Google’s Governance Agent project uses BigQuery column-level lineage to propagate trusted descriptions, tags, classifications, and quality metadata downstream.

Google’s Governance Agent project is built on Google Cloud Knowledge Catalog, BigQuery, and column-level lineage. It uses lineage records to determine where a downstream column originated and then carries existing governance metadata forward instead of requiring people to recreate it manually.

The project propagates column descriptions, governance tags, data classifications, and quality-related metadata from trusted upstream datasets. Google presented the approach as a response to the loss of context that occurs when raw tables are joined, filtered, reshaped into views, and then used to create additional downstream assets.

The underlying problem is that data meaning, personally identifiable information classifications, and quality context often do not travel with transformed data. As a result, a small number of core datasets may be thoroughly documented while downstream tables and views become progressively less documented even when the underlying data has not deteriorated.

The project is described as an automation approach to keeping governance metadata current in the background. The source presents Google’s architecture and intended workflow, but does not provide production-scale performance, customer adoption, operating cost, or accuracy measurements.