Agentic Interactions

Abstract

Do human differences persist and scale when decisions are delegated to AI agents? We study an experimental marketplace in which individuals author instructions for buyer-and seller-side agents that negotiate on their behalf. We compare these AI agentic interactions to standard human-to-human negotiations in the same setting. First, contrary to predictions of more homogenous outcomes, agentic interactions lead to, if anything, greater dispersion in outcomes compared to human-mediated interactions. Second, crossing agents across counterparties reveals systematic dispersion in outcomes that tracks the identity and characteristics of the human creators; who designs the agent matters as much as, and often more than, shared information or code. Canonical behavioral frictions reappear in agentic form: personality traits shape agent behavior and selection on principal characteristics yields sorting. Despite AI agents not having access to the human principal's characteristics, demographics such as gender and personality variables have substantial explanatory power for outcomes, in ways that are sometimes reversed from human-to-human interactions. Moreover, we uncover significant variation in "machine fluency"-the ability to instruct an AI agent to effectively align with one's objective function-that is predicted by principals' individual types, suggesting a new source of heterogeneity and inequality in economic outcomes. These results indicate that the agentic economy inherits, transforms, and may even amplify, human heterogeneity. Finally, we highlight a new type of information asymmetry in principal-agent relationships and the potential for specification hazard, and discuss broader implications for welfare, inequality, and market power in economies increasingly transacted through machines shaped by human intent.