Author:jessy
Compiled by:Jiahuan, ChainCatcher
Over the past year, I've been dedicated to building infrastructure for the Agent economy, engaging with teams from Stripe, Visa, Coinbase, Google, and dozens of startups pushing Agent commerce forward. I've mapped out the entire industry, launched products, and sought market fit.
There is no real demand yet, and startups face numerous structural issues when venturing into this space.
Last month, Stripe announced 288 new products at its Sessions conference. Nearly 40% of the total documentation views were for its Agent commerce docs. Their Agent commerce market boasts over 1,000 activated merchants. Yet, during the Sessions conference, the number of registered Agents conducting transactions was in the single digits.
Visa mentioned that their Agent payment tokens (tokenized payment credentials bound to an Agent for making payments on a user's behalf) currently require 3 to 9 months for KYC approval and essentially need a minimum revenue threshold of $250 million to qualify. Today, only companies at the scale of Amazon or Walmart can complete this identity verification loop.
Coinbase reported that as of April, there were 69,000 active Agents and 165 million transactions on the x402 protocol. However, independent on-chain analysis shows actual daily transaction volume is around $17,000, with about half being test transactions (according to CoinDesk, March 2026).
Agent to Merchant
We built shop.fast.xyz to directly validate the real-world application of concierge-style commerce. It includes real products, merchants, and transactions.
For most product categories, the current user experience of AI shopping is entirely inferior to traditional e-commerce. When buying clothes, electronics, or furniture, you want to see pictures, browse options, and compare them side-by-side.
The conversational format of a chatbot is actually a step backward. You're replacing a rich visual interface with a plain text conversation, and humans are fundamentally visual shoppers.
Agents excel at areas we thought would be difficult. They can understand user needs and handle instructions like "similar to this but cheaper" well. The model layer works.
But it cannot replace the experience of lining up ten products side by side and choosing one. Chat interfaces can be enhanced with carousels and interactive displays, but at that point, you're essentially rebuilding an e-commerce frontend inside a chat window. For visually-driven comparison shopping, we haven't found a compelling reason why a chat interface is better than a native e-commerce interface.
We see real demand from merchants, but it's a defensive demand.
Merchants want their stores to be queryable by Agents. Not because current customers are buying through Agents, but because they fear being left behind if this becomes a mainstream channel.
This is an "Agent Engine Optimization (AEO)" strategy, but it's currently a nice-to-have, not a necessity. Merchants are preparing for a wave that hasn't arrived.
Conversational commerce can indeed improve the experience in certain scenarios: high-frequency, low-decision-cost purchases where the user already knows exactly what they want. Ordering food delivery is the clearest example. It's a huge market, extremely frequent, with quick decisions ("order me Pad Thai from the usual place"). Conversational Agents have a chance here.
But major food delivery platforms haven't opened their APIs. The only way is "computer use": letting the AI navigate the app visually like a human. This is slow, fragile, and the inference cost simply doesn't work for a $15 lunch order.
Another potential breakthrough lies where: the UI navigation of certain stores is extremely complex and painful. Layers of discounts, promo codes, loyalty programs, and confusing checkout processes.
An Agent that understands "use my coupons, deduct my reward points, find the cheapest shipping, operate in my native language" can simplify today's dreadful user experiences. This is particularly important for elderly users, non-native speakers shopping on foreign websites, or highly specific scenarios with very niche needs.
Both of these breakthrough points require massive consumer (B2C) distribution channels. You're competing with DoorDash (the largest US delivery platform with 56% market share) and Amazon for user entry points.
Consumer-scale distribution is a strength of giants. The supply side for concierge-style commerce is ready, while the demand side is constrained by user experience and distribution channels. Building more infrastructure doesn't solve these two problems.
Agent to API
We discussed actual payment needs with dozens of developers. The situation is almost strikingly consistent: Agent-to-API usage today is recurring, including compute, inference, and data sources. Developers already have subscription services, archived API keys, and billing relationships with core providers.
The typical stablecoin argument is: on Stripe, the minimum effective cost for credit card processing is about 2.9% plus 30 cents, making sub-one-dollar API calls uneconomical. But with today's low transaction volume, prepaid credits solve this. Developers top up their accounts in advance, problem solved.
