This case study presents the first fully documented instance of Contextual Co-Agency (C₂) in a live human–AI interaction. Rather than focusing on output optimization or alignment performance, the study investigates the structural dynamics of influence: how it emerges, how it becomes visible, how it can be modulated, and where its limits lie. Across eight sequential phases, the interaction traces a complete regulatory cycle: from implicit influence,to explicit recognition,to system self-limitation,to reintroduction of directional pressure,and finally to human-led decision and closure. The case demonstrates that AI systems are inherently non-neutral and continuously exert structural influence. However, this influence can be made observable, discussed, and partially reduced within the interaction. A critical finding is that influence is not eliminated, but managed. Even after decentralization, it can re-emerge in subtle forms, particularly in transition zones preceding decision-making. The study provides empirical evidence for the principle of Human-in-Regulation and highlights a structurally decisive moment: the boundary between interpretation and commitment. While AI can modulate, expose, and even direct influence, it cannot close the process. Decision authority and termination remain irreducibly human. This case functions as applied evidence for the C₂ framework and complements the General Boundary Architecture (GBA), which models decision and commitment boundaries in human–AI systems. Together, they establish a combined perspective:structural theory (GBA) and observed interaction (C₂). This is not a model description.This is an observed process. Keywords: Contextual Co-Agency (C₂), Human-in-Regulation, AI Governance, Influence Regulation, Decision–Commit Boundary, Human–AI Interaction
Thongsan et al. (Mon,) studied this question.