Overview

What separates experimental AI agents from systems you can trust in production? In an era where large language models are everywhere, engineering teams are discovering that intelligence alone is not enough. Real-world agents must retrieve the right information, reason within constraints, and execute actions with consistency you can measure. Engineering Reliable AI Agents with MCP and RAG shows how to build AI systems that move beyond fragile demos and into dependable, scalable production deployments. This book presents a practical, engineering-first approach to agent design, combining Retrieval-Augmented Generation with the Model Context Protocol to create grounded, controllable, and predictable behavior. Rather than relying on oversized prompts or blind model calls, you'll learn how to architect modular pipelines that separate reasoning, retrieval, and tool execution into clearly defined components. The result is AI agents that base decisions on verified data, invoke tools safely, and behave consistently under real operational constraints. Through detailed explanations and real implementation patterns, the book walks you through the complete lifecycle of a production-ready agent system. You'll explore advanced retrieval strategies, embedding and indexing techniques, deterministic tool orchestration, and multi-agent coordination models designed for complex workflows. Performance optimization, token efficiency, caching, parallelism, and cost control are treated as first-class concerns, ensuring your systems remain fast and economical at scale. Beyond performance, reliability also means accountability and safety. This book dives into monitoring, audit logging, fallback strategies, and risk controls that allow teams to detect failures early and respond with confidence. You'll learn how to test agent behavior, enforce constraints, and deploy with observability, making reliability a measurable engineering property rather than an assumption. Written for software engineers, ML practitioners, architects, and technical leaders, Engineering Reliable AI Agents with MCP and RAG provides a clear blueprint for building AI agents that retrieve accurately, act deterministically, and scale responsibly. If you're ready to move from unpredictable AI behavior to systems you can trust in production, this book delivers the knowledge and patterns you need to get there.

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