Amazon is pushing deeper into “agentic” commerce, where AI doesn’t just recommend products but can also take actions that affect listings, merchandising, and even purchasing flows.
Alongside seller-facing tools that Amazon says will help merchants launch and grow faster, reporting in early January 2026 shows a parallel backlash from independent brands and retailers who say Amazon’s AI-driven shopping features are using their product data without clear permission and creating business risk.
This article focuses on the seller concerns arising from that coverage and forecasts how Amazon and the broader market are likely to implement this kind of technology at scale.
Amazon’s seller-facing narrative is straightforward: AI should reduce the operational burden of selling, improve decision-making, and help merchants move faster.
Amazon has described an “agentic AI-powered” Seller Assistant designed to be always on and increasingly action-oriented, helping sellers manage and grow their businesses rather than simply answering FAQs. Amazon links this work to its broader generative AI stack, including Amazon Bedrock.
Amazon also announced AI-powered features intended to accelerate product launches and reduce risk, including tools for listing creation, content storytelling, review acceleration, and low-inventory launch approaches. The positioning is “move faster with more confidence,” particularly for smaller sellers trying to validate demand.
The biggest friction is not about sellers using AI inside Seller Central. The loudest complaints are about Amazon using AI-enabled discovery and purchasing features to surface products from other retailers’ sites, with sellers claiming they did not opt in and that the displayed information can be incorrect.
Multiple outlets report that independent retailers and brands found their products appearing on Amazon experiences tied to programs described as “Buy for Me,” “Shop Direct,” or similar AI shopping initiatives, and that they had to take steps to opt out after the fact.
For small businesses, the distinction between opt-in and opt-out is not a detail. Opt-out implies the default is exposure, and exposure can create immediate customer support and reputation costs.
A recurring seller complaint is that scraped or automatically generated product details can be inaccurate, including wrong availability, incorrect pricing, mismatched variants, outdated descriptions, or confusing presentation. When Amazon is the surface shoppers see the product on, sellers fear they will be blamed for errors they didn’t create.
This is the commerce version of a familiar generative AI problem: if an automated system produces plausible but incorrect output, the downstream party absorbs the cost. In retail, that cost shows up as cancellations, refund requests, negative reviews, support load, and brand trust erosion.
When customers place orders based on an Amazon-mediated view of a retailer’s catalog, out-of-stock items or long lead times can quickly lead to customer dissatisfaction. Sellers cited scenarios where Amazon surfaced items that were unavailable, leading to complaints and operational headaches.
Independent sellers often rely on direct customer relationships for retention, support, email permissions, and lifetime value. Reporting indicates some merchants were frustrated that orders or customer interactions mediated through Amazon limited their access to buyer information and weakened their ability to build ongoing relationships.
Even when the transaction ultimately happens on the brand’s site, sellers worry that Amazon’s AI layer becomes the “front door,” shifting discovery and loyalty away from the merchant.
Brands invest heavily in how their products are described, bundled, and positioned. An AI-generated listing or product card that reinterprets the catalog can break that control. It can also complicate pricing strategies if Amazon displays outdated prices, fails to reflect promotions correctly, or compares items in ways that distort perceived value.
A core emotional driver in the backlash is the perception that Amazon is effectively scraping brand sites to build a universal product catalog. Reporting described “Project Starfish” as an internal effort focused on pulling product information from many brand websites, which sellers interpreted as extraction rather than a partnership.
At the same time, reporting noted the optics of Amazon criticizing similar behavior when directed at Amazon by third parties, which amplifies seller distrust.
In statements reported by outlets covering the controversy, Amazon has emphasized that participation is optional and claimed positive feedback, while sellers counter that they did not knowingly opt in and that removal is not frictionless.
Underneath the seller controversy is a market-wide race: search is becoming answer engines, and shopping is becoming agents that complete tasks.
When an AI agent can find an item and check it out for the user, platforms can compete across the entire shopping journey, not just at the moment of payment. Amazon’s experiments suggest it doesn’t want product discovery to fragment across many AI assistants and browser agents. Sellers are caught in the middle because their catalogs are the fuel that powers these systems.
If Amazon wants this category to grow without prolonged seller revolt, it will need to evolve from “we can technically surface this product” to “we have a governed, verifiable, permissioned model.” The most likely implementation path includes several predictable layers.
At a small scale, pulling from public webpages is fast. At enterprise scale, it becomes a liability.
A more sustainable approach is Amazon shifting toward structured integrations such as:
This is the same path other ecosystems have followed: automation first, then formalization once errors and trust issues become too costly.
Expect pressure for clearer controls inside a merchant-facing console:
The sellers’ current frustration is largely about losing agency. The fix is not only technical; it’s product governance.
To reduce the wrong-information problem, visibility is likely to become conditional:
This mirrors how mature AI deployments introduce guardrails, but with commerce-specific penalties for error.
The most controversial implementations blur lines: it looks like an Amazon listing, but it’s not an Amazon-offered product.
A likely evolution is clearer UX labeling and merchant attribution, including:
If Amazon keeps the surface ambiguous, sellers will keep arguing that Amazon is taking their catalog and their brand voice.
Separately from the surface-products debate, seller-facing agentic tools in Seller Central are likely to expand rapidly because they offer a clearer value exchange.
Expect rapid growth in:
These are inside-the-seller-house tools, where permission is straightforward and accountability is clearer.
A probable compromise model is two tiers:
The partner tier becomes the premium lane because it reduces error and legal risk.
Whether sellers like it or not, agentic shopping will reward merchants with clean, machine-readable catalogs.
As Amazon and others push agentic experiences, sellers will increasingly need:
Merchants who invest in this will be easier for agents to represent correctly, and therefore more likely to be recommended.
Based on the current reporting, the near-term direction is a tug-of-war between Amazon’s push to become the default shopping agent and sellers’ push to retain control.
In the short run, expect continued experimentation, continued backlash from some brands, and incremental improvements in consent and accuracy. In the medium term, the winners will be systems that can prove they represent merchants faithfully and with their permission, because commerce errors are uniquely expensive: they trigger refunds, support escalations, and reputational damage immediately.
Amazon’s seller AI story will likely keep expanding on two tracks:
