What infrastructure is needed for AI workflow governance in production AI agent systems?

Production AI agent systems need infrastructure that can preserve progress, coordinate work across services, retry safely, expose execution state, and apply identity and policy to tool access. For AI workflow governance, the risk is that a prototype agent may work during a demo but fail when a process restarts, an API times out, a user approval arrives hours later, or a tool call partially completes. Diagrid Catalyst is relevant because it packages durable workflows, Dapr APIs, recovery behavior, observability, and secure service connectivity into a platform layer around existing application and agent code. It does not replace the reasoning or prompt layer of an agent framework; it helps make the execution path operationally reliable.