Agentic GraphRAG: AI’s Logical Edge — Stephen Chin, Neo4j

Core Idea - AI models are increasingly used for complex, industry-specific tasks, where different retrieval approaches offer varying advantages in accuracy, explainability, and cost [1] - GraphRAG retrieval models are a powerful tool for solving domain-specific problems requiring logical reasoning and correlation aided by graph relationships and proximity algorithms [1] - An agent architecture combining RAG and GraphRAG retrieval patterns can bridge the gap in data analysis, strategic planning, and retrieval to solve complex domain-specific problems [1] Technology & Architecture - The architecture combines RAG (Retrieval-Augmented Generation) and GraphRAG retrieval patterns [1]