AI Agent Merchant Discovery: What Klarna Signals

Discovery is turning into infrastructure.

If you’re a retailer or payments leader, you’ve probably spent years treating “findability” as a marketing problem: SEO, ads, marketplaces, affiliates. Now AI agents are changing the shape of that funnel. When a shopper asks an agent to “find the best black boots under $200 that arrive before Christmas,” the agent doesn’t browse like a human—it assembles options from structured signals, product feeds, policies, and payment/checkout reliability.

That’s why Klarna’s move to give merchants tools to be discovered by AI agents matters. Even though the original article source wasn’t accessible at scrape time (403), the headline alone points to a very real shift: merchant discovery is becoming an AI-to-AI handshake. And payments providers are in a unique position to mediate that handshake—because they already sit at the intersection of identity, intent, risk, and conversion.

Why AI agents change merchant discovery (and why payments teams should care)

AI agent discovery favors merchants with clean data, predictable fulfillment, and trustworthy checkout. Traditional search ranking can tolerate messy catalogs and ambiguous policies; agents can’t. They need deterministic inputs.

Here’s the practical change: agent-driven shopping compresses the funnel.

• In classic e-commerce, customers bounce between product pages, reviews, shipping calculators, and checkout.
• With an AI shopping agent, the user’s intent is captured once, and the agent tries to resolve it into a short list—or a single recommended purchase.

Payments and fintech infrastructure become part of “discovery” because the agent is optimizing for conversion and safety, not just relevance. If the agent believes a merchant will fail at checkout (declines, friction, weak authentication), it will avoid sending the user there.

The new ranking factors: “Can this merchant complete the transaction?”

AI agents implicitly rank merchants on operational truth:

1. Product truth: accurate attributes, variants, availability.
2. Policy truth: shipping cutoffs, returns, warranties.
3. Price truth: total cost with taxes/shipping, promo rules.
4. Payment truth: supported payment methods, approval likelihood, fraud posture.
5. Service truth: delivery performance, dispute rates, customer support responsiveness.

Most companies get this wrong: they treat these as separate systems owned by separate teams. For AI agents, they’re one bundle.

Discovery is no longer “who has the best ad.” It’s “who can confidently fulfill the promise.”

What “tools for discovery by AI agents” likely means in practice

Merchants don’t need another chatbot. They need machine-readable commerce. When a provider like Klarna talks about AI agent discovery, the underlying mechanics usually look like this:

1) Structured merchant and product metadata

Agents can’t rely on scraped pages alone. They prefer feeds and schemas.

• Normalized product attributes (size, material, compatibility)
• Real-time inventory signals
• Shipping options and cutoff times (especially in December)
• Clear return windows and conditions

This is where retailers in the “AI in Retail & E-Commerce” series often start: personalization and product intelligence. But the agent era forces the next step—standardization.

2) Trust signals tied to payments

Payments networks and BNPL providers see what most marketing stacks don’t:

• approval/decline patterns
• chargeback and dispute ratios
• fraud attack signatures
• checkout latency and drop-off

If discovery engines increasingly factor “transaction success,” payment-layer signals become inputs to visibility.

3) Agent-friendly offers and financing options

Klarna’s ecosystem has a specific advantage: it can package offers (pay-in-4, monthly financing, dynamic promos) in a way an agent can reason about.

For example, an agent comparing two merchants might weigh:

• Merchant A: $180 boots, 5–7 day shipping, no financing
• Merchant B: $195 boots, 2–3 day shipping, pay-in-4 available

In late December, shipping certainty can beat raw price. Agents will behave that way because users behave that way.

The infrastructure angle: discovery, routing, and risk are merging

The smartest way to view AI agent discovery is as a routing problem. The agent is routing demand to supply, and it will prefer routes that are less risky and more predictable.

Payments teams already understand routing—just in a different context:

• smart transaction routing across processors
• failover and retries
• step-up authentication
• fraud scoring and decisioning

Agent discovery is similar, only upstream. Instead of routing an authorization to the best acquirer, you’re routing a shopper to the best merchant experience.

Smarter discovery can reduce fraud (if you do it right)

Fraud thrives in ambiguity: mismatched product data, unclear policies, low-friction onboarding, and fragmented identity.

