Error handling is the set of decisions about what should happen when a step fails: the network drops, an API returns an error, data arrives in the wrong shape, or an AI model returns something unusable.
Good handling does a few things. It detects the failure instead of ignoring it, records enough detail to diagnose it, decides whether to try again (retry logic), stop, or take another route, and tells a person when human action is needed. It also keeps the system in a safe state, which is where idempotency helps.
In a workflow, that often means an error branch or an alert channel for failed runs, plus clear messages that include the input and the failing step. Distinguish temporary errors, which may succeed on retry, from permanent ones, such as invalid data, which never will. For AI steps, validate the output before trusting it, since a model can return a wrong or malformed answer.
Related: Workflow Automation, Webhook, Tool Calling.