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Coles Agentic GraphRAG: Integrating Knowledge Graphs, Reasoning, and Agency for Enterprise AI at Bayshore Shopping Centre in Ottawa, ON
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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
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
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