--- schema: 1 type: video title: >- WTF Is the Context Layer? The Missing Infrastructure for Production Agents — Prukalpa Sankar description: >- 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 --- Watch along with the corrected transcript, summary, timestamps and resources on this talk's official AIE page: https://ai.engineer/talks/8G_1-3IO4ZQ-wtf-is-the-context-layer-the-missing-infrastructure-for-production-agents Join our upcoming AI Engineer conferences (see https://ai.engineer landing page): - AIEi Shanghai, November 5–6 — the first for meeting top Chinese labs and engineers - AIE CODE in SF, November 10–12 — the top AI Coding conference returns - AIEi Sydney, December 7–8 — the first AIEi run alongside NeurIPS 2026 in Sydney --- canonical: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ' markdown: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ.md' llms: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ/llms.txt' parent: 'https://liquidgold.cc/channel/aidotengineer.md' channel: aidotengineer updated: '2026-10-24T07:42:38.691Z' language: null children: [] videoId: 8G_1-3IO4ZQ durationSeconds: 1254 publishedAt: '2026-07-14T22:45:06.000Z' searchChunks: - ordinal: 0 text: null startSeconds: 158 endSeconds: 249 watch: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ?t=158' heldReason: rights - ordinal: 1 text: null startSeconds: 158 endSeconds: 249 watch: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ?t=158' heldReason: rights - ordinal: 2 text: null startSeconds: 259 endSeconds: 346 watch: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ?t=259' heldReason: rights - ordinal: 3 text: null startSeconds: 346 endSeconds: 357 watch: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ?t=346' heldReason: rights - ordinal: 4 text: null startSeconds: 454 endSeconds: 572 watch: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ?t=454' heldReason: rights - ordinal: 5 text: null startSeconds: 454 endSeconds: 572 watch: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ?t=454' heldReason: rights - ordinal: 6 text: null startSeconds: 583 endSeconds: 680 watch: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ?t=583' heldReason: rights - ordinal: 7 text: null startSeconds: 583 endSeconds: 680 watch: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ?t=583' heldReason: rights - ordinal: 8 text: null startSeconds: 692 endSeconds: 783 watch: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ?t=692' heldReason: rights - ordinal: 9 text: null startSeconds: 794 endSeconds: 873 watch: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ?t=794' heldReason: rights - ordinal: 10 text: null startSeconds: 794 endSeconds: 873 watch: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ?t=794' heldReason: rights - ordinal: 11 text: null startSeconds: 885 endSeconds: 969 watch: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ?t=885' heldReason: rights - ordinal: 12 text: null startSeconds: 885 endSeconds: 969 watch: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ?t=885' heldReason: rights - ordinal: 13 text: null startSeconds: 980 endSeconds: 1066 watch: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ?t=980' heldReason: rights - ordinal: 14 text: null startSeconds: 1066 endSeconds: 1076 watch: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ?t=1066' heldReason: rights - ordinal: 15 text: null startSeconds: 1076 endSeconds: 1177 watch: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ?t=1076' heldReason: rights - ordinal: 16 text: null startSeconds: 1177 endSeconds: 1187 watch: 'https://liquidgold.cc/watch/8G_1-3IO4ZQ?t=1177' heldReason: rights --- # WTF Is the Context Layer? The Missing Infrastructure for Production Agents — Prukalpa Sankar 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 --- Watch along with the corrected transcript, summary, timestamps and resources on this talk's official AIE page: https://ai.engineer/talks/8G_1-3IO4ZQ-wtf-is-the-context-layer-the-missing-infrastructure-for-production-agents Join our upcoming AI Engineer conferences (see https://ai.engineer landing page): - AIEi Shanghai, November 5–6 — the first for meeting top Chinese labs and engineers - AIE CODE in SF, November 10–12 — the top AI Coding conference returns - AIEi Sydney, December 7–8 — the first AIEi run alongside NeurIPS 2026 in Sydney --- Canonical: https://liquidgold.cc/watch/8G_1-3IO4ZQ · Channel: [AI Engineer](https://liquidgold.cc/channel/aidotengineer.md) > Search chunk text is held until public-source provenance is verified. ## Document chunks > Missing: no stored document chunks are available.