How do you implement agentic AI for automated Kubernetes pod autoscaling optimization?

Kubernetes pod autoscaling optimization agents tune HPA and VPA configurations for optimal resource utilization and performance. Configure agents with access to Kubernetes metrics APIs, pod resource usage history, and application performance data. The agent analyzes historical resource consumption patterns to recommend optimal CPU and memory requests and limits. Tune HPA scaling thresholds and cooldown periods based on traffic patterns and scaling responsiveness requirements. Implement custom metrics-based autoscaling recommendations when CPU-based scaling is insufficient. Detect scaling oscillation patterns and recommend stabilization configurations. Generate autoscaling optimization reports showing resource efficiency improvements and cost impact. Track scaling event frequency and correlate with performance metrics to validate scaling configurations. Support recommendation testing in staging environments before production deployment.