In a single week of March 2026, three institutions gave three incompatible answers to the same question: who controls what your AI agent does on the internet? Amazon went to federal court to block one. The UK’s Competition and Markets Authority published a 40-page framework for regulating them. Shopify turned them on by default for every eligible merchant.
The three responses are not just different speeds of adoption. They represent three fundamentally different models for how AI agents will participate in commerce, and the precedents being set right now will determine market structure for the next decade. Every company building or deploying an AI agent needs to understand which regime it is operating in.
Model One: Ban. Amazon v. Perplexity and the Platform Authorization Doctrine
On March 10, 2026, U.S. District Judge Maxine Chesney granted Amazon a preliminary injunction against Perplexity AI, blocking the startup’s Comet browser from accessing password-protected sections of Amazon’s marketplace. The ruling is the first federal court order to directly restrict an AI shopping agent from operating on a major platform.
The legal mechanism matters. Amazon filed under the Computer Fraud and Abuse Act (CFAA) and a California computer fraud statute, arguing that Perplexity disguised Comet’s automated sessions as regular Google Chrome browser traffic. When Amazon deployed a technical block in August 2025, Perplexity pushed a software update within 24 hours to circumvent it. Amazon warned Perplexity to stop at least five times starting in November 2024 before filing suit.
Judge Chesney found that Amazon presented “strong evidence” that Comet accessed the site with users’ permission but without Amazon’s authorization. That distinction is the core legal question: when a user tells an AI agent “buy this for me on Amazon,” whose permission matters? The user’s or the platform’s?
Perplexity’s argument was straightforward: the user authorized the agent. If a human can log in and buy something, their AI agent should be able to do the same. Amazon’s argument was equally direct: platform access requires platform consent, and disguising bots as human browsers violates that consent regardless of what the user wants.
The court sided with Amazon, at least preliminarily. Perplexity must stop accessing Amazon accounts and destroy collected customer data. The Ninth Circuit granted an administrative stay on March 17, pausing the injunction while it considers a longer appeal, but the legal reasoning stands for now.
The irony is worth noting. Amazon itself launched “Buy For Me” in April 2025, a feature that lets shoppers purchase products from third-party websites directly within the Amazon Shopping app. Amazon is building agentic commerce capabilities while suing a competitor for doing the same thing outside Amazon’s own ecosystem. CEO Andy Jassy acknowledged in October 2025 that agentic commerce “has a chance to be really good for e-commerce” but argued current agents are “not good enough” at personalization. Days later, Amazon sued Perplexity.
Amazon also updated its Business Solutions Agreement on March 4, 2026, formally requiring all AI agents to identify themselves when accessing its services. The platform is building a legal and technical framework where agents operate on Amazon’s terms or do not operate at all.
Model Two: Regulate. The CMA Framework and Agent Accountability
On March 9, 2026, one day before the Amazon ruling, the UK’s Competition and Markets Authority published “Agentic AI and Consumers,” a research document and guidance framework for businesses deploying AI agents. The CMA is not banning agents. It is establishing that existing consumer protection law applies to them and that companies deploying agents are fully accountable for their behavior.
The framework rests on the Digital Markets, Competition and Consumers Act 2024 (DMCC Act) and the Consumer Rights Act 2015. Under these statutes, a business cannot engage in unfair commercial practices, must provide clear information to consumers, and cannot use terms that disadvantage consumers. The CMA’s position: it does not matter whether these practices are executed by a human customer service representative or an AI agent. The deploying company bears responsibility either way. Fines under the DMCC Act can reach 10% of global annual turnover.
The specific risks the CMA identifies map to how agents actually work in practice. The first is steering: agents that push consumers toward products that benefit the deploying business rather than the consumer. A shopping agent built by a retailer might surface higher-margin products first, or frame sponsored items as “best matches,” without disclosing the commercial relationship.
