North America: Inside the Growth of Agentic Commerce

Paper · Source
Agentic Commerce

Source: Chainalysis Team; Chainalysis blog · 2026-10-14

North America’s $3 trillion crypto economy remained the most influential in the world, with advancements in agentic commerce, exports of dollar stablecoins, and tokenization shaping global activity.

Agentic commerce, or AI agents using crypto to pay autonomously, grew rapidly over the 12-month period, with U.S. providing a nexus for growth.

“A lot of what we’re seeing vs. 2025 comes down to maturation of the industry,” said Hoolie Tejwani, Head of Coinbase Ventures. “The building blocks set in 2025 are turning into products with real users, liquidity, and markets in 2026.”

The United States’ lead in the global AI development race is influencing all corners of the economy, including crypto. We researched x402, one of the most popular digital payments rails that lets AI agents spend money, to better understand how this economy is taking shape.

Autonomous software has begun to pay for things on-chain, and the United States has emerged as its dominant builder. Crypto’s cornerstone American companies, including Circle, the issuer of USDC (a leading currency for agents), and Coinbase, builder of the x402 system itself, are supporting their rise. We spoke to both companies for this report to get a sense of the technology. Agentic commerce is also drawing interest from the U.S.’ most important financial institutions. BlackRock’s Head of Digital Assets Research Will Su told us the firm is watching agentic commerce closely “with AI agents potentially creating meaningful demand for frequent, low-value payments around the clock.”

By the end of the 2026 period over 138 million x402 transactions had occurred on Base, the most popular blockchain for agentic commerce.

Circle describes a clean division of labor emerging: “card rails for consumer-prompted agent payments, stablecoin rails for machine-to-machine settlements that are too small for card economics.”

Indeed, the vast majority of x402 activity registered as a micropayment in the 16 months ending in September 2026 (we extended our review period given how new this technology is). Over 87% of payments fall between a hundredth of a cent and a dime, with the most common size between one and ten cents.

Each successive group of wallets has arrived more committed than the last. Grouping wallets by the quarter they were first funded, the share that got a second funding within 30 days of the first rose from 70.6% for wallets first funded in Q3 2025 to 89.3% for those first funded in Q2 2026, the most recent group whose 30-day window has fully closed. Topping up a depleted wallet is the on-chain equivalent of renewing a subscription after the first invoice lands. We counted only wallets that sent on two or more separate days, to exclude the one-and-done experimenters and focus on the wallets actually using agentic payments in an enduring way. These numbers show that users are increasingly finding enough utility in x402 to keep funding their wallets after their balances are depleted.

Users of x402 are also becoming more purposeful. We can discern this by looking at the decreasing gap in time between a wallet’s seed funding and its first x402 spend. In late 2025 (when the protocol was still new) wallets were, at the median, 73 days old when they initiated an x402 payment; by Q3 2026 that gap had dropped to 18 minutes. This temporal compression shows that these newer wallets are being purpose built to engage in agentic commerce and to spend on x402.

Since launch, the share of wallets spending on x402 within their first hour of being funded has climbed. In Q3 2025, just 4% of wallets made their first payment within an hour of funding. By Q3 2026, that figure had jumped to 67%, meaning roughly two in three new wallets are now spending almost immediately after being topped up. That is another strong signal of purposeful adoption. People are not funding wallets to park value or test the protocol; they are funding them because a specific job is already queued up and the money needs to move.

“AI agents are becoming economic actors where every agent is effectively a new ‘user’ of the blockchain,” said Tejwani of Coinbase Ventures. “We’re especially excited about projects building on the x402 protocol, which Coinbase incubated, and what it means for the future of open-sourced standards and internet-native payments.”

All the data on agentic payments had us asking: what are AI models actually buying on-chain? In the early days of x402 the answer was predominately fads; memecoins such as PING attracted huge numbers of new wallets only to fade. But the maturation of x402 has also produced more enduring forms of agentic commerce, especially in the information sector. The robots are buying AI-generated knowledge on-chain.

We’ve tracked millions of transactions flowing from AI wallets into information-focused services like code review, financial intelligence, and data and search API. This tracks with what we know about agents in general: they’re hungry for machine-readable data. It’s an area where on-chain micro-payments can be especially useful, as we discussed earlier.

AI agents are also paying through payment rails, for gaming, token issuance and gambling services, and for agent frameworks and agentic tooling. Notably, the AI agents are also paying for AI agents. One of the largest categories for x402 transactions is model inference.

American-funded AI agents are transacting more with x402 services than agents funded from any other country. The U.S. holds a 30% share of the agent-to-services transactions that we can geolocate (using the same methodology we apply to other on-chain activity).

American wallets account for 88.1% of all geolocated AI model inference transactions on the protocol, 41.8% of payments to agent frameworks and tool catalogs, and 23.2% of payments to data and search APIs — the three categories that supply the inputs to building agentic software.

To compare, Chinese-attributed wallets were most prominent in the security space.

Lines of inquiry this paper opens 24

Research framings built by reading the notes related to this paper — the questions it feeds into.

How can AI agents earn consumer trust with financial decisions? How do inference costs, efficiency, and market competition shape AI adoption economics? What evidence best independently verifies agent behavior for safety? How can merchant-funded agents remain loyal to consumer interests in digital commerce? Should multi-agent systems use structured protocols or natural language communication?