Debugging an AI agent that misinterprets product requirements begins with a thorough analysis of specific misinterpretations to identify common patterns or areas of consistent failure. Initially, review the original product requirements document itself for any ambiguities, inconsistencies, or unstated assumptions that might confuse the agent. Then, leverage AI interpretability tools to trace the agent's internal reasoning and attention mechanisms, revealing which parts of the input were misunderstood or overlooked. This often highlights issues with the agent's semantic understanding of technical jargon or complex logical dependencies within the requirements. Solutions typically involve refining the agent's training data with more diverse, clearly labeled examples that address the identified gaps, or meticulously improving prompt engineering strategies by providing clearer instructions, constraints, and few-shot demonstrations of correct interpretations. Finally, implementing a robust human feedback loop is crucial, allowing subject matter experts to correct erroneous outputs and incrementally fine-tune the agent for better accuracy. More details: https://www.smpn1-pamekasan.sch.id/redirect/?alamat=https://infoguide.com.ua/