What if your AI systems could retrieve information, reason over complex knowledge, plan actions, and continuously learn--with enterprise-grade security and compliance? Agentic GraphRAG guides technical leaders, engineers, and architects through the next evolution of GenAI. Combining retrieval-augmented generation (RAG) with graph-based reasoning and agentic capabilities, this guide is a blueprint for building scalable, auditable, intelligent systems. Written by Anthony Alcaraz and Sam Julien, this book demystifies knowledge graphs, graph memory, neural-symbolic reasoning, and agent orchestration through real-world case studies, hands-on design patterns, and production-ready architectures. Readers will learn how to construct graph-native retrieval systems, integrate advanced reasoning into agent workflows, and address enterprise challenges around governance, scalability, and transparency. - Design graph-augmented architectures that surpass traditional RAG - Implement agents with dynamic memory, planning, and decision-making capabilities - Integrate knowledge graphs with LLMs - Deploy scalable, governable multi-agent systems ready for production environments
