The Empirical Test: A Structured Field Study of Agent-Habitat Regimes in Industrial AI Deployment

The Agent-Habitat Dynamics (AHD) series has developed a framework for Physical AI deployment built around specific constructs: the habitat as a first-class architectural object, the agent-habitat pairing as the unit of analysis, regimes defined by stability margin and write-back, the Trust-Maturity Ladder (TML) of earned operating authority, the Habitat Viability Ladder, the Digital Organism’s five functional groups, and consequence learning as the mechanism by which operational experience enriches the environment. This paper is the first structured attempt to apply those constructs, through a coded diagnostic instrument, to real startup-corporate collaborations in the field. The question is whether the framework’s vocabulary is adequate to describe what practitioners actually encounter and whether its predicted patterns hold.