Running AI agents in Kubernetes?

Running AI agents in Kubernetes? You’ve likely seen it: actions are invisible, drift creeps in, and costs spike without explanation.

Agentic AI is powerful, but in production clusters, it can feel like a black box. Without fine-grained telemetry (agent ↔ tool calls, inference usage, policy context), SREs lose the ability to debug, govern, or even trust the outcomes. A single misfired reconciliation can cascade into failed deployments, while untracked GPU calls inflate spend unnoticed. The ecosystem is now evolving toward context-aware runtimes for agentic AI:Identity binding per agent → trace who/what made the changeObservability hooks → logs, traces, and metrics across agent-tool-LLM pipelines Governance layers → enforce limits, record cost attribution, and audit every action.With these in place, operators can answer critical questions: Which agent updated this Ingress? Why did inference costs spike yesterday? Where did the failed rollout start?

This is exactly the philosophy behind opsworker.ai: AI-driven ops should be auditable, observable, and governed by default—not a leap of faith. By embedding observability and policy into automation, we give teams resilience without losing control.