9 min read
5 hours ago
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Text-to-SQL was never the destination — it was a proof of concept. The real challenge is giving AI agents governed, scalable access to your entire data stack without rebuilding integrations from scratch every time
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The Connectivity Gap: Why Text-to-SQL is failing the enterprise-grade agent test
Picture the typical enterprise data stack: a PostgreSQL warehouse here, a Snowflake instance there, a sprinkling of REST APIs, a legacy Oracle database your team has been “planning to migrate” since 2019. Now picture an AI agent trying to answer a business question that spans all of them.
This is where Text-to-SQL breaks. It was never designed for this. Text-to-SQL is a translation layer — it converts natural language into a SQL statement and fires it at a single, pre-configured database. It assumes a known schema, a fixed connection, and an end user who just wants to SELECT something. None of those assumptions hold in a real enterprise data environment.
The failure modes are predictable and well-documented by now. Hallucinated column names. JOIN errors on schemas the model has never seen. No transaction safety. Zero awareness of data contracts or governance policies. And…