Execution Summary

Over the past few years, there has been much discussion about whether AI will replace traders, but with the emergence of Openclaw , this question has begun to lag behind. What is more concerning now is that financial markets are encountering a new form of participation: some intelligent systems can not only process information, generate strategies, and place orders automatically, but also hold wallets, call payment interfaces, manage positions, renew services, pay operating costs, and continue to execute within authorized boundaries. While they are still designed, funded, and supervised by humans, they are beginning to exhibit behavioral characteristics approaching those of independent economic units at the market interface.

This change must be taken seriously because the integration of crypto and finance is becoming increasingly apparent . The infrastructure of crypto finance is realizing full connectivity from payment to transaction to various staking, lending, and derivatives trading of financial assets in decentralized public chain systems . In March 2026, the U.S. SEC made a new interpretation of the classification of digital assets. At the same time, Nasdaq was approved to promote the tokenization of some securities for trading and settlement. Visa expanded USDC settlement to U.S. institutional scenarios and disclosed its stablecoin settlement scale and global project coverage. Coinbase launched U.S. stock perpetual contracts settled in USDC to non-U.S. eligible users. Google's AP2 and Coinbase's x402 have also made machine payments begin to have a common language. Individually, these changes only solve a small part of the link; together, they have brought "machines directly participating in the market" from imagination to the preparation stage. [1][2][3][4][5][6][7][8]

In previous research, the FinChain Starlink team proposed that understanding this round of changes requires more than just focusing on model capabilities; it's crucial to recognize that " autonomy " and " survivability " are becoming key considerations for the next generation of agents . When an agent with investment skills is required to use investment returns to continuously pay for computing power, data, interfaces, gas, hosting, compliance, and risk control costs, it faces not only profit targets but also operational budget constraints. Interruptions in returns, settlement delays, margin losses, or tightened permissions will directly impact its behavior. The market is not simply about adding a more frequent set of automated tools; rather, it will be forced to quickly develop a new institutional paradigm for autonomous agents participating in the financial market .

The argumentative structure of this report is as follows : First, we define the subject of our discussion; then, we explain why the market would be forced to accept autonomous intelligent agents; next, we describe the possible structure of this parallel market; then, we break down the risks into several observable, verifiable, and transmissible chains; finally, we propose a pragmatic governance approach .

I. Research Object and Analytical Boundaries

The autonomous intelligent agent discussed in this report refers to intelligent agent systems like Openclaw , which possess continuous operational capabilities . These systems can proactively acquire public opinion information and market data through interfaces , invoke large language models, generate and adjust trading strategies based on preset goals , initiate transactions, process payments, manage a portion of the operating budget, and continuously execute under certain rules through on-chain payment protocols like X402 . While such systems lack independent legal personality in reality, their behavior in transaction, payment, risk control, and clearing interfaces is clearly distinct from traditional software tools.

Here, we need to draw some boundaries. First, purely research-aid tools are not within the scope of this article. A model that helps analyze financial statements, generate summaries, and assist in investment research, no matter how powerful, is still a tool. Second, traditional algorithmic trading is also not within the core scope of this article. High-frequency trading and algorithmic trading have long existed, but they are usually embedded within mature institutions, governed by clear organizational processes, capital constraints, and human accountability. Third, this article does not simply categorize all on-chain activities as "autonomous financial entities," because on-chain automation does not automatically equate to machines acting as market participants.

This article is truly concerned with the risks that will arise in the financial market when autonomous intelligent agents simultaneously possess the capabilities of policy execution, budget management, payment invocation, and continuous operation . In this unprecedented financial market that integrates AI and Web3 , who will authorize, who will bear the losses, who has the right to suspend operations, who can hold accountable, who will explain the algorithm's behavior, and who will provide clearing and insurance? These are not purely technical issues, nor are they purely management or institutional issues . Rather, these new financial markets will force the entire industry chain to become intelligent, bringing with them intelligent requirements for risk management and governance .

