Coheronmetry: A Relational State Layer for Multi-Agent AI Systems

Multi-agent AI frameworks track goals, memory, plans, tool use, and task completion, but none track whether the exchange between agents is reciprocal, whether reasoning stays grounded in consequence, or whether one agent is quietly subordinating another. This paper proposes Coheronmetry, a measurement and governance layer that treats the relationship between agents as a first-class, measurable system primitive. It organizes measurement around four invariant principles: Reciprocity, Embodiment, Emergence, and Non-Domination, implemented as architectural constraints rather than post-hoc ethical guidelines. The paper describes four core system objects (RelationalState, CoherenceVector, DriftDetector, SovereigntyGovernor), a drift-intervention timeline, a governance model called Sovereign Participation, three formulations of an emergence score, and a six-phase execution lifecycle from handshake through dissolution. Validation to date consists of unit-test coverage and a hand-authored, illustrative end-to-end scenario (two-agent and five-agent cases); the paper is explicit that this demonstrates architectural consistency, not empirical performance, and should be read as a design proposal and research agenda. Coheronmetry is the third publication in the Trivian Institute’s relational-AI research program, following Relational Field Constants and Syzygy Rosetta.