Using RL Agent to Detect and Remediate ETL Pipeline Failures - Anna Marie Benzon
An RL agent autonomously diagnoses and remediates ETL pipeline failures with safety bounds, cutting manual recovery from ~2.5 days.
“The central question is simply whether an agent can act, but whether it can act usefully, explainably, and within the boundaries that an operation would actually trust.”
A capstone project demonstrates an RL-based agent on AWS (Glue, EventBridge, Lambda, CloudWatch) that detects ETL job failures, classifies them, and proposes bounded remediation actions gated by a safety layer, escalating novel or high-risk cases to humans. It matters as a concrete pattern for trustworthy, explainable autonomous remediation in data ops, reducing a ~2.5-day manual recovery baseline, though it remains an early synthetic-data implementation rather than a major industry signal.