Stephen Chin of Neo4j argues that traditional RAG is insufficient because AI agents lose the reasoning behind past decisions. He introduces Context Graphs as a solution to capture the 'why' behind decisions, creating a queryable system of precedent that provides grounded, explainable, and auditable results.
Dhravya Shah, founder of Super Memory, details the evolution of his company from a simple RAG-based consumer app to a sophisticated, open-source context infrastructure for AI, and introduces a novel hooks-based memory solution for OpenClaw.
Graph analytics expert Amy Hodler explores the power of graph data structures, covering fundamental concepts, graph algorithms like PageRank, and their application in fraud detection and supply chain optimization. She delves into the emergence of Graph RAG for enhancing AI systems and discusses the future of graphs as memory for AI agents and in causal inference.