Durable Execution
What production gaps remain after adopting the OpenAI Agents SDK?

The OpenAI Agents SDK helps developers build agent behavior, but production teams still need to evaluate the surrounding infrastructure. Common gaps include durable execution, failure recovery, persistent workflow state, access control around tool calls, observability, deployment controls, and security review evidence. These are not the same as model or framework features; they are operating requirements for agents acting in real systems. Diagrid positions Catalyst as a complementary platform layer for agent frameworks, including OpenAI Agents, adding durable workflows, identity, policy, tracing, and governance so teams can move from implementation to production operations.
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- Durable Execution
What is durable execution in AI agent workflows?
Durable execution means an AI agent workflow can keep its progress even when a process crashes, a tool call fails, or the system restarts.
- Durable Execution
Why do production AI agents need durable workflows?
Production AI agents need durable workflows because real agent tasks rarely finish in a single clean request.
- Durable Execution
Is checkpointing enough for production AI agents?
Checkpointing helps, but it is usually not enough by itself for production AI agents. It explains the production reliability impact for AI agent workflows.