Multi-agent systems primarily involve:

Multi-agent systems are characterized by the interaction and collaboration of multiple autonomous agents, which can include AI entities, working together toward a common goal or to solve specific problems. This collaborative aspect is crucial, as it allows these agents to share information, distribute tasks, and leverage their individual strengths to achieve more complex outcomes than any single agent could accomplish alone.

The nature of multi-agent systems supports a variety of applications, such as distributed problem-solving, where agents communicate and coordinate their efforts. These systems can also adapt to dynamic environments, allowing the agents to respond to changes and challenges in a flexible manner.

The other scenarios do not encapsulate the essence of multi-agent systems. A single entity performing isolated tasks suggests a more traditional, singular approach rather than collaboration. A group of human agents making decisions implies a human-centric approach rather than the focus on autonomous AI entities. A centralized system managing all operations contradicts the decentralized and cooperative principles fundamental to multi-agent systems, where agents operate independently yet contribute to a collective effort. Thus, the collaboration among multiple AI entities to solve problems is indeed the defining characteristic of multi-agent systems.