Generative AI in Production: Building Autonomous Agents with LangChain and LangGraph

You have likely run a Python script that calls OpenAI and prints a response. It feels like magic. But taking that script and turning it into a reliable, scalable application—one that handles failures, remembers context, and interacts with your internal data—is a different engineering challenge entirely.Generative AI in Production bridges the gap between a weekend prototype and robust software. This book does not waste time on hype; it focuses on the architectural patterns required to tame the probabilistic nature of Large Language Models (LLMs). You will stop viewing the LLM as a black box and start treating it as a reasoning engine that must be integrated into a deterministic stack.Using the industry-standard frameworks LangChain and LangGraph, you will learn to build agents that are stateful, controllable, and autonomous. From forcing models to output valid JSON with Pydantic to orchestrating complex multi-agent teams that research, code, and review their own work, this guide covers the full lifecycle of AI development.In this book, you will learn how to:Master the Stack: Transition from brittle scripts to modular chains using the LangChain Expression Language (LCEL).Control the Output: Eliminate parsing errors by binding tools and forcing structured data schemas using Pydantic.Build Advanced RAG: Go beyond naive retrieval with hybrid search, query reformulation, and reranking to eliminate hallucinations.Orchestrate Agents: Move from linear chains to cyclic graphs with LangGraph, enabling memory persistence, human-in-the-loop approval, and complex reasoning loops.Scale and Deploy: Containerize your application with Docker, expose it via the LangServe API, and monitor performance with LangSmith traces.Whether you are a software engineer, data scientist, or technical founder, this book provides the mental models and code required to build the next generation of software. Read more