Snowflake builds internal semantic layer to unify data for AI agents

Snowflake's semantic layer addresses a foundational problem in enterprise data: when millions of data sources exist across a business, raw data alone does not answer questions reliably. The article illustrates this with the simple example of "active customer"—a single phrase that can mean three entirely different things depending on the table and metric definition used. This ambiguity compounds when AI agents are introduced, because agents operate at scale and speed, surfacing answers faster than humans can verify them. If one agent reports one number while a legacy dashboard shows another, no one knows which to trust.

The semantic layer solves this by sitting between raw data and downstream consumers (dashboards, AI agents, analytics tools) and translating business language into standardized metrics and dimensions. The article backs this with concrete results: in July 2025 alone, Snowflake's internal usage reached over 320,000 queries across teams, and benchmark data shows that semantic context raised text-to-SQL accuracy from 20% to more than 90%. Beyond accuracy, the semantic layer also improves performance and cost—agents spend less time discovering and sampling data, and consume fewer tokens when table relationships are pre-defined in the semantic view.

Snowflake's approach emphasizes treating the semantic layer with the rigor of production software: versioning it with dbt, testing it via the UI, and curating it relentlessly to match what users actually ask. The company is also moving toward interoperability by adopting Apache Ossie, an open standard, signaling that semantic layers may become a shared infrastructure layer across multiple platforms rather than a vendor-specific artifact.