Future military operations will require teams of Soldiers and intelligent systems to plan and execute collective action in a dynamic and adversarial environment. In human teams, teamwork processes such as effective communication and shared understanding underlie effective team performance. Recent work proposes a vision for generalizing this theory to human-agent teams and facilitating teamwork via individualized, adaptive technologies. We propose a dynamical system model to understand how individualized, adaptive technology can facilitate teamwork in human-agent teams. The model reveals three scientific challenges: describing the dynamics of team state, understanding how technological interventions will manifest in team states, and observing latent teamwork states. Using this model, we motivate a problem in which we predict team outcomes from non-obtrusive observation of a military staff during a training exercise. Representing pairwise interactions between team members as a weighted adjacency matrix, we use low-rank matrix recovery techniques to identify communication patterns that predict external evaluations of three team processes during task completion: effective communication, shared understanding, and positive affect.