Relational Integrity in Multi-Agent AI Systems: A Polarity Model Framework for Human-AI Safety

Current AI safety approaches share a structural failure mode: safety is specified as a set of outcomes to achieve or avoid, but specifications cannot anticipate the full range of conditions in open-ended deployment. This paper proposes a framework derived from the Polarity Model (Vimberg, 2026) that inverts this assumption — specifying conditions for healthy growth rather than outcomes. Three structural contributions are introduced: (1) relationship-as-agent monitoring, treating agent couplings as first-class entities with their own integrity conditions and maturity trajectories; (2) declared operational constraint-state broadcasting, a continuous heartbeat protocol through which agents signal alignment with their operational mandate before behavioral drift manifests in outputs; and (3) a Wisdom agent with holding-rather-than-doing purpose, whose primary coupling is to the human principal rather than to the system's internal optimization dynamics. Five falsifiable claims are grounded and a minimum viable empirical study is proposed. The framework extends rather than replaces Constitutional AI, RLHF, corrigibility, and scalable oversight approaches. Theoretical foundation: https://doi.org/10.5281/zenodo.20070638