WARD is a runtime-adaptive Vision Transformer framework for dependable edge AI that adapts to changing power budgets, reliability requirements, and input distributions. It uses channel-wise subnetwork partitioning, reliability-aware continual learning, and dynamic operating-mode scheduling to optimize performance, fault tolerance, and adaptation.