Systems Relational Control for Multi-Agent Systems: A Framework for Conditional Execution in AI Systems

This paper introduces Relational Control as a system-level execution governance framework for multi-agent AI systems. Conventional AI systems implicitly assume that execution is the default response to input. In multi-agent environments, this assumption produces structural instability, including over-execution, excessive intervention, failure to incorporate relational history, and misalignment across interacting entities. The paper demonstrates that these failures arise because execution is assumed rather than governed. To address this limitation, the paper formalizes Relational Control as a pre-execution control paradigm in which execution decisions are conditionally determined through relational conditions among interacting entities. The framework establishes:- conditional execution governance,- relationally determined execution decisions,- intervention degree regulation,- dynamic evaluation criteria,- relational history integration,- and non-execution as a valid system outcome. Execution decisions are determined through relational state, relational dynamics, relational history, and temporal interaction structures, producing outcomes including:- Execute- Delay- Delegate- Do Not Act Crucially, Relational Control does not optimize outputs first; it regulates execution prior to inference. The proposed architecture introduces a model-agnostic Relational Control Layer operating as an external pre-execution control plane for multi-agent AI systems. This publication serves as a foundational systems control paper for the Relationship-Aware AI Research initiative, establishing Relational Control as the governing framework for conditional execution in multi-agent AI systems.