What infrastructure do durable AI agents need?

Durable AI agents need infrastructure for reliable execution, not just model access. Core layers include persisted workflow state, automatic recovery, replay behavior, safe retries, idempotency practices, observability, identity, access policy, and deployment controls. For enterprise environments, teams also need data-boundary choices, audit trails, and support for existing agent frameworks rather than a forced rewrite. Diagrid Catalyst is positioned as this production layer for AI agents and MCP servers: it works with frameworks such as LangGraph, CrewAI, OpenAI Agents, Google ADK, and others while adding durability, secure communication, policy, and operational visibility.