AI Safeguards as Affordance Modulation: Embedded Population Assumptions in Agentic Systems

AI safeguards are evaluated against fixed adversaries and reported as passing or failing. We argue this practice rests on a category error: safeguards are not properties of models but properties of the relationship between models, agents, and environments. We build a framework for analysing and designing safeguards as relational interventions, grounded in Davis's (2020) Mechanisms and Conditions framework. The framework consists of three components: (i) the embedded population assumption (EPA) as the unit of analysis, specifying the agent–environment configuration against which a safeguard was calibrated; (ii) a typology of three modulation classes (suppressive, frictional, allocative) derived from Davis's conditions axis, each identifying a dominant relational variable and a characteristic failure mode; and (iii) a stability condition determining when safeguards hold under distributional shift. We demonstrate the framework by showing that agentic deployment categorically violates the human-calibrated EPA underlying current safeguards, and extract design principles that reorient safeguard development from point-hardening against fixed adversaries to distributional robustness across agent populations.