Earlier this 12 months, eBay updated its user agreement to explicitly ban third-party “buy for me” agents and AI chatbots from interacting with its platform without permission. The move highlights a broader query facing online marketplaces as AI shopping agents turn out to be more capable: Who controls the transaction when software acts on the client’s behalf?
Blaine Nielsen, president of outlets at Rithum, suggested that eBay’s motion makes a very important statement that autonomous purchasing shouldn’t operate without accountability.
Rithum, formed by the 2023 merger of CommerceHub and ChannelAdvisor, is an end-to-end commerce operations platform connecting brands, retailers, and suppliers to automate product listings, manage dropship programs, and scale success across a whole lot of online marketplaces.
He expects a human-in-the-loop partnership to at all times exist. But as marketplaces construct technical standards and AI earns more trust, the concept will likely evolve. Consumers will approve spending thresholds, delivery preferences, and trusted-seller rules, with AI handling execution inside those limits.
“Setting clear boundaries gives marketplaces the flexibility to guard themselves within the short term while establishing guardrails, formal access points, and monetization models for AI-driven transactions as they proceed to mature,” he told the E-Commerce Times.
Why AI Buying Agents Aren’t Ready Yet
Nielsen noted that AI buying agents cannot yet fully deliver confidence and control in the net shopping experience. Rithum’s recent survey found that only 15% of shoppers have used AI to finish a purchase order.
Consumers want less friction. Nevertheless, today’s AI buying agents cannot yet deliver that convenience without introducing latest risks and challenges, he observed. Detecting highly sophisticated, large language model (LLM)-driven AI agents that mimic human browsing behavior is notoriously difficult.
eBay’s blocking of AI agents risks a long-term consumer backlash in favor of platforms that actively welcome agentic shopping, he warned. They usually tend to construct long-term consumer trust in the event that they balance automation with proper safeguards and human oversight.
“Platforms that actively welcome agentic shopping may reap early advantages of added simplicity to the shopping experience, but it surely’s more likely they run into obstacles, like upset customers over unauthorized purchases or incorrect items, in the long run,” Nielsen said.
The Limits of Bot Detection
Sophisticated AI agents have gotten increasingly difficult to discover because they’ll mimic legitimate human browsing behavior, including realistic navigation patterns, residential IP usage, and interactions with accessibility tools.
Marketplaces can limit abusive automation through behavioral analytics, device intelligence, status scoring, and risk-based verification. Yet every detection system faces the identical balancing act: stopping bad actors without creating friction for legitimate users.
“The larger challenge is avoiding friction for legitimate users, including high-value shoppers, enterprise buyers, and customers using assistive technologies reminiscent of screen readers or voice navigation,” said Nielsen.
As AI agents turn out to be more advanced, he sees marketplaces moving away from distinguishing between humans and bots toward specializing in whether activity is allowed, trustworthy, and policy-compliant.
“In practice, enforcement will increasingly depend on identity, account status, permissions, and rate limits fairly than traditional bot-detection methods alone,” he suggested.
Who’s at Fault When Payment Issues Occur
If an autonomous AI agent purchases a counterfeit item, enters the mistaken shipping address, or misinterprets an inventory, determining legal and operational responsibility may remain a gray area.
In response to Nielsen, legal and operational responsibility generally stays with the human user or business deploying the AI agent. AI systems are usually not independent legal actors.
If an AI agent acts outside its authorized parameters, responsibility will often depend upon where the failure occurred. The fault could stem from user instructions, the vendor’s listing, marketplace controls, or the AI provider’s system behavior.
From an e-commerce perspective, AI-agent transactions will likely be treated similarly to other delegated purchasing systems, Nielsen offered. The account holder stays responsible. Marketplaces and AI providers must maintain strong authentication, audit trails, disclosures, and dispute-resolution processes.
“Over time, the industry may evolve toward formal agent authorization frameworks with defined permissions, spending limits, and liability boundaries,” he said.
Query of Intent
Nielsen pointed to a challenge for traditional e-commerce trust models, which have long been built around human intent. Agentic commerce complicates that assumption, particularly when disputes arise.
He explained that shoppers could use AI-assisted purchasing as grounds to hunt refunds or return items outside the traditional return window by claiming they didn’t personally make the acquisition.
“We saw this friction increase when chatbots and automatic call centers emerged. Now there’s a risk AI buying brings this to the following level if there aren’t proper guardrails in place to mitigate the chance,” he warned.
When AI Agents Start Bidding
eBay’s bidding and auction format raises additional legal concerns because timing, bidding psychology, and competitive dynamics make autonomous AI participation in auctions inherently more complex.
“Just like an unintentional purchase, autonomous agents could overbid, react too quickly based on bidding patterns and timelines, or create artificial price inflation because they’ll’t balance winning a bid with true shopper value,” Nielsen said.
He cited a good greater risk that the vendor and buyer would need to settle a dispute with out a human-in-the-loop in bidding and auction formats. Having proper boundaries, reminiscent of spending limits and human user purchase confirmation, could help weigh the risks and rewards of using an AI agent to take part in auctions, especially when a client is bidding on several items in a brief timeframe.
Nielsen views eBay’s decision to ban unauthorized buy-for-me agents as an early indication that auction marketplaces could face dynamics much like those seen in high-frequency trading. Autonomous bidding creates speed and algorithmic benefits that might make it nearly inconceivable for humans to participate.
“This reinforces the necessity for more accountability and intent in agentic shopping experiences to maintain human trust at the middle of every transaction,” he urged.
The way to Handle Future Machine-to-Machine Commerce
While much of today’s debate focuses on how AI agents interact with marketplaces, Nielsen believes the longer-term impact could be the rise of machine-to-machine commerce, where software increasingly represents each buyers and sellers.
In response to Nielsen, agentic shopping represents the following step within the broader shift toward more connected, data-driven commerce. Brands and retailers need accurate, real-time product information, success visibility, pricing consistency, and trusted marketplace connectivity now greater than ever.
He advises retailers to arrange for a future through which seller-side AI agents negotiate directly with buyer-side agents. Many retailers and marketplaces are already exploring versions of seller-side AI agents, particularly for dynamic pricing, inventory management, product recommendations, and automatic customer engagement.
“Over time, e-commerce likely evolves toward agent-to-agent interactions, where buyer agents and seller agents negotiate around price, success speed, bundles, warranties, and promotions in real time,” he predicted.
Retailers will want their very own intelligent agents to make sure their products, policies, and brand preferences are represented accurately inside these automated purchasing ecosystems. As AI-driven commerce evolves, marketplaces will likely establish official APIs, certification programs, and trusted-access frameworks for approved buying agents, he concluded.
“From a platform perspective, unmanaged scraping and autonomous purchasing create operational, fraud, and infrastructure risks, while authenticated AI-agent access creates opportunities for governance, rate controls, identity verification, and monetization,” he said.