AI Agent Commerce: Make Your Store Discoverable in 2025

Discovery is getting redistributed—fast. Over the past two years, shoppers have shifted meaningful time from “search and scroll” to “ask and decide.” They’re asking chatbots for gift lists, asking assistants to compare brands, and asking in-app agents to find “the best option under $50 that arrives before Tuesday.” That’s not a UI change. It’s a new distribution channel.

Klarna’s push to give merchants tools for discovery by AI agents is a signal: payments companies aren’t just competing on checkout anymore. They’re moving upstream into the moment a customer decides what to buy.

For retailers and marketplaces, this matters because AI-driven discovery changes the rules you’ve optimized for—SEO, paid social, affiliate, and even onsite personalization. If an AI agent is choosing which products to present, your storefront needs to be readable by machines, trustworthy in the data it exposes, and integrated into the payment rails that finalize the purchase.

What “discovery by AI agents” actually means for merchants

Discovery by AI agents means your product catalog, offers, and fulfillment promises become machine-readable so an assistant can recommend—and increasingly purchase—on a shopper’s behalf. It’s the next step in AI in retail & e-commerce: from “personalize my homepage” to “help a bot confidently pick my product.”

The practical shift is simple: instead of competing for clicks, you’re competing to be the selected option in an agent’s shortlist.

From search ranking to agent selection

Classic e-commerce discovery is built around:

• Keywords and category pages
• Ads and retargeting
• Influencer/affiliate links
• Merchandising and onsite search

Agent-based discovery adds new selection criteria that look more like procurement:

• Does the product have structured attributes (size, materials, compatibility, allergens)?
• Is pricing and availability reliable right now?
• Are shipping cutoffs, delivery dates, and return policies clear?
• Does the merchant have strong trust signals (ratings, dispute rates, fraud controls)?

If your catalog is messy, your inventory is stale, or your delivery promises are vague, an agent will default to the brand that’s easier to “reason about.”

Why a payments player is taking the lead

Klarna sits at a junction where discovery and conversion meet:

• It has shopper intent signals (what people browse and finance)
• It’s embedded in checkout flows (where drop-off happens)
• It can standardize merchant data formats at scale

That combination makes it logical for a fintech platform to offer “merchant tools for AI agents.” The bet is that whoever mediates agent shopping decisions will influence transaction routing, financing attach, and long-term customer value.

The infrastructure underneath: agent-ready product data + agent-safe checkout

AI agents don’t browse the way humans do; they query. They need clean inputs and predictable outputs. That’s an infrastructure problem as much as a marketing one.

Here’s what I’ve found in practice: when teams treat agent readiness as “just another marketing channel,” they underinvest in the unglamorous plumbing—feeds, schemas, reconciliation, and fraud controls. Then they wonder why recommendations don’t convert.

Agent-ready product data: the new baseline for retail AI

To be “discoverable” by AI agents, merchants need to expose structured, accurate, frequently refreshed product data. That typically includes:

• Core attributes: title, brand, category, variants, GTIN/UPC where applicable
• Decision attributes: materials, dimensions, compatibility, dietary info, sustainability markers
• Commercials: price, currency, promotions, subscription options, financing eligibility
• Availability: inventory levels or availability status, backorder rules
• Fulfillment promises: shipping methods, cutoffs, delivery ETA by region, pickup options
• Policy: returns window, restocking fees, warranty

If this sounds like “feeds,” it is. But agent commerce puts more weight on completeness and consistency than traditional channels, because the assistant can’t “guess” what you meant.

Snippet-worthy truth: Agents prefer the merchant with the clearest data, not the prettiest site.

Agent-safe checkout: fewer steps, more guarantees

Even if an agent can recommend your product, it still needs to complete a transaction. That introduces hard requirements:

1. Reliable price and stock validation at the moment of purchase
2. Strong customer authentication without breaking the flow
3. Fraud controls tuned for agents (new device patterns, API-driven behavior)
4. Dispute and returns handling that doesn’t penalize legitimate automation

Payments infrastructure is where this becomes real. If your checkout can’t support fast, verified purchases (or it rejects “agent-like” traffic), you’ll lose sales to merchants with smoother payment orchestration.

Klarna as a case study: why discovery and payments are merging

The big idea: the company that helps customers decide can also help them pay. Klarna’s merchant tooling for AI discovery fits a broader pattern across fintech infrastructure in 2025:

• Upstream expansion: payment platforms moving into shopping, offers, and intent
• Downstream optimization: more control over authorization, routing, and post-purchase service

From a merchant perspective, this creates a new playbook: treat your payment partner as part of your growth stack—not just a cost center.

