Artificial Intelligence (AI) has infiltrated nearly all aspects of our lives, improving the efficiency of human goal-directed behavior, and subsequently (in some cases) the quality of life. Our future will likely have more integration between technology and humans for rapid and accurate decision-making to complex problems of ever-increasing complexity. An important component of this is the seamless integration of humans with technology (with underlying AI operating it) to solve complex problems together. To develop strategies to better integrate humans with technology, we might learn from the continuous monitoring of individuals and groups as they work on a problem together, uncovering key features that technology (e.g.,AI) can leverage to predict the future of the team and modify teaming behavior as needed. In other words, we hope to do foundational research for a future synthetic human-technology “team metacognitive”-like monitoring agent. Working from a dataset that contains simultaneously monitored physiological signals (electroencephalographic recordings, heart rate, etc.) recorded from more than 20 groups of 3-4 individuals (and an “intelligent agent”) to work together on consensus building and creativity tasks, we hope to use methods borrowed from dynamical systems to create abstract representations of inter- and intra- brain-body interactions that underlie optimal teaming behavior.