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  • AI Agents in Action, Second Edition: Intelligent workflows with LLMs, MCP, A2A, and more
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AI Agents in Action, Second Edition: Intelligent workflows with LLMs, MCP, A2A, and more

by Micheal Lanham (Author) Format: Paperback
4.7 out of 5 stars (41)
#1 New Release in Natural Language Processing

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"Great contents, broad coverage, fun exercises. This book has it all.”
—Saurabh Sawant, Microsoft

AI Agents in Action, Second Edition is a substantial revision and update of the first edition. It is a practical and comprehensive guide to building AI agents—not just understanding what they are, but designing, implementing, evaluating, and deploying them. Its strength is in the way it combines conceptual clarity with working code examples, so readers build progressively rather than absorb isolated ideas. The examples form a continuous learning path, moving from a minimal agent to more capable, tool-using, multi-agent, and deployable systems. Each step adds a new skill while reinforcing what came before.

The book begins by giving readers a usable mental model for agent design. Its central organizing idea is the five functional layers: persona, actions and tools, reasoning and planning, knowledge and memory, and evaluation and feedback. This framework helps readers understand where an agent’s behavior comes from and how to diagnose weaknesses. Rather than randomly adding prompts, tools, or memory, readers learn to ask which layer needs improvement. This is especially valuable because the model is not tied to one vendor or framework; it remains useful even as APIs and tools continue to change.

From there, the book moves into the practical building blocks of agents: LLMs, prompting, typed outputs, tracing, tool use, and the OpenAI Agents SDK. Typed outputs reduce brittle text parsing. Tracing exposes what the agent is doing. Tool integration gives agents the ability to act rather than merely respond. The cumulative benefit is that readers learn to build agents that are more predictable, inspectable, and maintainable.

A highlight is the treatment of Model Context Protocol. Readers liked the book’s “USB-C” analogy, because it explains MCP as a standard connector between agents and external capabilities. The book shows how MCP can flatten “a mess of bespoke integrations” into cleaner, swappable components, helping developers build agents that are modular instead of tangled.

The book also covers multi-agent architectures, reasoning patterns, planning strategies, RAG, memory, evaluation, feedback, observability, and deployment. Each topic is tied to a practical benefit: multi-agent patterns help divide complex work; reasoning and planning help agents handle multi-step tasks; RAG and memory let agents use external and retained knowledge; evaluation and feedback help make them safer and more reliable.

Physically, this is a substantial but focused book covering 392 pages across 11 chapters. Its tables and figures are a valuable part of the learning experience. While building, readers will want to return to the easy-to-use tables summarizing complex trade-offs.

AI Agents in Action shows developers how to build agents they can ship, trust, and maintain.

What's inside

• Autonomous agent design and deployment
• MCP-based tools, resources, prompts, memory, and server integrations
• Reasoning and planning patterns including ReAct, Reflexion, Tree-of- Thought, and Sequential Thinking

About the reader

For intermediate Python programmers. No experience with AI agents and agentic systems required.

About the author

Micheal Lanham is a software and technology innovator with over 20 years of industry experience. He has authored books on deep learning, including Manning’s Evolutionary Deep Learning.

Table of Contents

1 The rise of AI agents
2 Core components: Large language models, prompting, and agents
3 Actions with Model Context Protocol for AI agents
4 Architecting and building multi-agent systems
5 Agent reasoning and planning
6 Working with memory and knowledge RAG for agents
7 Building robust agents with evaluation and feedback
8 Deploying agents and agentic systems
9 Understanding the agentic loop
10 Exploring the cognitive agent that thinks, monitors, and adapts
11 Tips for building agentic systems
A Setting up the sample code repository
B Node.js setup for local MCP servers
Read more
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  1. ISBN-10
    1633434532
  2. ISBN-13
    978-1633434530
  3. Edition
    2nd
  4. Publication date
    July 14, 2026
  5. Part of series
    In Action
  6. Language
    English
  7. Dimensions
    7.38 x 0.98 x 9.25 inches
  8. Print length
    391 pages
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From the Publisher

AI Agents in Action, Second Edition header

AI Agents in Action, Second Edition quote 1

"One of the most practical introductions to agent development available today."

