A lot of businesses that use AI for real work have added a safety net. A second model reviews the first model's output, or a script checks that every quote in a summary actually appears in the source document, or a person signs off at the end. That is good practice. The uncomfortable question is how you know the safety net catches anything.
Usually nobody knows. The review step runs, it passes most things, and everyone assumes the passes mean the work was right. A checker that approves everything looks exactly like a checker that is working well on good input.
Plant errors you already know about
Software engineers have a name for the fix: mutation testing. You take work you know is correct and deliberately break it in small, realistic ways, then run it through your review step and count how many of the breaks get flagged.
For an AI workflow, the planted errors should look like the mistakes AI actually makes. Change a dollar figure. Swap two dates. Insert a quote that sounds right but isn't in the source. Keep a real quote and attach it to the wrong page or section. Drop a key condition from a contract summary, such as a deadline or an exclusion. Each of these is a mistake a reader would trust.
Run it like a small experiment
Take five documents your process has handled correctly. Make three or four altered copies of each, with one planted error per copy, and keep a list of what you changed. Run all of them through the review step exactly as it runs in production. Then compare what was flagged with your list.
Anything that gets through tells you something specific. If fabricated quotes slip past, your checker is probably reading the summary without going back to the source. If wrong numbers slip past, it probably isn't recalculating anything. Those are fixable problems once you can see them.
What this won't tell you
A checker that catches every error you planted can still miss a kind of error you didn't think to plant. Mutation testing measures the net against the holes you imagined. Keep adding new error types as real mistakes turn up in your work, and rerun the test whenever you change the model, the prompt or the documents the process handles.
The takeaway
Set aside an afternoon to try to fool your own review step. If it catches everything, you have earned the confidence you were already assuming. If it doesn't, you found the gap in a test file instead of in front of a client.