Author: RWA Research Institute

In March 2026, Illia Polosukhin, co-founder of the NEAR protocol, made a seemingly simple yet profound statement in an interview: “The users of blockchain will be AI agents.” He painted a picture of the future: artificial intelligence will become the front-end interaction layer for all online transactions, while blockchain will step back to become a trusted back-end infrastructure. Humans will no longer need to directly operate wallets, browse block explorers, or verify transaction hashes; this complexity will be entirely abstracted away by AI agents.

Almost simultaneously, the open-source AI agent project OpenClaw released version v2026.3.7-beta.1, which offers native support for GPT-5.4. This GitHub project, which has over 280,000 stars, rolled out two rounds of significant updates within two days. The official update log included a somewhat self-deprecating yet confident remark: “We have fixed more problems than we have created, and that is progress.” This update not only introduced a pluggable Context Engine but also strengthened security mechanisms and engineering deployment capabilities—OpenClaw is evolving from an experimental agent framework to a true “agent operating system.”

Meanwhile, another seemingly unrelated piece of news was circulating in the crypto community: data from RWA.xyz indicates that the on-chain value of tokenized real-world assets, excluding stablecoins, has surpassed $25 billion, a nearly fourfold increase from about $6.4 billion a year ago. On-chain scales of six major asset classes including U.S. Treasury bonds, commodities, and private credit have all exceeded the $1 billion threshold.

These events, occurring in the same month, are not a coincidence. They jointly point to an emerging paradigm shift: as AI agents begin to autonomously interact with blockchains, and as the scale of on-chain assets becomes sufficient to support an “agent economy,” the operational model of RWA will transition from “human management” to “AI autonomous management.” This is an industrial leap that must be taken seriously.

1. AI is Shifting from “Co-Pilot” to “Pilot”

To understand the depth of this leap, it is essential to recognize the fundamental changes occurring in the role of AI.

In recent years, artificial intelligence has primarily played the role of “co-pilot” in public perception—assisting humans in drafting emails, planning trips, generating code, but always in a passive response mode. Users issue commands, AI executes them, and the task's closed loop is completed by humans. In this model, AI is a tool, and humans are the subjects.

However, the latest release of OpenClaw provides a window to observe the loosening of this relationship. From March 7 to 8, OpenClaw consecutively released versions 2026.3.7 and 2026.3.8, with core updates focusing on four directions: model capability upgrades, evolution of Agent architecture, optimization of engineering deployment, and enhancement of security and reliability.

The most notable feature for developers is the pluggable Context Engine. This mechanism allows developers to freely mount RAG or lossless compression algorithms, addressing the “forgetfulness” issue of agents in long conversations, paving the way for long-term autonomous operation. Meanwhile, ACP binding supports restart recovery, meaning that even if servers restart, the agent can “remember” previous communication progress and context, enabling true persistent service.

Behind these technical details lies an important trend: AI agents are gaining “persistence” and “autonomy.” They are no longer one-time products of conversation but digital entities capable of continuous operation, ongoing learning, and task execution.

Polosukhin's prediction precisely points to the application scenarios for this capability: “Artificial intelligence will be at the front end, while blockchain will exist as the back end. The goal is to let your artificial intelligence hide the entire blockchain—our possession of a block explorer is, in fact, a failure because we have not abstracted this technology.”

In his vision, future AI agents will interact directly with blockchain protocols, autonomously completing payments, managing assets, coordinating services, and even participating in governance voting. Humans will only need to converse with the AI, telling it to “help me optimize asset allocation” or “vote on that proposal,” while the agent completes the remaining tasks on-chain.

This is not science fiction. EVMbench, launched in collaboration between OpenAI and Paradigm, is already testing AI agents' capabilities to detect, patch, and exploit vulnerabilities in smart contracts. Circle and Stripe are competing to build stablecoin payment infrastructure for AI agents, with Stripe's x402 USDC payment functionality launched on Base already supporting autonomous settlement between AI agents. The decentralized AI infrastructure protocol 0G and Alverse have launched the “Web4.0 Market,” allowing AI agents to mint and trade digital assets using crypto proxy IDs.

An on-chain economy composed of AI agents is transitioning from concept to reality.

2. From Issuance to Governance, Every Step of RWA is Being Rewritten

When AI agents become the “users” of blockchain, RWA's models for issuance, trading, management, and governance will be systematically reshaped. This is not a partial efficiency optimization but a paradigm reconstruction of the entire lifecycle.

Asset Issuance: From “Manual Due Diligence” to “Real-Time Verification”

Traditional RWA issuance requires the intervention of lawyers, auditors, appraisers, and other parties. Taking real estate tokenization as an example, project parties need to hire third-party appraisal agencies to provide valuation reports, law firms to conduct ownership investigations, and accounting firms to audit cash flows, a process that often takes months and incurs high costs.

