Explore how agentic GraphRAG architectures enhance AI's logical reasoning capabilities in this 15-minute conference talk. Discover the distinct advantages of GraphRAG retrieval models over traditional approaches, particularly in accuracy, explainability, and cost-effectiveness for complex, industry-specific tasks. Learn how graph relationships and proximity algorithms enable logical reasoning and correlation that traditional RAG systems cannot achieve. Examine practical demonstrations of agent architectures that combine RAG and GraphRAG retrieval patterns to bridge gaps in data analysis, strategic planning, and information retrieval. Understand how these hybrid approaches solve complex domain-specific problems by leveraging the structural advantages of graph databases and the reasoning capabilities of modern AI agents.