aiops-autonomous-incident-response

Automating incident diagnosis for Kubernetes CrashLoopBackOff and OOMKilled alerts uses an alert-driven loop to classify severity and correlate pod logs, Prometheus metrics, and Airflow metadata. This autonomous incident response confirms the failure mode and produces an RCA draft to reduce MTTR.

Yes, you can use Slack approvals to gate risky self-healing actions during an Airflow incident response. The automated workflow executes only low-risk corrective actions immediately, while routing medium and high-risk recovery operations through Slack to require explicit human approval before execution.

The best way to generate an RCA draft from Prometheus alert signals and failed DAG tasks is through an autonomous incident response workflow that correlates metrics, logs, and events into a failure taxonomy. This produces a structured incident report with a timeline and recommended runbook actions suitable for postmortem follow-up.

Yes, autonomous incident response works with both Kubernetes pod failures and Airflow DAG task errors. The automated diagnosis workflow targets Kubernetes events like CrashLoopBackOff and OOMKilled, alongside Airflow-based failure scenarios such as failed DAG tasks, by analyzing metadata and pod logs to confirm likely failure modes.

Routing recovery actions based on alert severity during a data platform incident is handled by classifying the alert and executing only low-risk corrective actions automatically. Medium and high-risk recovery actions are routed through Slack approval gating to ensure human oversight before any risky healing is executed.

You should not use fully automated self-healing for data platform incidents when the required recovery actions are classified as medium or high risk. These risky healing actions must be routed through Slack for explicit human approval, ensuring that potentially destructive or impactful operations are not executed without oversight.