When an AI agent consistently fails to route product requirements, the first crucial step is to diagnose the underlying problem. This could involve issues with input data quality, ambiguities within the requirements themselves, or limitations in the AI model's understanding or training data. Focus on refining the prompt engineering or the structured format of the requirements, ensuring clarity and consistency for the agent. If the model is the bottleneck, consider retraining or fine-tuning the AI agent with more diverse and specific examples relevant to your product categories and routing logic. Implementing a human-in-the-loop validation process can provide immediate corrections and invaluable feedback for continuous AI improvement. Additionally, evaluate if a hybrid approach, where complex or ambiguous requirements are flagged for manual review, is more effective until the AI's accuracy significantly improves. More details: https://www.lmgdata.com/LinkTracker/track.aspx?rec=[recipientIDEncoded]&clientID=[clientGUID]&link=https://infoguide.com.ua