AI Agents & Harnesses Foundations

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 FeaturesBuild and debug AI agents using LangChain, LangGraph, and LangSmithImplement ReAct reasoning loops, tool calling, and structured outputsApply context engineering, MCP integration, and RAG to real-world agent workflowsBook DescriptionAI 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 learnBuild AI agents with LangChain primitives, tools, and structured outputsUnderstand the agent loop: call the model, run tools, feed results back, repeatBuild stateful agents with LangGraph and apply context engineeringTrace and debug agent behavior with LangSmithDesign reliable agent harnesses for long-horizon executionSecure and improve production AI agent systems with memory, persistence, and guardrailsWho this book is forThis 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.