Incident remediation in Kubernetes environments is often slow, manual, and error-prone due to undocumented operational knowledge and inconsistent runbooks. While AIOps can detect anomalies, acting on them safely remains a challenge.

This talk presents a spec-driven approach to automated incident remediation, where operational intent is defined as structured specifications and executed through Backstage as the system of record. We show how incident signals from Kubernetes observability tools are mapped to remediation specs, enriched with AI-assisted analysis, and executed through deterministic workflows.

Using real Kubernetes failure scenarios, attendees will see how specs flow from Backstage into AIOps pipelines to trigger safe, auditable remediation actions. The session focuses on reducing MTTR while maintaining operator trust and platform safety, combining AI insights with explicit engineering controls.