Building Agentic AI Systems with RAG and MCP: A Developer's Guide to Designing, Orchestrating, and Scaling Multi-Agent Workflows with LangGraph and Op

Build AI systems that don’t just respond they collaborate, reason, and self-orchestrate.

This book shows you how to combine RAG, MCP, and LangGraph to create scalable, agentic AI workflows ready for real-world deployment.

Building Agentic AI Systems with RAG and MCP is a practical guide for developers who want to design intelligent, multi-agent architectures that behave more like teams than tools. Using Retrieval-Augmented Generation (RAG), LangGraph, and OpenAI’s Model Context Protocol (MCP), this book teaches you how to build AI systems that autonomously coordinate tasks, share context, and operate safely at scale.

You’ll learn how to architect agents that communicate through structured protocols, retrieve and reason over knowledge dynamically, and interact with external tools in secure, fully auditable ways. Each chapter combines real examples, best practices, and production-tested patterns to help you move from simple prototypes to enterprise-grade AI pipelines.

Whether you’re an engineer, researcher, startup builder, or AI enthusiast, this book gives you the technical foundation and practical strategies needed to create modern, modular, and highly effective agentic AI systems.

• Hands-on guidance for integrating RAG, MCP servers, and LangGraph orchestration.

• Blueprints for scalable multi-agent workflows, including planning, memory, routing, and tool invocation.

• Production patterns for safety, observability, and error handling in agentic systems.

• Real-world case studies and complete project walkthroughs, from prototypes to deployable agents.

• Developer-focused insights on performance tuning, API design, and long-term maintainability.

Ready to build the next generation of intelligent, collaborative AI systems?
Grab your copy of Building Agentic AI Systems with RAG and MCP and start building agent workflows that scale from idea to production.