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 RAGImplement agents with dynamic memory, planning, and decision-making capabilitiesIntegrate knowledge graphs with large language models for robust, explainable AIDeploy scalable, governable multiagent systems ready for production environments
| Format |
Häftad |
| Omfång |
300 sidor |
| Språk |
Engelska |
| Förlag |
O'Reilly Media |
| Utgivningsdatum |
2026-09-04 |
| ISBN |
9798341623170 |
