Test more than the happy path
Run examples with missing fields, duplicate events, unexpected text and an unavailable downstream service. Write down the expected response before running each case. This creates a useful acceptance test instead of a demonstration that only succeeds on carefully chosen inputs.
Inspect what the AI changed
Show source values beside generated values where possible. Ask whether the model introduced a price, deadline or commitment that was never in the input. Review steps should make those differences visible rather than require a person to reconstruct them from several screens.
Make recovery an ordinary operation
An operator should know how to pause the workflow, find a failed item and resume from the right point. Keep the original input and relevant run history. Avoid retries that silently repeat payments, messages or record creation.
Measure the useful result
Track completed outcomes, review time and corrections. Raw execution count says little about business value. A workflow that processes more items but creates more cleanup may not be an improvement. Keep a manual route available until the configured process has earned confidence.
Practical guidance for this comparison. The examples describe work to configure and validate, not a tested product deployment. Editorial method and product sources.