To debug an AI agent misinterpreting customer emails, we first collect specific examples of problematic interactions and their corresponding incorrect outputs. We then analyze these errors systematically to identify patterns, such as difficulties with jargon, sarcasm, complex sentence structures, or a failure to correctly infer the user's intent or context. This often reveals issues with the quality or diversity of the training data, or limitations in the model's ability to capture subtle linguistic nuances. Debugging involves iterative model fine-tuning, which includes enriching the training dataset with more diverse and correctly labeled examples, enhancing text preprocessing techniques like normalization and entity recognition, or adjusting the model's architecture. We also scrutinize the AI's confidence scores and attention mechanisms to understand where it misplaces focus. Finally, the agent is re-evaluated on a fresh set of unseen customer emails to confirm that the changes have rectified the misinterpretations and improved overall accuracy. More details: https://www.romyee.com/link.aspx?url=https://infoguide.com.ua