There is a continuing trend of letting fewer people deal with larger amounts of information in more complex situations using highly automated systems. In such circumstances, there is a risk that people are overwhelmed by information during intense periods or, on the other hand, do not build sufficient situational awareness during periods of slack to deal with situations where human intervention becomes necessary. A number of studies show encouraging results in increasing the efficiency of human–machine systems by making the automation adapt itself to the human needs. Current literature shows no examples of adaptive automation in real operational settings, however. We introduce a fine-grained adaptation methodology based on well-established concepts that is easy to comprehend and likely to be accepted by the end user. At the same time, we let the machine operate like a virtual team member in that it continuously builds its own view of the situation independent from the human. Working agreements between human and machine provide lower and upper bounds of automation that are in advance determined by the end user so that unwanted appropriation of responsibility by the machine is avoided. The framework is domain neutral and therefore thought to be applicable across a wide range of complex systems, both military and civilian. It gives researchers an architecture that they can use in their own work to get adaptive automation up and running quickly and easily.