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Building Intelligent Agents with Knowledge Graphs: Boost Agent Reasoning, Reduce Hallucinations, and Achieve Explainable AI Outcomes
Paperback
Publisher: Independently Published
Published: Nov 10 2025
Pages: 122
Weight: 0.50
Height: 0.26 Width: 7.00 Depth: 10.00
Language: English
Building Intelligent Agents with Knowledge Graphs: Boost Agent Reasoning, Reduce Hallucinations, and Achieve Explainable AI Outcomes
Building Intelligent Agents with Knowledge Graphs begins with a question every AI builder faces today: How do you create agents that reason reliably, avoid hallucinations, and explain their decisions with confidence? As LLM-powered systems spread across industries, the pressure to deliver accuracy, transparency, and trust has never been greater. This book gives you a practical, industry-ready path forward.At its core, this guide shows you how to combine knowledge graphs with modern AI agents to build systems that understand context, maintain memory, and perform structured reasoning far beyond what statistical models can do alone. You'll learn how to design graph-powered architectures that strengthen agent logic, reduce errors, and support the demands of real-world deployment-whether you're building enterprise copilots, domain-specific assistants, or multi-agent systems that coordinate across teams and tools.
Inside, you'll gain the skills to:
- Model entities, relationships, and schemas that give agents a reliable foundation for decision-making.
- Use knowledge graphs to cut hallucinations, enforce factual consistency, and support transparent reasoning.
- Integrate LLMs with graph querying, retrieval strategies, and hybrid vector-graph pipelines.
- Build agents that store, update, and reference long-term memory using evolving, temporal knowledge graphs.
- Apply graph techniques to biomedical QA, supply chain operations, legal reasoning, customer support workflows, and more.
- Design governance, explainability, and trust frameworks essential for enterprise-grade AI.
- Implement scalable graph infrastructures, evaluate reasoning accuracy, optimize latency, and manage cost.
- Explore emerging directions such as neuro-symbolic AI, autonomous graph evolution, and multi-modal agent architectures.
Each chapter translates complex concepts into clear, actionable steps supported by real code, grounded examples, and proven design patterns. Whether you're a software engineer, data architect, AI researcher, or technical leader, you'll find strategies you can apply immediately and expand as your systems grow.
If you're ready to build AI agents that are not only powerful but consistently correct, transparent in their decisions, and aligned with human expectations, this book will give you the blueprint. Start building AI systems you-and your users-can trust.
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