January 29, 2026
Who checks the checker?
OpenAI's In-House Data Agent
OpenAI made a data butler; the crowd asks: trust the bot or fix the org
TLDR: OpenAI built an internal AI agent to quickly answer company data questions. The community split: some love the speed, others warn that without human-defined metrics and better organization, you can’t trust the results—plus rivals are already doing similar things. The stakes are accurate decisions across big teams.
OpenAI unveiled an in-house AI “data agent” that acts like a tireless office data butler—ask it a plain-English question, get answers fast across the company’s giant data stash. It’s powered by the same OpenAI tools developers use and promises end-to-end analysis, self-correction, and a memory that gets smarter over time. Cool flex, right? The comments immediately turned the spotlight to the real show: trust, control, and who’s actually to blame when numbers lie.
The hottest fight: “AI will fix messy data vs. data is messy because your org is messy.” One camp cheers the bot, with sjsishah quipping BI (business dashboards) is already “make believe land,” so let the agent handle the error-prone first steps. Another camp fires back: htrp says this isn’t a tech problem—it’s an org problem. Meanwhile, maxchehab drops the governance hammer: you still need human-approved ‘canonical metrics’ or non-technical teams will gamble on guesses. Rival energy enters as 0xferruccio waves Amplitude’s similar tool, Moda, and drops a demo. And spiderfarmer stirs the pot with a “what about Kimi?” drive-by. The meme of the day: “Who babysits the babysitter?”—because if the agent auto-fixes itself, who checks the checker? Drama aside, everyone agrees: faster answers are great—but only if you can trust them.
Key Points
- •OpenAI built an internal-only AI data agent to analyze its own platform data using natural language.
- •The agent uses Codex, GPT‑5, the Evals API, and the Embeddings API—tools OpenAI also offers to developers.
- •It serves a large internal data environment: 3.5k+ users, 600+ petabytes, and 70k datasets.
- •The agent performs end-to-end analytics with self-evaluation, adjusting when intermediate results look wrong.
- •It aims to reduce common SQL analysis errors and accelerate insights across multiple OpenAI teams.