AUTO-OPS: Predictive Incident Detection and Self-Healing Remediation for Cloud-Native Enterprise Platforms

Abstract

Reactive alert handling and manual intervention continue to limit resilience in cloud-native enterprise platforms. This paper presents AUTO-OPS, an implemented AIOps decision layer that connects predictive incident detection, dependency-aware event correlation, non-compensatory admission gates, bounded remediation, rollback readiness, and post-action verification. The contribution is architectural and methodological rather than a new anomaly detector: anomaly ranking is separated from remediation authority, and no score can compensate for incomplete evidence, excessive blast radius, missing rollback, or failed policy authorization. A single-site production case study used a 90-day reactive baseline, a 60-day calibration period, and a 30-day blind window with 46 confirmed incidents. AUTO-OPS detected 44/46 incidents, achieved 39/44 top-attribution precision, reduced median time to detect from 18.0 to 6.1 min, reduced median time to remediate from 42.0 to 11.1 min, consolidated alert storms by 9.0x, and reduced false-positive alert groups from 34.0% to 12.0%. Quantitative ablations isolate the effects of correlation, forecasting, remediation, and gates. The results are bounded associational evidence from one non-concurrent deployment and do not establish universal or comparator superiority.