In 1781, Immanuel Kant argued that cognitive agents ignored the underlying structure of their world " as such " (the noumenal reality), and could only know phenomenal reality (the world " as it appears " through their experience). We introduce design principles to implement these theoretical ideas. Our agent's input data is not a direct function of the environment's state as it is in most symbolic or reinforcement-learning models. The agent is designed to discover and learn regularities in its stream of experience and to construct knowledge about phenomena whose hypothetical presence in the environment explains these regularities. We report a proof-of-concept experiment in which the agent constructs categories of phenomena, and exploits this knowledge to satisfy innate preferences. This work suggests a new approach to cognitive modeling that focuses on the agent's internal stream of experience. We argue that this approach complies with theories of embodied cognition and enaction.