Researchers writing in Nature Machine Intelligence proposed building AI agents around internal state signals modelled on how living bodies monitor and regulate themselves, such as hunger or temperature. The authors argue these internal signals can act as a stable reference that guides learning and behaviour when the outside environment keeps changing. It is a conceptual perspective piece rather than a working system, and the full text sits behind a subscription.