Fluid antenna systems (FAS) provide continuous spatial degrees of freedom for next-generation wireless networks. However, deploying FAS on high-speed mobile embodied intelligent agents introduces Doppler shifts that shorten the channel coherence time, causing conventional full-port scanning to incur excessive pilot overhead. To address this issue, this paper proposes a prediction-aided adaptive communication architecture that constructs a dynamic local sounding window centered on upper-layer prediction results. By exploiting the spatial channel correlation among FAS ports, the proposed approach balances pilot overhead and spatial diversity gain under prediction deviations. We establish a quantitative model for the long-term expected throughput by integrating a nonlinear bounded hit probability function and a gain bridging model based on the zeroth-order Bessel function of the first kind. We then formulate a discrete optimization problem under practical constraints to maximize the expected transmission capacity, and develop a two-stage low-complexity algorithm that decouples the online computational complexity from the number of antenna ports. Simulation results show that the proposed mechanism adaptively adjusts the sounding window size according to moving velocity and prediction accuracy, reducing pilot overhead in high-mobility scenarios and achieving higher throughput than fixed-window and global scanning schemes.