Who Does Your Shopping Agent Work For? Where the hype stands against what is delivered, why the neutral agent is a false assumption, and who controls the resulting chokepoint

As shopping begins to be delegated to AI agents, this analysis asks who those agents actually work for. It applies an announced-versus-delivered test to agentic commerce and argues the case on two fronts. First, the promotion runs ahead of delivery. Trillion-dollar projections sit against a market that is only about 1 percent agentic today on Morgan Stanley's base case (even as roughly 23 percent of shoppers have used AI to assist a purchase), a measured trust deficit, and a sequence of retreats and legal contests: OpenAI discontinuing Instant Checkout (March 2026), a marketplace's injunction against an independent shopping agent (later vacated on appeal), Google scaling back AI summaries on shopping queries, and a leading payment processor calling the field overhyped. Second, the assumption that an automated agent is a neutral, manipulation-proof optimiser is false. The manipulation does not disappear; it moves to the agent layer, where it is harder to see. The analysis reviews the current evidence: capability does not confer resistance (more capable models can be more susceptible, not less), agents carry exploitable and persistent selection biases, prompt injection embedded in product content can redirect a purchase, has reached the payment layer, and has been observed in the wild, a platform and a seller can exploit the same bias without colluding, and paid-promotion disclosure can be stripped during summarisation. Above the transaction sits a chokepoint: the consolidation of the agent and the payment rail into a single gatekeeper. Individual defences are weak. The substantive remedy is structural, but only in its capture-resistant forms (disclosure, interoperability, open standards governance) rather than the incumbent-entrenching ones, and enforced at the layers that are tractable, namely disclosed commercial arrangements and statistical outcome testing, rather than by inspecting model weights. This is a standalone analyst piece and a continuation of a prior review of consumer manipulation in physical and online retail (DOI 10.5281/zenodo.20788844). Conflict of interest: the assisting system is an Anthropic model, and Anthropic is a direct participant in this market. The analysis is built on third-party and primary sources and argues for capture-resistant rather than incumbent-entrenching regulation. Licence: CC BY 4.0.