The rise of artificial agents that excel in complex tasks makes it feasible for humans and machines to form efficient teams. However, the design and requirements of the necessary software agent are still unclear. We identify important factors for human–machine teaming and characterize teaming situations by the presence of shared (sub-)goals, communication, interdependence, and the ability to learn and adapt. Given this, we introduce a frame-work for software teaming agents, which utilizes state-of-the-art deep neural networks and addresses how communication and conceptual information can be incorporated into such a design. Moreover, we suggest information-seeking behavior, based on uncertainty assessment, to deal with the variability of the environment and the agent’s imperfectness. Finally, we address some inter-disciplinary research directions in human–machine teaming which arise from our conception.