Using RL Agent to Detect and Remediate ETL Pipeline Failures - Anna Marie Benzon
Jun 29, 2026 · 14:41
Anna Marie Benzon presents an RL-guided ETL pipeline health agent that automates failure diagnosis and remediation using deterministic anomaly detection, tabular Q-learning, and an external safety layer, achieving a 99.85% reduction in mean time to recovery (to 5.24 minutes) across 30 synthetic runs. The system detects schema drift, null-rate spikes, type changes, and runtime errors, then selects from six bounded actions (retry, coerce, rollback, quarantine, escalate, log). A key design principle separates deterministic rules for observable facts from learning for contextual action selection, with safety overrides outside the learned policy to prevent unsafe autonomy. Ablation results show the Q-learning policy matches a hand-defined deterministic policy (0% difference), while the safety override reduces non-escalation by 15.03 percentage points intentionally. The talk emphasizes that reliability comes from structured state, sensible logic, and guardrails, not from RL alone, and that escalation is a capability, not a failure. Future work involves shadow-mode deployment on production incident traces.
