Read “The Global State of Fraud and Returns” report
Imagine AI agents landing on your platform right now, comparing products, building carts and (in some cases) checking out. Is your marketplace ready to support that journey? Probably not. Agentic commerce for marketplaces is still early, and no marketplace has the full playbook yet. But there are steps you can take now to start preparing, and we’ve listed them below.
TL;DR
- Marketplaces need a different agentic commerce playbook than single retailers because they have to manage seller quality, buyer trust and platform risk at the same time.
- Marketplaces can prepare for agentic commerce by doing things like standardizing catalog data for agent discovery, strengthening seller verification and payout controls, rethinking fraud signals built around human browsing behavior and preparing checkout flows for trusted agent-led transactions.
What does agentic commerce mean for marketplaces?
For online marketplaces, agentic commerce means AI agents are starting to shape how products are discovered, evaluated and purchased across your platform. They surface products before a shopper ever reaches your website or app, changing how sellers compete for visibility and making listing quality more important. And once those agents move into the transaction flow — whether for low-risk purchases now or higher dollar amount transactions in the future — they also change how your systems interpret intent, risk and identity when the one checking out isn’t always human.
Understanding the agent-led customer journey
Agentic commerce is influencing the entire purchase journey, from how online shoppers discover products to how transactions are completed. While the traditional customer journey (awareness, consideration and decision) still exists, the mechanics have changed.
In a traditional ecommerce shopping session, a human buyer arrives and takes their time to make a decision. They browse, click around, open a few tabs, add items to the cart and eventually check out or abandon the transaction. And all of those actions leave behind a behavioral footprint that your systems track and learn from.
An agent-led session looks nothing like that. The agent arrives with intent already formed. It navigates programmatically after being given a prompt (“Hi Claude, find the best black tights for winter in a size L that will ship to me ASAP”), executes the task and exits. Sessions are shorter, browsing is narrower and traffic lands further down the funnel. And many of the familiar behavioral signals (like more time spent clicking around your site or reading your product pages) are distorted or missing entirely. For single-brand retailers, those missing signals affect one storefront. For marketplaces, the same problem plays out across hundreds, if not thousands, of sellers at once.
Do online marketplaces need a different playbook for agentic commerce than retailers?
Marketplaces can’t follow the same agentic commerce playbook as single-brand ecommerce retailers. Why? The operating model is fundamentally different. Retailers are mostly protecting their own storefronts and customers. Marketplaces like yours, on the other hand, have to manage systemic risk across an entire ecosystem — governing seller-generated catalog data, onboarding integrity, buyer trust and payout controls all at once.
“How well products are described and named will make or break marketplace discoverability in agent-led shopping.”
Tara Mitchell, Signifyd senior director, risk operations
Start by replacing freeform fields with structured inputs wherever you can. Require category-specific fields for details like brand, size, material, color, condition, compatibility and shipping speed instead of leaving sellers to explain everything in open text boxes. Use listing templates by category so sellers are prompted to provide the information that really matters for discoverability.
Next, tighten how values get entered. Having sellers select from standardized attribute options in dropdowns, for example, make it easier to keep similar products comparable. You can reinforce that with title and description rules, character limits, banned keyword stuffing and guidance on what belongs in each field.
You can also make adjustments on the platform-side. Consider leveraging AI to:
- Auto-suggest cleaner titles
- Offer more accurate translation services
- Extract missing attributes
- Flag vague or incomplete listings for review before they go live
Strengthen seller verification and payout controls
A seller may look legitimate at first, then once they understand how your platform works, start using agent-led buyer accounts or automated workflows for fraudulent and abusive activities. The abuse often doesn’t look dramatic and usually shows up as a string of low-dollar customer issues instead of obvious fraud.
Should an untrustworthy seller be banned from a marketplace, they might use AI agents to mask their identity and return to open another unscrupulous shop.
In the case of marketplaces, needing to understand the intent of both buyers and sellers doubles the difficulty of avoiding fraud schemes and scams for businesses operating marketplaces. Consider the possibilities for mischief, for instance, when the same individual is on both ends of the deal.
By relying on AI agents to do the buying, a marketplace seller could:
- Spin up multiple agents to repeatedly purchase moderately priced items from its own store before requesting a refund through the marketplace. In some cases, the marketplace itself will issue the refund without requiring the seller to pay up, given the cost and effort involved in securing the reimbursement. Using agents masks the evidence that it’s the same seller behind all the purchases and refund requests. By using promotional discounts for first-time buyers for each new agentic buyer, a wayward seller can significantly boost their profit.
- Launder money through the marketplace by using many agents to make purchases from their storefront. The seller then deposits those receipts into a bank account or onto a reloadable debit card. Because the purchases are made by many agents, certain identifiers — like IP addresses — that might otherwise link the many buyers to the same person (and expose them as the seller themselves) can be harder to detect. That obfuscation gives the seller the time and space to create legitimate-looking transactions.
- Create multiple storefronts with inventory they don’t have or don’t intend to deliver. They can then use agents to place multiple orders that mimic good buyers and generate positive reviews to attract other shoppers.That makes the storefront look trustworthy while making the scheme harder for marketplaces to detect, giving illegitimate sellers more time to collect funds before disappearing.
Because of this heightened vulnerability, your seller verification has to extend past onboarding. Know Your Business (KYB) and identity checks (i.e. government-issued ID verification and bank account ownership confirmation) are the starting point. Ongoing monitoring is where you catch the behavior you can’t predict during onboarding — things like sudden listing changes or repeated refund requests activity on orders under $10 for certain sellers.
