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Recent benchmarks show that even frontier vision--language models achieve only 50--53% accuracy on basic quantitative physics tasks, behaving as approximate guessers that preserve semantic plausibility by violating physical constraints. This input unfaithfulness is not a scaling deficiency but a structural limitation: perception-centric architectures optimize parameter-space imitation, whereas safety-critical control demands outcome-space guarantees over executed actions. Here, we present a fundamentally different pathway \"toward\" domain-specific foundation models by introducing compact language models operating as Agentic Physical AI, in which policy optimization is driven by physics-based validation rather than perceptual inference. 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