The Vanishing User: Web Analytics in an Agent-Dominated Internet

Conventional web analytics treats the human user as its fundamental unit of analysis, assuming stable preferences, identifiable intentions, and behavioral patterns that unfold over time. That assumption is under strain. Crawlers and traditional bots already account for a substantial fraction of online interactions, and autonomous AI agents are emerging as a further class of actors layered on top of this automated traffic. Unlike either, these agents do not possess persistent identities or psychologically grounded motivations. They are task-specific, dynamically instantiated processes whose behaviors are contingent and often orchestrated by external systems. Their presence weakens the interpretive value of core metrics, including sessions, engagement, conversion, and retention. A click may reflect an optimization routine, a proxy objective, or a recursive agent-to-agent exchange rather than meaningful human intent, and traditional inference frameworks cannot reliably distinguish among these possibilities. This is a position paper. It synthesizes literature across bot and agent detection, agent architecture, web measurement validity, governance of automated systems in adjacent sectors, and the epistemology of digital trace data, and it argues that web analytics should supplement, and in places replace, its human-centered model with an agent-aware model focused on interaction dynamics within hybrid ecosystems of human and non-human actors. The paper develops a working taxonomy of crawlers, traditional bots, AI agents, LLM-powered agents, and autonomous agents; identifies three properties of LLM agents (identity discontinuity by design, task-based instantiation, agent-to-agent loops) that distinguish the present challenge from prior bot-detection problems; examines opaque agent objectives, synthetic traffic loops, and the indistinguishability between human-originated and agent-mediated signals; and proposes five candidate measurement primitives (task chain, actor class, interaction provenance, objective alignment, signal authenticity) with explicit operational definitions. Governance machinery from energy systems and critical infrastructure offers a partial template, and we delimit which dimensions transfer and which do not. The contribution is conceptual and programmatic, presenting a vocabulary, set of candidate primitives, and research agenda for a field whose foundational unit of analysis is becoming unreliable.