Inside OpenAI’s in-house data agent

Data powers how systems learn, products evolve, and how companies make choices. But getting answers quickly, correctly, and with the right context is often harder than it should be. To make this easier as OpenAI scales, we built our own bespoke in-house AI data agent that explores and reasons over our own platform.

Our agent is a custom internal-only tool (not an external offering), built specifically around OpenAI’s data, permissions, and workflows. We’re showing how we built and use it to help surface examples of the real, impactful ways AI can support day-to-day work across our teams. The OpenAI tools we used to build and run it (Codex, our GPT‑5 flagship model, the Evals API⁠(opens in a new window), and the Embeddings API⁠(opens in a new window)) are the same tools we make available to developers everywhere.

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.

Why we needed a custom tool

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.

As one internal user put it: