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AI shopping agents are becoming a new sales channel and a new attack surface at the same time. They can send qualified shoppers to product pages, but they also generate expensive catalog requests, obscure attribution, imitate human browsing and probe APIs. Online sellers should not make a blanket allow-or-block decision: identify traffic by intent, measure its value and cost, expose controlled product data, and apply stronger controls to accounts, payments and checkout.
Why AI-agent traffic is arriving so quickly
Agents now perform more than web searches. They can discover products, compare prices, select a cart, complete parts of a purchase and handle post-purchase tasks. Akamai reported in 2026 that 47.9% of all AI-bot traffic on its network came from commerce between July and December 2025. The same report said commerce faced nearly three trillion Layer 7 distributed-denial-of-service attacks during 2025, with retail responsible for 84% of that volume.
That activity is not automatically malicious. A legitimate agent may be a useful referral source, while a crawler collecting every variant, price and availability change can consume bandwidth, database capacity and large-language-model (LLM) resources. Salesforce also warns that agent visits can arrive on product-detail pages without a referrer or UTM parameters, so analytics may label them as direct traffic and hide the channel that caused the visit.
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The four traffic types a seller must distinguish
Intent, business value, operating cost and security exposure differ sharply across agent traffic. Treating every automated request alike either blocks useful demand or leaves sensitive systems open.
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| Traffic type | Typical intent | Potential value | Main cost or risk |
|---|---|---|---|
| Training or indexing crawler | Collects pages or text for discovery or model development | Possible discoverability, but conversion intent is uncertain | High catalog load and uncontrolled reuse of content |
| Shopping or comparison agent | Finds products, compares offers and may send a shopper to checkout | Referral demand and potential conversion | Frequent price and inventory requests; attribution can be missing |
| Scraper | Copies prices, inventory, listings or seller intelligence | Usually no direct seller benefit | Bandwidth, competitive leakage and database/API pressure |
| Fraud or hijacked agent | Automates account abuse, payment misuse or checkout attacks | None | Credential theft, chargebacks, privacy exposure and operational disruption |
Why normal bot defenses are less reliable
Akamai says autonomous shopping agents can reproduce the small timing and navigation patterns associated with human users. Attackers can also hijack legitimate assistants or exploit stored payment credentials. LLM-enabled synthetic identities, sometimes assembled from “Frankenstein” combinations of real and fabricated data, can bypass defenses that rely on a few static signals.
Scraping is the largest measured category of bad-bot activity in DataDome’s 2026 report: 70.9% of bad-bot traffic in its customer base. DataDome said scraping rose 185.2% during the report’s study period. A request that looks like ordinary browsing therefore needs context—account history, velocity, endpoint sensitivity and behavioral consistency—not only a user-agent string.
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APIs are a separate seller risk
Product pages are only one path into a store. Price, inventory, search, account, order and checkout APIs may expose more valuable data and perform more consequential actions. Akamai reported that web attacks targeting APIs rose 9% year over year. In its commerce findings, 85% of respondents had experienced at least one API-related incident in the preceding year, while only 22% knew which of their APIs exposed sensitive data.
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Build and maintain an API inventory that records the owner, data returned, authentication method, write capabilities, rate limits and logging status for every endpoint. Apply least-privilege credentials, separate read-only catalog access from account and payment functions, and require step-up verification for high-impact actions.
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A practical governance plan for an online store
- Inventory what can be reached. List storefront routes and every API used for products, prices, stock, carts, accounts, orders and payments. Mark which endpoints reveal personal, commercial or security-sensitive information.
- Identify automated clients. Combine declared identity with request behavior, authentication state, IP and network reputation, request volume, navigation sequence and endpoint sensitivity. A self-identified agent is not automatically trustworthy, and an unidentified client is not automatically hostile.
- Measure before enforcing. Create analytics segments for known agents, suspected scrapers and ordinary customers. Compare conversion or assisted-conversion value with bandwidth, database work, LLM calls, support tickets and abandoned carts. Investigate “direct” product-page sessions instead of assuming they are typed-in traffic.