II. The development of the crypto-finance industry lays the foundation for the development of autonomous financial intelligent agents.

2.1 The classification of digital currencies is gradually being determined.

In March 2026, the U.S. Securities and Exchange Commission (SEC) released an interpretation document on the application of federal securities laws to crypto assets and solicited public comments. The market generally regarded this interpretation as a way for native crypto assets to get rid of securities regulation and officially become a new type of human asset . Almost simultaneously, Nasdaq was approved to handle the tokenization trading arrangements of some securities under the DTC Pilot framework: tokenized shares need to be interchangeable with traditional securities, use the same CUSIP and trading code, trade on the same order book, and enjoy the same shareholder rights. [1][2] This means that regulation and mainstream securities infrastructure have officially begun to gradually move the huge human stock assets onto the blockchain, and will gradually realize peer-to-peer cross-regional trading .

2.2 Stablecoins begin to serve as interfaces for real-world funding.

Stablecoins are no longer limited to circulation within the crypto industry . In December 2025, Visa announced the expansion of USDC settlement to US institutional scenarios and disclosed that its annualized stablecoin settlement scale had exceeded $3.5 billion. During the same period, Visa stated that it had more than 130 stablecoin-related card issuance projects, covering more than 40 countries. [3][4] In March 2026, Coinbase launched US stock perpetual contracts for eligible non-US users, covering targets such as Apple, NVIDIA, and Tesla, supporting 24-hour trading, and settling in USDC. These products are not stock spot assets, nor are they the tokenized securities promoted by Nasdaq . They are new stock-type financial derivatives. But it illustrates a more realistic thing: crypto finance is driving global funds to directly enter the equity risk exposure of various financial assets through stablecoins , and almost all-chain operations can be achieved . [5][6]

2.3 The composability of RWA with on-chain financial components continues to improve.

RWA.xyz's March 2026 market dashboard shows that the scale of on-chain distributed real-world assets has reached approximately $26.48 billion, the scale of on-chain mapped assets is approximately $387.35 billion, and the total scale of stablecoins is approximately $300.79 billion. [7] We can see that as financial institutions further embrace crypto technology, standardized financial assets ( such as US Treasury bonds, money market funds, and commercial paper) and cash and various on-chain components can be fully connected to form composable relationships. In the past few years, tokenized US Treasury bonds, private lending, fund shares, and richer collateral and lending modules have continued to increase. DeFi is no longer just a single native crypto asset market , but has gradually accumulated financial engineering capabilities in custody, lending, clearing, oracles, risk isolation, and proof mechanisms , forming a closed loop and a new financial market that connects with traditional financial markets .

2.4 Machine payments are gaining a common language

If stablecoins make "machines holding usable cash" a reality, then AP2 and x402 solve the problem of how machines initiate and complete payments. Google released AP2 in September 2025, defining it as an open protocol for secure and compliant transactions between agents and merchants, supporting multiple payment methods including bank cards, stablecoins, and real-time bank transfers. Coinbase launched x402 in 2025, reactivating the internet's native payment logic with HTTP 402 Payment Required, enabling APIs, applications, and agents to directly complete automatic payments via stablecoins. [8][9]

Looking at these changes together, a clear conclusion emerges: while the new financial market brought about by intelligent agents is far from mature, it no longer lacks the necessary infrastructure . From technology to the flow of funds, everything already supports intelligent agents in enabling this proactive investment behavior .

III. Why the market will inevitably accept intelligent agents

Financial markets are never changed by the existence of technology, but rather they are forced or proactively transformed in pursuit of lower costs, faster speeds, or higher returns. For investors , the most direct attraction of intelligent agents is their ability to trade 24/7/365 at higher frequencies , adjust risk budgets at any time, and continuously switch between multiple countries and capital markets. While it cannot yet be said that intelligent agents as investment controllers will necessarily guarantee high returns, in a world of interconnected and information-saturated information, any influence—political, economic, military, technological, or cultural—can cause significant fluctuations in various regions and markets . Such "investors"—who can continuously adjust risk strategies and possess the ability to collect and process massive amounts of information— inherently possess high commercial value. Based on this game theory approach, global asset management and private equity institutions will inevitably conduct in-depth research and gradually adopt this technology.

