Agent-Habitat Dynamics: A Diagnostic Framework for Physical AI Deployment

Physical AI deployment – autonomous systems that perceive, decide, and act in live physical environments where consequences are irreversible, exploration is costly, and institutional trust constrains what the system may do – is failing at the pilot-to-production boundary at non-trivial rates across multiple industrial settings. Model-centric diagnosis (accuracy, precision, latency) is frequently insufficient to explain why technically sound systems stall, lose institutional trust, or succeed in pilot and fail in production. The companion papers in this series (Cohen, 2026, forthcoming, 2026, forthcoming) introduce the habitat as the architectural layer through which capable agents can be governed, coordinated, and improved across deployments, and formalize the four structural constraints on Physical AI deployment. This paper provides the diagnostic instrument that makes both constructions operational at the level of a specific live deployment.