SKU/Artículo: AMZ-B0GHK7XDPY
Agentic AI Engineering with LangChain & LangGraph: Design, Build, Test, and Deploy Production-Ready LLM Systems — 2026 and Beyond
Format:
Hardcover
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.70 kg
0.70 kg
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Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
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USA
USA
Sobre este producto
- Agentic AI Engineering with LangChain & LangGraph is the definitive guide for building production-ready large language model (LLM) systems in 2026 and beyond. As organizations move past prototypes and demos, this book addresses the real challenge facing modern AI teams: designing, testing, deploying, and scaling reliable agentic AI systems that operate safely and efficiently in real-world environments.Unlike introductory LangChain tutorials, this book focuses on engineering discipline, system architecture, and enterprise-grade practices. You will learn how to move from prompt-driven experiments to robust agentic workflows powered by LangChain and orchestrated with LangGraph. Each chapter is grounded in practical design patterns used in production, with clear guidance on what actually matters when building AI systems that must scale, evolve, and remain trustworthy over time. This book dives deep into single-agent and multi-agent architectures, showing how to design planners, executors, tool-centric agents, and human-in-the-loop systems without chaos or runaway behavior. You will master LangGraph’s graph-based execution model, learn how to handle errors, retries, state, and recovery, and understand when agent autonomy helps—and when it becomes a liability. A major focus is placed on Retrieval-Augmented Generation (RAG) as an engineering problem, not a buzzword. You will learn how to design document ingestion pipelines, chunking strategies, hybrid search, re-ranking, validation, and evaluation pipelines that dramatically improve accuracy and reduce hallucinations. The book goes beyond naive RAG to show how agentic retrieval planning improves performance and trustworthiness. Testing, observability, and cost control are treated as first-class concerns. You will learn how to unit test prompts, tools, and graph nodes, run end-to-end scenario tests, detect reasoning drift, trace agent decisions, and monitor latency, errors, and token usage in production. Security, compliance, and ethical considerations are woven throughout, helping you design systems resilient to prompt injection, tool abuse, and regulatory scrutiny. Written for Python developers, AI engineers, platform teams, and technical leaders, this book provides the mental models, architectural guidance, and practical checklists needed to deploy agentic AI with confidence. Whether you are extending existing workflows or architecting multi-agent systems from scratch, this guide equips you to build scalable, maintainable, and future-proof LLM systems that deliver real business value. If you are serious about moving from experimentation to production-grade agentic AI, this book is your roadmap.
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AR$111.844
44% OFF
AR$62.135
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