Experimental Protocol for Evaluating Load Minimization Theory in Agentic AI Systems: Testing Relational Load-Aware Design with AAIO

This protocol outlines an experimental framework to empirically test Load Minimization Theory (LMT) in agentic AI systems. LMT posits that simultaneous minimization of epistemic burden (U), relational tension (F), and capability burden (E) leads to more sustainable human–AI interaction. While Agentic AI Optimisation (AAIO) focuses on functional efficiency and agent–platform coordination, it does not directly address the psychological and relational costs experienced by users—costs that increasingly shape the long‑term viability of agentic AI. The proposed study uses a between-subjects A/B design comparing an AAIO baseline agent with an AAIO + LMT load-aware agent. Key measures include prediction mismatch rate and subjective uncertainty (U), trust-drop per interaction and alignment correction frequency (F), and NASA‑TLX cognitive load, session fatigue, and energy consumption proxies such as joules‑per‑inference (E). Participants will complete both everyday and high-load agentic tasks, with quantitative metrics complemented by qualitative data from post-task reflections. The protocol aims to evaluate whether LMT-guided design reduces relational load while maintaining or improving functional performance, providing a reproducible foundation for relational optimisation research in AI psychology.