Auto-remediation without memory fails because every incident gets treated as the first one. The AIOps engine reacts to symptoms, not patterns. It restarts the same pod, rolls back the same deploy, and scales the same service again and again. Without a memory layer that learns what worked, what backfired, and what the system actually needed, automation becomes a faster way to cause the same outage twice. The fix is not better automation. It is reliability intelligence that remembers. Your auto-remediation engine just restarted the same payments pod for the eighth time this quarter. It worked. Pager cleared. SLO held. Everyone went back to sleep. Nobody asked the obvious question: why does this keep happening? That gap, between resolution and understanding, is where modern AIOps quietly bleeds money. The promise of self-healing infrastructure was never wrong. The execution is. Most AIOps and auto-remediation tools today are reactive systems wearing AI clothing. They detect patterns. They run playbooks.…