How OpenAI handles 600PB of data with self-correcting agents, six context layers, and closed-loop validation — a technical guide you can replicate
20 min readJust now
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It’s 4:55pm.
Someone pings you: “What was WAU on Oct 6, 2025? Compare it to DevDay 2023. Round to the nearest 100M. I need it for the 5pm meeting.”
You can write SQL. But you can’t, in five minutes, untangle which table is canonical, which users should be included, how the metric is defined this quarter, and whether a logging incident made last week’s numbers weird. That’s the real job. The SQL part is almost trivial compared to navigating your data warehouse’s institutional knowledge.
OpenAI recently published a clear loo…
How OpenAI handles 600PB of data with self-correcting agents, six context layers, and closed-loop validation — a technical guide you can replicate
20 min readJust now
–
Press enter or click to view image in full size
Image Generated by Author Using AI
It’s 4:55pm.
Someone pings you: “What was WAU on Oct 6, 2025? Compare it to DevDay 2023. Round to the nearest 100M. I need it for the 5pm meeting.”
You can write SQL. But you can’t, in five minutes, untangle which table is canonical, which users should be included, how the metric is defined this quarter, and whether a logging incident made last week’s numbers weird. That’s the real job. The SQL part is almost trivial compared to navigating your data warehouse’s institutional knowledge.
OpenAI recently published a clear look inside their internal-only data agent that tackles exactly this problem. The system combines a text-to-SQL loop with deep data context and self-correction capabilities that go far beyond simple query generation. This article turns that write-up into a build guide you can adapt to your own warehouse, focusing on the architecture patterns and context layers that make the difference between a demo and a production system.