From Chat to Agent: Why Consent Breaks When AI Acts on Your Behalf

The rapid evolution from conversational chatbots to autonomous AI agents has outpaced consent

mechanisms designed to protect users. While millions share sensitive information with LLM-based

systems, evidence shows a widening gap between users’ mental models of data handling and actual

platform practices. This disconnect deepens as AI browsers navigate the web, handle credentials, and

execute transactions. This paper argues that consent is structurally broken across the LLM ecosystem

and identifies three design tensions: personalization versus data minimization, agent autonomy

versus user control, and transparency versus usability. Building on these tensions, this paper proposes

LLM-specific consent mechanisms: contextual in-flow transparency, task-scoped permissions,

and AI-mediated privacy guardians, and highlight their consequences for security, comprehension,

and power asymmetries in agentic systems.