Go beyond simple prompts and build production-grade AI agents with LangChain and LangGraph, from ReAct reasoning loops to tool-calling, RAG, and context engineering. Key Features
Build and debug AI agents using LangChain, LangGraph, and LangSmith
Implement ReAct reasoning loops, tool calling, and structured outputs
Apply context engineering, MCP integration, and RAG to real-world agent workflows
Book Description AI Agents & Harnesses Foundations shows you how to build the AI agents and agent harnesses powering modern agentic AI applications. You’ll start with LangChain’s core primitives, including models, messages, prompts, tools, and structured outputs, and use them to build increasingly capable agents. From there, you’ll go under the hood to understand the agent loop, tool-calling LLMs, state, memory, context engineering, orchestration, and long-horizon agents. Using LangChain, LangGraph, and LangSmith, you’ll see how modern agent systems are built, orchestrated, traced, and observed. By the end, you’ll understand modern agent harness architecture and have the practical foundations to build, debug, secure, and evolve reliable production AI agents. What you will learn
Build AI agents with LangChain primitives, tools, and structured outputs
Understand the agent loop: call the model, run tools, feed results back, repeat
Build stateful agents with LangGraph and apply context engineering
Trace and debug agent behavior with LangSmith
Design reliable agent harnesses for long-horizon execution
Secure and improve production AI agent systems with memory, persistence, and guardrails
Who this book is for This book is for software developers and engineers, AI engineers, data scientists, researchers, and technical builders who want to understand how modern AI agents work and how to build them with LangChain and LangGraph. You should be comfortable with Python, Git, APIs, and basic debugging. No machine learning background is required, but the book assumes some programming experience and focuses on practical implementation rather than introductory coding.
