Utility-based agents in AI operate by assessing multiple possible actions and choosing the one with optimal utility. Rather than fixed rule execution, they reason about trade-offs, risk, and rewards. This approach enhances flexibility in decision environments like resource allocation, scheduling, or autonomous systems. As complexity grows, these agents offer better balance autonomy and control.