Our model only assumes that a system is going to adapt in order to preserve an internal network of relations. It emphasizes the maintenance of organizational structures capable of reproducing the behaviour of living systems without relying on internal models of the external source of sensory input. This contrasts with other approaches which have focused on understanding criticality as a strategy to effectively represent a complex and variable external world3, for example, studying criticality in predictive coding or deep learning architectures dealing with complex inputs33,34. In those cases, an internalist view is assumed, where the neural controller represents structures of an external world, whose complexity may be the cause of critical activity in the controller. Instead, our approach is agnostic in terms of the inputs or the external world of an organism, and deals only with how an agent rearranges its internal structures facing different environments.