From Information Retrieval to Agentic Action: A Framework for Brand Visibility in AI-Mediated Markets

The competitive ground of digital visibility has moved twice in eighteen months. The first move was from the ten blue links to the AI-generated answer; the second, still in progress, is from the answer to the autonomous action. This article develops a framework that integrates three optimization disciplines emerging in response to that shift—Answer Engine Optimization (AEO), Generative Engine Optimization (GEO), and Agentic Optimization (AgO)—under a single theoretical lens: delegated consumer–AI agency. Building on Puntoni et al.’s (2021) experiential perspective on consumer AI and Davenport et al.’s (2020) account of AI in marketing, we treat the AI assistant not as a channel but as a delegated decision-maker whose choices are governedby retrievability, attribution cost, and embedded brand associations. We argue that the central strategic risk of this transition is brand erasure—the systematic elimination of brand identity from synthesized answers and completed agentic actions—and we develop six propositions that link content and infrastructure choices to brand outcomes in this environment. We close with managerial implications for brands operating across the dual audience of human readers andmachine intermediaries, and a research agenda focused on measurement, audit methods, and governance.