Agentic Loops in the Data Stack: From Pipeline Failure to Auto-Remediation

Session description

Every data engineer knows the 2 AM pipeline failure — the one nobody notices until Friday's report is wrong. In this session, we break down five AI agents that are changing how data teams operate: from monitoring pipelines 24/7 and catching schema drift at ingestion, to closing the gap between a production failure and its root cause in minutes. We'll walk through real implementation patterns, including a baseline-learning monitoring agent and a tool-use driven incident response loop, and discuss what the shift to agentic data engineering actually means for the way teams are built and how engineers grow. Whether you're evaluating agents for your platform or already running them in production, you'll leave with concrete patterns you can apply immediately.