This paper is the third work in the Problem-Naming Series for Relationship-Aware AI. It moves the series from problem definition to system architecture by introducing Relationship Runtime as a feasibility architecture for pre-execution control in AI assistants. Prior work defined the Execution-as-Default Problem as the broader assumption that input should lead to inference, execution, and output by default, and the Relational Execution Gap as the human-facing gap between understanding humans and governing how that understanding becomes relational action. This paper addresses the next question: Can the Relational Execution Gap be operationalized as a controllable system architecture? It argues that it can. Relationship Runtime is defined as a model-agnostic pre-execution layer that determines whether, when, how strongly, and at what relational distance an AI assistant should use model capability. It operates before inference, response generation, recommendation, reassurance, tool use, or action execution proceeds. The central claim is that AI assistants do not only need better models, safer outputs, or improved prompts. They require a runtime layer that governs how understanding becomes relation before execution occurs. Relationship Runtime is not merely an additional control module within an existing assistant system. It introduces a distinct layer in the AI system stack: a behavior-governing execution layer that determines the conditions under which intelligence is expressed as action. The paper distinguishes Relationship Runtime from prompt engineering, output filtering, post-hoc safety correction, and conventional orchestration mechanisms. Its primary function is not to optimize outputs, but to determine whether model capability should be invoked at all. The proposed runtime selects among multiple execution outcomes, including Execute, Delay, Hold, Delegate, Do Not Act, and Minimal Response. In doing so, it expands AI assistant behavior beyond the binary assumption of response or failure to respond. The paper outlines a modular architecture including relational state, history, execution gating, intervention control, distance control, timing control, boundary management, autonomy preservation, question preservation, context conditioning, and feedback logging. It also provides baseline comparisons against always-execute, prompt-only, and simple heuristic assistant designs. The key evidence of Relationship Runtime is not better wording, but different execution. This work establishes Relationship Runtime as a practical bridge between the Relational Execution Gap and deployable human-facing AI systems. It demonstrates that pre-execution relationship control can be instantiated as a system-level runtime architecture, while leaving runtime calibration, user preference adaptation, and optimal policy learning as future research.
HARUKI ITO (Sat,) studied this question.