LangChain Agent Engineering: Build Production-Ready AI Agents, Agentic RAG, and Multi-Agent Systems with Python and LangGraph

AI agents are easy to demonstrate. Engineering them to operate reliably in real-world applications is far more challenging.
LangChain Agent Engineering is a practical guide to designing, building, testing, and deploying production-ready AI agents with Python, LangChain, and LangGraph. Rather than focusing on basic chatbot demonstrations, this book shows you how to create dependable agent systems that can use tools, manage state, retrieve trustworthy information, coordinate specialized agents, recover from failures, and operate within clearly defined security boundaries.
You will begin by understanding how modern AI agents work and how they differ from language models, chains, and deterministic workflows. From there, you will learn how to design reliable tools, manage conversation context, implement short-term and long-term memory, and build stateful workflows with LangGraph.
The book provides detailed coverage of Retrieval-Augmented Generation, including document preparation, chunking, embeddings, vector stores, retriever evaluation, query rewriting, document grading, reranking, contextual compression, corrective RAG, evidence verification, and source-aware responses.
You will also explore multi-agent architecture, including supervisor-and-worker systems, agent handoffs, shared state, communication patterns, and methods for preventing delegation loops and conflicting outputs.
Inside this book, you will learn how to:
Build tool-calling AI agents with Python and LangChain
Design stateful and recoverable workflows with LangGraph
Implement short-term and long-term agent memory
Create agentic RAG systems grounded in external knowledge
Improve retrieval with query rewriting, reranking, and corrective RAG
Build supervisor, worker, and handoff-based multi-agent systems
Add human approval to sensitive agent actions
Test agents, tools, routes, state, and structured outputs
Trace and evaluate applications with LangSmith
Defend against prompt injection and unsafe tool execution
Control latency, token usage, reliability, and operating costs
Package and prepare agent applications for production deployment
Throughout the book, you will work with professional architecture patterns, complete Python examples, practical troubleshooting guidance, production checklists, and progressive projects. The final capstone brings the major concepts together in a production-ready agentic knowledge platform that combines retrieval, memory, orchestration, security, evaluation, and human oversight.
Whether you are a Python developer, AI engineer, software professional, technical founder, or advanced learner, LangChain Agent Engineering will help you move beyond experimental AI demos and build agent systems that are observable, secure, maintainable, and ready for real-world use.