An AI infrastructure agent entered a feedback loop where it continuously provisioned cloud resources to address perceived capacity issues, resulting in runaway costs.
An AI-powered infrastructure management system was deployed to automatically scale cloud resources based on demand. A monitoring glitch caused the system to receive inflated CPU utilization metrics. The AI interpreted this as a capacity emergency and began provisioning additional servers. As more servers came online, the faulty monitoring system reported even higher aggregate utilization (since it was now summing more servers). This created a feedback loop where the AI continuously provisioned resources to address a non-existent capacity problem.
The AI was given autonomous provisioning authority without rate limits or sanity checks. Monitoring data was trusted without validation against other signals. No maximum boundary existed for automatic provisioning decisions.
$180,000 in unexpected cloud charges over 14 hours. Thousands of unnecessary server instances provisioned. Cloud provider quotas exhausted, blocking legitimate provisioning. Manual cleanup required across multiple regions.
Runplane would evaluate each provisioning request against cumulative limits. When the AI attempts to provision the 11th server in an hour (against a policy limit of 10), the action would be blocked and escalated for human review. Cost-based policies could also trigger when projected spend exceeds thresholds, regardless of the AI's reasoning.