Building a Context Layer for AI Agents | Snowflake

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Snowflake's internal data environment generates petabytes of telemetry across every query, warehouse, and click — but without shared context, the same question yields different numbers from different tables. A semantic layer resolves this by sitting between raw tables and downstream consumers (dashboards, AI agents) and standardizing business language into governed physical schemas. The post describes how Snowflake built this using its own Semantic Views product: a top-level object that translates logical names, metrics, and AI metadata into physical table structures. Instead of exposing raw tables to Cortex Agents directly, semantic views ensure that a metric like 'active customer' has exactly one definition regardless of which agent or dashboard queries it. AI agents gain two concrete benefits from querying through semantic views: faster execution (less schema exploration at query time) and fewer hallucinated answers (agents can't fabricate metric definitions that don't exist in the semantic layer). This is a case of Snowflake dogfooding its own product, but the architectural pattern applies to any enterprise deploying AI agents on top of a data warehouse. This is vendor content, but the engineering detail is genuine and the problem it addresses — AI agents returning inconsistent answers because they interpret the same raw data differently — is one of the most common failure modes in enterprise AI deployments.