AI infrastructure agents are exciting, but they are also difficult to trust. A Kubernetes debugging agent can generate a remediation plan, but if we cannot inspect its iterations, validation results, latency, artifacts, and failure modes, the system becomes a black box. That is a problem, especially when the agent is working near infrastructure. I built Kube-AutoFix as an autonomous Kubernetes SRE agent prototype that uses structured outputs, Pydantic validation, YAML safety checks, dry-run support, and namespace isolation to reduce risky behavior. Recently, I added an MLflow observability layer so each agent run can be tracked, inspected, and compared. This article explains how I added optional MLflow tracking to Kube-AutoFix and why observability is essential for evaluating AI infrastructure agents. Why You Should Learn This Adding observability to AI agents moves you from "vibes-based" testing to rigorous, data-driven engineering.…