A novel way to model an agent interacting with an environment is introduced, called an Enactive Markov Decision Process (EMDP). An EMDP keeps perception and action embedded within sensorimotor schemes rather than dissociated, in compliance with theories of embodied cognition. Rather than seeking a goal associated with a reward, as in reinforcement learning, an EMDP agent learns to master the sensorimotor contingencies offered by its coupling with the environment. In doing so, the agent exhibits a form of intrinsic motivation related to the autotelic principle (Steels, 2004), and a value system attached to interactions called interactional motivation. This modeling approach allows the design of agents capable of autonomous self-programming, which provides rudimentary constitutive autonomy—a property that theoreticians of enaction consider necessary for autonomous sense-making (e.g., Froese & Ziemke, 2009). A cognitive architecture is presented that allows the agent to autonomously discover, memorize, and exploit spatio-sequential regularities of interaction, called Enactive Cognitive Architecture (ECA). In our experiments, behavioral analysis shows that ECA agents develop active perception and begin to construct their own ontological perspective on the environment.