Author Description

Engineering Deep Agents with LangChain is a practical guide for developers, engineers, and AI practitioners who want to move beyond simple chatbots and start building structured, intelligent multi-agent AI systems. This book walks you through the essential concepts, tools, and workflows for designing agentic AI powered by LangChain, large and small language models, and vector databases.<br>The rapid progress of AI has shifted the focus from single LLM-driven assistants toward modular, multi-agent systems that can collaborate, specialize, and scale. LangChain has emerged as the leading framework for connecting models, memory, tools, and APIs into coherent AI workflows. This book provides the knowledge and hands-on strategies you need to engineer deep agents capable of reasoning, retrieving information, and interacting with real-world systems.<br><b><b>Inside, you'll learn how to: </b></b><br>Build your first LangChain pipelines and agents from scratch.<br>Integrate small language models (SLMs) and large language models (LLMs) for balanced performance, cost, and efficiency.<br>Design tools, memory components, and orchestration patterns for reliable AI workflows.<br>Implement retrieval-augmented generation (RAG) using FAISS, Chroma, or Pinecone for grounded, knowledge-driven responses.<br>Apply design patterns such as coordinator-executor roles, parallel workflows, and error handling.<br>Debug and evaluate your agentic systems with LangSmith for transparency and reliability.<br>Combine theory with real-world examples through practical, annotated code walkthroughs.<br>Written in a clear and approachable style, this book doesn't stop at abstract concepts, it shows you how to actually implement agentic AI step by step. Whether you are experimenting with small prototypes or preparing to scale production systems, you will gain both the foundational understanding and the practical skills to succeed.<br>If you are a developer, AI engineer, researcher, or product builder looki