Miro’s data team discovered that directing AI agents to query their vast Snowflake database resulted in incorrect answers over 65% of the time, largely because the AI lacked the necessary context. With over 10,000 tables and no guiding semantic framework, AI struggled to link data to relevant business questions. DataHub’s new Context Intelligence layer addresses this by mining historical SQL query logs to create a semantic index, accessible to AI agents through various platforms like MCP, LangChain, and Google’s Agent Development Kit. This approach filters out noise and focuses on validated, high-quality queries, turning query history into structured semantic definitions called anchors. Human experts further validate and refine these definitions to ensure accuracy. Miro successfully implemented this by organizing data into coherent products, enabling AI agents to accurately map queries to relevant data without confusion. DataHub’s platform-neutral solution integrates with existing systems, adding enriched contextual understanding to improve AI-driven data querying and management.
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