AI Agent Discovery: What Merchants Need in 2026

Holiday traffic compresses a year’s worth of customer intent into a few weeks. When that happens, merchants don’t just compete on price or shipping speed—they compete on being findable in the places where purchase decisions now start: chat-style assistants, shopping copilots, and “agentic” tools that shortlist products before a shopper ever reaches a traditional search page.

That’s why the recent signal from Klarna—giving merchants tools designed for discovery by AI agents—matters even if you don’t use Klarna today. It points to a broader shift in fintech infrastructure: payments players are moving upstream from “checkout button” to shopping discovery layer, and AI agents are becoming a new kind of traffic source.

This post is part of our AI in Retail & E-Commerce series, and it makes a clear point: AI agent discovery isn’t just a marketing problem. It’s also a payments and risk problem—because the moment an agent decides where it sends a shopper, it also shapes transaction routing, approval rates, fraud exposure, and visibility across the payment stack.

AI agent discovery is becoming a new acquisition channel

AI agent discovery is a simple idea: software agents choose what to show the shopper. Instead of ten blue links, an agent returns two or three recommended products, a preferred merchant, and sometimes even a pre-filled checkout path.

For merchants, that changes the playbook. You’re no longer optimizing only for human scanning behavior (titles, thumbnails, reviews). You’re optimizing for machine selection: structured product attributes, clear policies, reliable delivery promises, and high-confidence signals that a transaction will complete cleanly.

Why fintech companies are getting involved

Payments providers see what happens after a shopper clicks “buy”: drop-offs, declines, chargebacks, refunds, shipping disputes. If an AI agent is going to recommend a merchant, it needs confidence on basics like:

• Is the item in stock right now?
• What’s the true landed cost (shipping, tax, fees)?
• Are returns predictable and fair?
• Will the payment likely be approved?
• Is this merchant low-risk for fraud or dispute?

That’s not “marketing data.” That’s commerce infrastructure data, and fintech networks are well-positioned to package it into agent-readable signals.

Snippet-worthy reality: In an AI-agent shopping flow, “discovery” and “checkout” are no longer separate steps—they’re one continuous decision pipeline.

The practical impact for retailers

In agent-led commerce, you can lose the sale without ever seeing the shopper. If your product data is incomplete, your policies are ambiguous, or your fulfillment performance is inconsistent, the agent may never surface you—no matter how good your site looks.

And when you do get surfaced, the payment has to work. Agent traffic tends to be high-intent and impatient. That raises the cost of declines, latency, and friction.

What Klarna’s move signals about fintech infrastructure

Even though the RSS scrape didn’t expose the full original article text (the source returned a 403), the headline itself—“Klarna gives merchants the tools for discovery by AI agents”—is enough to read the direction of travel.

Klarna built its business on smoothing checkout with flexible payment options. If it’s now emphasizing tools for AI agent discovery, it’s effectively saying: distribution will be mediated by agents, and merchants will need new tooling to stay visible.

Here’s the infrastructure angle I’d bet on (and I’ve seen this pattern repeat across payments):

1) Discovery tooling becomes a data standardization project

Agents rely on clean inputs. That pushes platforms to help merchants standardize:

• Product catalogs (variants, availability, substitutions)
• Shipping promises (cutoffs, carriers, regions)
• Returns and refunds rules (time windows, fees, exceptions)
• Pricing integrity (no surprise fees, consistent totals)

Merchants should treat this like SEO 2.0: structured, measurable, and audited.

2) Agent discovery connects directly to transaction routing

“Transaction routing” sounds like back-office plumbing. It isn’t anymore.

If an agent can choose how a customer pays (card, bank transfer, pay-later, wallet) and which rails carry the transaction, then routing becomes part of the customer experience. The best routing:

• Improves approval rates by choosing the right path for each shopper
• Reduces fraud by aligning risk signals to the payment method
• Lowers cost by selecting efficient rails when risk is low

Bridge point: AI agent discovery supports better transaction routing and visibility because the agent needs real-time confidence that the purchase will complete.

3) Discovery requires stronger visibility and accountability

Once agents influence where shoppers go, merchants will ask: “Why did I show up—or not show up?” That pushes platforms toward better observability:

• Attribution that explains agent referrals vs. human search
• Diagnostics for declines and drop-offs tied to agent flows
• Policy compliance reporting (returns, delivery, dispute rates)

This is where fintech infrastructure can genuinely help: the payments layer has the cleanest view of “attempted purchase” vs. “successful purchase” vs. “refunded” vs. “charged back.”

The security side: agents raise the stakes on fraud and disputes

AI agents will be a magnet for abuse because they aggregate intent at scale. Fraudsters love systems that:

• Auto-fill identity and payment details
• Reduce friction
• Make fast decisions

So if you’re a merchant, don’t treat agent discovery as a traffic gift. Treat it as a new threat model.

