AI Agents & Harnesses Foundations: Building from ReAct Loops to Long Horizon Agent Harnesses with LangChain and LangGraph
暫譯: AI 代理與 Harness 基礎：使用 LangChain 與 LangGraph，從 ReAct 迴圈打造長時程代理 Harness

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.

Table of Contents

- Understanding LangChain Foundations

- Building Your First Agent

- Understanding Agents Under the Hood

- Building Agent Loop Without a Framework

- ReAct From Scratch: Building an Agent Loop Without Function Calling

- Prompt and Context Engineering

- Agent Harnesses and Deep Agents