Agent-based discovery can lower fraud when it:

• prefers verified merchants with consistent fulfillment
• penalizes merchants with abnormal dispute patterns
• uses entity resolution to detect cloned storefronts

But it can also create new attack paths:

• data poisoning: attackers manipulate feeds/reviews that agents consume
• prompt injection: malicious content that tries to redirect an agent
• synthetic identities: agents interacting with fake shopper accounts at scale

If you’re building fintech infrastructure, the stance should be clear: agent discovery needs a security model, not just a UX.

Payments reliability becomes a discovery feature

From what I’ve seen, many merchants still treat declines as “a payments issue.” In an agent world, declines become a marketing issue because they reduce the agent’s confidence.

A merchant with:

• high false-positive fraud blocks,
• poor SCA handling,
• weak network tokenization coverage,
• inconsistent descriptor and dispute flows,

…is going to look “unreliable” to an optimization-minded agent.

What fintechs and merchants can do now (a practical checklist)

If you want AI agents to send customers your way, your commerce data and payment stack must be agent-readable and outcome-driven. Here’s a tight plan you can run in Q1 planning while holiday lessons are still fresh.

1) Make your catalog agent-ready

Focus on precision over volume.

• Normalize attributes (sizes, colors, materials) into consistent enums
• Publish variant-level availability (not “in stock” at the parent level)
• Provide delivery promises as structured ranges (and keep them true)
• Tag products with compatibility and constraints (e.g., “fits iPhone 15 Pro”)

If you’re already investing in AI for personalization, reuse that work: the same feature store that powers recommendations can power agent feeds.

2) Treat policies as data, not paragraphs

Agents can’t negotiate with a wall of text.

• return window in days
• restocking fees (yes/no + amount)
• free return shipping (yes/no)
• warranty duration
• shipping cutoff timestamps by region

This reduces customer support load too, because fewer customers arrive surprised.

3) Improve checkout certainty with payments instrumentation

You can’t optimize what you don’t measure.

Track these weekly, by market and device:

• authorization rate (approved / attempted)
• soft decline rate and recovery rate
• 3DS challenge rate and completion rate
• time-to-authorize (p95)
• chargeback rate by reason code

Then connect them to discovery outcomes (traffic quality, conversion rate, repeat purchases). That’s where “AI in payments” stops being theory.

4) Build “agent APIs” the boring way: stable, versioned, testable

If your organization wants agent discovery, don’t start by building a flashy agent. Start with an interface.

• versioned product and offer endpoints
• signed webhooks for inventory and price changes
• clear idempotency rules
• rate limits and abuse controls

Agents will call you more often than humans click you.

5) Use risk signals to guide discovery—not just to block

This is the contrarian take I agree with: fraud tools shouldn’t only stop bad events; they should also amplify good merchants and good customers.

Examples:

• fast-track returning customers with strong history
• reduce friction (and step-ups) on low-risk carts
• surface “trusted merchant” badges based on measurable outcomes

The ethical line matters here. Don’t create opaque “credit scoring for visibility.” Do publish understandable criteria: dispute rate, on-time delivery, verified policies.

People also ask: common questions about AI agent shopping discovery

Will AI agents replace marketplaces and comparison sites?

No. They’ll compress them. Marketplaces still aggregate supply, but agents will increasingly act as the front door, pulling from marketplaces, DTC sites, and retail partners.

Does BNPL improve discovery, or just conversion?

Both—if the financing offer is machine-readable and reliable. Agents weigh affordability and approval probability. A BNPL option that often fails at checkout won’t help.

What’s the biggest mistake retailers will make in 2026?

Treating agent discovery like another channel campaign instead of a data-quality and reliability program. Agents reward operational consistency.

The real signal from Klarna: distribution is becoming programmable

Klarna’s positioning around AI agent discovery fits a bigger infrastructure trend: distribution is moving from human attention to machine intent. Retailers already adopted AI for personalization, demand forecasting, and dynamic pricing. Agent discovery is the next layer—because it decides where the customer goes before your personalization even runs.

If you’re a merchant, the priority is simple: make your products, policies, and checkout legible to machines. If you’re a fintech or payments provider, the opportunity is even bigger: you can provide the trust and routing layer that agents rely on.

If you’re planning your 2026 roadmap, ask one uncomfortable question: when an AI agent chooses where to send a customer, what evidence will it have that your checkout will succeed?