Our data agent lets employees go from question to insight in minutes, not days. This lowers the bar to pulling data and nuanced analysis across all functions, not just by our data team. Today, teams across Engineering, Data Science, Go-To-Market, Finance, and Research at OpenAI lean on the agent to answer high-impact data questions. For example, it can help answer how to evaluate launches and understand business health, all through the intuitive format of natural language. The agent combines Codex-powered table-level knowledge with product and organizational context. Its continuously learning memory system means it also improves with every turn.

In this post, we’ll break down why we needed a bespoke AI data agent, what makes its code-enriched data context and self-learning so useful, and lessons we learned along the way.

OpenAI’s data platform serves more than 3.5k internal users working across Engineering, Product, and Research, spanning over 600 petabytes of data across 70k datasets. At that size, simply finding the right table can be one of the most time-consuming parts of doing analysis.

“We have a lot of tables that are fairly similar, and I spend tons of time trying to figure out how they’re different and which to use. Some include logged-out users, some don’t. Some have overlapping fields; it’s hard to tell what is what.”