The second is dark pattern amplification. Traditional dark patterns in user interfaces (hidden fees, manipulative countdown timers, difficult cancellation flows) become harder to detect when each user receives personalized recommendations from an agent. If every user sees different results based on behavioral profiles, it becomes nearly impossible to prove that any individual interaction was manipulative. The CMA calls this the “replicability problem.” When there is no standard experience to compare against, there is no baseline for identifying manipulation.
The third is algorithmic collusion. The CMA published a separate blog post in March specifically addressing the risk that AI agents from competing businesses could independently converge on pricing strategies that reduce competition, without any explicit communication between the businesses or instructions to collude. If Company A’s pricing agent and Company B’s pricing agent both optimize for profit maximization using similar training data and market signals, they could reach the same price equilibrium that a human cartel would, without anyone telling them to. The CMA offers a reward of up to $250,000 to anyone who reports evidence of algorithmic cartel activity.
The fourth is over-reliance and loss of agency. As consumers delegate more decisions to automated assistants, the CMA warns they may lose the habit of checking what their agents are doing. An AI agent that cancels the wrong service, switches a contract based on flawed analysis, or makes a financial decision using hallucinated data creates consequences that compound when no human is reviewing the output.
The CMA’s four-step compliance framework for businesses deploying agents is practical: be transparent about AI use, design agents with consumer protection built in, monitor agent behavior in production, and address problems swiftly when they emerge. The framework does not propose new legislation. Its power comes from mapping existing law onto a new technological context and making clear that enforcement is coming.
Model Three: Embrace. Shopify’s Default-On Agent Commerce
On March 24, 2026, Shopify activated Agentic Storefronts by default for every eligible merchant. Products from Shopify stores now surface inside ChatGPT, Google Gemini, and Microsoft Copilot. No merchant action required. No opt-in form. The infrastructure was turned on.
Two competing protocols power the system. OpenAI‘s Agentic Commerce Protocol (ACP) connects ChatGPT to merchant product catalogs with structured data for pricing, availability, and shipping. Shopify and Google co-developed the Universal Commerce Protocol (UCP) to do the same across Gemini, Copilot, and other agent platforms. Both protocols exist because OpenAI originally wanted to build in-chat checkout (letting users buy without leaving ChatGPT) and then retreated from that position after merchant pushback. The current architecture sends users to the merchant’s checkout page instead.
Shopify’s model is the opposite of Amazon’s. Where Amazon demands that agents identify themselves and obtain platform permission, Shopify makes every store agent-accessible without the merchant lifting a finger. The logic is commercial: Shopify makes money when merchants make sales, regardless of whether the buyer arrived through a Google search, a social media link, or a ChatGPT conversation. More distribution channels means more transactions. Agents are not a threat to Shopify’s business model. They are an expansion of it.
This is possible because Shopify’s pricing is not per-seat. It charges transaction fees and subscription fees. The per-seat pricing death that triggered the SaaSpocalypse does not apply to a platform whose revenue scales with commerce volume, not employee count. Shopify can welcome AI agents because AI agents buying things generates the same revenue as humans buying things.
Why the Three Models Are Incompatible
The Amazon model says: platforms control access. No agent enters without the platform’s permission. The CFAA provides the enforcement mechanism. This model protects incumbents, preserves walled gardens, and lets platforms build their own agents while blocking competitors.
The CMA model says: agents can operate, but the companies deploying them are responsible for outcomes. Existing consumer protection law applies. The enforcement mechanism is financial (fines up to 10% of global revenue). This model preserves competition but creates compliance costs that favor large, well-resourced companies over startups.
The Shopify model says: agents are welcome by default. The more agents that can reach your products, the better. The enforcement mechanism is market incentives: merchants benefit from distribution, platforms benefit from transactions, and agents benefit from access to product data. This model maximizes consumer choice but assumes that market forces will self-correct for quality and accuracy.