Ajay Prakash, LinkedIn

AI Agents in Action, Second Edition quote 2

“An outstanding book for beginner practitioners in the field of AI agent development.#

Roger Meli, IT Architect

AI Agents in Action, Second Edition quote 3

"A comprehensive yet digestible overview of agents, and by the end of it, you'll be able to build an agent that can even summarize it for you"

Daksh Gupta, Co-founder, CEO Greptile

AI Agents in Action, Second Edition about the book

why this book?

AI Agents in Action, Second Edition helps intermediate Python developers move from prompt tinkering to designing, building, evaluating, and deploying practical AI agents.

You learn a reusable architecture for agent systems through five functional layers—persona, tools/actions, reasoning/planning, knowledge/memory, and evaluation/feedback—while also gaining hands-on experience with OpenAI Agents SDK, MCP, RAG, multi-agent workflows, guardrails, observability, and deployment.

By the end, you will be able to build more reliable, inspectable, maintainable agents that use tools, reason over complex tasks, retrieve knowledge, coordinate with other agents, and operate closer to production standards.

about manning

about Manning

Manning helps developers and tech professionals stay ahead in a fast-moving industry with expert-led books, videos, and projects. Learning never stops, but it’s hard to keep up, so we focus on content that’s practical, clear, and trusted. As an independent publisher, we adapt quickly, from pioneering early-access books to offering DRM-free eBooks. Our series, like "In Action" and "In a Month of Lunches", reflect a commitment to making complex topics accessible.

Deep Learning with Python, Third Edition
AI Agents in Action
Build a Large Language Model (From Scratch)
LLMs in Production: From language models to successful products
Data Analysis with LLMs: Text, tables, images and sound (In Action)
Causal AI
Customer Reviews
4.2 out of 5 stars 42
4.0 out of 5 stars 51
4.5 out of 5 stars 613
4.5 out of 5 stars 36
4.8 out of 5 stars 7
4.4 out of 5 stars 14
Level of proficiency Intermediate Intermediate Intermediate Intermediate Intermediate Advanced
About the reader For readers with intermediate Python skills. For intermediate Python programmers. Readers need intermediate Python skills and some knowledge of machine learning. For data scientists and ML engineers. For data scientists and data analysts. For data scientists and machine learning engineers.
Special features Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant.
Pages 648 344 368 456 232 520

Editorial Reviews

About the Author

Micheal Lanham is a proven software and tech innovator with over 20 years of experience. He has developed a broad range of software applications in areas such as games, graphics, web, desktop, engineering, artificial intelligence, GIS, and machine learning applications for a variety of industries. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development.

Product details

  • Publisher ‏ : ‎ Manning Publications
  • Publication date ‏ : ‎ July 14, 2026
  • Edition ‏ : ‎ 2nd
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 391 pages
  • ISBN-10 ‏ : ‎ 1633434532
  • ISBN-13 ‏ : ‎ 978-1633434530
  • Item Weight ‏ : ‎ 13.8 ounces
  • Dimensions ‏ : ‎ 7.38 x 0.98 x 9.25 inches
  • Part of series ‏ : ‎ In Action
  • Best Sellers Rank: #25,023 in Books (See Top 100 in Books)
  • Customer Reviews:
    4.7 out of 5 stars (41)

About the author

Follow authors to get new release updates, plus improved recommendations.
Micheal Lanham

Micheal Lanham

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Full content visible, double tap to read brief content.

Michael Lanham is a proven software and tech innovator with 25+ years of experience. During that time, he has developed a broad range of software applications, including games, graphic, web, desktop, engineering, artificial intelligence, GIS, and machine learning applications for a variety of industries as an R&D developer. Michael has worked as an GIS Architect, Data Scientiest, Machine Learning Engineer, AI Engineer and Manager of an AI team He currently resides in Calgary, Alberta, Canada, with his family. At the turn of the millennium, Michael began working with neural networks and evolutionary algorithms in game development. He later applied many of those practices to several applications in Oil and gas, GIS and financial services.

Michael has written 10 books including "Augmented Reality Game Development," "Game Audio Development with Unity 5.x," and "Learn ARCore: Fundamentals of Google ARCore," all published by Packt Publishing. Additionally, he has written "Evolutionary Deep Learning" by Manning Publications and "Learn Python Game Development with ChatGPT" by BPB. He is currently writing "AI Agents In Action" for Manning Publications.

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