Load Minimization Theory as a Relational Extension of Agentic AI Optimisation: Implementation and Validation of U·F·E Dynamics in a Simple Chat Agent

Agentic AI Optimisation (AAIO) has advanced functional efficiency and platform coordination in autonomous AI systems, yet it provides limited tools for addressing the relational and psychological burdens experienced by human users. This paper extends AAIO through Load Minimization Theory (LMT) by implementing and validating a minimal LMT-aware chat agent. We operationalize LMT’s three relational burdens—epistemic burden (U), relational tension (F), and capability burden (E)—using simple heuristics based on message characteristics and conversation dynamics. The agent calculates these burdens in real time and modulates its response style accordingly (e.g., becoming more empathetic or concise when loads are high). Exploratory conversational experiments revealed that the system successfully captured basic load dynamics: U increased with message complexity, F rose with strong emotional or corrective expressions, and E accumulated with conversation length and intensity. Cross-burden interactions were observed, and load-aware response modulation produced subjectively more considerate interactions during high-load periods. While F and E exhibited a tendency to decrease slowly once elevated—potentially reflecting emotional persistence—this behavior suggests a plausible approximation of human-like relational dynamics. The results demonstrate that even a simplified implementation can make LMT principles observable and actionable, offering a practical bridge between theoretical relational optimisation and real-world agentic AI design. We discuss implications for sustainable human–AI interaction and outline directions for more sophisticated future implementations.