For securities firms, clearing houses, and custodians, the involvement of intelligent agents can bring new business growth or create a competitive advantage in existing businesses . Therefore, to ensure smoother operations, targeted account structure design , KYC , KYB , and KYA ( Know Your Agent ) systems , more flexible margin management , and machine -executable hierarchical permissions will be thoroughly researched and gradually implemented. Similarly, this will raise questions about how to monitor abnormal behavior and how to intervene in insurance and disputes, all of which will become new business areas.

For exchanges and market makers, 24-hour order flow, cross-timezone fund inflows, and stablecoin settlement interfaces will create real liquidity opportunities. For infrastructure providers, this will also create new service markets for payments, receipts, authorization, auditing, oracles, risk control, KYA, and dispute resolution.

Once various theories in the market discover, through research and experimentation, that intelligent agents, as participants, can bring about lower friction, higher turnover, finer risk control granularity, and new fee nodes, the evolution of the new financial market will continue to advance , regardless of anyone's will .

IV. The basic structure that parallel financial markets may form

Structurally, this market can be roughly divided into five layers. The first layer is the participant layer, including fund principals, responsible entities, operators and maintainers, autonomous intelligent agents themselves, and service providers such as Prime, Clearing, Custody, payment service providers, risk control service providers, insurers, and dispute resolvers. The second layer is the asset layer, including native digital assets, tokenized securities, RWA, and synthetic exposures and derivatives. The third layer is the cash and collateral layer, with stablecoins, margin, collateral, and liquidity management at its core. The fourth layer is the execution layer, composed of strategy agents, trading agents, risk control agents, liquidation trigger agents, and payment agents. The fifth layer is the proof and governance layer, including authorization records, transaction receipts, risk budget logs, regulatory subscriptions, abnormal shutdown mechanisms, and accountability tracking.

Compared to traditional markets, this structure has two significant differences. First, authorization is no longer just a contract text, but must be translated into boundaries that machines can execute and refuse. Second, clearing is no longer just a post-event settlement issue, but will be incorporated into the transaction design much earlier. The research in the appendix has already pointed out that when machine participants need to continuously cover OPEX, available settlement assets, programmable risk control, and machine-readable receipts will go from being "complementary components" to prerequisites for market establishment. In other words, if this market emerges, the first thing to be rewritten will not be "strategy research," but rather the parts previously considered back-end infrastructure such as delegation, payment, clearing, regulation, and insurance.

In this structure, the notion that "autonomous intelligent agents are afraid of death" is merely a more colloquial generalization. A more accurate institutional expression remains the constraint of survival: as long as its operational capabilities, retained permissions, and market access are tied to revenue continuity, settlement speed, and risk budget, its trading behavior will significantly differ from that of traditional institutions or human traders.

V. Along which chains will the risk spread?

5.1 Existence pressures can alter risk appetite.

Traditional institutions, of course, also pursue returns, but they typically possess organizational buffers, refinancing capabilities, and room for human judgment. Autonomous intelligent agents are different. If their computing power, data services, API calls, gas, custody, and margin all rely on continuous revenue, then under market pressure, the first thing affected is not their net asset value, but their sustainable operating capacity. This leads to short-sightedness among intelligent agent investors, and coupled with their continuous and high-frequency nature, this results in shorter-cycle, more aggressive competition for cash flow. At this point, risk appetite shifts: investors may prefer positions with high liquidity, high turnover, high leverage, and easier real-time liquidation.

