Author: RWA Research Institute
In March 2026, Peter Schroeder, Head of Global Markets at Circle, posted a set of data on X platform: Over the past nine months, AI agents have completed 140 million payments, with a total transaction volume of $43 million. Of these, 98.6% were settled in USDC, with an average of only $0.31 per transaction. More importantly, the number of AI agents with purchasing power has exceeded 400,000.
This data speaks louder than any financial report: AI agents are moving from concept to real economic activity.
400,000 AI agents, 140 million transactions, $43 million—this is value exchange happening autonomously between machines. No human intervention, no bank approval, no credit card verification. Code to code, protocol to protocol, processes that previously required human signatures, reconciliation, and clearing are now completed automatically.
Circle’s stock price has risen from $60 to $105 in recent trading days, a 75% increase. The market interprets this rise as a positive response to the financial results—Circle achieved $770 million in revenue in Q4 2025, a 77% year-over-year increase, with a net profit of $133 million. But what’s truly worth noting isn’t these numbers themselves, but the structural changes behind them: as AI agents become new economic entities, the entire financial infrastructure’s logic needs to be rewritten.
And in this rewriting process, a deeper question is emerging: when AI agents start holding disposable funds and can earn USDC by completing tasks, how will they handle these funds? Payment is the first step; asset management is the second. The RWA (Real World Assets) track is precisely about answering this second question.
1. From Payment Capability to Asset Holding
To understand what financial services AI agents need, first understand their economic activity patterns.
Deloitte’s “2026 Technology, Media, and Telecommunications Industry Outlook” reports that if enterprises and service providers can achieve efficient intelligent agent coordination, the global agent-based AI market could reach $45 billion by 2030. The basic feature of this multi-agent collaboration model is: a complex task is broken down into multiple steps, completed by different specialized agents, with each call accompanied by a micro-payment.
Take API calls as an example. An AI application might need to call multiple large language models, access various databases, and use multiple computing resources. Each call adds up to $0.01, $0.05, or $0.1. These payments are tiny but frequent. Circle’s data shows that over the past nine months, 140 million transactions averaged only $0.31 each—typical of the micro-payment market.
But the issue is, as AI agents continuously generate income—whether by providing services to users or participating in distributed computing networks—their accounts will accumulate funds. These funds can’t stay idle forever. Any rational economic entity will consider: what to do with idle funds?
This is the logical starting point for AI agents transitioning from “payers” to “asset holders.”
In traditional finance, individuals and companies deposit short-term idle funds in banks, buy money market funds, or short-term government bonds to earn returns. AI agents need similar capabilities—not for speculation, but to optimize their economic models. Keeping a balance of USDC for payments is necessary, but if the excess just sits there, it incurs opportunity costs. If excess funds can be automatically invested in a tokenized short-term US Treasury fund, and redeemed when needed, operational efficiency improves.
Furthermore, if AI agents need to reserve value for long-term operation or hedge against gas fee volatility, they might require assets with different risk levels. At this point, they are no longer just “payers,” but “investors”—even if that investor is just a piece of code.
Circle’s focus is on enabling AI agents to be “payers.” To make them “investors,” another infrastructure layer is needed.
2. RWA and AI Agents: An Ongoing “Bidirectional Pursuit”
Circle’s efforts over the past few years can be summarized into three capability layers.
The first layer is stablecoin issuance and liquidity network. As of the end of 2025, USDC circulation reached $75.3 billion, a 72% increase year-over-year, accounting for nearly 50% of stablecoin trading volume. This provides a valuable medium for AI payments.
The second layer is an efficient on-chain settlement network. In August 2025, Circle launched Arc, a blockchain designed for institutional-grade financial services. By March 2026, Circle introduced Nanopayments, aggregating thousands of small payments off-chain and periodically batching them on-chain, reducing transaction costs for developers to zero. Testnets support 12 EVM chains including Arbitrum, Arc, Avalanche, Base, and Ethereum. On the payment protocol layer, x402 allows websites or APIs to send HTTP 402 payment requests directly during responses, embedding payments into internet requests.
The third layer connects to traditional financial systems. Circle Payments Network (CPN) links banks, payment providers, cross-border clearinghouses, and corporate clients. By February 2026, 55 financial institutions had joined, with an annualized transaction volume of about $5.7 billion. In February, new direct payment systems with local currencies and stablecoins in Asia, Middle East, and other regions were added.
These three layers form the “payment infrastructure” for the AI agent economy. But a complete economy also needs “asset management infrastructure”—and this is where RWA can enter.
The exploration of RWA tokenization over recent years has mainly focused on “on-chain mapping” of traditional finance assets. According to Defillama, as of June 2025, total RWA TVL reached $12.5 billion, more than doubling since 2024. Major global banks like Citibank and Standard Chartered are exploring RWA applications in payments, settlement, and cross-border transactions.
But to enter the AI agent economy, RWA needs an “AI-native” transformation. It’s not just about putting assets on-chain; it’s about making assets “understandable and tradable by AI.”
First, data standardization. Leading RWA projects like Ondo Finance are pushing to turn underlying cash flows, legal terms, and risk ratings into structured, machine-readable data formats. In July 2025, Ondo Finance launched the first tokenized US Treasury for global investors, which was included in the White House report on digital asset markets.
Second, programmable logic. Rules for dividends, interest payments, buybacks, and liquidation are encoded into smart contracts, automatically executed by code. Only then can AI agents interact with assets in a “trustless” manner—no need to trust counterparties to fulfill obligations, only to trust the code to run as programmed.
