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.
Biswaroop Bhattacharjee from Prem AI discusses Cortex, a novel AI memory system inspired by human cognition. The conversation explores moving beyond traditional flat memory structures to hierarchical, context-aware systems that enable more sophisticated and less noisy retrieval for AI agents.
A deep dive into building expert AI systems using a Wisdom-Driven Knowledge Graph. This approach enhances Knowledge-Augmented Generation (KAG) to surpass traditional Retrieval-Augmented Generation (RAG) by enabling systems to understand, reason, and provide expert-level quantitative analysis and advice.
Dara Ladjevardian, founder of Delphi, discusses creating "digital minds" using an adaptive temporal knowledge graph. Inspired by Ray Kurzweil's theory of the mind, this technology aims to scale human thought and expertise, transforming content consumption from static feeds into interactive, conversational media while emphasizing the premium value of authentic human connection.