Meta Knowledge Graph: A Self Learning Agentic System

Session track: Data Intelligence

Session time: 02:00 pm - 02:30 pm UTC November 12

Session description:

This talk presents a practical architecture for building harness-agnostic intelligent agent systems that can persist context, retrieve knowledge, and improve across sessions. Rather than treating memory as a standalone database, it frames agent intelligence as a loop: tools provide on-demand access to enterprise data and memory, hooks passively capture execution traces, metadata layers help agents discover and reason over available knowledge, and background consolidation turns raw activity into durable learnings and prompt improvements. The session will cover the core design patterns, tradeoffs between agent-driven and harness-driven orchestration, and how a knowledge graph can act as the shared substrate for provenance, retrieval, memory, and self-improvement.