AI Hallucinations Halted by New Context Layer

AI agents faced significant challenges in understanding complex data environments, with one instance showing over 65% inaccuracy in Snowflake. The core issue was a lack of contextual understanding, especially when dealing with over 10,000 tables without a guiding semantic layer. DataHub has addressed this by releasing a Context Intelligence layer on 2026-05-28.

This new layer mines years of analyst query history to construct a semantic index. This index is then exposed to AI agents through various development kits, enabling them to access validated joins and business logic. The technology aims to transform historical query data into a retrievable knowledge base, stopping AI hallucinations.

The Context Intelligence layer is built upon DataHub's existing metadata management infrastructure, originally developed at LinkedIn. This foundation, used for lineage tracking across thousands of production deployments, allows the system to filter "golden queries" and invert SQL patterns into semantic anchors. Human validation is also integrated to refine AI-proposed context.

Miro, a digital collaboration platform, successfully implemented DataHub's solution to improve AI agent accuracy in its Snowflake environment. By organizing data into well-defined products and utilizing DataHub's context layer, Miro constrained agent access and significantly reduced incorrect query responses, enhancing overall data usability for AI.