Merchant visibility is becoming a fintech problem

Most retail leaders think of visibility as a marketing KPI: impressions, traffic share, ROAS. Agent commerce turns visibility into a data and infrastructure KPI:

• Feed accuracy rate
• Catalog attribute completeness
• Real-time price/stock match rate
• Authorization success rate by channel
• Refund/dispute resolution time

That’s why this belongs in an “AI in Payments & Fintech Infrastructure” campaign. The infrastructure determines whether you’re even eligible to be recommended.

Transaction efficiency is the hidden win

When discovery and conversion are integrated, you typically see improvements in:

• Lower drop-off (fewer handoffs between recommendation → checkout)
• Higher authorization rates (cleaner signals, better routing)
• Better unit economics (less paid traffic dependence)

It’s not magic. It’s fewer broken steps.

What merchants should do now: an “agent readiness” checklist

If you want AI agents to send you customers, treat this like a product launch, not a plugin install. Here’s a practical checklist you can hand to your e-commerce, data, and payments teams.

1) Clean up your catalog like an agent will judge it

Start with the 20% of SKUs that drive 80% of revenue.

• Standardize variant logic (size/color bundles that humans understand often confuse machines)
• Fill missing attributes that influence purchase decisions
• Normalize units (inches vs cm, ounces vs grams)
• Remove ambiguous titles (agents hate “Premium Classic Best Seller”)

A simple internal metric that works: attribute completeness % for priority SKUs.

2) Make availability and delivery promises machine-trustworthy

Agents will penalize uncertainty. Build confidence with:

• Real-time inventory status (or at least frequent refresh)
• Region-based delivery ETAs
• Clear cutoff times during peak season (December is brutal for this)
• Transparent backorder rules

Holiday context matters here: in late December, “arrives before Christmas” becomes “arrives before New Year’s,” and shipping cutoffs create customer frustration fast. If your agent-facing promise is wrong, your returns spike.

3) Reduce checkout friction and raise authorization reliability

This is where payments infrastructure earns its keep.

• Audit payment authorization rates by device and channel
• Ensure 3DS/SCA flows are as low-friction as possible while meeting compliance
• Use retries and smart routing if you operate multi-PSP setups
• Confirm idempotency and safe retry logic on payment APIs (agents will retry)

If you don’t know your baseline authorization rate, you can’t improve it.

4) Prepare fraud controls for agent behavior

Agent traffic can look like bots. Fraud systems can overreact.

• Coordinate with your PSP on what “agent purchase” signatures look like
• Prefer risk models that incorporate merchant and basket context, not just device heuristics
• Build step-up verification rules that don’t destroy conversion

The goal isn’t “block automation.” It’s “verify intent.”

5) Instrument everything: measure agent commerce like a funnel

You’ll want metrics that connect discovery to payment outcomes:

• Agent-referred sessions (or API-originated order attempts)
• Add-to-cart rate vs agent channel
• Checkout initiation and completion rates
• Authorization success rate
• Refund and dispute rate
• Net revenue per order after fees and returns

If you can’t attribute outcomes, your team will argue about anecdotes instead of improving the system.

Common questions teams ask (and direct answers)

Will AI agents replace SEO and paid social?

No, but they’ll siphon off high-intent traffic. SEO and ads still matter, especially for awareness. Agents will dominate the “I already know what I want, help me choose and buy” segment.

Do I need to expose my entire catalog to be discoverable?

Start with your highest-converting SKUs and the categories where you win. Agents reward reliable data. A smaller, cleaner set performs better than a massive, inconsistent feed.

Is this only relevant for big retailers?

Smaller merchants can benefit faster because they can standardize faster. Enterprise retailers often have fragmented catalog systems. A nimble team can become “agent-friendly” in weeks, not quarters.

What’s the biggest mistake merchants make?

They optimize the front-end experience while ignoring the back-end truth. Agents don’t care about your hero banner. They care that the product attributes, price, and delivery promise are correct.

Where this fits in the AI in Retail & E-Commerce series

In this series, we’ve talked about personalization, forecasting, and customer analytics. Agent commerce is the connective tissue between them. It forces retailers to operationalize their data discipline and payment reliability, because an AI assistant won’t compensate for messy infrastructure.

If you remember one line, make it this: Agent discovery is earned with clean data, and agent conversion is earned with resilient payments.

The next step is to assess where your stack is brittle—catalog quality, offer logic, fraud rules, authorization rates—and fix those before you chase shiny agent integrations.

If you’re building an AI-ready commerce stack and want a practical assessment of your payment flow and data readiness, map your current funnel from product data → recommendation surfaces → checkout → authorization → post-purchase. You’ll immediately see where the agent era will reward you—or expose you.

What will matter more in 2026: being the cheapest option, or being the most verifiable option for an AI agent to recommend and buy?