Your team runs a customer-support agent in Azure AI Foundry that answers questions using a retrieval-augmented workflow over a product knowledge base. Over the past month, support leads report that the agent increasingly returns answers that sound confident but are not supported by the retrieved documents. You need to continuously detect and quantify this problem in production so you can alert on it and track it over time. Which approach should you implement?