When an AI agent consistently fails to deny customer emails, the first crucial step is to conduct a thorough analysis of its performance logs and decision-making process. This involves examining specific failure instances to pinpoint why denials were missed, whether due to insufficient or ambiguous training data, or misinterpretation of rejection criteria. You should then refine the AI's training dataset with more diverse and explicit examples of emails requiring denial, paired with clear, decisive rejection responses. Consider adjusting the AI's confidence thresholds for denial decisions and fine-tuning its natural language understanding capabilities to better identify triggers for negative responses. Implementing a human-in-the-loop review system for uncertain or repeatedly failed denial attempts can provide essential feedback, enabling iterative model improvement. Ultimately, this combination of data refinement, model tuning, and human oversight is vital for enhancing the agent's accuracy and effectiveness in handling denials. More details: https://twogarin.info/bitrix/rk.php?goto=https://infoguide.com.ua