When should an AI workflow be tested again?
Test an AI workflow again when a change reaches it, and start by asking what changed. Find out which updates your team controls and which the provider applies. Then test typical tasks and a few known problems before a controllable change goes into regular use.
New AI releases came up all through the summit, and a new version can sound like a reason to hurry. For an existing workflow, the first question is whether your current evaluation still applies.
Ask the service owner how updates reach your environment. Record whatever version or configuration information you can see, and don't promise more visibility than the product gives you.
Those test tasks should look like the real work, and the stop cases belong in there too. Run them before a controllable change goes live and judge the results against your acceptance criteria, not against how the old tool used to sound.
A team can misunderstand a new behavior even when the underlying capability improves, so check whether instructions, permissions or training need to change. Keep a route for pausing the workflow if a change causes trouble.
I'd treat repeated evaluation as a habit that lets you notice when yesterday's approval no longer answers today's question. It's a proposed practice, with no promise of error free work.
What to try this week
List the AI workflows your team relies on and note who applies each update. For one of them, build a test set from real tasks and run it before the next controllable change. Write down how you'd pause it.