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AI shopping agents are starting to influence what people discover, compare, and may eventually buy—but product discovery is advancing faster than fully autonomous checkout. Brands can prepare now by making product information accurate and usable beyond their own websites, planning how to identify legitimate agents, and setting clear rules for delegated purchases. They should not assume that one protocol, adoption curve, or model of consumer trust has won.
What does it mean for an AI agent to shop?
Agentic commerce is commerce in which an AI agent can discover, decide, and complete purchases for a consumer within permissions and controls that consumer defines. That definition, published by Visa in 2026, describes a possible range of activity—not a claim that every shopping assistant can buy things independently.
Discovery is not the same as checkout
A shopping assistant may answer a product question, recommend an item, or compare options without having authority to place an order. Completing a purchase adds separate requirements: the agent needs a way to act on the shopper’s behalf, the merchant needs to support that action, and payment and authorization must be handled securely. Keep these stages distinct when assessing traffic, conversion, and readiness.
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Why brands are paying attention
When an agent narrows a shopper’s options, its answer may shape consideration before the shopper reaches a retailer’s site. Visa’s October 2025 protocol announcement cited Adobe Data Insights’ finding that AI-driven visit share to U.S. retail sites grew 4,700% over the prior year. That figure concerns U.S. retail-site visits, is attributed to Adobe Data Insights, and should not be read as a 4,700% increase in AI purchases.
How far has agentic commerce actually progressed?
The evidence points to momentum, but it mixes platform activity, organizational adoption, and consumer expectations. Those measures describe different things and should not be combined into one adoption rate.
- Discovery behavior: Salesforce’s September 2026 release reported 200% year-over-year growth in agentic search as the first step in shopping journeys.
- Organizational use in Singapore: Salesforce reported that 28% of Singapore commerce organizations were currently using agentic AI. This is a Singapore-specific company-reported figure, not a global rate.
- Planned deployment in Singapore: Salesforce said 52% of non-adopters in that context planned to deploy within the next six months. A stated plan is not a completed rollout.
- Consumer expectations: In its 2026 consumer research, Checkout.com reported that 33% of surveyed consumers expected at least 10% of their purchases to be AI-driven within a year. This is an expectation, not observed purchase share.
These findings support preparation for AI-mediated discovery; they do not establish that autonomous checkout is routine or that a particular region, category, or merchant has reached the same stage.
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What should brands make ready first?
1. Make product information machine-legible and dependable
Product data needs to answer the questions people actually ask, not just populate a product detail page. Review whether an agent can find consistent, current answers about a product’s identity, variants, attributes, availability, price, delivery, returns, and compatibility. Where those facts are missing, contradictory, or buried in unstructured copy, recommendations may be incomplete or inaccurate.
Salesforce’s release describes surveyed organizations working on content quality, conversational-query optimization, and feeds for AI search platforms. Google Cloud’s January 2026 article similarly emphasizes high-fidelity, brand-aligned content. Treat these as readiness priorities and vendor-reported descriptions, not proof that one feed format or content tactic guarantees recommendation placement.
- Assign owners for product facts and define how quickly price, inventory, and policy changes must propagate.
- Keep the same canonical facts aligned across product feeds, retailer systems, and brand-controlled content.
- Test conversational questions that matter to your category: “Will it fit?”, “What works with this?”, or “Can I return it?”
- Track errors and omissions in agent-mediated answers separately from ordinary site-search issues.
2. Treat agent traffic as an identity and operations issue
More automated visits can create a classification problem: a merchant’s bot defenses may block a legitimate shopping agent, while weak controls may allow harmful automation. Visa describes these challenges alongside the need to support agent-driven checkout and retain visibility into the consumer and payment information involved.
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Visa’s Trusted Agent Protocol uses agent-specific cryptographic signatures and signals related to agent intent, consumer recognition, and payment information. Visa says the initial specifications apply to its network in this phase. This is one effort to address agent identification and payment interactions; it is not evidence of a universal merchant standard or broad availability across all networks and markets.
