AI Agents to Make Sense of Data at OpenAI

OpenAI's internal data platform spans tens of thousands of tables and hundreds of petabytes of data. It’s powerful, but navigating it without deep institutional knowledge is hard. In this talk, we’ll share how we built an AI agent that uses MCP, RAG, and vector search over platform metadata to intelligently explore this ecosystem: discovering relevant datasets, generating safe and correct queries, interpreting results, and delivering insights through natural language.

We’ll walk through the core architecture that enables this, including an index of all the tables in our data lake that our agent understands and integration with existing platform tools. You’ll also hear what we’ve learned from real adoption across teams like Data Science, Go-to-Market, and Finance, who now rely on the agent for debugging and analysis.

Finally, we’ll show how these capabilities extend into other data products such as dashboards, where conversational intelligence and on-the-fly recommendations bring an entirely new level of interactivity to analytical workflows.

Attendees will walk away with practical patterns for building data-aware AI agents, deploying retrieval-augmented systems in complex data environments, and driving sustained adoption of AI-assisted analytics.