How AI will buy for you - The agentic commerce series
Part I: A framework for understanding how the buying experience evolves as AI moves from suggesting products to purchasing them
As an increasing share of our digital interactions go through AI apps, they will intuitively become the most relevant new gateway to commerce.
If agents really are the apps of the AI era, then commerce on these platforms becomes inevitable. The global opportunity could reach $3 to $5 trillion by 2030, and it’s quadrupling year-over-year.
I’ve written previously about what an AI agent actually is and how it works. Here, I want to propose a framework for how the agentic buying experience can evolve. Four stages, each building on prior capabilities: from pure guidance to autonomous orchestration.
Recommends. Initiates. Transacts. Orchestrates.
AI Recommends
AI functions as a smart advisor. You query a model for purchase suggestions; it returns tailored recommendations based on your prompts, preferences, history, and its reasoning.
No transactions executed. Guidance only. In the best cases, a bespoke interface and redirect-to-merchant experience.
This is where most AI-commerce interactions sit today. Generative AI traffic to retail sites grew 4,700% year-over-year by July 2025. 38% of US consumers have used generative AI specifically for shopping, 85% say it improved their experience, and when AI does send shoppers to merchants, they convert 31% better than traffic from traditional channels.
This stage is already reshaping marketing funnels. If product discovery shifts from search engines to AI chat, then SEO, paid advertising, and traditional digital marketing face a fundamental reconfiguration. The structural problem is simple: AIs don’t care about ads. The AI becomes the first touch point, and the business model just doesn’t translate.
But the AI can’t yet act on what it knows. It can recommend the perfect running shoes for your pronation and budget. You still need to complete the checkout. That limitation motivates the next stage.
AI Initiates
Agents begin taking action to start payments. AI tools interact with eCommerce platforms and initiate checkout journeys, though final confirmation stays with the user, one transaction at a time.
This is the current state. And it’s messier than the clean progression might suggest. Three models are emerging:
Browser-use agents. These process your prompt, break it into steps, and execute in a web browser by capturing screenshots and simulating human interaction. OpenAI’s Operator (January 2025) and Anthropic’s Computer Use are the most visible examples. They navigate commerce flows without API access to the merchant. The limitation: they’re clunky, stall on complex sites, and still hand over for payment credentials (besides residing in regulatory and T&Cs compliance grey areas).
Integrated plugins & apps. Merchants develop their own native presence inside the AI platform. Think of it as the App Store model applied to AI commerce: the platform provides distribution and the conversational interface; the merchant keeps their brand, loyalty programs, and checkout experience.
Target built a ChatGPT app where shoppers can buy groceries, link loyalty accounts, and choose fulfillment options. Walmart introduced an in-app service with its own payments. Shopify lets merchants connect storefronts so purchases complete via a branded in-app browser. The merchant retains the customer relationship. The AI is the channel, not the intermediary.
AI platform protocols. Here, the AI platform itself facilitates the transaction through its own protocols. Two architectures are emerging with very different dynamics:
The first is the aggregator model. Perplexity’s “Buy with Pro” expanded to over 5,000 merchants via PayPal in November 2025, with shopping intent queries growing 5x. Here, the AI platform acts as wallet and marketplace: it receives payment from the user, then settles with the merchant in a second transaction. Classic aggregator economics (think food delivery apps, OTAs) applied to AI.
The second is more open. A protocol lets merchants sell through the AI app without marketplace dynamics. OpenAI launched Instant Checkout in ChatGPT in September 2025, powered by Stripe’s Agentic Commerce Protocol. Merchants remain merchants of record. Stripe processes the payment inline, the buyer never leaves the chat, and the protocol handles discovery, cart, and checkout in a standardized way any merchant can plug into.
Conversion remains hard.
By March 2026, OpenAI had to revamp Instant Checkout entirely. Users asked product questions but didn’t buy inside the app. High intent, low conversion. The jump from “advise me” to “buy for me” is harder than it looks.
On the bright side, AI-attributed orders on Shopify grew 11x between January 2025 and March 2026, so the volume is building. But the transition between stages is genuinely difficult.
AI Transacts
Limited but increasing autonomy. Agents can complete transactions in multiple merchants, using open protocols after the user intent is clearly confirmed.
In some cases, they can initiate payments within narrow, predefined parameters without explicit confirmation for each one.
The critical enablers here: open protocols and tokenization.
Unlike the closed-loop models of the previous stage, Transacts sees a shift toward pass-through payment models. Agents interact with open payment networks through tokenized credentials, rather than relying on the AI platform to manage funds in a staged wallet.
Tokenization is the key primitive. In the last twelve months, every major payment player shipped its version.
