Moving autonomous AI agents from a local IDE demo to a high-scale production Kubernetes environment introduces an entirely new class of infrastructure volatility. Unlike traditional deterministic microservices, agentic workloads exhibit unpredictable runtime behaviors, spontaneous burst capacity demands, and non-deterministic memory footprints that easily bypass classic horizontal pod autoscaling.

This session steps away from the AI hype to address the raw infrastructure reality that engineering leaders and platform teams face when agents go rogue. We will explore real-world patterns for isolating probabilistic AI workloads from core business logic using advanced Kubernetes scheduling, custom metric-driven autoscaling, and intelligent resource quotas. Attendees will analyze a blueprint for building resilient platform guardrails that prevent cascading cluster failures, manage vector database state sync issues during outages, and maintain systemic stability when AI logic behaves unpredictably.