Systems Stability and Interference Control in Multi-Agent Systems: A Relational Control Perspective

This paper introduces a relational control framework for stability and interference regulation in multi-agent AI systems. Conventional multi-agent systems implicitly assume that execution is the default response to input. As interaction complexity increases, this assumption produces structural instability, including over-execution, mutual interference, recursive feedback loops, and evaluation inconsistency across interacting agents. The paper demonstrates that these failures are not fundamentally coordination problems, but consequences of unregulated execution. To address this limitation, the paper formalizes Relational Control as a framework for stabilization prior to execution in which execution decisions are conditionally governed through relational conditions among interacting entities. The framework establishes:- execution suppression as a primary stabilization mechanism,- interference reduction through relational governance,- recursive loop prevention via conditional execution,- dynamic evaluation alignment,- and non-execution as a first-class system outcome. Execution decisions are determined through relational state, relational dynamics, relational history, and dynamic evaluation criteria, producing outcomes including:- Execute- Delay- Delegate- Do Not Act Crucially, system stability is not achieved through post-hoc coordination, but through pre-execution regulation of execution eligibility. The proposed architecture introduces a model-agnostic Relational Control Layer operating before execution to suppress instability propagation and stabilize multi-agent interaction dynamics. This publication serves as a foundational systems stability paper for the Relationship-Aware AI Research initiative, establishing pre-execution regulation as the primary mechanism for scalable multi-agent system stability.