When an AI agent consistently fails to triage product requirements, the immediate action should be to introduce human oversight for correction and analysis. This involves manual review of misclassified requirements to understand the failure patterns and provide a crucial feedback loop for the system. Utilize these corrected examples to retrain or fine-tune the AI model with an expanded and more accurately labeled dataset, focusing on the areas where it struggles most. It's also essential to refine the input data quality or consider better feature engineering for the requirements themselves, ensuring clarity and consistency in their presentation to the AI. Finally, exploring alternative AI models or implementing a hybrid human-AI triage process where the AI provides initial suggestions that humans then validate and refine can significantly improve accuracy and reliability. More details: https://reformedsermons.org/screenSelect.asp/dom/infoguide.com.ua