When a traditional service fails, it usually returns a 5xx error. When a model fails, it might continue returning a 200 OK while serving hallucinated, biased, or nonsensical outputs. This makes AI incident response fundamentally different from standard SRE workflows.
To handle these "silent" failures, you need a runbook that connects observability dashboards with clear, actionable SLO targets.
Triage: Identifying the Failure Class
Is the failure in the infrastructure—perhaps a GPU pod OOMKilled event—the data pipeline, or the model weights themselves? Your runbook should provide specific commands for each stage:
# Check for model-server pod restarts and OOMs
kubectl get pods -n ai-production -l app=model-server --sort-by='.status.containerStatuses[0].restartCount'
# Check for KV cache pressure in vLLM logs
kubectl logs -f deployment/vllm-server | grep "cache_utilization"
Mitigation: Rollback and Failover
Once a failure is confirmed, the immediate goal is to restore service. This often involves graceful degradation, rolling back to a previous prompt version or failing over to a more stable multi-region cluster.
Final Takeaway
AI incident response is a muscle that must be trained before the incident happens. By building structured runbooks and automated triage paths, you ensure that your team can respond to model failures with precision rather than panic.
Is your team ready for the next production AI incident? We help teams build incident response runbooks, automated triage tools, and high-availability infrastructure. Book a free infrastructure audit and we’ll review your reliability and recovery path.
