AI Agent Doesn't Need a Bigger Prompt. It Needs a Data Catalog (aeroscissorz.github.io)

A developer has demonstrated that AI agents aimed at querying company data are hindered not by their ability to generate SQL but by a lack of contextual understanding of the data. The agent successfully executed queries but returned incorrect answers due to confusion over business-specific definitions, like what qualifies as "revenue" or "new customer." This highlights the need for a structured data catalog that serves as a context layer for AI agents, going beyond mere technical metadata to include business meanings, ownership, and usage norms. To address these challenges, the developer utilized Google's Knowledge Catalog to create a reliable inventory of datasets, augmenting it with detailed descriptions that convey business context. This approach ensures that information about what data exists and how it should be interpreted is readily accessible. By integrating verified queries to demonstrate how business questions are answered, the architecture allows AI agents to generate accurate responses without depending solely on complex prompts. Ultimately, this shift towards establishing a shared source of truth for data context is essential for enhancing the accuracy and relevance of AI agents in real-world applications.