AI Agents in Action: Intelligent workflows with LLMs, MCP, A2A, and more, 2nd Edition
English | 2026 | ISBN: 1633434532 | 392 pages | True PDF | 38.22 MB
Get the eBook free when you register your print book at Manning.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 Editionteaches 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 innovationsAgents start to finish-from prompting and planning, to memory and evaluationNew chapter!Evaluate agents with LLM-as-judge, test-driven-development, and observable behaviorNew chapter!Explore the agentic loop, build task-focused agents, and develop long-horizon, goal focused systemsNew chapter!Metacognition harnesses that structure agent thinking, planning, and learningNew chapter!Practical tips and techniques for building AI applications, help desks, and deep research agentsNew chapter!Voice-driven AI agents, including real-world deployment patterns for multi-agent systemsMulti-agent workflows with assembly-flow, orchestration, and collaboration patternsHands-on with the Model Context Protocol (MCP) to connect agents to tools and serversHybrid search for Retrieval Augmented Generation (RAG) and practical ways to use memory in agentsWith 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 insideAutonomous agent design and deploymentMCP-based tools, resources, prompts, memory, and server integrationsReasoning and planning patterns including ReAct, Reflexion, Tree-of- Thought, and Sequential ThinkingAbout the readerFor intermediate Python programmers. No experience with AI agents and agentic systems required.About the authorMicheal Lanhamis a software and technology innovator with over 20 years of industry experience. He has authored books on deep learning, including Manning'sEvolutionary Deep Learning.Table of Contents1 The rise of AI agents2 Core components: Large language models, prompting, and agents3 Actions with Model Context Protocol for AI agents4 Architecting and building multi-agent systems5 Agent reasoning and planning6 Working with memory and knowledge RAG for agents7 Building robust agents with evaluation and feedback8 Deploying agents and agentic systems9 Understanding the agentic loop10 Exploring the cognitive agent that thinks, monitors, and adapts11 Tips for building agentic systemsA Setting up the sample code repositoryB Node.js setup for local MCP servers
Get the eBook free when you register your print book at Manning.
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
Evaluate agents with LLM-as-judge, test-driven-development, and observable behavior
Links are Interchangeable - Single Extraction
