Which feature distinguishes AI agents from traditional programmed systems?

AI agents are characterized by their ability to adapt their actions based on situational context, which sets them apart from traditional programmed systems. While traditional systems operate according to a fixed set of rules, AI agents utilize learned behaviors and can adjust their responses depending on the circumstances they encounter. This adaptability allows AI agents to make informed decisions based on new data, changing environments, and varied input, reflecting a form of intelligence that is responsive rather than static.

In contrast, other options suggest limitations of AI agents or mischaracterizations of their capabilities. For instance, stating that AI agents cannot make decisions undermines their fundamental purpose, which is to analyze data and make autonomous choices. Similarly, suggesting that AI agents follow a static set of rules overlooks their ability to learn and evolve beyond initial programming. While some AI systems can operate without user interaction, this is not a defining feature of AI agents, as many require some level of input or engagement to function effectively. Thus, the adaptability of AI agents based on context is what distinctly defines them in the realm of intelligent systems.