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The Agentic Blueprint: Architecting Enterprise Intelligence with Knowledge Graphs, Neuro-Symbolic Reasoning, and Strategic Prompting
Paperback
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ISBN13: 9798195423315
Publisher: Independently Published
Published: May 13 2026
Pages: 314
Weight: 0.93
Height: 0.66 Width: 6.00 Depth: 9.00
Language: English
Publisher: Independently Published
Published: May 13 2026
Pages: 314
Weight: 0.93
Height: 0.66 Width: 6.00 Depth: 9.00
Language: English
The Reliability Ceiling of Generative AI has arrived. Is your enterprise ready to break through it? In the current landscape, Large Language Models (LLMs) have proven their brilliance at conversation but often falter at the foundational requirements of the enterprise: logic, long-term planning, and verifiable truth. The Agentic Blueprint is the definitive guide for AI architects, CTOs, and senior engineers who are ready to move beyond simple chatbots and build autonomous systems that actually perform in production.
Authored by Saiyam Arora, an AI Researcher specializing in neuro-symbolic systems, this book bridges the gap between probabilistic AI and the deterministic rigor of Knowledge Graphs. Drawing on advanced research in graph-optimized multi-agent architectures, this volume provides a rigorous, systems-first approach to building reliable Agentic Intelligence. Why Knowledge Graphs are the Backbone of Agency
While Vector databases and RAG (Retrieval-Augmented Generation) provided a starting point, they lack the structural logic required for complex, multi-step reasoning. This book introduces Knowledge-Augmented Generation (KAG), explaining how to use Knowledge Graphs as an agent's long-term memory and reasoning rail to eliminate hallucinations and ensure auditability. Inside the Blueprint:
Putting AI into production is vastly different from running a demo. This book addresses the Day 2 challenges of enterprise AI:
Saiyam Arora is a professional AI Research Engineer specializing in the intersection of Knowledge Engineering and Autonomous Multi-Agent Systems. A frequent contributor to international venues including IEEE and Springer Nature, his work focuses on overcoming the reliability barriers in generative AI through neuro-symbolic integration.
While Vector databases and RAG (Retrieval-Augmented Generation) provided a starting point, they lack the structural logic required for complex, multi-step reasoning. This book introduces Knowledge-Augmented Generation (KAG), explaining how to use Knowledge Graphs as an agent's long-term memory and reasoning rail to eliminate hallucinations and ensure auditability. Inside the Blueprint:
- The Business Value of AI Agents: Move past the hype with a deep-dive into ROI, cost-benefit analysis of token consumption vs. human capital, and real-world deployment strategies.
- The Business Value of Knowledge Graphs: Learn how to transform fragmented data silos into a unified Source of Truth that powers autonomous decision-making.
- Neuro-Symbolic Integration: Master the technical synthesis of LLMs with structured ontologies to achieve multi-hop reasoning capabilities.
- The Art of Strategic Prompting: Move beyond creative writing to deterministic prompt engineering, including text-to-graph traversal and self-correction loops.
- Multi-Agent Orchestration: Discover methods for tool allocation and agent coordination based on the author's research in information-theoretic optimization.
Putting AI into production is vastly different from running a demo. This book addresses the Day 2 challenges of enterprise AI:
- Scalability in billion-node environments using Neo4j and AWS Neptune.
- Security protocols to protect against prompt injection and data leakage.
- The Trust Horizon-achieving interpretability and safety in regulated industries.
Saiyam Arora is a professional AI Research Engineer specializing in the intersection of Knowledge Engineering and Autonomous Multi-Agent Systems. A frequent contributor to international venues including IEEE and Springer Nature, his work focuses on overcoming the reliability barriers in generative AI through neuro-symbolic integration.
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