learning Loop
This is the chapter that pulls together everything scattered across chapter 10 (Skills) and 7 (Context) into a single, continuous cycle — exactly as the slogan “the agent that grows with you” promises. Nous Research calls this the closed learning loop, and describes it as the one differentiating feature versus other agent frameworks on the market: most other agents run on a take-task → plan → execute → return-results model, then the session ends, nothing is retained, and the next session starts again from the same starting point.
Five steps, one closed loop
Hermes adds a whole layer after execution: it evaluates what just happened, extracts reusable patterns, and stores them — while continuously building a model of the user themselves (preferences, decision history, working habits) that persists across many sessions. The five sub-sections below are precisely the five consecutive steps of that loop:
- Memorize What Matters (20.1) — retain what is worth remembering.
- Retrieve Past Decisions (20.2) — bring back old decisions when needed.
- Turn a Method into a Skill (20.3) — turn a procedure into a reusable asset.
- Reuse the Skill (20.4) — reuse instead of starting over from scratch.
- Improve It with Each Use (20.5) — refine the skill after every use.
The key point: this is not an agent “retraining the model” — it is building a library of reusable procedures based on how you actually use it, plus a model of you that grows deeper over time.