Durable Execution
What operational challenges arise when running durable execution for AI agents?
Operationally, durable execution requires monitoring checkpoint storage and replay performance, as large state histories can slow recovery. Catalyst manages this, but you must design activities to be deterministic and idempotent to avoid replay issues. It does not automatically handle LLM rate limits or token costs—those remain your responsibility. Plan for capacity planning around state persistence and replay frequency.
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.