https://drive.google.com/file/d/1fTSRdxgfz-ykrRa3eO9Iw48GHs1rO3p0/view?usp=share_link
AI agents don't search. They need to understand. And understanding requires a world model: a living representation of how your business actually works.

The knowledge graph is the right foundation. But three hard problems stand between today's knowledge graphs and the organisational world model enterprises need: bootstrapping context at scale across 80–150 systems, maintaining bounded context where the same term means different things to different teams, and building learning loops so the graph gets smarter with every agent interaction.

This talk shows how to solve all three — and transform your knowledge graph from static infrastructure into a Universal Context Layer: an open, portable organisational world model that governs every AI agent in your enterprise.

The knowledge graph was always the right answer. Now it has the right question.

Target audience: Knowledge graph practitioners and architects; data leaders evaluating AI agent infrastructure; enterprise AI and data engineering teams hitting the context wall in production deployments.

Learning objectives:
1. Understand why the context layer is the missing infrastructure for enterprise AI agents — and why the knowledge graph is its natural foundation
2. Learn the three hard problems (bootstrapping at scale, bounded context, learning loops) and concrete architectural approaches to each
3. Walk away with a practical 4-layer context audit framework to assess your organisation's AI readiness today