Semantic Layer: The Missing Layer Between AI Agents and Enterprise Data
Semantic Model Descriptions: The Missing Layer Between AI Agents and Enterprise Data The Hallucination Problem in Data Queries AI agents querying enterprise data warehouses face a fundamental challenge: context collapse . Without business semantics, technically correct queries produce business-incorrect results. Common Failures : Metric Ambiguity : "Revenue" could mean gross, net, recognized, or billed across different tables Calculation Errors : Agents average ratio columns instead of recalculating from components (e.g., averaging efficiency percentages instead of SUM(output)/SUM(input)) Schema Misinterpretation : Joining dimension tables directly causes query timeouts and incorrect aggregations Missing Thresholds : Without target values, agents can't interpret if 85% performance is good or concerning Impact : 40-60% of agent-generated queries return plausible but business-invalid answers. Semantic Metadata as Ground Truth Semantic model descriptions embed ...