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.
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