Natural language to SQL has always been a brittle last mile for enterprise AI. Snowflake's new Cortex Sense proposes a different approach: instead of you manually defining a semantic layer, it automatically builds a working model of your business by observing how analysts and tools already query your data. This moves the bottleneck from manual curation to automated inference, tackling the context problem head-on.
the accuracy floor
The core problem with text-to-SQL is context, not syntax. Large language models are perfectly capable of writing SQL. What they lack is the deep, implicit knowledge of your business encoded in your database schema: which user_id joins to which account_id, what a "power user" actually means, and which cryptic enum value signifies a churned customer.
Without this semantic grounding, agent accuracy is predictably low. Internal Snowflake data and independent measurements from Anthropic both put the baseline accuracy for text-to-SQL agents without a context layer at around 21-25%. This is simply not reliable enough for production business intelligence. The traditional answer has been a manually curated semantic layer, but this creates its own bottleneck and struggles to keep pace as the business changes.