What changes when agents shop

1. Synthetic identities get more convincing. Agents can create consistent profiles, browsing histories, and shopping patterns.
2. Refund and returns abuse becomes easier. Agents can optimize exploitation of policy loopholes.
3. Account takeover can scale. If agents can transact quickly, stolen sessions become more valuable.

Controls that actually help (and don’t kill conversion)

You want layered defenses that keep checkout fast for good customers:

• Risk-based step-up authentication: only challenge when signals warrant it (device change, velocity spikes, odd shipping).
• Stronger linkage between order, delivery, and payment: consistent timestamps, carrier scans, proof-of-delivery where relevant.
• Policy clarity as a risk control: agents will avoid ambiguous merchants; fraudsters prefer them.
• Real-time monitoring for agent-like velocity: unusual patterning across SKUs, addresses, or payment tokens.

Bridge point: AI in commerce strengthens digital payment ecosystems when discovery, routing, and risk signals reinforce each other.

How to make your catalog “agent-readable” (practical checklist)

Most companies get this wrong by focusing on “AI content” instead of AI-ready operations. Agents reward consistency.

Product and inventory fundamentals

• Structured attributes: size, material, compatibility, certifications, warranty terms
• Accurate availability: avoid “in stock” if fulfillment is constrained
• Variant clarity: map variants to distinct IDs with consistent pricing and images
• Substitution rules (if applicable): what’s allowed, what isn’t

Pricing and policy clarity

Agents penalize surprises. So do customers.

• Display full cost early (shipping thresholds, taxes, surcharges)
• Keep return windows and fees unambiguous
• State delivery estimates by region, not a single generic range

Payments and checkout performance

Agent traffic amplifies weak points in your payments setup.

• Decline rate tracking by segment (geo, issuer, payment type)
• Fallback payment methods (wallets, A2A, pay-later where appropriate)
• Latency monitoring (slow checkout loses high-intent shoppers)
• Dispute readiness: capture evidence automatically (order confirmation, shipping status, customer comms)

Snippet-worthy stance: If your checkout isn’t observable, you can’t improve it—and agents will quietly route around you.

What merchants should ask providers offering “AI discovery tools”

Whether it’s Klarna or any other platform, ask blunt questions. You’re building dependency, so you need clarity.

1) “What inputs does the agent use to rank or recommend?”

Look for specifics:

• Which product attributes matter most?
• How are stockouts handled?
• How are shipping SLAs verified?

2) “How do you report performance and attribution?”

You need more than clicks.

• Agent impressions → agent referrals → checkout starts → approvals → completed orders
• Refunds and disputes attributed back to the traffic source

3) “How do you handle security and merchant protection?”

• What fraud signals are shared?
• How are returns and disputes managed?
• Are there protections for merchant-friendly evidence submission?

4) “Can I export my data and learnings?”

If the provider becomes your discovery layer, you still need portability:

• Product feed exports
• Performance reporting exports
• Clear APIs or batch files you can integrate into your own analytics

People also ask: quick answers for teams evaluating AI agents

Do AI agents replace SEO for e-commerce?

No. They sit on top of it. Classic SEO still matters for discoverability, but agent discovery adds a new layer focused on structured data, policy clarity, and transaction reliability.

Will AI agent discovery favor big brands only?

Not automatically. Agents often prioritize confidence: accurate delivery promises, clear policies, strong reviews, and reliable payments. Smaller merchants can win if their operational data is cleaner and their customer experience is consistent.

How does this affect payment approval rates?

Agent-led traffic is typically higher intent, which should improve conversion—if your routing and risk logic are tuned. If you have noisy fraud rules, mismatched payment methods, or frequent issuer declines, agents will reduce your visibility over time.

The next 12 months: what to prepare for

Expect three changes through 2026 planning cycles:

1. Agent referral becomes a line item in your acquisition mix, alongside paid search, marketplaces, and affiliates.
2. Checkout reliability becomes a ranking factor. Declines, disputes, and delayed refunds won’t stay hidden in back-office dashboards.
3. Fintech infrastructure will compete on merchant intelligence: who can provide the best signals for trust, routing, and visibility.

If you’re leading e-commerce, payments, or risk, your next step is straightforward: map your current discovery inputs (catalog + policies + fulfillment) to your payments outputs (approvals + refunds + disputes). Then close the gaps.

The merchants who win agent discovery won’t be the loudest. They’ll be the most predictable—to customers, to networks, and to the agents making decisions on everyone’s behalf.

Where do you think your operation is least “agent-readable” right now: product data, fulfillment promises, or payments performance?