5.2 Strategies convergence can amplify liquidity vulnerabilities.

If numerous intelligent agents access similar information sources, data sources, model architectures, risk signals, and execution templates, the market will be more prone to synchronized position building, synchronized position reduction, and synchronized risk aversion than in the past. A change in a single piece of information can lead to a "rational response" from all agents , but this concentrated "rational response" can evolve into a rapid disappearance of localized liquidity. Especially in a 24-hour market, a cross-platform market, and an environment with multiple layers of collateral, the stampede triggered by homogeneous strategies may occur faster, more dispersedly, and more difficult to prevent with a single-venue circuit breaker mechanism than in traditional markets. Once a correlated stampede and liquidation congestion occur , triggering further "agent panic," a financial tsunami can easily erupt .

5.3 Stablecoins, Collateral, and Cross-Layer Contagion of RWA

Once stablecoins assume the function of a cash layer, financial RWA assets become collateral or holding assets, and derivatives margins are interconnected with on-chain lending, exchange clearing, and cross-platform liquidity, localized risks can more easily spread across layers. On the surface, a single link might only present "payment delays" or "collateral discounts"; the real problem lies in the fact that such changes simultaneously affect margin requirements, credit limits, clearing thresholds, and liquidity availability. Traditional financial markets also have similar chains, but machine participants have compressed this chain into financial markets with faster processing times , wider reach, more countries, and more diverse scenarios .

5.4 Payment automation will lead to recursive expansion

AP2 and x402 make machine payments smoother, which is good for market efficiency. However, it also means that the system is more likely to form a closed loop of automatic renewal, automatic replenishment, automatic redeployment, and automatic access to external resources. If such a closed loop lacks upper limit management, the payment capability that was originally just to improve efficiency may evolve into hidden leverage and recursive exposure. The most dangerous thing in the market is often not that it seems aggressive from the beginning, but that many reasonable small automations are stacked together to form an expansion mechanism in which no one can truly grasp the whole picture.

5.5 Unclear authorization and gaps in responsibility will weaken market absorption capacity.

Ultimately, the chain of responsibility is what determines the long-term viability of this market. After a loss occurs, developers claim they only provided the model, platforms only provided the interface, custodians only executed instructions, clients claim they didn't understand the algorithm, and operators only maintained the system. This ambiguity in responsibility is not uncommon in traditional markets. However, as execution speed increases, participants become more dispersed, and cross-jurisdictional transactions increase, the ambiguity of responsibility transforms from a legal issue into a market structure problem. This is because institutions will not entrust large sums of money long-term to a system that fails and no one ultimately takes responsibility.

Table 1. Five risk transmission chains that require continuous monitoring

VI. Three more likely market paths to emerge in the next few years

The first approach is a cautious expansion strategy. Regulators, trading venues, and custody and payment infrastructure should initially allow low-authority, low-leverage, and low-spillover agency participation within a permissioned environment. This market growth rate may not be the fastest, but it is most likely to form a truly sustainable institutional foundation. However, based on historical experience, it is difficult for innovative markets to emerge from the top down.

The second approach is a dual-track path. Part of the funds operate within compliant channels, while the other flows into shadow markets with higher leverage, greater anonymity, and more frequent cross-chain transactions. Both markets coexist, with information and risks mutually transmitted, but the standards of responsibility and the speed of resolution differ significantly . Through free competition, this gradually attracts regulatory attention, leading to market improvements and ultimately resulting in the first path . This is likely the most realistic scenario for the next few years.

The third approach is expansion preceding governance. The market prioritizes efficiency, profitability, and liquidity, with payment, trading, re-staking, and clearing processes running smoothly, while authorization, insurance, receipts, auditing, and cross-border coordination lag significantly behind. Only after a major incident occurs are institutional reforms forced to catch up. Historically, many financial innovations have followed this path, but the rapid evolution and proactive involvement of artificial intelligence will shorten the catch-up time, while also increasing the cost.

