What should an AI agent runtime handle beyond model calls?

An AI agent runtime should handle more than prompts and model responses. In production, it should coordinate tool calls, preserve state, recover from failures, manage sessions, enforce identity and access policy, provide traces, and support deployment across the team's infrastructure. Model calls are only one part of an agent run; the harder production work is making the agent safe, observable, and resilient while it acts on external systems. Diagrid's framing separates agent reasoning from the production runtime layer: teams keep their chosen framework while Catalyst adds durable workflows, secure communication, policy, and operational visibility.