Building Applications with AI Agents: Designing and Implementing Multiagent Systems

by Michael Albada

Generative AI has revolutionized how organizations tackle problems, accelerating the journey from concept to prototype to solution. As the models become increasingly capable, we have witnessed a new design pattern AI agents. By combining tools, knowledge, memory, and learning with advanced foundation models, we can now sequence multiple model inferences together to solve ambiguous and difficult problems. From coding agents to research agents to analyst agents and more, we've already seen agents accelerate teams and organizations. While these agents enhance efficiency, they often require extensive planning, drafting, and revising to complete complex tasks, and deploying them remains a challenge for many organizations, especially as technology and research rapidly develops.


This book is your indispensable guide through this intricate and fast-moving landscape. Author Michael Albada provides a practical and research-based approach to designing and implementing single- and multiagent systems. It simplifies the complexities and equips you with the tools to move from concept to solution efficiently.



Understand the distinct features of foundation model-enabled AI agents
Discover the core components and design principles of AI agents
Explore design trade-offs and implement effective multiagent systems
Design and deploy tailored AI solutions, enhancing efficiency and innovation in your field

Genres: Artificial Intelligence, Computer Science, Nonfiction, Technical, Technology

352 pages, Paperback

Published October 21, 2025

Reviews

[Andrew Hamara · Rating 2 out of 5]

After getting assigned an agentic AI project at work, I was excited to crack open this book and start learning about the current state of the topic. While some parts of this book were genuinely useful, the text is overwhelmingly AI-generated, which frustrated me and took me out of the experience. As I said, many of the chapters do have good content, but it's hidden in the weeds of AI slop that you have to parse through to find it. For example, there are a multitude of checklists such as "reasons you might do x" or "when to y instead of z" and then of course there has to be a "when to z instead of y...". These simply don't add to the main point of the book in my opinion. If you are willing to skim through this, then in some chapters you will find engaging content and code. In some other chapters, these lists are all you will get, and you will leave the chapter having not learned anything meaningful.

It's sad to leave this review, because I think with this book I did get a good idea of how agentic AI works under the hood, I just think that I could have learned it in about a third of the pages in this book.

[Khan · Rating 5 out of 5]

4.6

[Patsy Hancock · Rating 5 out of 5]

Building Applications with AI Agents: Designing and Implementing Multiagent Systems by Michael Albada earns a well-deserved 5/5 stars.
Published by O'Reilly, this book stands out as a practical, research-informed guide to the rapidly evolving world of AI agents. Albada, a machine learning engineer with hands-on experience at companies like Uber, ServiceNow, and Microsoft (including large-scale multi-agent systems for cybersecurity), delivers a clear and comprehensive approach to designing and building both single-agent and multi-agent applications.
The book covers essential topics like core agent components (tools, memory, orchestration), popular frameworks (such as LangGraph, AutoGen, CrewAI, and OpenAI's SDK), coordination patterns for multi-agent setups, scalability considerations, security, evaluation strategies, and human-agent collaboration. It balances theory with actionable insights, code examples, and real-world trade-offs, making complex ideas accessible without oversimplifying.
What makes it particularly valuable is its forward-looking yet grounded perspective—perfect for engineers, developers, or technical leaders moving from basic LLM prompts to production-grade agentic systems.
I highly recommend it as an excellent reference and starting point for anyone new to AI agents. It equips readers with the foundational knowledge and best practices needed to experiment, prototype, and scale effectively in this fast-moving field. If you're serious about building intelligent, autonomous applications, this is one of the strongest single-volume resources available.

[Jiří Mareš · Rating 4 out of 5]

I read the book to found out what the AI agents are. That I realized in first few chapters.

Following chapters was a little bit boring for me but they cover all aspects of using AI agents and that is interesting (al least it is good to summaries knowledge from time to time).

[Mr.Shore · Rating 4 out of 5]

把主流的技术都说了一下，内容比较浅，适合面试突击

[Mikhail Filatov · Rating 1 out of 5]

A lot of the text is AI generated and extremely boring and repetitive with every second sentence ending “a,b and c”.