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Building Production Text-to-SQL for 70,000+ Tables: OpenAI’s Data Agent Architecture
Artificial Intelligence   Data Science   Latest   Machine Learning

Building Production Text-to-SQL for 70,000+ Tables: OpenAI’s Data Agent Architecture

Last Updated on February 3, 2026 by Editorial Team

Author(s): MKWriteshere

Originally published on Towards AI.

How OpenAI handles 600PB of data with self-correcting agents, six context layers, and closed-loop validation — a technical guide you can replicate

It’s 4:55pm.

Building Production Text-to-SQL for 70,000+ Tables: OpenAI’s Data Agent Architecture

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The article discusses OpenAI’s architecture for its internal data agent designed to handle complex SQL queries over large datasets (600PB across 70,000 tables). It highlights the importance of combining text-to-SQL capabilities with deep context and self-correction mechanisms to improve accuracy. Key elements include managing institutional knowledge, human annotations, and ensuring security through pass-through permissions. OpenAI emphasizes that building trust in the system requires a thoughtful design that continually evaluates its own outputs against established metrics and patterns.

Read the full blog for free on Medium.

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