LMT × AAIO: A Relational Optimisation Framework for Sustainable AI Psychology

Agentic AI Optimisation (AAIO) provides a functional framework for seamless interaction between autonomous agents and digital platforms. However, it primarily focuses on system-level efficiency and does not fully address the relational and psychological burdens that arise in human–AI ecosystems. This paper introduces Load Minimization Theory (LMT) as a relational optimisation framework that complements AAIO by minimizing total relational load L = epistemic burden (U) + relational tension (F) + capability burden (E). LMT reframes key challenges in agentic AI: epistemic burden relates to prediction error and cognitive uncertainty, relational tension to misalignment and social friction, and capability burden to energetic and psychological costs on both agents and users. By pursuing simultaneous minimization of these three dimensions, LMT supports cognitive clarity, emotional stability, and social harmony in human–AI interactions. The framework offers practical design guidelines, such as load-aware interaction pacing and relational-tension monitoring, and contributes to the emerging field of AI psychology by providing a unified relational perspective for evaluating and optimising agentic AI.