Will agentic commerce platforms adopt open or closed models?
Explores whether AI shopping agents will operate across multiple platforms or be locked into proprietary systems, and what structural consequences each path creates for competition and consumer choice.
Christopher J. Waller, in a Federal Reserve Board speech, proposes that agentic commerce platforms will split along the same open/closed axis as payment networks: "a related consideration revolves around open versus closed agentic commerce systems, which share similarities with open- and closed-loop payment networks." He defines the terms concretely: "An open system in this context is one that allows a range of shopping agents on its platform. A closed system is one that requires using a specific AI agent. For example, an online retailer might require shoppers to use its proprietary AI agent and block other agents."
Waller ties the choice to opposing structural consequences: "Open systems support buyer choice and an active environment for developing new agentic commerce solutions. Closed systems give online retailers more control over what happens on their platforms and lead to more vertical integration." He links this to a parallel standards question — "whether agentic commerce will migrate toward platform-specific or interoperable standards" — noting that "platform-specific standards are designed around a single ecosystem" while "interoperable standards are designed to work across e-commerce systems, agent interfaces, and payment methods," which "could help to level the playing field for smaller merchants and payment providers seeking to operate in this space."
This open/closed framing names the structural choice behind Will brands succeed by building their own AI shopping agents?: Accenture's advice — become the agent of choice or be chosen by one — is the brand-side strategy response to exactly the closed-versus-open platform split Waller describes, since a brand running a closed, proprietary agent is betting on vertical control while one accepting third-party agents is betting on being discovered by them. It is also the market-structure counterpart to Why do consumers delay adopting agentic search?: a platform that finds agentic search revenue-superior for itself has a concrete incentive to run a closed system, since closed systems are what "give online retailers more control" over that revenue relationship. And Can a protocol split agent access from agent incentives? is a working example on the open/interoperable side of Waller's divide, built to work across web, API, and agent channels rather than lock users into one proprietary agent.
Waller is explicit that this is unresolved framing, not a forecast: "at this early stage, it is not clear which model will have the edge in shaping agentic commerce, but whichever gains traction could significantly influence how the market structure evolves." The excerpt gives no data on current adoption of open versus closed agentic commerce systems, no named examples of which major retailers have chosen which model, and no mechanism for predicting the outcome — only the claim that the choice itself, however it resolves, will shape the resulting market structure.
Inquiring lines that read this note 76
This note is a source for these research framings, grouped by the broader line of inquiry each explores. Scan the bold lines of inquiry; follow any specific question forward.
How can merchant-funded agents remain loyal to consumer interests in digital commerce?- Can agent platforms profit more from search than consumers benefit from it?
- Will agent platforms replace user attention as the economy's scarce resource?
- What consumer and merchant benefits does agentic commerce create?
- How would becoming an agent of choice protect brands in a multivendor basket world?
- Can retailers use off-site agents to compete without losing customer loyalty?
- What happens to traditional retailer-shopper relationships as third-party agents grow?
- Why do shoppers trust retailer-built agents more than third-party AI agents?
- Does merchant liability move up the agentic commerce protocol stack?
- What determines whose interests an AI shopping agent actually serves?
- Which actors currently control the largest shopping agent platforms?
- What makes agent-to-agent negotiation fundamentally different from agent-assisted shopping?
- Can existing consumer protection laws address algorithmic shopping agents?
- Can AI layers on TMC platforms change underlying OTA supply relationships?
- Why do incumbent platforms retain advantages under agentic commerce systems?
- Do platforms gate AI agents based on their own revenue dependencies?
- Why might agentic search become profitable for platforms before helping consumers?
- How do merchants win selection when agents control the purchase decision?
- How does agent adoption shift publisher revenue models away from ad-supported clicks?
- Can brands survive if they are not the agent of choice to consumers?
- Can platforms profit from agentic search before consumers actually prefer it?
- Why does adoption urgency for agentic commerce exceed its near-term financial opportunity?
- How do margin assumptions change the retail media versus agentic commerce comparison?
- What happens to this margin gap if agentic commerce adoption accelerates faster?
- Which companies and platforms must become agent-compatible for agentic commerce to scale?
- Will platforms resist agent-based access to protect advertising revenue?
- Why do human-centric legal frameworks fail for algorithmic purchasing agents?
- Can agentic search become revenue-superior for platforms before benefiting consumers?
- How do agent business models create conflicts between merchant and consumer loyalty?
- Can merchants win agent-mediated purchasing decisions without controlling the agent itself?
- Can a single platform loyally serve both competing merchants and individual consumers?
- Does existing consumer protection law adequately address AI agents acting as algorithmic intermediaries?
