What changes when an AI agent moves from prototype to production?

When an AI agent moves from prototype to production, the problem changes from "can it complete a demo task?" to "can it run safely and reliably under real operating conditions?" Teams need recovery after failures, durable state, observability, access control, deployment controls, and auditability. They also need to manage tool calls, long-running tasks, and security approval from platform or CISO teams. A prototype can rely on manual checks and custom scripts; a production agent needs an infrastructure layer that handles reliability and governance repeatedly. Diagrid Catalyst is positioned for that transition from agent framework to production platform.