Data quality work is constant: monitoring, investigating drift, chasing broken dashboards, and responding to incidents after stakeholders already noticed the numbers are wrong. It keeps data engineers reactive and on call because the failure modes are subtle and the fixes require context.

AI coding agents can now do a large part of this work. They can write SQL validations, schema checks, anomaly detection rules, and even propose fixes.

The real blocker is operational safety: most data stacks make it hard to let an agent run changes against real production data without risking partial writes, messy staging, or hard-to-debug side effects.

In this webinar, we show how to use AI agents (Claude Code, Cursor, and similar tools) to continuously harden analytics pipelines with robust data quality checks.

We walk through a concrete workflow where an agent proposes checks, executes them on production-scale data in isolation, inspects failures, iterates on the logic, and publishes only when validation passes. When something fails, production stays unchanged and the failure state is preserved for debugging.

The goal is practical: fewer incidents, faster detection, less pager fatigue, and higher trust from downstream stakeholders. We focus on what teams can automate today, and what needs guardrails to stay reliable.

What you’ll learn
• Where AI agents can take real ownership in data quality (checks, drift detection, regression testing, remediation)
• Why “just add AI” fails on traditional stacks (unsafe execution, partial writes, brittle staging)
• How to run agent-generated checks safely on production-scale data, repeatedly • How validation gates and atomic publish reduce incidents and rebuild trust

Format
- 15 min: AI agents in data quality: what is realistic today
- 15 min: Live walkthrough: agent-driven checks on a real pipeline
- 15 min: Q&A

From Alerts to Agents Rethinking Data Quality with Claude Code