Deciphering the behaviour of intelligent others is a fundamental characteristic of our own intelligence. As we interact with complex intelligent artefacts, humans inevitably construct mental models to understand and predict their behaviour. If these models are incorrect or inadequate, we run the risk of self deception or even harm. This paper reports progress on a programme of work investigating approaches for implementing robot transparency, and the effects of these approaches on utility, trust and the perception of agency. Preliminary findings indicate that building transparency into robot action selection can help users build a more accurate understanding of the robot.