Overview

RAG and MCP for AI Agents: Build Reliable LLM Systems That Retrieve Knowledge and Execute Actions AI agents promise automation, speed, and intelligence-but without the right foundation, they quickly become brittle. Teams struggle with hallucinations, broken tool calls, and context loss. The problem isn't the model-it's the system architecture. This book gives engineers, architects, and practitioners a practical blueprint for building reliable, production-grade agent systems using Retrieval-Augmented Generation (RAG) and the Model Context Protocol (MCP). It bridges the gap between academic theory and real-world implementation, showing how to design agents that not only reason but also retrieve, act, and adapt safely. You'll learn how to: Build retrieval pipelines that surface accurate, timely context from massive data sources. Implement MCP servers that connect language models to structured tools, APIs, and live systems. Combine RAG and MCP to create intelligent agents capable of grounded reasoning and dependable execution. Secure your agents against data leaks, drift, and prompt injection attacks. Deploy scalable, observable, and version-controlled agent workflows that stand up to production demand. Every chapter translates hard-won lessons from real systems into tested, implementable patterns. You'll move from building vector-based retrieval layers and robust MCP integrations to managing latency, observability, and security in hybrid AI environments. Whether you're architecting enterprise-grade automation, augmenting human workflows, or exploring next-generation AI platforms, this book equips you with the engineering discipline and operational clarity needed to move beyond prototypes and deliver systems that last.

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