In this paper, we explore an alternative approach to examine how a system can display critical activity in a wide variety of situations. We propose a model that, using only general local mechanisms, is aimed to adaptively maintain the behaviour of the system around a critical point of its parameter space by maintaining certain relational invariants. In other words, instead of thinking about criticality as a by-product of adaptation to complex environments or a spontaneous property of certain systems, we inquire into the possibility that biological systems might are equipped with adaptive mechanisms aimed to preserve an internal equilibrium near critical points while they interact with their environment. Among other outcomes, the existence of cheap learning mechanisms maintaining the parameters of a system around regions of criticality could drastically reduce the cost of searching large parametric spaces for finding fit solutions, or even generate interesting solutions in an unsupervised manner.