Why Enterprise AI Agents Need Versioned Business Semantics, Not Just SQL

Enterprise AI data agents face a critical gap when business metric definitions change over time, as valid SQL alone cannot determine which version of a term like 'Revenue' should apply to a historical query. A static semantic registry that simply overwrites old definitions loses crucial information about what changed, when it took effect, and who approved it. Experts recommend modeling semantic objects as immutable, versioned artifacts that separate a concept's stable identity from its evolving definitions. Each version should carry both a publication timestamp and an explicit effective date range, since finance teams may approve a new definition days after its intended start date. Resolving queries then requires a time-aware semantic layer that matches the right metric version to the right period, with a clear organizational policy on whether historical data is recalculated under old or current definitions.