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3. Define delegated authority before enabling purchase
Permission should be specific enough that a consumer understands what the agent may do and can stop it. In Checkout.com’s 2026 survey, 30% of surveyed consumers named spending caps among their top confidence requirements; 29% named instant revocation, and 28% named easy cancellation. Visa’s 2026 discussion also stresses verified identity, secure credentials, human control, and the ability to override.
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Translate those principles into operational rules: what categories or merchants are allowed, whether an order requires confirmation, how a maximum spend is enforced, how a permission is revoked, and what happens if an order is canceled or disputed. Decide which actions remain subject to human approval, and make the answer visible at the point where permission is granted.
4. Prepare for comparison without assuming loyalty disappears
Checkout.com reported in 2026 that 57% of its surveyed consumers would let an AI shopping agent switch brands if it found better value. That finding points to a possible pressure on brands when agents compare offers against stated criteria; it does not show that consumers generally intend to abandon brand preferences. Value may include more than price, so make the differentiators your customers care about—such as compatibility, durability, service, or return terms—clear and verifiable.
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Which agent protocols should a brand plan around?
There is no settled, standardized architecture in the evidence available. Different initiatives address different parts of the agent-to-merchant journey, and their names alone do not tell a retailer whether a system supports discovery, cart actions, completed checkout, identity verification, or permission management.
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Visa has described its Trusted Agent Protocol. Mastercard’s 2026 article describes work with Google on Universal Commerce Protocol and collaboration across Google protocols and OpenAI’s Agentic Commerce Protocol. The International Monetary Fund’s April 2026 note characterizes these initiatives as emerging design patterns rather than settled or standardized architectures.
Evaluate any proposed integration against the same practical questions before committing:
- Journey stage: Does it support discovery, recommendations, cart changes, checkout, or only some of these?
- Identity and intent: How does the merchant distinguish an authorized agent and its purpose from other automated traffic?
- Consumer control: Where are payment credentials, limits, confirmation, revocation, and cancellation handled?
- Interoperability: Can it work with your commerce, inventory, payment, and customer-service systems—and with other approaches you may need?
- Availability: Which merchants, payment networks, regions, and product categories can use it now?
- Human approval: Which actions still require the shopper to review or confirm?
The reviewed sources do not establish a neutral, like-for-like scorecard or justify ranking these approaches. Treat protocol claims as implementation proposals to validate against your own merchant systems, geography, risk requirements, and customer experience.
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- Audit product facts. Sample high-value and high-question products. Check whether core attributes, availability, policies, and compatibility answers agree across your feeds and customer-facing channels.
- Test discovery questions. Use real customer phrasing to assess whether the information available to AI-mediated discovery is complete and brand-aligned. Record incorrect answers and missing facts rather than treating a recommendation mention as a sales outcome.
- Map the transaction boundary. Document what an agent may do today, what it cannot do, and where customer confirmation would be required if agent-led checkout becomes available.
- Review bot and fraud controls. Identify how legitimate agents could be challenged or misclassified, without relaxing protections for unverified automation.
- Write a delegated-purchase policy. Specify limits, confirmation, revocation, cancellation, customer support, and dispute handling before enabling any purchasing authority.
- Monitor separately. Distinguish AI-referred visits, product consideration, cart activity, and completed purchases in reporting. Avoid labeling discovery traffic as autonomous commerce.
What should leaders avoid claiming?
- Do not say AI agents have broadly replaced conventional shopping or that autonomous checkout is already routine; Salesforce says discovery is moving first and fully autonomous purchasing remains early.
- Do not treat survey expectations as observed behavior, or combine percentages from different studies into a single adoption curve.
- Do not generalize Salesforce’s Singapore adoption figures to global commerce organizations.
- Do not describe one protocol as the industry standard while multiple efforts are emerging and the IMF characterizes the architecture as unsettled.
- Do not claim agent comparisons inevitably erase brand loyalty; the Checkout.com switching result is a survey finding, not a universal forecast.
The near-term brand opportunity is to be accurately understood during discovery and operationally prepared for delegated transactions. The near-term risk is confusing those two states: a product being surfaced by an AI is not the same as an agent having permission, infrastructure, and consumer trust to buy it.
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