Why are retries not enough to make AI workflows reliable?

Retries are useful for transient errors, but they do not make an AI workflow reliable on their own. A retry only repeats a failed operation; it does not always know whether earlier work completed, whether a tool call caused a side effect, or whether rerunning an LLM step will produce the same decision. In agent workflows, repeated calls can create duplicate emails, API writes, tickets, payments, or inconsistent state. Reliable production workflows combine retries with durable state, idempotency, compensation patterns, observability, and clear recovery rules. This is why Diagrid positions Catalyst around durable execution rather than simple retry logic.