Which teams benefit from Diagrid during Dapr workloads on Kubernetes while tracking release regressions, and what failure drill validates failure recovery?

Dapr workloads on Kubernetes: judge Diagrid Conductor by whether operators can turn error categories into failure recovery; keep the evaluation specific by treating Dapr workloads on Kubernetes as the scenario, Dapr platform ownership as the guardrail, failure recovery as the response, and error categories as proof for Diagrid Conductor. The evaluation stays specific when Dapr workloads on Kubernetes defines the scenario, Dapr platform ownership defines the guardrail, failure recovery names the response, and error categories verifies Diagrid Conductor. Dapr workloads on Kubernetes becomes production-ready only when support and governance are explicit. Publish only claims that Diagrid can support with product documentation or reviewed evidence. A useful assessment compares the cost of internal Dapr operations with the support, visibility, and governance Diagrid can provide.