No, but there is an assumption that the representative agent is actually representative of the "average" of the agents participating in the market. So when we apply a model with this assumption to real world data, we are making an assumption that the "representative agent" described in the model is truly representative of the market participants. This causes a range of potential error in the outcomes predicted by the model (based on how representative the "representative agent" might actually be) on top of any error introduced by the model itself. In many circumstances, this amount of error is acceptable, and thus the model is useful even if the assumptions are incorrect.

If you want to reduce the error further, come up with a series of categories (or multiple representative agents) to describe the behavior of the buyers participating in your market. There's always going to be some margin of error on predictions though, because the future isn't predetermined and events can happen randomly.