Using RL Agent to Detect and Remediate ETL Pipeline Failures - Anna Marie Benzon

Automating ETL failure remediation requires a hybrid architecture that balances autonomous decision-making with strict operational guardrails. By integrating deterministic anomaly detection, tabular Q-learning for action selection, and external safety overrides, systems can effectively compress incident response loops for routine failures. This approach reduces Mean Time To Recovery (MTTR) by approximately 99.85% compared to manual workflows, as demonstrated in controlled synthetic benchmarks. Rather than replacing human judgment, the system prioritizes clarity and inspectability, delegating only well-defined, low-risk tasks to the agent while escalating novel or high-risk incidents for manual review. This design ensures that autonomy remains bounded by authority, transforming the engineering experience from midnight manual troubleshooting to event-triggered, validated recovery.