Human-Control Framework for AI Factories: Regulating Latent State Drift with Control Language

This mini report presents a concrete, traceable case demonstrating human control over AI latent-state drift in extended human–AI interaction. As AI systems evolve toward agentic behavior and large-scale “AI factory” deployment, existing discourse emphasizes capability and scale while leaving human control mechanisms under-specified. Using a single longitudinal interaction sequence, this report applies a state-based coding framework (P / T / N) derived from a 48-state interaction model to track how AI outputs shift from stable alignment into transitional instability or collapse (e.g., structural flattening, over-generalization, constitutional short-circuit). Each turn is annotated alongside the human’s control language, categorized into four operational types: negative (boundary), ratio (proportional constraint), structural (explicit organization), and meta-constitutional (reassertion of governing rules). The analysis shows that AI drift is frequent even in constrained tasks, but that targeted human language can reliably restore stability. In particular, negative and ratio-based interventions are effective against flattening, while meta-constitutional language is critical for terminating runaway loops. This report reframes AI literacy from prompt optimization to human-as-supervisor state regulation, with implications for AI factory operations, agentic AI oversight, and scalable AI literacy training.