AI-on-AI monitoring is becoming infrastructure-level critical. As autonomous agent deployments scale beyond human oversight capacity, we're seeing AI supervisors emerge as a necessary layer for real-time quality control, security enforcement, permission management, and performance tracking. The architecture shift: instead of humans monitoring individual AI tasks, you need meta-AI systems that can parse logs, detect anomalies, and enforce policies across entire agent fleets. This isn't just about efficiency—it's about making large-scale AI deployment operationally feasible. Without this layer, you're basically running distributed systems with zero observability. The technical challenge: building supervisory AI that's robust enough to catch edge cases without introducing new failure modes. Think of it as the control plane for AI infrastructure.