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Building AI agents to query your databases
How Dust's Query Tables agent tool evolved from parsing CSVs to parsing data warehouses, creating a unified SQL interface for AI data analysis.
This blog post details that journey – the technical challenges we faced, the architectural decisions we made, and how we've maintained a unified abstraction layer that makes it easy for our users to work with structured data regardless of its source. What makes this approach powerful is that it allows users to join tables from completely different sources – combining data from a CSV file with a Notion database and a Google Sheet in a single SQL query, something that would be difficult to accomplish manually. By making every query result a potential input for the next operation, users can build complex analytical workflows without leaving the conversation, seamlessly bridging data across previously isolated sources.
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