Sponsored by: Atlan | The Enterprise Context Layer: Why Agents Fail — and the Fix — Demystified and Demoed

Everyone's talking about context layers. Nobody agrees on what one is — a semantic layer with a better name? A knowledge graph in a trench coat? AI is moving from pilot to production — and it made data teams into janitors: cleaning up hallucinations, debugging agents. But the context agents need most is what data teams already own. The definitions, the lineage, the institutional knowledge. That's not a cost center. It's your most strategic AI asset. You already have Databricks — maybe Unity Catalog too. But your agents still don't know which "revenue" definition to use, which tables to trust, or what the business actually means. The gap between pilot and production isn't a model problem. It's a context problem. Join Prukalpa Sankar and Varun Banka to see it live: dark Databricks tables become AI-ready — automated lineage mapped, AI-bootstrapped definitions added, Context Quality Score green — and a Genie query returns a governed response via MCP. All live. All on stage.