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 Sankar Founder & Co-CEO · Atlan [X/Twitter](https://x.com/prukalpa) · [LinkedIn](https://www.linkedin.com/in/prukalpa) 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