In this talk I will present the synthesis of control laws for interacting agent-based dynamics and their mean-field limit. In particular, a linearization-based approach is used for the computation of suboptimal feedback laws obtained from the solution of differential matrix Riccati equations. Quantification of dynamic performance of such control laws leads to theoretical estimates on suitable linearization points of the nonlinear dynamics. Subsequently, the feedback laws are embedded into a nonlinear model predictive control framework where the control is updated adaptively in time according to dynamic information on moments of linear mean-field dynamics.