Autonomous Agents (AAs) intrinsically have to exploit some forms of causal reasoning, i.e., the ability to understand what actions can bring about intended effects. However, such reasoning is not usually grounded in formally defined and homogeneous causal models, but is instead implicitly represented within the AA model itself. In this paper, we discuss the role that sound causal modelling and learning can play in conceiving and developing AAs and Multi-Agent Systems: reasoning activities would be made available by a uniform, explicit model, that would be amenable to autonomous manipulation by the AAs. Open challenges towards achieving this goal are also discussed.