What do you think?
Rate this book
Building Applications with AI Agents: Designing and Implementing Multiagent Systems
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
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
352 pages, Paperback
Published October 21, 2025
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 - 14 of 14 reviews
January 25, 2026
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.
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.
March 23, 2026
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.
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.
May 28, 2026
I rarely get the feeling that a book about AI genuinely brings clarity to the topic instead of adding yet another layer of buzzword-driven chaos.
“AI Agent-Based Applications” is one of those books.
Its biggest strength? Consistency and structure. The content is well organized, concepts are explained logically, and the author systematically guides the reader through different aspects of the AI agent landscape. There are not too many examples — and paradoxically, that works in the book’s favor, leaving more room for substance and structured thinking.
What I particularly appreciated was the analytical approach to defining concepts and systematizing solutions. The book makes a strong case that agentic systems are not “magic AI,” but complex engineering systems that can be analyzed, designed, and improved in a structured way. I especially liked the perspective of applying traditional IT system quality criteria to modern AI systems. That is a direction we definitely need more of.
At the same time, I occasionally felt a sense of incompleteness. The author often presents broad principles and good practices but leaves the reader with natural questions: *why exactly this approach?* or *how would this work in practice?* In many places, I wanted to go one level deeper. On the other hand, I suspect this is partly intentional and reflects the philosophy of the book: you do not need a 30-page explanation to start building a solid agentic system.
And to be fair — this approach works. At first glance, the book does not feel overly complicated, but in reality it describes a highly complex world. It introduces new concepts effectively, and when a technical or unintuitive term appears, it is usually explained almost immediately. This makes it much easier to keep momentum and avoid getting lost in unfamiliar terminology.
Another major plus: even when presenting external concepts — such as the autonomy slider — the author adds commentary and interpretation. That makes the book more than just a catalog of ideas; it becomes an attempt to place them into practical context.
I also strongly appreciated the dedicated chapter on security. This topic is too often overlooked in AI discussions, unfairly so. Here, it gets proper attention and is treated seriously, which says a lot about the maturity of the overall approach.
That maturity is visible more broadly as well: the book emphasizes the need to control both costs and effectiveness. There is no blind enthusiasm for the biggest models or the most complex architectures. I especially appreciated the balanced and honest perspective on small models and their practical advantages — something that is often underestimated in the industry.
That said, the book is not without flaws. In the second half, the pace noticeably accelerates — at times, perhaps too much. The style shifts from guided narrative toward something closer to a lexicon: more concepts, more categories, less time to pause and build deeper understanding. I also missed a few practical considerations, such as a brief discussion of the costs of multi-agent approaches. It may seem obvious, but when discussing benefits and applications, it would still be worth explicitly acknowledging.
Since this is effectively a starter guide, it would also benefit from stronger mechanisms to support revisiting the material later: chapter summaries, highlighted key ideas, or similar navigational aids. This is a book worth coming back to, and it would be a shame to make key insights harder to rediscover.
Despite a few shortcomings, my overall impression is very positive. Even with prior experience in application security and AI, I still learned a number of valuable and genuinely useful things.
I would especially recommend this book to people starting to build AI agent systems — and not only engineers. It is also a valuable read for architects, product managers, and more broadly, people managing products and technology. Engineers will expand their toolkit with a structured approach to agents built on familiar patterns, while more business-oriented readers will better understand the variety of agent types and the number of decisions and dependencies worth thinking through. That said, I do wonder whether some less technical readers may occasionally struggle with terminology and concepts that feel natural to people with an IT background.
In short: a very strong book to start with. It does not cover everything, but it provides solid foundations and brings structure to an area that many people still perceive as chaotic. Definitely worth reading.
“AI Agent-Based Applications” is one of those books.
Its biggest strength? Consistency and structure. The content is well organized, concepts are explained logically, and the author systematically guides the reader through different aspects of the AI agent landscape. There are not too many examples — and paradoxically, that works in the book’s favor, leaving more room for substance and structured thinking.
