Vision-Language-Action (VLA) models equipped with billion-parameter architectures offer unprecedented reasoning capabilities for embodied agents. However, their direct deployment in continuous control systems faces critical challenges. These challenges include high inference latency, kinematic instability, and severe fragility to visual perturbations. In this paper, we propose an uncertainty-aware hierarchical control architecture alongside a comprehensive evaluation framework. Our primary contributions are threefold: (1) an information-theoretic meta-controller that maps the VLA's predictive uncertainty heuristics (Shannon entropy and logit variance) directly to the planner's update rate and control policy; (2) a POMDP-grounded formalization of Cognitive Deadlock, which we resolve using an Extended Kalman Filter (EKF) as a Kinematic Spatial Memory; and (3) a Control-Coupled Chain-of-Thought (CoT) system where spatial reasoning actively constrains an Artificial Potential Field (APF). We validate our approach through rigorous statistical analysis across 15 independent simulation seeds. The empirical results demonstrate that direct End to End (E2E) deployment of State Of The Art (SOTA) VLAs frequently fails due to cognitive deadlocks. In contrast, our entropy-guided APF architecture maintains empirically bounded tracking behavior under severe visual disturbances. It successfully restores continuous kinematic actuation, completely overcoming the physical paralysis observed in direct policy execution.