Agentic Graphrag: Integrating Knowledge Graphs, Reasoning, and Agency for Enterprise AI
暫譯: 代理圖譜：整合知識圖譜、推理與企業AI的自主性

What if your AI systems could retrieve information, reason over complex knowledge, plan actions, and continuously learn--all while maintaining enterprise-grade security and compliance? Agentic Graph RAG guides technical leaders, engineers, and architects through the next evolution of generative AI. Combining retrieval-augmented generation (RAG) with graph-based reasoning and agentic capabilities, this guide provides a practical blueprint for building scalable, auditable, and intelligent AI 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 large language models for robust, explainable AI
• Deploy scalable, governable multiagent systems ready for production environments