AI Agents and Applications (with LangChain, LangGraph, and MCP)

This book is a comprehensive, code-driven journey through the full spectrum of LLM-powered application development. Beginning with the fundamentals of prompt engineering and the OpenAI API, it progressively builds toward sophisticated architectures: summarization engines, Q&A chatbots grounded in private knowledge bases via Retrieval-Augmented Generation, and finally autonomous multi-tool AI agents and multi-agent systems. Each concept is illustrated with practical, runnable Python examples centered on a travel industry theme. The book covers LangChain's modular component model, LangGraph's stateful graph-based agent framework, LangSmith's observability tooling, and the emerging Model Context Protocol (MCP) standard. Readers learn not just how to wire components together but how to reason about trade-offs in chunk size, embedding strategy, query transformation, routing, and production concerns like memory and guardrails—making this an indispensable reference for any software developer building real-world AI applications.

What it argues

A causal model describing how design levers in LLM application development—spanning prompt engineering, indexing strategy, query transformation, retrieval architecture, agent orchestration design, and observability—drive intermediate system states such as retrieval relevance and agent reasoning quality, which in turn determine outcome metrics including answer accuracy, hallucination rate, system reliability, and developer productivity.