The Second Customer

On 31 October 2025, Amazon sent a cease-and-desist letter to Perplexity AI.

The accusation was not that Perplexity had built a competing product. It was that Perplexity’s Comet browser was helping people use Amazon’s own product, in a way Amazon had not designed for and had not authorised.

Comet is a shopping agent. You tell it what you want. It navigates the web, finds the product, and completes the purchase on your behalf. The person using Comet gave it explicit permission to act for them. Amazon, the platform Comet visited, had not agreed to that arrangement.

On 9 March 2026, a federal judge in California granted Amazon a preliminary injunction. US District Judge Maxine Chesney wrote that Amazon had provided strong evidence that Comet had accessed its website “at the user’s direction, but without authorisation” from the platform itself.

The legal proceedings may run for years. The distinction Judge Chesney drew is not a legal technicality. It is the structural problem every product now faces, whether they have been to court about it or not.

That gap, between what the user authorised and what the platform was built for, is the interface problem of 2026.

The scale of that problem became visible in Q1 2026, when the traffic data arrived.

The Traffic You Did Not Send For

Adobe Analytics measured AI-sourced traffic across more than one trillion US retail visits in Q1 2026.

It grew 393% year over year. During the 2025 holiday season, the growth had been 693%. The rate is not levelling off (Adobe Analytics, 2026).

Salesforce’s analysis of the same period found that AI and agents drove 20% of all retail sales and influenced $262B of global online spend over November and December 2025. Autonomous agent actions increased by 142% over the same window (Salesforce, 2025).

The most striking figure is the conversion reversal. In March 2025, AI-sourced traffic converted 38% worse than human traffic. In March 2026, it converted 42% better. Revenue per visit was 37% higher. Time on site was 48% longer (Adobe Analytics, 2026).

The product did not change. Most likely, the agent had learned to identify what the human actually wanted, and was filtering to the sessions most likely to convert.

Two Users, One Interface

The human and the agent want the same outcome: the right thing, at the right price, quickly.

The paths they take are entirely different.

The human navigates. They land on the homepage, scan the headline, watch the video, read the pricing comparison, hesitate, close the tab, come back. The interface earns their trust across several visits. The copy matters. The personality matters. The design matters.

The agent parses. It arrives at a URL and extracts: price, specifications, availability, a countable proof signal (a specific, verifiable number: review count, star rating, units sold). It compares across three competitors in the time the human was reading the first paragraph. It executes when the criteria match.

The agent does not watch the video. It does not respond to brand warmth. It does not care about the scroll animation. What it needs is information that is structured and labelled (organised so software can find and extract specific values), and readable by a machine.

What the Machine Cannot Read

Adobe’s April 2026 analysis of more than one trillion US retail visits measured what agents can and cannot see.

The average homepage is 75% machine-readable (structured so software can understand and extract it). The average product page: 66% (Adobe Analytics, 2026).

The 25 to 34% that is invisible to an agent is, in most cases, the differentiating content: the main promotional video at the top of the page, JavaScript-rendered pricing comparisons (content that only appears after the page loads fully in a browser, invisible to anything reading the underlying document), CSS star ratings (visual stars rendered by browser code, invisible to anything reading the raw page), interactive demos, scroll-triggered copy. Every design pattern that builds trust with a human reader is, to an agent, a void (Adobe Analytics, 2026).

The 34% invisible on a product page frequently contains the specific differentiators that separate the product from its competitors: the copy written to justify the price, the proof that addresses the main objection, the specification that wins the decision. An agent that cannot read those sections defaults to price alone (Adobe Analytics, 2026).

Early analysis of Shopify merchant data by Presta (a commerce analytics platform) in 2026 found that stores optimised for agentic discovery saw 28% higher conversion from AI-driven traffic compared to stores that had not made those changes (Presta, 2026). These results come from a single study; real-world variation by category is expected. But the direction is consistent with Adobe’s broader data: the structured surface converts better for agents and, in most cases, better for humans too.

What the Protocols Require

MCP: The Interaction Layer

Two protocols now govern how agents interact with and complete purchases in digital products.

The Model Context Protocol (MCP) is the agreed standard for AI agents to interact with products without scraping webpages. Anthropic published it in November 2024. In plain terms: rather than guessing what the price is from the HTML, the agent has a direct, structured conversation with the product itself.

Within ten months, OpenAI, Google, and Microsoft had adopted it. By March 2026, over 10,000 active MCP servers were running in production, with 97 million monthly downloads of the SDK (Anthropic/MCP Registry, 2026). MCP is infrastructure, not experiment.

ACP: The Transactional Layer

The Agentic Commerce Protocol (ACP), co-developed by OpenAI and Stripe, published as an open standard in September 2025, is the checkout flow for agents. If MCP is the door, ACP is the checkout. It handles how an agent identifies itself to a merchant, how payment credentials are handled securely, and how a purchase completes without a human clicking through a single screen. Salesforce, Etsy, and major Shopify merchants have adopted it. US ChatGPT users can already complete purchases from Etsy sellers inside the chat interface, without visiting Etsy’s website. The specification is open-sourced on GitHub.

The Amazon case turns on the Computer Fraud and Abuse Act (the US federal law making it illegal to access a computer system without the owner’s permission). The distinction: the user’s authorisation versus the platform’s authorisation. The Agentic Commerce Protocol is precisely what is designed to resolve that gap before it reaches a court.

The legal picture is still settling. The commercial case for acting now is not.

One Fix, Two Better Users

The changes that make a product agent-readable also make it a better human product.

Optimising for agents is not a concession to a secondary user at the expense of the primary one. The improvements compound.

Clear information hierarchy (price stated plainly, specifications structured, social proof as text with a countable number, a single unambiguous action) is what an agent needs to parse and act. It is also precisely what reduces friction for the human buyer. The page that gets out of the way of the decision converts better for both.

The agent’s requirements push product teams to make the human-readable improvements that most should have made already. The plays that follow serve both users.

Three Plays

Serve the Second User on Your Terms