Which books should you read to build agentic AI applications in 2026?
If you build software, read Building Applications with AI Agents by Michael Albada first, then Building Agentic AI Systems by Anjanava Biswas and Wrick Talukdar. The first teaches you how to design single agents and multi-agent systems and evaluate them, and the second goes deeper on planning, reflection and coordinating many agents. Add Chip Huyen's AI Engineering for everything around the agent, and Ethan Mollick's Co-Intelligence if you lead a team.
When I first published this list in February 2025, it leaned towards the broad picture of AI and society. Since then, several books written specifically about building agents have come out, and they are the ones I hand to engineers now. This update puts them first and keeps the earlier titles that still earn their place.
Building Applications with AI Agents, by Michael Albada
O'Reilly, September 2025. Albada is a machine learning engineer, and the book is about designing and shipping agents built on foundation models: the components of an agent, when one agent is enough and when to split the work across several, and the trade-offs of each design.
What I like most is the attention to evaluation and operations. Building an agent that works in a demo is quick, and keeping it working in production is the real job, so the chapters on testing and observing agents are worth the price. The stated level is beginner to intermediate.
Read it if you are a developer or technical lead about to build your first production agent.
Building Agentic AI Systems, by Anjanava Biswas and Wrick Talukdar
Packt, April 2025. This one goes deeper into how agents think: architectures, planning, reasoning, reflection and multi-step tool use, plus a coordinator, worker and delegator approach for orchestrating larger systems. It also covers trust, transparency and safety.
It comes with a companion repository of notebooks, so you can run each chapter's examples. Expect to know Python and some machine learning.
Read it after Albada's book, when you want to build systems with several agents working together.
AI Engineering, by Chip Huyen
O'Reilly, December 2024. This book covers the whole practice of building applications on foundation models: prompt engineering, retrieval and agents, fine-tuning, dataset engineering, inference optimisation and evaluation.
An agent is one part of an application, and this is the book for the other parts. Its chapters on evaluation are the best I have read on the topic.
Read it if you are responsible for an AI product end to end.
AI Agents in Action, by Micheal Lanham
Manning. The first edition came out in March 2025, and a second edition is listed for June 2026. It is a hands-on book about building, orchestrating and deploying agents and multi-agent systems, with plenty of code.
Read it if you learn best by building along with a book.
Build a Large Language Model (From Scratch), by Sebastian Raschka
Manning. You build a small GPT-style model step by step, pretrain it, then fine-tune it into a classifier and a chatbot, on an ordinary laptop. It covers attention, pretraining and instruction fine-tuning.
You will build agents on someone else's model, and knowing how that model works inside helps you understand why it fails the way it does.
Read it if you want to understand the engine under every agent.
Co-Intelligence, by Ethan Mollick
Portfolio, April 2024. Mollick, a Wharton professor, writes about working with AI as a co-worker, co-teacher and coach, with many real examples from business and education.
It is written for every reader, and it is the book I give to managers and founders who need to decide where AI fits in their team.
Read it if you lead people, or if you want to explain AI to someone who does.
The order I would read them in
- Developers: Albada, then Biswas and Talukdar, then Huyen. Raschka when you want the internals.
- Technical leads and founders: Huyen, then Albada, then Mollick.
- Managers and non-technical readers: Mollick first, then the first chapters of Huyen.
Questions people ask
What is the best book for building AI agents in 2026?
For most developers, Building Applications with AI Agents by Michael Albada is the best starting point, because it covers design, multi-agent systems and evaluation in one book. Follow it with Building Agentic AI Systems for deeper multi-agent patterns.
Do I need to know machine learning to read these books?
Albada's and Huyen's books assume you can program and explain the AI concepts as they go. Biswas and Talukdar expect Python and some machine learning. Mollick's book suits any reader.
Are books still useful when AI changes so fast?
Tools change every few months, and design principles change slowly. Use books for the principles (how to structure an agent, how to evaluate it) and documentation for the current tools.
