A comparative experiment using the same Kimi K3 model across three agent harnesses—Claude Code, Hermes, and Kimi Code—shows that while success rates are similar, token usage can differ by as much as thirty‑fold, dramatically affecting cost and latency.
The article breaks down AI agent continual learning into three layers—model, harness, and context—explains their distinct challenges, shows how traces link them, and argues that focusing on harness and context yields faster, more practical improvements than merely retraining models.
Meta‑Harness introduces an external‑loop optimization framework that lets coding agents automatically search and improve large‑language‑model harnesses, achieving up to ten‑fold faster search, ten‑times token efficiency, and significant performance gains across text classification, math reasoning, and agentic coding tasks.