The same logic applies to payouts. Once a bad actor has platform access, the next step is often cashing out or moving funds in ways that are harder to trace. Monitor payout trails closely: watch for suspicious account changes, reused bank details, reloadable debit cards and other signals that suggest connected abuse.
Use network analysis to detect connected abuse
Seller verification and payout monitoring help you assess individual sellers and watch for obvious signs of risk. But abuse rarely stays isolated to one account. A bad actor can use agent-led buyer accounts, automated purchasing flows or tokenized payment layers to make related activity look like separate, legitimate transactions.
Network analysis can help you catch the patterns that individual account reviews miss. Look for the same seller sitting behind multiple buyer accounts, the same payout details appearing across different sellers or the same listing tied to repeated refund and appeasement activity.
Rethink fraud signals for agent-led transactions
Most legacy marketplace fraud models were built around human behavior: how long someone browses, how many listings they click, if they move through search and category pages in a familiar sequence and what the path to checkout looks like. Agent-led sessions break those assumptions. An agent may land deep in the funnel with very little visible exploration. While that doesn’t automatically make the session suspicious, it does mean some signals your systems rely on will carry less weight than they used to.
Start by auditing the rules, models and manual review cues that depend on browsing depth, session length, click paths or direct-to-product traffic. Session length and click depth, for example, break quickly in agent-led flows. Device and identity consistency, order value patterns and behavioral velocity across accounts tend to hold up better. To help figure out which signals still stand and which need to be replaced, consider tapping into Signifyd’s Commerce Network and broader agentic commerce capabilities.
How to maximize agentic commerce approvals
Prepare checkout and payment flows for trusted agents
Even when an agent is authorized, your checkout may still create friction if it’s designed for human flows. Extra redirects, confusing error messages, unnecessary fields, brittle session handling and steps that break when the buyer arrives late in the funnel can all block a legitimate order. The payment layer adds a second layer of complexity. An agent buying on a person’s behalf is a delegated transaction. Though the cardholder isn’t initiating the payment in real time, they did grant permission upstream. Not all payment service providers (PSPs) are built for that, and not all checkout flows know what to do with a request that arrives without a normal browsing session behind it.
To get your checkout flow ready for agentic commerce, clean up the basics: cut unnecessary redirects, simplify forms, sharpen error messages and make sure shipping, tax, payment and order-confirmation steps work cleanly when the purchase path is shorter and more direct.
Those fixes handle the surface. The harder work is making sure your payments infrastructure can handle a transaction that happens without a human. At minimum, your stack needs to be able to receive and validate agent-originated requests without rejecting them as anomalous, handle delegated payment authorization through your PSP and pass agent metadata — what agent, what permissions, what session context — downstream to your fraud, risk and order systems so they’re not making decisions with blindspots.
However, passing the right data downstream only helps if your risk systems know what to do with it. Risk assessment needs to work at two levels simultaneously, and needs to ask three key questions:
- Is the agent legitimate and acting within its authorized scope?
- Is the human who delegated that authority actually who they claim to be?
- Does the transaction fit that person’s normal online purchase behavior?
After all, a compromised account can produce a technically valid agent request. If your systems only check one layer, you may block good orders or accidentally approve bad ones.
Improve your post-purchase communications
When a person uses an AI agent to handle more of the end-to-end purchase, they may not recognize the charge immediately, especially on a marketplace where multiple sellers, listings and fulfillment paths are involved. That creates a real chargeback risk. A buyer who doesn’t remember authorizing the purchase doesn’t call you. They call their issuing bank.
Make your post-purchase communication explicit enough to close that gap. Send immediate order confirmations that clearly show the product, seller, price, shipping method and expected delivery window. Follow up at key moments, like when the order is confirmed, shipped, delayed or delivered. Keep messages consistent across email, SMS and in-platform notifications so buyers aren’t left trying to figure out what happened or immediately disputing the charge when it shows up on their statement.
To get ahead of agentic commerce, focus on what you can control first
Marketplaces don’t need all the answers today to make real progress in preparing for agent-led shopping. To get ahead of agentic commerce, start with what you can control now: cleaner catalog data, stronger seller oversight, better risk signals, more resilient checkout flows and clearer post-purchase communications.
Want a broader view of where commerce is heading and how leading merchants and marketplaces are preparing for it? Read Signifyd’s “Global State of Commerce 2026” report.
FAQs
When will agentic commerce impact marketplaces?
Agentic commerce is already starting to affect marketplaces, even if adoption is still early. For example, Etsy has integrated with ChatGPT, letting users discover and browse listings directly within the chat experience.
Why do marketplaces need a different approach to agentic commerce than traditional online retailers?
Marketplaces operate with more moving parts than traditional retailers. They have to manage third-party sellers, inconsistent catalog data, buyer trust and platform-wide risk all at once. That means they need an agentic commerce strategy that goes beyond storefront optimization and addresses things like seller controls, listing quality and systemic abuse across the platform.
How does agentic commerce change fraud prevention for marketplaces?
Agentic commerce can make legitimate shopping behavior look different from the human patterns many fraud systems were built to recognize. Shorter sessions, fewer clicks and faster paths to checkout may no longer be reliable risk signals on their own. For marketplaces, fraud prevention will need to rely less on traditional browsing behavior and more on stronger identity signals, seller monitoring and connected-risk detection across buyers, sellers and payouts.
