The present contribution investigates the role of intentionality in human–machine interactions. Building on previous research on intentionality in human–human interactions, it is shown that different “levels of intending” (including complex nested intentions and beliefs) need to be taken into account; dichotomies such as “implicit versus explicit” are insufficient to capture the necessary distinctions. Adequately guessing the degree of intending of interaction behavior, from basic actions up to and including communication processes, requires taking into account all relevant aspects of the behavior (speech, gesture, gaze, and further aspects of movement and/or body behavior). Furthermore, an adequate “model of intentionality” is needed in order to infer the degree of intending of an interaction based on various types of cues. It will be argued that Embodied Digital Technologies (EDTs) with the capabilities necessary to adequately infer and represent intentions and beliefs in their mental models of themselves and of interactants may be able to achieve improved situational awareness and a more human-like interaction quality.