As AI systems evolve into agentic workflows, they move beyond simple inference APIs. Modern agents can invoke tools, interact with external systems, and execute multi-step reasoning chains. When deployed on Kubernetes, this introduces new challenges in observability, control, and runtime security.

Traditional monitoring approaches are not enough to explain agent behavior. Logs and metrics alone cannot answer key questions: What actions did the agent take? Which services or tools were invoked? Did execution remain within expected boundaries? Was sensitive data exposed?

This session focuses on building runtime visibility and policy enforcement for agentic AI workloads on Kubernetes. We will explore how OpenTelemetry can trace agent workflows across distributed services and how KubeArmor can enforce runtime policies at the container level to prevent unsafe execution.

We will also discuss Kubernetes-native guardrails such as namespaces, RBAC, and workload isolation to reduce blast radius and maintain control in production environments.

Through practical architectural patterns, attendees will learn how to transform opaque, black-box AI systems into observable, accountable, and secure agent workloads running in cloud-native environments.