While many Kubernetes practitioners embrace automated deployment cycles, they remain reluctant to let systems manage CPU and memory resource rightsizing without human oversight. This trust gap creates significant operational challenges, specifically as the surging costs of AI inference workloads make manual optimization inefficient and increasingly expensive.