# AI Agents in Action Releases Second Edition Guide > Source: > Published: 2026-06-22 08:14:53.383074+00:00 # AI Agents in Action Releases Second Edition Guide The new book **AI Agents in Action, Second Edition** by **Micheal Lanham** is a practical guide to building agentic systems, covering personas, tools, memory, retrieval, reasoning, evaluation, multi-agent patterns, and the Model Context Protocol (MCP). Manning offers a free online extract and lists the edition as published in June 2026, per the publisher preview on Manning.com. Retail listings on **Amazon** show a paperback release date of **July 14, 2026** and a pre-order price of **$59.99**. The edition is listed as **392 pages** with ISBN-13 **978-1633434530** in catalog entries on WOW! eBook and O'Reilly. The Manning preview includes a brief author note describing the book's aim to make agentic development accessible to developers and practitioners. ### What happened **AI Agents in Action, Second Edition**, authored by **Micheal Lanham**, has been published in a new, substantially updated edition that concentrates on building and deploying intelligent agent systems. Per Manning's online preview, the edition was published in June 2026 and the publisher provides a free extract of early chapters on Manning.com. The book is listed as **392 pages** with ISBN-13 **978-1633434530** on catalog pages including WOW! eBook and O'Reilly. Retail listings on **Amazon** show a paperback release date of **July 14, 2026** and a pre-order price of **$59.99**. ### Technical details The second edition documents core agent components and practical workflows. Manning and O'Reilly table-of-contents excerpts list chapter-level material on **LLMs**, agent personas, tools and actions, knowledge and memory, retrieval and augmentation, reasoning and planning, evaluation and feedback, and multi-agent architectures such as hub-and-spoke orchestrations and agent collaboration. The book explicitly introduces the Model Context Protocol (MCP) and discusses agent-to-agent interactions (A2A) and multi-agent coordination patterns, per the publisher preview on Manning.com and the O'Reilly catalog. ### Editorial analysis - technical context Industry-pattern observations: developer-focused, example-driven books that combine conceptual layers (persona, tools, memory, planning) with concrete code examples accelerate team adoption of agentic patterns. Readers looking to move from single-turn LLM prompts to longer-lived, stateful workflows often need hands-on recipes for memory management, tool integration, and evaluation; the book's chapter breakdown mirrors those practitioner needs. ### Context and significance agentic systems remain a key area for applied ML and software engineering because they bridge LLM capabilities with external tools and state. Comprehensive, up-to-date resources that cover MCP, agent orchestration, and evaluation frameworks address recurring gaps in practitioner knowledge: designing typed outputs, tracing tool use, and assembling multi-agent flows. Manning's author note in the preview captures this teaching aim: "I hope this book inspires you to see intelligent agents as partners in innovation," per the Manning online extract. ### What to watch For practitioners: watch for sample repositories, code walkthroughs, and any publisher-provided exercises that accompany the book; Manning's live preview notes exercises in agent building and O'Reilly lists chapter exercises and runtime topics. Observers should also track whether the author or publisher publishes companion code, notebooks, or a GitHub repo that implements the MCP examples and multi-agent orchestrations described in the text. ## Scoring Rationale This is a useful practitioner resource that consolidates agent design patterns and protocols. It is valuable for engineers adopting agentic workflows but does not introduce a new model or paradigm-shifting technology. Practice interview problems based on real data 1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with. [Try 250 free problems](/problems)