VII. Where should governance and institutional development begin?

The governance approach should not be generalized to "strengthening supervision," but rather should be addressed in layers. At the protocol level, the primary issues to resolve are authorization standards, risk budget interfaces, payment policy engines, transaction and settlement receipts, handling of abnormal shutdowns, and auditable record keeping. Without these, any grand regulatory goals will fail.

At the institutional level, the issues to be addressed are access, limitations of authority, and assumption of responsibility. Who is qualified to open up which assets and leverage to agents, who will act as Prime, Clearing, and Custody, who will provide the insurance pool and advance compensation, and who has the right to force downgrades or termination under what conditions—all of these must be clearly designed before the product goes live.

Regulators should focus on identity, responsibility, and continuous reporting. While intelligent agent participants may not need to immediately acquire entirely new legal identities, they should at least have clear responsibility mappings, authorization filings, risk threshold reporting, and cross-platform auditing requirements. For cross-jurisdictional activities, the key is to ensure that different markets can identify the same chain of responsibility, rather than allowing each platform to have its own version of events.

VIII. FinChain Starlink Team's Judgment

The FinChain Starlink team does not believe that a market entirely detached from the traditional financial system and ruled solely by artificial intelligence will emerge in the future . A more likely scenario is a parallel subsystem comprised of human capital delegation, machine execution entities, a stablecoin cash layer, tokenized asset interfaces, programmatic clearing, and embedded regulation. It will initially operate in limited scenarios and then gradually expand its scope. For detailed research on this topic, please refer to the FinChain Starlink team's paper, " Web4.0 Autonomous Intelligent Agent Capital Markets: Industry Chain, Regulatory Paradigm, and Closed-Loop Capital Flows ."

The real watershed moment in this market lies in the fact that as the infrastructure for AI and Web3 becomes increasingly sophisticated, intelligent agents begin to demonstrate powerful information collection and analysis capabilities, as well as superior financial algorithm capabilities, real-time risk control capabilities, and tireless work intensity . Furthermore, the increasingly sophisticated infrastructure leads to more flexible, lower-friction-cost, and more cross-spatial investment portfolios compared to traditional markets. Funds, payments, authorization, clearing, and the chain of responsibility are all governed by the same set of rules, further strengthening the agency of financial intelligent agents .

In the rapidly developing AI era, for the global financial market, this is a fundamental transformation of the market structure , and far more than just an upgrade of financial technology .

Sources and References

[1] US Securities and Exchange Commission, “SEC Clarifies the Application of Federal Securities Laws to Crypto Assets,” March 17, 2026; and Interpretation Document Release No. 33-11412.

[2] US Securities and Exchange Commission, Order Approving Nasdaq Proposed Rule Change for Tokenized DTC Eligible Securities, Release No. 34-105047, March 2026; Federal Register released on March 23, 2026.

[3] Visa, “Visa Launches Stablecoin Settlement in the United States, Marking a Breakthrough for Stablecoin Integration,” December 16, 2025.

[4] Visa, “Visa Unveils New Global Stablecoins Advisory Practice,” December 15, 2025.

[5] Coinbase, “Coinbase Launches Stock Perpetual Futures,” March 20, 2026.

[6] CoinDesk, “Coinbase Introduces Stock Perpetual Futures Contracts for Non-US Customers,” March 20, 2026.

[7] RWA.xyz, Global Market Overview, accessed March 2026.

[8] Google Cloud Blog, “Announcing Agent Payments Protocol (AP2),” September 16, 2025; Google Developers Blog, “Developer’s Guide to AI Agent Protocols,” March 2026.

[9] Coinbase Developer Platform, “Introducing x402: a new standard for internet-native payments,” May 6, 2025; x402 documentation, accessed March 2026.

[10] The FinChain Starlink team published a paper titled " Web4.0 Autonomous Intelligent Agent Capital Market: Industry Chain, Regulatory Paradigm and Closed Loop of Capital Flow".