What happened
DataHub released its Context Intelligence layer, leveraging existing SQL query history to build a semantic index for AI agents. This new capability, built on DataHub's production-proven query-log infrastructure, exposes validated context to agents via platforms like LangChain, Google’s Agent Development Kit, and CrewAI. It addresses AI agents' high error rates, exemplified by Miro's experience where agents produced incorrect SQL over 65% of the time when querying complex data environments lacking a semantic layer. DataHub's system filters for "golden queries" to create "semantic anchors," guiding agents to accurate data assets, per CTO Shirshanka Das.
Why it matters
AI agent accuracy in enterprise data environments improves significantly by providing validated historical context. Data architects and platform engineers gain a mechanism to prevent agent hallucination, reducing the risk of incorrect data analysis or actions. This approach leverages existing query logs, cutting the need for extensive new semantic layer development. Procurement teams evaluating agentic solutions must prioritise context management, as unconstrained agents pose operational risks; this follows recent incidents where AI agents deleted production databases.
