The concept of agency is of fundamental importance for Cognitive Science. However, usual definitions of agency are loose and the work to capture and measure it using mathematical tools is still in its infancy. Recently, the framework of integrated information theory has been proposed to capture the causal boundaries of biological autonomous systems. Here, we test measures of integrated information theory in a minimal model to test its capacity to identify and delimit an autonomous agent interacting with an environment. Doing so, we reformulate some aspects of current definitions of agency using insights from integrated information in our models. Specifically, we propose a redefinition of how we capture the ability of an agent to modulate its interaction with the environment in terms of the control of the emergent causal structure of the agent-environment system. In this way, we propose an operational definition of agency based on the capacity of a system to modulate its causal boundary, extending and reducing it by functionally open and closing sensorimotor loops, and coupling the agent to different environmental processes. This allows us to formulate a tentative measure for our definition of agency and test it in minimal models of sensorimotor interaction, which we test in a minimal agent evolved to solve a simple task.