Get Discount 10% Off
Subscribe to our newsletters now and stay up-to-date with new arrivals, updates and deals.
MILLIONS OF TITLES TO CHOOSE FROM
Currency
AI Agents in Action, Second Edition: Intelligent Workflows with Llms, McP, A2a, and More
Get the eBook free when you register your print book at Manning. "Great contents, broad coverage, fun...
- $95.98 USD
- $95.98 USD
- Unit price
- per
Please hurry! Only 31 left in stock
Couldn't load pickup availability
Get the eBook free when you register your print book at Manning. "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
Author: Micheal Lanham
Publisher: Manning Publications
Published: 07/14/2026
Pages: 392
Binding Type: Paperback
Weight: 1.25lbs
Size: 9.26h x 7.47w x 1.01d
ISBN: 9781633434530
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.
--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
Author: Micheal Lanham
Publisher: Manning Publications
Published: 07/14/2026
Pages: 392
Binding Type: Paperback
Weight: 1.25lbs
Size: 9.26h x 7.47w x 1.01d
ISBN: 9781633434530
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.
Returns Policy
You may return most new, unopened items within 30 days of delivery for a full refund. We'll also pay the return shipping costs if the return is a result of our error (you received an incorrect or defective item, etc.).
You should expect to receive your refund within four weeks of giving your package to the return shipper, however, in many cases you will receive a refund more quickly. This time period includes the transit time for us to receive your return from the shipper (5 to 10 business days), the time it takes us to process your return once we receive it (3 to 5 business days), and the time it takes your bank to process our refund request (5 to 10 business days).
If you need to return an item, simply login to your account, view the order using the "Complete Orders" link under the My Account menu and click the Return Item(s) button. We'll notify you via e-mail of your refund once we've received and processed the returned item.
Shipping
We can ship to virtually any address in the world. Note that there are restrictions on some products, and some products cannot be shipped to international destinations.
When you place an order, we will estimate shipping and delivery dates for you based on the availability of your items and the shipping options you choose. Depending on the shipping provider you choose, shipping date estimates may appear on the shipping quotes page.
Please also note that the shipping rates for many items we sell are weight-based. The weight of any such item can be found on its detail page. To reflect the policies of the shipping companies we use, all weights will be rounded up to the next full pound.
- Choosing a selection results in a full page refresh.