Snowflake Semantic Views for AI agents: define the business question before the view

A Snowflake team builds a semantic view and connects it to an agent. The agent writes SQL, but the team cannot tell whether it used the intended metric definition or merely found a plausible table. A recent practitioner question asks how to scope one view and how verified queries interact with generated SQL. That is the right operational concern: a semantic view is a contract only if the consuming workflow honors it.

Begin with a bounded question set

Collect ten to twenty real business questions from the people who will use the agent. For each, specify the measure, grain, time zone, filters, join path and expected answer shape. Group questions by business domain and ownership. One enormous view may expose incompatible definitions and make evaluation hard; too many narrow views can force the agent to choose among near duplicates. Scope follows the decisions users need to make.

Snowflake's semantic view best practices provide the product-specific modeling guidance. Apply them to a small domain first. Document synonyms, relationships and measures in business terms, then have a domain owner sign off on ambiguous words such as “active,” “customer” and “revenue.”

Treat verified queries as tests

A verified query is useful evidence of intended behavior, but its existence does not prove every answer used that exact query. Keep a review set with the question, expected SQL or result constraints, generated SQL, answer and reason for any deviation. Test paraphrases and boundary cases: empty periods, duplicate customers, refunds, slowly changing dimensions and permission-limited users.

If an agent repeatedly bypasses the right definition, inspect view scope and question ambiguity before adding more examples. The fix may be a missing relationship, competing measures or a question that should be answered by a different domain view.

Give the buyer a measurable handoff

Ask an implementation partner for a semantic contract, evaluation questions, access rules, a change owner and a regression process. Demonstrate a question that should be refused or clarified. Reliable analytics agents need boundaries as much as fluent answers.

Adovo’s view

The semantic layer is where a company decides what its numbers mean. An agent can accelerate the conversation, but it cannot settle a disputed definition by writing faster SQL. Make the question, metric and owner agree first. Then the view becomes an asset instead of another place for ambiguity to hide.