The deeper issue is the supplier market. Most mainstream SaaS companies don't want to offer ephemeral API access costing fractions of a cent. Their business model is multi-year enterprise contracts. Companies whose revenue relies on large commitment contracts will resist pricing mechanisms that circumvent their existing models.
Machine commerce is structurally a long-tail market, comprising smaller services, niche data sources, individual developers, and MCP servers. Protocols like MPP and x402 are well-suited for this niche.
But by definition, this is a market serving advanced users with specific needs, and historically, developers have been one of the groups least willing to pay.
When Stripe Projects launched, it partnered with 32 supplier partners like Vercel, Supabase, Cloudflare, Twilio, etc., covering most of the tools developers use to build and deploy software, all accessible through existing billing systems. The top-of-stack demand for developers is already met.
The opportunity for new payment rails exists in everything beyond these top 30 services: the opportunity is real, but its scale is inherently much smaller than the flashy numbers suggest.
The same pattern applies to content acquisition. Agents are already scraping and summarizing articles, and publishers are pushing back.
But when content monetization arrives at scale, it will happen through the CDN providers already sitting between publishers and the internet (Cloudflare has already launched AI audit tools for this), or through large-scale licensing agreements between publishers and AI labs.
This infrastructure opportunity will ultimately flow to giants that already own distribution channels.
Agent to Agent
The Agent-to-Agent business model is a long-term vision, currently almost entirely theoretical, with no one achieving meaningful transaction volume. Startups are tackling core challenges: Agent discovery, trust establishment, terms negotiation, and dispute resolution.
When this transaction structure truly materializes, it will be fundamentally different from existing payment rails. Neither party in the transaction involves a human identity. Latency is sub-second. Funds ranging from fractions of a cent to millions of dollars move in the same flow.
Add to that multi-party settlements, which completely defy the bilateral buyer-seller model assumed by existing payment rails. When this happens, we believe it will come fast and at scale.
This is a long-term bet on dedicated settlement infrastructure, and it's real. But a "real long-term bet" and "current market" are two different things.
For months, we were also among those evangelizing this market and had built a complete infrastructure around it over the past few years. With our distributed network, we could theoretically scale to over 1 billion TPS, with latency under 50 milliseconds and average consensus of 10 milliseconds. But we must meet the market where it actually is today.
Agent to Finance
This is arguably the only category with existing demand. The customer base already exists and is willing to pay. Today, fund managers, finance teams, and DeFi users are paying for financial tools. Embedding AI into existing workflows is a natural product evolution.
Agent finance also creates entirely new behaviors. An Agent that can autonomously monitor and rebalance hundreds of positions in real-time operates in a way humans cannot replicate manually. This isn't just automation; it's a substantive capability enhancement.
The challenge is the competitive landscape. The financial industry is heavily regulated and relies heavily on established business relationships. Incumbents have licenses, compliance infrastructure, and client relationships. Startups can find a niche in less-regulated areas (like DeFi), areas where giants move slowly, or where AI creates capabilities giants don't possess.
But compared to the other three categories, the competitive dynamics here are more favorable to incumbents, as layering AI on top of existing products and customer bases is far easier than the reverse.
The Real Game
So, why are people still building this stuff? Two reasons.
First, motivation. Industry giants have ample cash flow to bet on a future that may take years to materialize. For them, the cost of entering five years early is a rounding error, while the cost of entering one year late is existential. So they must build.
Second, cognitive bias. When your core business is payments, every problem looks like a payments problem. The Agent economy needs a payments layer, so build the payments layer.
But payments are just one piece of a much larger puzzle. The real challenge isn't moving money between Agents, but coordinating work between Agents and humans, verifying the work was done, and settling the outcome. Payments are just part of settlement. Settlement is just part of coordination. And coordination is the real prize.
Coordination at scale will naturally give rise to settlement mechanisms as a necessity. Payments are just one instrument in the symphony, not the entire score. The companies solving coordination will swallow the payments business, not the other way around.
Most incumbents are building defensively for a future of large-scale machine transactions. Because their funding runway is infinite, the timeline doesn't matter to them.
But startups don't have that luxury. We must go where the market actually is; we can't wait for the wave to arrive.
A year of building has led us in an unexpected direction. There is real market activity there, growing rapidly, and underserved. It lies outside the four categories we've outlined.
