https://video.ut0pia.org/videos/watch/deb20b7d-777c-492c-9b78-bff08096c05b In the last two years, models have gotten exponentially smarter. Two years ago they couldn't pass the bar. Today, top 1% of test scorers. And yet most agents still can't answer a simple business question correctly. You ship a demo that works. You deploy it. The business abandons it in a month. The missing variable is context: the business definitions, procedural knowledge, and operational norms that make a human expert valuable. Drawing on hundreds of production deployments, Prukalpa Sankar will break down what it actually takes to give agents contextual intelligence — and get them past the demo stage. She'll walk through the architecture of a context layer: how context repos work (versioned, testable, portable), how simulation environments catch failures before deployment, how agent traces compound back into shared context, and why context engineering scales where fine-tuning and prompting don't. She'll also cover why your context needs to be open (MCP, Iceberg, deploy to any framework) — and what happens when it isn't. Prukalpa SankarFounder & Co-CEO · Atlan X/Twitter · LinkedIn Prukalpa Sankar is the Founder & Co-CEO of Atlan, the context layer for AI. She's been early to a defining idea of the AI era: context is king. AI systems are only as good as the business context behind the data they rely on. Under her leadership, Atlan has become a Leader in the Gartner Magic Quadrants for both Data & Analytics and Metadata Management, serves 300+ enterprises including Mastercard, GM, JPMorgan Chase, and Nasdaq, and has raised $200M+ from Sequoia, GIC, and Salesforce Ventures. Before Atlan, Prukalpa co-founded SocialCops, the world's largest government data lake powering the UN's SDG monitoring — recognized by the New York Times and the World Economic Forum. She's been featured in Forbes 30 Under 30 and Fortune 40 Under 40. Timestamps 0:12 Introduction: The Context Moment, 1:51 Why AI Agents Struggle with Business Context, 3:19 Performance = Intelligence + Context, 4:27 The Human Learning Model: Lessons from Maya, 7:20 Evolution of Agent Architecture at Atlan, 10:00 The Challenges of Isolated Agent Systems, 11:06 Transitioning to General Purpose Agents, 12:43 Marketing Team Case Study: The Context Layer, 14:36 The Challenges of Context Engineering, 15:31 Defining the Context Layer: The GitHub for Context, 16:43 Compounding Learning Loops and Traces, 17:18 How to Start Building Your Company Brain, 18:07 Defining the Context Layer Architecture, 19:31 Conclusion: Context is IP Thu, 16 Jul 2026 03:14:47 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://video.ut0pia.org https://video.ut0pia.org/lazy-static/avatars/0287a09a-aae7-4840-9843-b416426e7046.webp https://video.ut0pia.org/videos/watch/deb20b7d-777c-492c-9b78-bff08096c05b All rights reserved, unless otherwise specified in the terms specified at https://video.ut0pia.org/about and potential licenses granted by each content's rightholder.