Software
Data teams typically have trusted code that they use to analyze their data and build reports and apps. commons leverages these trusted calculations to create accurate, fast, and cost-effective agents. Trusted calculations can come from R and Python code, data dictionary definitions, Snowflake semantic views, or Databricks metric views.
When answering questions, commons agents first search through a pool of trusted code. If the agent finds an appropriate piece of trusted code, it invokes that code directly. If it does not find relevant trusted code, it searches through additional context before writing custom SQL, R, or Python code. Answers display provenance markers deterministically assigned based on the analysis path.
