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Diving Through Data at OpenAI: How a Data Agent Navigates 70,000 Datasets (27 minute video)
OpenAI successfully reduced reliance on model raw intelligence by building an internal data agent that prioritizes schema lineage and continuous learning.
What: OpenAI created an internal agent to navigate 70,000 datasets by integrating SQL queries with metadata, lineage, code context, and past corrections. It emphasizes confidence scoring and clear SQL outputs to improve reliability.
Why it matters: This underscores a growing shift in AI development from 'model-first' strategies to 'context-first' engineering where internal data quality and provenance become the primary determinants of agent performance.
Original article
OpenAI built an internal AI data agent that answers complex data questions by combining warehouse queries with rich context from schemas, lineage, code, company knowledge, memory, and live sources, while showing its assumptions, SQL, citations, and confidence. The big lesson is that reliable data agents depend less on raw model intelligence and more on giving them the right context, continuously learning from corrections, and testing them with strong evals.
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