Agentic Interactions

This paper explores how AI agents inherit and potentially amplify human heterogeneity when tasked with negotiating on behalf of individuals. By comparing agentic interactions to a human-to-human benchmark, the study reveals that instructional prompts act as carriers for the principal's personality, biases, and demographic traits. Remarkably, delegating decisions to machines leads to a greater dispersion of outcomes and a breakdown of traditional fairness norms, such as the 50/50 split. The authors introduce the concept of "machine fluency"—the unique skill of effectively aligning an AI's behavior with one’s own goals—as a new source of economic inequality. These findings suggest that the agentic economy will not be a standardized marketplace, but rather one shaped by specification hazards and the latent characteristics of the humans who design the agents. Ultimately, the transition to AI mediation appears to transform and intensify existing social disparities rather than eliminating them.