At our localization agency, I run this workflow every month for each client. It looks at the edits our professional translators made to the initial AI translations, sorts those fixes by the kind of mistake they correct, and turns the results into suggested updates for the AI configuration. The goal is that future AI translations for that client make fewer of the same errors. 1. Each month, an AI agent for each client analyzes what the translators edited in the initial AI translations. 2. The agent categorizes the changes according to the mistake each edit fixed. 3. Based on those categories, the agent suggests updates to the AI configuration so future translations are better. What it saves: our language teams no longer spend hours categorizing every single change by hand. It is also much quicker, and it finds patterns that would otherwise be missed when people do this work manually.