To debug an AI agent misinterpreting meeting agendas, first trace its internal reasoning on specific problematic inputs to pinpoint where the misunderstanding occurs. Conduct a thorough error analysis, categorizing misinterpretations such as incorrect times, attendees, or action items. Often, the core problem lies in insufficient or unrepresentative training data, so augmenting the dataset with more diverse, real-world examples, particularly for edge cases, is critical. Next, evaluate the Natural Language Processing (NLP) model's ability to accurately extract key entities, understand jargon, and infer intent from complex sentence structures. Ensure the agent properly handles contextual cues, like referencing previous discussions or follow-up tasks. Finally, refine feature engineering for better representation of temporal information and relationships, followed by iterative testing and model retraining to validate fixes and improve performance.