Load Minimization Theory as a Relational Extension of Agentic AI Optimisation: Bridging Compute Efficiency, Emergent Abilities, and Sustainable Human-AI Coexistence

As autonomous Agentic AI (AAI) systems increasingly initiate digital interactions independently, optimisation paradigms such as Agentic AI Optimisation (AAIO) primarily focus on functional efficiency and seamless agent–platform integration. However, these approaches do not fully address the relational burdens that emerge when autonomous agents operate within human-centered ecosystems. This paper proposes Load Minimization Theory (LMT) as a relational extension of AAIO that formalizes total relational load as L = epistemic burden (U) + relational tension (F) + capability burden (E). LMT maps epistemic burden onto prediction error and compute inefficiency in scaling laws, relational tension onto challenges in emergent coherence and alignment drift, and capability burden onto training and inference energy costs. By explicitly minimizing these three dimensions simultaneously, LMT enhances computational efficiency while reducing unintended spillover effects—such as increased human cognitive load or relational friction—that AAIO alone may overlook. This relational extension yields concrete design implications for agentic AI, including load-aware scaling strategies, transparent interaction protocols, and governance mechanisms that prioritize relational rest—a low-friction state in agent–human–platform interactions. LMT thus provides a practical pathway toward sustainable human-AI coexistence while extending the functional optimisation paradigm established by AAIO.