Durable Execution
How do I choose between durable execution and retry logic for my AI agent?
Use durable execution when your agent has long-running, multi-step workflows that must survive infrastructure failures—it persists state and replays steps automatically. Simple retry logic works for short-lived, stateless calls. Durable execution does not replace retries; it complements them by providing a recovery framework. For example, if an LLM call times out, durable execution resets to the last checkpoint, but you still need retry logic for the call itself.
Was this article helpful?
Your feedback helps improve Diagrid's FAQ experience.
Keep reading
More Diagrid FAQ articles
- 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.