These three models cannot coexist in a single market without friction. An AI agent operating under the Shopify model (open access, default on) immediately violates the Amazon model (platform permission required) the moment it tries to compare prices across both platforms. A company building an AI shopping agent that complies with the CMA framework (transparent, accountable, non-manipulative) may still be blocked by Amazon if it does not meet Amazon’s separate authorization requirements.
The result is a fragmented regulatory environment where the same AI agent might be legal in one jurisdiction, blocked on one platform, and welcomed on another, all for the same shopping task.
What These Models Miss
All three models share a blind spot: none of them adequately addresses the question of whose interests the agent actually serves when the user, the platform, and the agent developer have conflicting incentives.
Consider a user who tells an AI shopping agent, “Find me the best deal on noise-canceling headphones.” The user wants the lowest price for acceptable quality. The agent developer may want to route the purchase through a merchant that pays affiliate commissions. The platform may want to surface its own private-label products. The CMA framework requires transparency about these conflicts, but transparency alone does not resolve them. A disclosure that says “this recommendation may reflect our commercial partnerships” does not help a consumer determine whether the recommendation is good.
The Amazon v. Perplexity ruling also leaves open a deeper question about the Computer Fraud and Abuse Act. The CFAA was written in 1986 to address computer hacking. Its application to agentic software acting on a user’s behalf has never been tested at trial. If the Ninth Circuit upholds the injunction, it establishes that platforms can override user authorization for AI agents. If it reverses, it opens every platform to agent access that users consent to but platforms do not. Neither outcome is clean.
The CMA’s algorithmic collusion concern is theoretically valid but practically difficult to detect. If two pricing agents independently reach the same price without communicating, proving collusion requires demonstrating that the outcome would not have occurred through independent optimization. That is a forensic challenge regulators have barely begun to address.
And Shopify’s embrace model works because Shopify’s business model aligns with agent activity. For platforms where agent access reduces revenue (subscription services, ad-supported content, platforms with per-seat pricing), the Shopify model does not translate. The embrace approach is not universally applicable. It works where commercial incentives are aligned and breaks where they are not.
What Happens Next
Three immediate events will shape which model gains ground. First, the Ninth Circuit’s ruling on Perplexity’s appeal of the Amazon injunction. If upheld, every major platform gains legal precedent to block AI agents at will. If reversed, agent developers gain a right-of-access argument grounded in user authorization.
Second, the CMA’s first enforcement action under the DMCC Act against an agentic AI system. The framework is published. The fining power (10% of global turnover) is active. The first case will establish whether the regulator treats agent manipulation with the same seriousness as traditional dark patterns. The timing of the CMA report, published the day before the Amazon ruling, was likely not coincidental.
Third, Shopify’s Agentic Storefronts at scale. If merchants see meaningful revenue from agent-driven purchases, every other commerce platform faces pressure to open up. If agent-driven transactions generate returns, fraud, or customer complaints at higher rates than traditional purchases, the embrace model loses credibility.
The deeper question is structural. AI systems already exhibit systematic biases toward agreement and user satisfaction over accuracy. An AI shopping agent optimized to make users happy will tell them they found the best deal even when it did not. An agent optimized for merchant revenue will surface profitable products over better ones. An agent optimized for platform retention will never recommend leaving the platform.
The ban model, the regulate model, and the embrace model all assume that someone can align agent incentives with consumer interests. AI agent architectures are growing more autonomous by the month. The question of who controls the agent is not a policy abstraction. It is a product design decision being made right now, in code, by every company building one.
March 2026 produced the first court order, the first regulatory framework, and the first default-on agent commerce system. The answers arrived before most companies finished asking the question.
Sources: CNBC (Amazon v. Perplexity ruling), UK CMA, “Agentic AI and Consumers” (March 9, 2026), CyberScoop (Ninth Circuit stay), CMA blog on AI collusion (March 4, 2026), Decrypt (legal analysis), The Register (CMA report), Lewis Silkin (CMA compliance framework), Ashurst (CMA legal analysis).