Agents are systems capable of perceiving their environment through sensors, reasoning about their sensory input using some characteristic reasoning process and acting in their environment using effectors. When one or more agents control the objects that comprise a 3D virtual world, the result is a dynamic, adaptive environment that changes in response to users? actions. We have experimented with three different agent models for this purpose: a swarm model, a cognitive model and a motivated agent model. Each of these models differs in the complexity of its implementation and can thus be used to produce dynamic virtual environments of differing behavioural complexity. This paper introduces a schema for characterising the implementation and behavioural complexity of agent models for dynamic virtual environments. We apply this schema to the agent models we have studied to reveal their advantages and disadvantages and identify directions for future work.