State Coupling Risk in Long-Horizon Human–AI Interaction: A State-Based Analytical Perspective

This working paper introduces state coupling risk as an interaction-level risk emerging in agentic and tool-using AI systems operating over extended task horizons. Rather than attributing failures to model error , hallucination, or misalignment, the paper argues that risk arises from the gradual coupling of human judgment, model behaviour , tool invocation, and work continuity. Using the 48-state inference system as a non-disclosed dynamic state framework, the paper demonstrates how decision authority and responsibility can progressively drift without explicit delegation, and why output-level evaluation is insucient for understanding risk in long-horizon human–AI collaboration. This paper is intended as a methodological contribution to AI governance, human–AI interaction (HAI), and HCI research. It does not propose model modications, restrictions, or alignment frameworks, but instead relocates risk analysis to the interaction-state level —a governance layer distinct from model capability and output accuracy.