AI Agents in Action, Second Edition: Intelligent workflows with LLMs, MCP, A2A, and more

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Build AI agents that do more than respond. Give them tools, connect them to knowledge, coordinate their work, and evaluate how well they perform.

AI Agents in Action, Second Edition teaches you how to design, build, evaluate, and deploy AI agents through working Python examples. You’ll progress from a simple agent to tool-using assistants, coordinated multi-agent workflows, and deployable systems. Along the way, you’ll learn how the pieces fit together and where to look when an agent needs improvement.

What’s new and included in the second edition?

A near-total rewrite with new and extensive coverage of the latest agent innovations Agents start to finish—from prompting and planning, to memory and evaluation New chapter! Evaluate agents with LLM-as-judge, test-driven-development, and observable behavior New chapter! Explore the agentic loop, build task-focused agents, and develop long-horizon, goal focused systems New chapter! Metacognition harnesses that structure agent thinking, planning, and learning New chapter! Practical tips and techniques for building AI applications, help desks, and deep research agents New chapter! Voice-driven AI agents, including real-world deployment patterns for multi-agent systems Multi-agent workflows with assembly-flow, orchestration, and collaboration patterns Hands-on with the Model Context Protocol (MCP) to connect agents to tools and servers Hybrid search for Retrieval Augmented Generation (RAG) and practical ways to use memory in agents
With more than 95% rewritten from the first edition, AI Agents in Action is far more than a refresh! You’ll go right from your agent’s first response, to systems that use tools, work together, draw on knowledge, and incorporate feedback. All illustrated with practical examples throughout.

What's inside

Autonomous agent design and deployment MCP-based tools, resources, prompts, memory, and server integrations Reasoning and planning patterns including ReAct, Reflexion, Tree-of- Thought, and Sequential Thinking
About the reader

For intermediate Python programmers. No experience with AI agents and agentic systems required.

About the author

Micheal Lanham is a software and technology innovator with over 20 years of industry experience. He has authored books on deep learning, including Manning’s Evolutionary Deep Learning .

Table of Contents

1 The rise of AI agents
2 Core components: Large language models, prompting, and agents
3 Actions with Model Context Protocol for AI agents
4 Architecting and building multi-agent systems
5 Agent reasoning and planning
6 Working with memory and knowledge RAG for agents
7 Building robust agents with evaluation and feedback
8 Deploying agents and agentic systems
9 Understanding the agentic loop
10 Exploring the cognitive agent that thinks, monitors, and adapts
11 Tips for building agentic systems
A Setting up the sample code repository
B Node.js setup for local MCP servers