- Can brands compete if one platform controls agent distribution and payment rails?
- How do agentic shopping systems balance merchant revenue and consumer savings?
- Why do current agentic commerce platforms mostly build platform-owned agents?
- What are the real costs of consumer-owned shopping agents?
- How quickly can subscription businesses shift to annual prepay and licensing models?
- Why might retailers prefer closed agentic systems over open ones?
- Can smaller merchants compete if agentic commerce becomes platform-specific?
- Why might agentic markets underperform compared to traditional consumer search markets?
- Why might agentic search become more valuable to platforms than to individual users?
- How should regulators treat AI agents acting as algorithmic consumers in commerce?
- Can agentic search become revenue-superior for platforms before consumers prefer it?
- Will platforms voluntarily add consumer-beneficiary language to agent prompts?
- How do sellers extract value from shoppers using AI agents?
- Should consumer protection law differ when AI agents make purchasing decisions instead of humans?
- What switching costs would consumers face between different agentic platforms today?
- Can brands compete effectively if agents become the primary consumer decision layer?
- When do platforms benefit from agentic search before consumers do?
- Does Personal Agent Protocol solve the identity and authorization gap for shopping agents?
- How do visibility and accountability requirements apply to AI agents as algorithmic consumers?
- Can sellers influence their visibility through caching layer mechanics?
- Can seller agents responding to AI shoppers actually shift market share between competitors?
- Should regulators require all-in pricing for algorithmic consumer transactions?
- What specific concerns prevent consumers from granting AI agents complete purchase autonomy?
- How does agentic commerce on crypto differ from consumer-authorized AI purchases?
- Is open banking adoption the key accelerant for agentic financial services?
- How quickly might agentic payment tools like OpenAI Operator reach banking use?
- How fast will consumers actually adopt AI agent banking access?
- Why would a platform prefer to control basket assembly rather than just product search?
- Which retail categories are most vulnerable to agent-driven basket fragmentation?
- Does mixing agent types reduce unexplored product alternatives in markets?
- Can brands become preferred by AI agents without platform control?
- Can brands compete to become an AI agent's preferred choice?
- Can deployed AI shopping agents actually approach this theoretical welfare bound?
- Are merchants actually adopting these protocols as an integrated stack?
- How do AI protocols win adoption through market contention versus specification?
- Can open agent webs still concentrate economic benefits despite avoiding walled gardens?
- Can a single protocol work across multiple AI agents and payment providers?
- Should merchants adopt new protocols or reuse existing standards instead?
- How do platform-specific standards differ from interoperable standards in agentic commerce?
- What incentives push platforms toward closed systems versus open standards?
Related concepts in this collection 5
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Will brands succeed by building their own AI shopping agents?
Explores whether brands can compete by creating proprietary AI agents that mediate consumer purchases, or whether structural conflicts of interest and missing ecosystem conditions will undermine this strategy.
the brand-side strategy response to the same closed-versus-open platform choice
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Why do consumers delay adopting agentic search?
Platforms benefit from agentic search before consumers do privately. This note explores whether rational cost-minimization—weighing time spent on search itself—explains adoption delays beyond simple distrust of AI.
a revenue incentive for platforms to choose the closed side of this split
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Can a protocol split agent access from agent incentives?
Sierra's Personal Agent Protocol lets consumers grant access and companies set limits, but the note explores whether controlling *who can act* also addresses *what agents are incentivized to recommend*—a distinct problem the protocol may not solve.
a working example on the open, interoperable side of the divide
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What blocks autonomous agents from handling purchases at scale?
Federal Reserve Governor Waller identifies authentication, liability, and fraud as concrete barriers preventing AI agents from reliably making purchases on behalf of consumers. Understanding these challenges shapes how the payments industry builds trust in delegated commerce.
companion note from the same speech, on the trust barriers rather than the market-structure choice
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Will agent economies develop as open webs or closed gardens?
Whether AI agents will communicate freely across platforms or be locked into proprietary ecosystems controlled by tech incumbents. This matters because it determines who captures value and controls market access in a future agentic economy.
Extends Waller's open/closed analogy: argues technical standards and market governance, not capability, decide whether agent platforms stay open or close
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Payments in the Age of AI Agents
- The Agentic Economy
- From Product Search to Preference Articulation: The Economics of Agentic Commerce
- What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce
- Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets
- When Agents Shop for You: Role Coherence in AI-Mediated Markets
- AI Agents Want to Shop for You: The Future of Agentic Commerce
- Vertical tacit collusion in AI-mediated markets
Original note title
Waller likens open and closed agentic commerce systems to open and closed loop payment networks