Lanham5-HI1107×1388 347 KB

Micheal Lanham

Getting an agent to complete a task is one challenge. Making it improve over time—and proving that a change actually helped—is another.

This book explores how agents can improve without retraining the underlying model. The focus is on the agent’s harness: its context, memory, tools, and code, with changes measured, versioned, and audited rather than accepted on faith.

The running project is HelixAgent, a RAG-based agent that gains a new self-improvement layer as the book progresses. Along the way, the book tackles practical questions about trustworthy evaluation, learning from live traffic, and keeping changes under control with human approval gates and rollout safeguards.

It’s aimed at intermediate Python developers building or operating LLM agents. The book is now available through Manning’s Early Access Program, with four chapters ready to read and more to come.

• Full details: Self-Improving Agents - Micheal Lanham

Don’t forget you can get 45% off with your Devtalk discount! Just use the coupon code “devtalk.com” at checkout