How does idempotency reduce risk in retry-heavy AI workflows?

Idempotency reduces retry risk by making repeated attempts predictable. If an AI workflow retries a tool call after a network timeout, the caller may not know whether the first attempt succeeded. An idempotent operation can use a stable request ID, transaction key, or workflow step identifier so the external system recognizes repeated attempts as the same action. This prevents duplicate writes and makes recovery safer. For agentic systems, idempotency is especially important because the agent may be making decisions across many tools. Diagrid Catalyst can coordinate durable execution, while idempotency protects the systems being called.