Durable Execution
What is the difference between replay and restart in durable execution?
Replay resumes a workflow from the last persisted checkpoint, re-executing deterministic steps to reconstruct state without redoing external side effects. Restart begins from scratch, losing all prior progress. Catalyst uses replay after crashes or deploys, so your agent workflow continues from where it left off, not from zero. Replay is efficient because only nondeterministic code (like random numbers) must be avoided; side-effecting calls are skipped via cached results.
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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.