Self-Improving Agents Teaches Adaptive Harnesses

The book’s strongest idea is that reliable agent improvement is an engineering loop—act, measure, search, apply—not a vague promise of autonomous learning.

• –Treats the agent harness as a versioned, auditable improvement surface.
• –Covers ground-truth evaluation, LLM judges, prompt optimization, evolutionary search, and online memory updates.
• –Companion code builds HelixAgent from a basic RAG/ReAct system toward self-improving workflows.
• –Production concerns such as drift detection, rollout gates, cost tracking, and reward hacking receive explicit attention.
• –The approach remains dependent on trustworthy signals; noisy evaluation can amplify mistakes instead of fixing them.