Although it might seem that single-agent systems should be simpler than multiagent systems, when dealing with a fixed, complex task, the opposite is often the case (see Figure 1). Distributing control among multiple agents allows each agent to be simpler. No one agent has to be able to complete a given task on its own. Thus centralized, single-agent systems belong at the end of the progression from simple to complex multiagent systems in Sections 4 - 6. They are described here first because to many people single-agent (centralized) approaches are more intuitive than multiagent (distributed) ones.
In general, the agent in a single-agent system models itself, the environment, and their interactions. Of course the agent is itself part of the environment, but for the purposes of this article, agents are considered to have extra-environmental components as well. They are independent entities with their own goals, actions, and knowledge. In a single-agent system, no other such entities are recognized by the agent. Thus, even if there are indeed other agents in the world, they are not modeled as having goals, etc.: they are just considered part of the environment. The point being emphasized is that although agents are also a part of the environment, they are explicitly modeled as having their own goals, actions, and domain knowledge (see Figure 2).
Figure 2: A general single-agent framework. The agent models itself,
the environment, and their interactions. If other agents exist,
they are considered part of the environment.