AI Agents for DevOps on Kubernetes Need Real Engineering, Not Magic

Source: DZone DevOps

In a real Kubernetes cluster, incidents rarely appear as a single, clean alert. They arrive as waves of Kubernetes events, latency spikes, pod restarts, rollout failures, and unpredictable autoscaling behavior all at once. The hard part is usually not “Can we fix it?” but “Can we understand what’s happening fast enough to make a safe decision?”

AI agents for DevOps can help here — but only when they sit on solid engineering foundations. They should compress the early correlation and triage phase, not take opaque, unsafe control of production.