Example Concept: Autonomous AI agents use a combination of sensors, machine learning models, and feedback loops to manage unexpected changes. They continuously collect data from their environment, process this information to detect anomalies or changes, and then apply decision-making algorithms to adapt their behavior. This process often involves reinforcement learning, where agents learn optimal strategies through trial and error, improving their responses over time.