The development of autonomous agents for wayfinding tasks has long maintained the usage of naive, omniscient models for navigation. The simplicity of these models improves the scalability of crowd simulations, but limits the utility of such simulations to the visualization of general behaviors. This restricted scope does not allow for the observation of more nuanced, individualized behaviors. In this paper, we demonstrate a novel framework for agent simulations that does not rely on omniscience. Instead, each agent is equipped with a memory architecture that enables wayfinding by maintaining a cognitive map of the space explored by the agent. Based on findings from simulation studies, cognitive science, and psychology, we describe a wayfinding procedure that simulates human behavior and human cognitive processes, incorporating landmark navigation, path integration, and memory. This cognitive approach makes observations of agent behavior more comparable to those of human behavior.