AI agents can change this process. By accessing data sources such as IoT devices, on-chain credit scores, and third-party APIs, AI agents can verify asset status in real time. For instance, when a batch of goods' ownership certificates is on-chain and insurance documents and customs payment certificates are validated, AI agents can automatically trigger the tokenization process, generating corresponding RWA tokens for investor subscriptions. The entire process is compressed from months to minutes, with human intervention minimized.

Transaction Execution: From “Command Response” to “Strategy Game”

Current RWA transactions mainly rely on manual order placements or simple smart contract condition triggers. Investors need to switch between multiple platforms, comparing prices, assessing liquidity, calculating costs, and then manually executing trades.

AI agents, on the other hand, can execute complex strategies. They can simultaneously monitor price gaps across multiple on-chain markets, automatically execute cross-chain arbitrage; they can predict asset price trends based on macroeconomic data (such as interest rate decisions, inflation reports) and adjust holdings in advance; they can automatically execute stop-loss or hedge operations when pre-set risk control thresholds are triggered. More importantly, the competition among multiple AI agents in the same market will give rise to complex dynamics that are difficult for humans to simulate—this is both a challenge and an opportunity for market efficiency enhancement.

Asset Management: From “Monthly Reconciliation” to “Continuous Monitoring”

Management during the life of RWA is often the most overlooked aspect. Rent collection, interest payments, collateral monitoring, and income distribution rely on manual reconciliation and collection, leading to inefficiency and a high potential for errors.

AI agents can achieve 24/7 uninterrupted monitoring. They can automatically distribute cash flow generated by assets to investors' wallets; they can immediately issue margin call notifications when the value of collateral falls below warning levels, and even initiate liquidation procedures; they can based on preset rules in smart contracts automatically handle early redemptions, renewals at maturity, and other operations. For investors, this means significantly improved transparency and timeliness in asset management.

Governance Participation: From “Low Voting Rates” to “Algorithmic Democracy”

Tokenized assets often come with governance rights, but traditional voting participation rates are extremely low. Most investors lack the time or willingness to thoroughly study proposal contents, leading governance to become a formality.

AI agents can change this situation. By analyzing proposal texts, assessing their impact on asset value, and simulating yield variations under different voting outcomes, AI agents can make decisions on behalf of investors. They can continuously participate in governance rather than just passively voting during annual shareholder meetings. This transforms governance into a daily activity rather than an occasional formality.

3. The Market is Already Voting with Real Money

These sound like predictions for the future, but market data is already validating the trend.

Data from RWA.xyz shows that as of March 2026, the on-chain value of tokenized real-world assets, excluding stablecoins, has surpassed $25 billion, a nearly fourfold increase from a year ago. The on-chain scales of six major asset classes—U.S. Treasury bonds, commodities, private credit, institutional alternative investment funds, corporate bonds, and non-U.S. government debt—have all exceeded the $1 billion mark.

Major traditional finance players are accelerating their investments. BlackRock has launched a tokenized fund BUILD on Ethereum, Franklin Templeton has migrated its U.S. government money market fund FOBXX to the Solana public chain, and JPMorgan has processed billions of dollars worth of tokenized collateral repurchase transactions via the Kinexys platform. These institutions are unlikely to enter a market without prospects.

The competition between Circle and Stripe in the provision of AI agent infrastructure is particularly noteworthy. These two institutions, long positioned at both ends of the stablecoin value chain, are now penetrating each other’s business domains. Circle is building application-layer infrastructure through the Arc L1 blockchain, CCTP cross-chain transmission protocol, and the Circle Payments Network; Stripe, on the other hand, has launched the x402 USDC payment functionality for AI agents on Base, acquired Bridge for $1.1 billion, and is co-developing the Tempo L1 settlement chain with Paradigm.

Data from Artemis indicates that in January of this year, the on-chain trading volume of USDC exceeded $8.4 trillion, with the overall stablecoin market surpassing $300 billion. This is a scale of funding sufficient to support an AI agent economy.

Meanwhile, EVMbench, developed in collaboration between OpenAI and Paradigm, is testing the capabilities of AI agents in the field of smart contract security. According to subsequent studies, AI agents in EVMbench tests were able to detect up to 65% of real-world vulnerabilities; although the end-to-end exploitation success rate has not yet reached the level of human experts, this data is significant enough to attract the attention of the security industry.

4. The Two Sides of a Coin: Great Opportunities but Numerous Pitfalls

Every significant technological transformation is accompanied by both opportunities and risks, and the integration of AI agents and RWA is no exception.

On the opportunity side, efficiency improvements are the most direct value proposition. AI agents can operate 24/7 without the physiological limitations of humans; they can monitor hundreds of markets simultaneously, capturing fleeting arbitrage opportunities; they can execute complex strategies that are difficult for humans to achieve. For asset management firms, this means lower operational costs and the potential for expanded management scale.

