Practical Guide to Building AI Agents Reviewed
A hands-on review of AI Agents in Action, a book for developers looking to build real AI agent systems.
If you're looking to move beyond theoretical discussions and start building actual AI agents, AI Agents in Action by Michael Lanham might be the resource you need. This book is designed for developers and technical leaders who want to create real systems using language models, agent frameworks, and orchestration tools.
About the Author
Michael Lanham, Lead AI Developer at Brilliant Harvest, brings over 25 years of industry experience and is the author of 10 books, including Evolutionary Deep Learning. His latest work focuses on practical applications rather than abstract concepts.
What's Inside the Book
The book is structured to gradually increase in complexity:
- Starts with OpenAI’s GPT Assistants
- Moves into multi-agent systems using CrewAI and AutoGen
- Covers sophisticated orchestration with behavior trees and platforms like Nexus
Code examples are clearly annotated, and the tooling discussed is open source. The book includes working projects on GitHub with guidance for both local and API-hosted LLMs.
Strengths and Weaknesses
Strengths:
- Practical, applied approach
- Clear code annotations
- Open source tooling
Weaknesses:
- Lacks critique of tools' limitations
- No extended real-world use cases
- Assumes technical proficiency
Who Should Read It?
This book is not for nontechnical audiences. It's ideal for:
- Developers comfortable with Python, GitHub, and LLM APIs
- Technical leaders investigating LLM-driven agents for enterprise workflows
- Researchers looking to build intelligent, interactive systems
In summary, AI Agents in Action bridges the gap between raw model capabilities and full agent systems. It won't answer all your questions, but it will help you ask better ones and build smarter agents.
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About the Author
Alex Thompson
AI Technology Editor
Senior technology editor specializing in AI and machine learning content creation for 8 years. Former technical editor at AI Magazine, now provides technical documentation and content strategy services for multiple AI companies. Excels at transforming complex AI technical concepts into accessible content.