Agentic GraphRAG. Integrating Knowledge Graphs, Reasoning, and Agency for Enterprise AI Anthony Alcaraz

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