Building AI agents to query your databases

In the world of AI agents, the ability to understand and analyze structured data is a game-changer. While large language models excel at understanding natural language, they struggle with quantitative analysis when data is presented as unstructured text. This is where Query Tables comes in – a powerful feature that enables agents to execute SQL queries on structured data, providing precise answers to analytical questions. At Dust, we've been on a journey to build and evolve our Query Table agent tool from a simple CSV file parser to a sophisticated system that can connect to enterprise data warehouses. 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. 0:00 / 0:17 1× The problem: why semantic search falls short for quantitative analysis. Before diving into our solution, it's important ... Read full article.