Large Language Models (LLMs) demonstrate remarkable reasoning capabilities but often suffer from "semantic drift" and inefficiency in long-horizon autonomous tasks. Current industry approaches predominantly rely on "prompt engineering"—attempting to govern agent behavior through psychological instructions—which fails to address the fundamental lack of stateful proprioception in Transformer architectures. Building upon our previous work on the Artificial Intelligence Control Loop (AICL) which established the structural separation of planning and execution, this paper presents CyberLoop v2.1. While v1.0 focused on modular architecture, v2.1 introduces Semantic Kinematics: a control-theoretic layer that applies PID control and Extended Kalman Filters (EKF) to enforce trajectory stability. We evaluate CyberLoop v2.1 on a high-entropy Wikipedia navigation benchmark. Our approach successfully reaches distant semantic targets using a purely kinematic inner loop without invoking the LLM for intermediate steps. Results demonstrate a 60x reduction in latency and a 99% reduction in token costs compared to standard Chain-of-Thought agents, validating that adding a physiological 'body' is a prerequisite for stabilizing long-horizon tasks and achieving sustainable AGI.