Theoretical framework demonstrates how consumers mentally construct relational ad agents from generative AI interactions, indicating a shift from discrete exposure to continuous social evaluation.
Advertising theory has traditionally conceptualized persuasion as arising from discrete persuasive exposures. Generative artificial intelligence challenges this assumption by enabling persistent, contingent, and perceptibly coordinated persuasion across interactions, platforms, and contexts. This article develops a relational inference framework for agentic advertising to explain how consumers organize recurring adaptive coordination into inferred ad agents that become the object of relational evaluation. The framework identifies three structural conditions for ad-agent emergence, specifies relational inference as the cognitive mechanism underlying that emergence, and proposes three complementary first-order relational judgments—artificial empathy, artificial recognition, and super-being social presence—that shape the second-order evaluations of authenticity and trust. It further explains how generative persuasion knowledge moderates these evaluations and identifies the recursive dynamics and boundary conditions of agentic advertising. By introducing inferred ad agents as a new unit of analysis, the framework extends exposure-centered advertising theory to continuously adaptive advertising environments.
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Guohua Wu (2026) studied this question.
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