What I particularly appreciated was the analytical approach to defining concepts and systematizing solutions. The book makes a strong case that agentic systems are not “magic AI,” but complex engineering systems that can be analyzed, designed, and improved in a structured way. I especially liked the perspective of applying traditional IT system quality criteria to modern AI systems. That is a direction we definitely need more of.
At the same time, I occasionally felt a sense of incompleteness. The author often presents broad principles and good practices but leaves the reader with natural questions: *why exactly this approach?* or *how would this work in practice?* In many places, I wanted to go one level deeper. On the other hand, I suspect this is partly intentional and reflects the philosophy of the book: you do not need a 30-page explanation to start building a solid agentic system.
And to be fair — this approach works. At first glance, the book does not feel overly complicated, but in reality it describes a highly complex world. It introduces new concepts effectively, and when a technical or unintuitive term appears, it is usually explained almost immediately. This makes it much easier to keep momentum and avoid getting lost in unfamiliar terminology.
Another major plus: even when presenting external concepts — such as the autonomy slider — the author adds commentary and interpretation. That makes the book more than just a catalog of ideas; it becomes an attempt to place them into practical context.
I also strongly appreciated the dedicated chapter on security. This topic is too often overlooked in AI discussions, unfairly so. Here, it gets proper attention and is treated seriously, which says a lot about the maturity of the overall approach.
That maturity is visible more broadly as well: the book emphasizes the need to control both costs and effectiveness. There is no blind enthusiasm for the biggest models or the most complex architectures. I especially appreciated the balanced and honest perspective on small models and their practical advantages — something that is often underestimated in the industry.
That said, the book is not without flaws. In the second half, the pace noticeably accelerates — at times, perhaps too much. The style shifts from guided narrative toward something closer to a lexicon: more concepts, more categories, less time to pause and build deeper understanding. I also missed a few practical considerations, such as a brief discussion of the costs of multi-agent approaches. It may seem obvious, but when discussing benefits and applications, it would still be worth explicitly acknowledging.
Since this is effectively a starter guide, it would also benefit from stronger mechanisms to support revisiting the material later: chapter summaries, highlighted key ideas, or similar navigational aids. This is a book worth coming back to, and it would be a shame to make key insights harder to rediscover.
Despite a few shortcomings, my overall impression is very positive. Even with prior experience in application security and AI, I still learned a number of valuable and genuinely useful things.
I would especially recommend this book to people starting to build AI agent systems — and not only engineers. It is also a valuable read for architects, product managers, and more broadly, people managing products and technology. Engineers will expand their toolkit with a structured approach to agents built on familiar patterns, while more business-oriented readers will better understand the variety of agent types and the number of decisions and dependencies worth thinking through. That said, I do wonder whether some less technical readers may occasionally struggle with terminology and concepts that feel natural to people with an IT background.
In short: a very strong book to start with. It does not cover everything, but it provides solid foundations and brings structure to an area that many people still perceive as chaotic. Definitely worth reading.
January 19, 2026
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.
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.
February 25, 2026
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.
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.
May 26, 2026
Agent systems represent one of the most transformative technologies of our time, redefining how we interact with software, automate tasks, and solve complex problems. This book has explored agent system design, orchestration, security, UX, and ethical considerations comprehensively—from foundations and core principles (skills, planning, memory, learning) through single-agent to multiagent coordination, measurement, validation, production monitoring, security and resilience, and ethical responsibilities.
March 21, 2026
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.
January 7, 2026
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).
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).
May 2, 2026
This book felt a lot like text generated by a modern LLM: verbose, polished, but often low in information density. There were a few interesting points, but overall it came across as rather shallow compared with other technical books.
January 13, 2026
把主流的技术都说了一下，内容比较浅，适合面试突击
January 27, 2026
A lot of the text is AI generated and extremely boring and repetitive with every second sentence ending “a,b and c”.
April 22, 2026
Good for getting an overall picture and quick reference
April 24, 2026
Important clarification on how to read the title:
This book is about building applications that contain AI agents NOT about how to use AI agents to build applications.
This book is about building applications that contain AI agents NOT about how to use AI agents to build applications.
Displaying 1 - 14 of 14 reviews