Data You Can Trust: Agentic Data Quality Monitoring from Lakeflow Jobs to Genie

Organizations have more data than ever — but less confidence to use it. Traditional quality checks rely on manual rules applied to a handful of tables, leaving blind spots across the data estate.

In this session, we’ll show how Databricks reimagines data quality with an agentic AI approach. Instead of writing rules, AI agents learn your data’s normal behavior and seasonlity. Data quality anomalies are detected automatically and scale with your data.

Detection is only the first step. We’ll cover how quality signals integrate directly into Lakeflow Autopilot for automated root cause analysis, tracing anomalies upstream to the responsible job so engineers fix the right thing fast. And to prevent the business from using unreliable datasets, we’ll show you how health is surfaced in Genie, warning business users in real time when data behind their answer may be stale, before a critical decision is made on bad numbers.