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, Nonfiction, Computer Science, Technical, Technology

610 pages, Kindle Edition

Published September 16, 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.

[FL Jia · Rating 3 out of 5]

The content is 4.5 star, but the writing style is 1.5 star, so 3 star on average.
The book gives me a pretty comprehensive view of AI Agents. A lot of details. This book is a very good overview of AI Agents like LLM, Tools, Orchestration, Memory, Learning, Monitoring, Multi-agent, MCP, UX, organization pivot, RAG, etc. The most intriguing point is the Meta Agent Search, which is used to design AI Agents. As suggested by the book, my next step is to build agents to gain some hands-on experience. I am sure I will need to re-visit this book multiple times while learning each every component of AI Agents.

However, for the writing style of the book, it is very dry. A lot of repetitive words. A lot of nouns. Maybe the writing style is good for scholars in the field of AI, but definitely not for the general public. I can only give 1.5 star for writing style.

[Alvaro Solano · Rating 2 out of 5]

I really wanted to like this book, but it fell well short of expectations. The core problem is simple: a lot of words, but very few that do any real work. It has a frustrating habit of listing concepts without ever explaining why they matter or how they actually work — like a very long table of contents that never leads anywhere.

The lack of technical depth is the biggest letdown. For a book at this level, you'd expect rigorous explanations and content that challenges the reader. Instead, it stays firmly on the surface, recycling the same ideas without adding anything new.

On top of that, the editing is poor — code snippets contain errors, paragraphs are duplicated back to back, and generic filler pads the page count without contributing anything meaningful.

If you're looking for something that actually deepens your understanding of the subject, look elsewhere.

[Chris Pachovsky · Rating 2 out of 5]

It's so verbose and tiring to get through. You can clearly see the author used AI for generating this book. While the topic is interesting and there is actual knowledge to be found there, it's very surface level and repetitive. You feel like reading the same paragraph over and over while learning very little. I'd be pretty mad if I bought this for a full price - fortunately I got it in a bundle with other books.

[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”.