Agent Memory & State
How do I identify which agent state must be replayable vs recomputable?
Replayable agent state is any data required to resume and correctly execute a workflow from a saved checkpoint. Catalyst’s AI-native, agentic durable execution, built on Dapr and covering both deterministic and probabilistic work, mandates retaining this critical state, while recomputable state such as transient LLM inference outputs can be regenerated on demand without persistent storage. Misclassifying these states can lead to broken workflow replay or unnecessary persistent storage overhead.
Was this article helpful?
Your feedback helps improve Diagrid's FAQ experience.
Keep reading
More Diagrid FAQ articles
- Agent Memory & State
How do workflow state, conversation memory, and retrieval corpora differ for AI agents?
Explain how workflow state, conversation memory, and retrieval corpora differ for AI agents, including their unique roles in durable execution systems.
- Agent Memory & State
Which agent memory and state should be included in durable execution?
Outline which types of agent memory and state are appropriate for inclusion in durable execution layers for production AI agent workloads.
- Agent Memory & State
What agent state survives a workflow crash or restart?
Explain which agent state is preserved following a workflow crash or unexpected restart, and how Catalyst manages state persistence.