- Publish a controlled catalog interface. Where possible, give shopping agents a documented, read-only feed or API with explicit fields, freshness limits and rate limits. Keep secrets, customer records, purchase history and internal margins out of that interface.
- Set category-specific controls. Permit reasonable discovery and comparison requests, throttle repetitive catalog harvesting, and deny access to account, payment and checkout operations unless the client passes stronger authentication and fraud checks.
- Protect expensive and sensitive actions. Use microsegmentation, risk-based multi-factor authentication, behavioral analysis and rapid “kill switch” rules for suspicious sessions. Coordinate fraud and security teams so a bot detected at checkout can be blocked across account and API layers.
- Review the policy continuously. Recheck agent identities, crawler declarations, rate limits, false positives, referral value and incident data. Change rules when a legitimate partner changes infrastructure or when an attacker changes behavior.
Amazon’s 2026 Agent Policy changes seller software obligations
Amazon announced that an update to its Business Solutions Agreement takes effect on March 4, 2026. Automated agents accessing Amazon Services must clearly identify themselves as automated systems, follow the Agent Policy and stop accessing the services when Amazon requests it.
Sellers should audit repricing, listing, product-research and customer-support tools that connect to Amazon. Confirm which credentials they use, whether they identify automation correctly, what data they retain and how access can be halted quickly. The policy applies to automated access; it does not make every seller tool unlawful, but non-compliant software can put a seller’s account or service access at risk.
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Responsibility also remains with the person or business delegating the action. UK government consumer-law guidance states: “If an AI agent you use does something illegal, you are responsible.” Treat an agent as an authorized operator whose actions require the same legal, contractual and audit controls as a human employee.
Platforms are combining verification with continuous monitoring
Amazon’s 2025 Trustworthy Shopping Experience Report says all new sellers complete verification before selling. It also says Amazon’s automated technology and AI scan billions of attempted product-detail-page changes every day for abuse. This model—verify the participant, then monitor behavior continuously—will likely create friction for legitimate sellers as well as disrupt attackers. Keep business identity, catalog provenance and change logs accurate so automated reviews can be resolved with evidence.
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What shoppers expect from agent-mediated buying
Visa surveyed 1,600 U.S. adults, 1,600 Australian adults and 500 New Zealand adults who shop online monthly and hold a debit or credit card. Across the three markets, roughly two-thirds said they use or would use shopping agents to save time or find better prices.
Convenience does not remove the need for control. Nearly nine in ten wanted transparency into how an agent makes decisions, and about 85% of American respondents considered visibility into data collection plus the ability to customize or delete shopping and purchase information important. Average concerns across the three markets were data security (50%), privacy (44%), purchase accuracy (42%), reliability (40%) and loss of control (36%).
Make the information an agent and its user need easy to retrieve: total price and fees, stock status and freshness, delivery estimates, refund conditions, payment-security practices, data collected, retention choices and a clear way to reverse or dispute an action. These details reduce mistaken purchases and give a legitimate agent enough information to make a transparent recommendation.
How to recognize a legitimate agent without trusting it blindly
- It declares that it is automated and provides an accountable operator or service identity.
- It uses documented, read-only catalog access for discovery instead of probing account or checkout endpoints.
- Its request rate, geography and navigation pattern are consistent with the task it claims to perform.
- It respects published rate limits, robots or agent instructions and promptly honors access changes.
- It does not ask for seller credentials, payment secrets or unnecessary customer data.
None of these signals proves legitimacy alone. Combine them with authentication, behavior, endpoint risk and a revocable access grant.
What to do when traffic suddenly spikes
- Separate catalog, account and checkout telemetry so a harmless discovery surge is not confused with an attack.
- Check API latency, database load, LLM consumption, error rates and conversion before blocking a source.
- Rate-limit or challenge the narrowest risky route first; preserve read-only product access when evidence supports it.
- Revoke compromised tokens, require risk-based MFA and coordinate a fraud-and-security response.
- Record the client identity, affected endpoints, timestamps and decisions for marketplace, contractual or legal follow-up.
The durable strategy is selective openness: welcome identifiable shopping demand, make catalog data available through controlled interfaces, and aggressively isolate automation that touches credentials, personal data, payment instruments or high-impact actions.
Quick Recap
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