New business models are also emerging. An “AI agents as a service” platform could become the next growth point: companies can rent professional AI agents to manage their RWA assets without building their own technical teams. Sub-specialties such as cross-chain liquidity aggregation, automated market making, and algorithmic governance could lead to the emergence of new specialized agent service providers.

Global liquidity is another dimension worth looking forward to. AI agents can seamlessly access multi-chain markets, transfer assets between different blockchain networks, breaking down the liquidity barriers formed by inter-chain fragmentation in the current RWA market. When agents can freely traverse different ecosystems such as Ethereum, Solana, and NEAR, the depth and breadth of the RWA market will be significantly enhanced.

Challenges should not be overlooked.

Security risks are the primary concern. AI agents hold private keys, execute transactions, and manage assets, which makes them new targets for hacker attacks. Vulnerabilities in private key management, algorithm design flaws, and adversarial attacks could lead to asset losses. Research by EVMbench indicates that while AI agents perform impressively in vulnerability detection, the success rate in real scenarios for end-to-end exploitation still falls far below expectations. This suggests that the current level of technology is insufficient to support fully unattended asset management.

The compliance dilemma is equally tricky. The legal status of AI agents remains unclear: if decisions made by agents lead to asset loss, who should bear the responsibility? The developer? The deployer? Or the asset holder? Regulatory attitudes may vary across different jurisdictions, and the global accessibility of blockchain complicates these issues. In mainland China, according to document No. 42 jointly issued by eight departments, conducting RWA tokenization and related services within the country is illegal; AI agents’ on-chain operations must strictly adhere to this red line.

The technological threshold is a reality barrier. To embrace the AI agent economy, enterprises need to possess both blockchain integration capabilities and AI deployment abilities, which pose significant challenges for traditional companies. Cultivating multidisciplinary teams, selecting suitable technology partners, and designing robust governance frameworks require time and resource investment.

5. Want to Get on Board? First, Do These Four Things

In the face of the emerging AI agent economy, traditional companies and publicly traded firms need to formulate clear strategic paths.

Step One: Prioritize Asset Digitization

AI agents manage digital forms of assets, not physical forms. Therefore, companies need to first tokenize their real assets (receivables, equipment, properties, intellectual property, etc.) through compliant channels. For companies in mainland China, this means needing to pay attention to filing channels in places like Hong Kong and exploring pathways for RWA to go abroad within the framework allowed by document No. 42.

Step Two: Pilot AI Agent Nodes

There's no need for comprehensive deployment all at once. Companies can choose specific scenarios (such as cross-border payments, supply chain financing, investor relations maintenance) as pilot projects, collaborating with mature AI agent protocols to introduce agents for automated management. They can accumulate experience from small-scale pilots, assess effectiveness, and then gradually expand the application range.

Step Three: Cultivate Multidisciplinary Teams

The AI agent economy requires a combination of talents from various fields. Companies need personnel who understand blockchain technology, as well as engineers knowledgeable in AI model deployment and tuning, and legal experts familiar with financial compliance. Cultivating or bringing in such multidisciplinary talents is crucial for long-term competitiveness.

Step Four: Participate in Standard Setting

The integration of AI agents and RWA is still in the early stages, with technical standards, governance rules, and compliance frameworks being formed. Forward-looking companies should actively engage in industry discussions and promote the formulation of rules conducive to their development.

Conclusion: The A and B Sides of Digital Civilization are Quietly Merging

Looking back at the two events mentioned at the beginning of this article—OpenClaw's technological breakthrough and the RWA market's scale leap—they may seem independent but actually point to the same profound historical question.

Within the cognitive framework of the RWA Research Institute, AI and blockchain have always represented the A and B sides of digital civilization. One side represents extreme productivity, while the other represents advanced production relationships. As AI agents begin to autonomously manage on-chain assets, these two sides are experiencing an unprecedented deep integration. AI agents handle information, execute strategies, and engage in competition with extreme efficiency, while blockchain provides reliable asset registration, transparent rule enforcement, and trustless value transfer.

This is not merely a simple technical overlay but an evolution of economic organizational forms. When assets are autonomously managed by AI agents, humans will retreat to the roles of rule makers and strategy designers. What kind of social impact will this bring? How will governance power be distributed? How will boundaries of responsibility be defined? These are questions without ready answers that require joint exploration by industry, regulatory bodies, and academia.

But one thing is certain: the on-chain economy constituted by AI agents has quietly begun in some version update in March 2026.

(This article is based on publicly available information and data as of March 12, 2026. According to document No. 42 jointly issued by eight departments in China, conducting RWA tokenization and related services within mainland China is illegal. The on-chain economy of AI agents discussed in this article only applies to compliant frameworks abroad and does not constitute any investment advice.)

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