I think this question is quite similar to the question previously asked and that is "how do you keep an AI agent output relevant and up-to-date while depending on having humans-in-the-loop as a significant part of keeping continuous improvements information up-to-date?" I'm paraphrasing what I remember that question to be.
The importance of an AI Solutions Architect isn't that we merely create targeted, robust, reliable, predictable, sustainable, scalable, etc... AI solutions and that have been designed through a rigorous, thoughtful, intentional, and successfully proven design process, but we are also, and I think most importantly, stewards and overseers that AI isn't running so autonomously that we have released our responsibility over what AI "does for us". In other words, we are trusting and thinking AI doesn't need human intervention because we say it's smarter, faster, better as what humans can do. This is a completely and wholly improper perspective to have of AI.
Therefore, the ultimate "tool" is really us staying engaged and by implementing ways to help US monitor that the AI solution stays true to it's design intent. I'm not an expert at knowing or leveraging these tools because I'm not formally trained in them, but they certainly make sense to me as I read about them, in keeping AI solutions "on track". They make sense because I have implemented these tools in overseeing projects and business intelligence platforms, just not knowing their professional labels. Here are some tools and techniques I have found, without their explanations, because I know you are the MBB expert that will recognize and understand what they are and their applications.
Control Plans, Statistical Process Control (SPC), Poka-Yoke (mistake proofing), standard operating procedures (SOPs), training and continuous education, visual management tools, audits and reviews, response and reaction plans, process ownership and accountability, leader standard work, regular communication, and continuous improvement culture (Kaizen).