Installing a CRD and controller does not prove that an AI workload will become useful compute. Across implemented validation of Kubernetes Job, RayJob, PyTorchJob, JobSet, and AppWrapper with Kueue, apparently similar "stuck" workloads came from different layers: API and schema readiness, controller reconciliation and RBAC, or scheduler admission and quota accounting.

This session turns those integration lessons into a three-gate diagnostic method. It explains how to test API readiness, controller execution, and Kueue admission independently; how the status returned at each layer narrows the failure domain; and why admission alone is not proof that the intended compute will run. The examples are grounded in implemented findings, including framework-specific placement behavior, an RBAC gap that prevented controller reconciliation, and a Dynamic Resource Allocation quota-accounting gap where a workload could receive a GPU without consuming the expected Kueue quota.

Attendees will leave with a practical test sequence and failure taxonomy for onboarding heterogeneous AI workload controllers before investing in GPU packing, platform UX, or performance tuning.