Visa Intelligent Commerce ships agent-bound tokens with revocable spend limits. Google’s Agent Payments Protocol (AP2, co-created with Amex, 60+ financial institutions) and Universal Commerce Protocol (UCP, with Shopify, Walmart, Target, and 20+ others) standardize the full commerce journey. Stripe launched Shared Payment Tokens. Visa is building a connector on top of them all.
There are ten active agentic commerce protocols in Q2 2026. Ten. The protocol wars, some call it.
A potential user experience: you’re having a conversation with your AI assistant. You ask it to find a birthday gift and a restaurant for dinner. Through back-and-forth, it narrows options across two different merchants. When you’ve settled on choices, the agent presents an explicit summary: “I’d like to purchase the leather wallet from Store A ($85) and reserve a table at Restaurant B ($50 deposit). Total: $135. Shall I proceed?” You confirm once. Transactions execute across both merchants through tokenized credentials. Explicit intent. You never left the chat.
A complementary one: you set permissions upfront and let the agent act within bounds. Maximum spend per transaction ($25). Total budget ($200). Allowlisted merchants in specific categories. Picture a trip to Colorado: you give an agent $1,300 and your preferences. Small purchases ($16 kayaking, $8 trail pass) proceed automatically. Larger ones ($185 cabin) sit pending until you confirm. One interface, many merchants, standard payment rails.
Both modes represent the same stage but different trust postures.
The first is early Transacts: human confirms each purchase, but the agent assembles multi-merchant journeys. The second is late Transacts: the human sets the parameters and the agent executes autonomously within them.
AI Orchestrates
The final stage shows agents managing complex workflows, including purchases and payments, with minimal human input. Beyond discrete transactions, they plan, execute, and iterate over multi-step processes autonomously.
I expect enterprise and B2B to lead here.
Ramp launched AI agents for procurement in 2026: triage requests, source vendors, execute payments. 40% of enterprise applications will feature task-specific agents by end of 2026 (up from less than 5% in 2025), with 90% of B2B buying projected to be AI-intermediated by 2028, pushing over $15 trillion through agent exchanges.
A logistics agent scheduling vehicle maintenance, arranging payments to repair shops, and optimizing fleet downtime.
A marketing agent dynamically monitoring keyword pricing to purchase ad campaigns within a monthly budget.
A procurement agent negotiating with suppliers and executing purchase orders when conditions are met.
These may sound speculative. They are. And also being piloted now.
For consumers, the pattern looks like standing orders executed when criteria align. “Restock my running supplements when the price drops below $X.” “Book the cheapest direct flight to London for any weekend in March under $Y.” The agent monitors permanently and acts when conditions match. No browsing, no comparison shopping, no checkout. You set the intent once; the agent handles the rest.
At this level, agents need three things: an assigned budget (pre-funded or linked to a tokenized credential), access to payment credentials across multiple merchants, and agent identification for secure authentication: authentication must shift from “proving you are the right human” to “proving you are the authorized agent on behalf of the right human.”
Multi-agent orchestration also enters the picture here. An agent negotiating with another agent (or a merchant’s automated system) to find the best price. An agent delegating sub-tasks to specialized agents for research, comparison, and execution.
The double trust gap
A fair objection: this framework describes a direction, but getting there requires trust that hasn’t been earned yet. The gap is double-sided.
On the consumer side: only 24% feel comfortable using AI to complete purchases today. Just 10% have actually bought something using AI in any form. OpenAI’s own Instant Checkout confirmed it: enthusiastic questions, no follow-through to purchase.
On the merchant side: retailers who’ve spent years optimizing checkout, SEO, and acquisition funnels won’t eagerly cede control to AI intermediaries. If the agent becomes the interface, the merchant loses the customer relationship.
The same tension that defined brands vs. marketplaces, hotels vs. OTAs.
Though Shopify, representing millions of merchants, is betting the other way: making every store agent-ready by default. It is true that these types of merchants, unlike major and consolidated retailers, have the most to gain in being discovered by agents.
Even if the timeline is uncertain, the direction looks clearer. The same objections were raised about mobile payments, about buying from unknown sellers on marketplaces, about storing card details online.
Trust followed utility, not the other way around.
What next?
This framework is an imperfect attempt at structuring something evolving fast.
But you might have noticed that several hard problems sit between stages. Payment infrastructure, fraud controls, legal frameworks, and UX patterns: all built around the assumption of a human in the loop. In an AI-led environment, that assumption may no longer hold.
Retailers will need to rethink how to place their brands in an “agent as personal shopper” era. Authentication systems will need to verify software, not biometrics. Dispute resolution will need to account for decisions made by algorithms within user-defined parameters.
That rethinking, and more, will be the focus of the next pieces in this series.
I publish a post a week on key ideas around AI, Agents and everything around their diffusion into the enterprise and people’s lives. You can read them all here.