AI Agents in Action (Second Edition): Intelligent Workflows with LLMs, MCP, A2A, and More (Unabridged‪)‬

AI agent technology is changing fast! This totally revised Second Edition of "AI Agents in Action" by Micheal Lanham guides you through the latest breakthroughs in large language model (LLM)-powered autonomy, from the Model Context Protocol (MCP) to advanced multi-agent collaboration and deployment.

Whether you're upgrading your customer service systems, creating a research assistant, or deploying a fleet of internal agents to automate enterprise tasks, this hands-on guide will help you build trustworthy agents that can handle high-stakes tasks. Extensively revised, this second edition contains new chapters on MCP, containerized deployment, and voice agent orchestration, along with refreshed coverage of LangChain, Prompt Flow, CrewAI, and more.

In "AI Agents in Action, Second Edition," you'll learn how to:

Design and deploy production-grade autonomous agents
Use MCP, the OpenAI Assistants API, and tools to build adaptable systems
Leverage retrieval-augmented memory, feedback loops, and self-improving behaviors
Orchestrate voice and vision-enabled agents
Containerize and scale your agents


About the book:

"AI Agents in Action, Second Edition" provides a solid foundation for listeners just starting with agents, while introducing advanced techniques. You'll work through the core layers of an agentic system, including prompt engineering, working with tools, reasoning and planning, knowledge and memory, evaluation, and feedback patterns. Each chapter combines real-world examples that cover reasoning frameworks like ReAct and Sequential Thinking MCP, advanced RAG systems, and multi-agent collaboration patterns. You'll find extensive tips, tricks, and best practice advice taken from Micheal Lanham's AI career.

About the listener:

For intermediate Python programmers.

About the author:

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