AI Engineering In Practice: Multi-Agent System, RAG, MCP, and LLMs

Build Smarter AI Systems—from Blueprint to Production AI Engineering in Practice is your hands-on guide to designing, building, and deploying intelligent systems using Multi-Agent Architectures, Retrieval-Augmented Generation (RAG), the Model Context Protocol (MCP), and Large Language Models (LLMs). Whether you're a machine learning engineer, AI product lead, or aspiring technical founder, this book gives you practical tools, proven design patterns, and expert insights to go beyond theory and deliver real-world AI solutions. What You’ll Learn: • How to build and orchestrate multi-agent systems with context-aware collaboration • Step-by-step RAG workflows using external knowledge sources • Techniques for effective prompt engineering and managing LLM output • Applying MCP to structure intelligent communication between agents • Real-world case studies from enterprise AI deployments This Book Is For: • AI engineers and software developers • Data scientists transitioning into AI productization • Technical PMs and architects building scalable LLM-powered applications Includes diagrams, code templates, performance metrics, and best practices. No fluff—just actionable AI engineering knowledge.