Gorkem Sevinc

Traditional quality frameworks validate data at rest, after ingestion, after transformation, and at the end of the pipeline. But AI agents don't follow that path. They query on demand, pulling from Unity Catalog tables that may have drifted, and they ingest new data from APIs and external sources that have never passed through a quality gate. This is the control gap: the space between where you enforce quality and where AI actually consumes data. In this session, Gorkem Sevinc (CEO, Qualytics) introduces validate-at-use, the principle that data quality should be enforced when an agent reads or receives data. He walks through a live Databricks lakehouse pipeline where an agent encounters stale-validated data and net-new inputs, then closes the gap in real time using Qualytics as the data control layer. You'll leave with a concrete architecture pattern for making your pipelines agent-ready by embedding validate-at-use controls directly into the agent loop.