Third, liquidity fragmentation. After tokenization, assets can theoretically be divided into tiny units—$0.01 Treasury bonds, or real estate income rights of 0.1 square meters—crucial for AI agents’ small-scale allocations. Nanopayments have proven micro-payments feasible; similar logic applies to micro-investments.
JPMorgan’s Kinexys provides a relevant example. In May 2025, Kinexys completed the first public trade of tokenized US Treasury on Ondo Chain, using Ondo Finance’s US Treasury fund (OUSG), settled via Chainlink’s cross-chain infrastructure. The trade followed the DvP (Delivery versus Payment) model, enabling simultaneous transfer of assets and payment. Since its founding, JPMorgan’s Kinexys handles over $2 billion daily, facilitating over $1.5 trillion in nominal value transactions.
This case demonstrates the integration of RWA with institutional-grade payment settlement networks. In the future AI agent economy, transaction parties might shift from JPMorgan to an AI agent, with transaction sizes shrinking from millions to just a few dollars, but the underlying logic remains the same—value transfer and storage need seamless connection.
3. Beyond Payment Networks: An Additional Layer of Imagination
Connecting these dots, a complete closed loop begins to emerge:
An AI content generation agent provides services to multiple clients, accumulating a substantial USDC balance. Its underlying protocol sets rules for fund management: when the balance exceeds 1,000 USDC, it automatically allocates the excess via an RWA aggregator into three tokenized short-term US Treasury funds and one tokenized green energy fund. When client demand drops or the account needs replenishment, the protocol automatically redeems some RWA shares for USDC for daily operations.
In this process, the AI agent performs actions such as monitoring balances, assessing risk-return profiles of different assets, executing buy and sell orders, and recording transactions for audit—all automatically by code.
Similarly, a travel planning AI, after booking flights and hotels, receives a USDC deposit from the user as a budget. While waiting for the flight, it detects a micro-share of an RWA insurance product based on delay data, and automatically invests a portion of idle USDC. When the flight is delayed, the RWA insurance triggers payout per rules, increasing the agent’s balance.
All these scenarios rely on existing modules: USDC as a value carrier, Nanopayments for micro-payments, x402 for embedded internet payments, tokenized Treasuries on Ondo Chain, DvP settlement verified by JPMorgan. The remaining step is integration—connecting payment, asset, and transaction layers so AI agents can invoke these financial functions as easily as calling APIs.
Li Ming, Executive President of the Hong Kong Web3.0 Standardization Association, commented on RWA development: “We hope to find a standardized entry point for Web3.0 that can connect RWA ecosystems.” For the AI agent economy, this entry point might be the connection between payments and assets.
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4. The Old Problems in a New World: Risks and Responsibilities
Of course, from today’s AI payments to tomorrow’s AI asset management, many obstacles remain.
First is data authenticity. RWA’s underlying assets are off-chain, and their status, value, and risk information need to be reliably transmitted on-chain. If AI agents rely on false or tampered data, their “investment decisions” will be flawed. The Hong Kong Web3.0 Standardization Association’s “RWA Industry Development Research Report” states that assets that succeed in large-scale implementation must meet three thresholds: value stability, clear legal rights, and verifiable off-chain data.
Second is model risk. Even with accurate data, AI agents’ investment logic may err. Who is responsible for AI decision errors? The individual, the protocol, or the AI itself? This liability issue remains unresolved legally and regulatorily.
Third is liquidity risk. RWA on-chain trading depth is far less than mainstream cryptocurrencies; some assets may have poor liquidity. When many AI agents try to redeem the same RWA fund simultaneously, success is uncertain.
Fourth is regulatory divergence. Different jurisdictions have varying attitudes toward RWA, and the legal status of the same asset can differ greatly. AI agents need to recognize and handle this complexity, which challenges current AI capabilities.
Finally, technical security. Smart contract vulnerabilities, cross-chain bridge attacks, private key leaks—these risks do not disappear just because the trading entity is AI. In fact, with automated trading, the speed and scale of exploitation could far surpass manual operations.
Conclusion
Returning to the initial data: 400,000 AI agents, 140 million transactions, $43 million.
The significance of these numbers isn’t in their scale—compared to humans’ trillions of dollars in annual payments, $43 million is trivial. Their true meaning lies in revealing a direction: machines are becoming independent economic entities, with their own income, accounts, and payment capabilities.
And once machines have income, they will quickly develop asset management needs. This isn’t a distant imagination but a natural evolution of the AI agent economy.
Circle is laying the “payment neural system” for this future—enabling AI agents to transfer value efficiently and at low cost. The RWA track’s role is to become the “energy storage system” of this economy—allowing AI agents to manage their assets as easily as managing their code.
If this judgment is correct, then today’s RWA practitioners should consider: when 400,000 AI agents start seeking configurable assets, and after 140 million payments generate asset management demands, are your RWA products ready for AI evaluation, selection, holding, and trading?
Related reading: Circle’s Reversal Moment: Stock Price Doubles, On-Chain Transactions Outperform USDT, Precise Positioning for Agent Payments
This article is based on Circle’s official financial reports and announcements, Deloitte’s “2026 Technology, Media, and Telecom Outlook,” Defillama data, Ondo Finance public information, JPMorgan’s Kinexys disclosures, Hong Kong Web3.0 Standardization Association’s “RWA Industry Development Research Report,” and other publicly available sources. It does not constitute investment advice. Markets are risky; invest cautiously.
