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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
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
| Erscheinungsjahr: | 2026 |
|---|---|
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: | Einband - flex.(Paperback) |
| ISBN-13: | 9798341623170 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Alcaraz, Anthony
Julien, Sam |
| Hersteller: | O'Reilly Media |
| Verantwortliche Person für die EU: | Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de |
| Maße: | 231 x 174 x 23 mm |
| Von/Mit: | Anthony Alcaraz (u. a.) |
| Erscheinungsdatum: | 30.09.2026 |
| Gewicht: | 0,676 kg |
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