Beyond AEO: The AI Visibility Stack and the Era of Agent Discovery Optimization

The emergence of autonomous AI agents as active participants in B2B commerce requires a fundamental revision of how enterprises conceptualize digital visibility. Existing disciplines — Search Engine Optimization (SEO), Answer Engine Optimization (AEO), and Generative Engine Optimization (GEO) — address visibility in contexts where a human user ultimately drives the decision process. This paper argues that a new category of visibility challenges arises when autonomous agents, acting on behalf of organizations, evaluate and select suppliers, initiate negotiations, and execute transactions without direct human involvement at the moment of decision. We introduce the AI Visibility Stack, a three-layer conceptual framework that maps the complete spectrum of enterprise visibility in AI-mediated environments: (1) LLM Visibility, addressed by AEO and GEO; (2) Agent Discoverability, addressed by the newly proposed discipline of Agent Discovery Optimization (ADO); and (3) Agent Participation, addressed by Agent Presence Optimization (APO). We describe the technical infrastructure underpinning this ecosystem — principally Google's Agent2Agent (A2A) protocol and Anthropic's Model Context Protocol (MCP) — and propose the ADO Score as a composite metric for measuring agentic discoverability. The framework is contextualized against projected market data indicating that AI agents will intermediate over $15 trillion in B2B spending globally by 2028. Implications for enterprise digital strategy, particularly for B2B companies in Spanish-speaking markets, are discussed.