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." She painted a vision of the future: AI will become the front-end interaction layer for all online transactions, while blockchain will recede into the background, becoming a trusted back-end infrastructure. Humans will no longer need to directly operate wallets, browse block explorers, or check transaction hashes; these complexities will be completely abstracted away by AI agents.

Almost simultaneously, the open-source AI agent project OpenClaw released version v2026.3.7-beta.1, achieving native support for GPT-5.4. This project, with over 280,000 stars on GitHub, released two major updates within two days. The official changelog included a slightly self-deprecating yet confident statement: "We fix more problems than we create—that's 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 into a true "agent operating system."

Meanwhile, another seemingly unrelated piece of news is circulating in the crypto community: data from RWA.xyz shows that the on-chain value of tokenized real-world assets, excluding stablecoins, has surpassed $25 billion, nearly quadrupling from approximately $6.4 billion a year ago. The on-chain size of six major asset classes, including US Treasury bonds, commodities, and private credit, has all exceeded the $1 billion threshold.

The timing of these events, occurring within the same month, is no coincidence. They all point to an emerging paradigm shift: as AI agents begin to interact autonomously with the blockchain, and as the scale of on-chain assets becomes sufficient to support an "agent economy," RWA's operating model will shift from "human management" to "AI autonomous management." This is an industrial leap that needs to be taken seriously.

I. AI is transitioning from a "co-pilot" to a "driver".

To understand the depth of this leap, we need to first see the fundamental changes that the role of AI is undergoing.

In the past few years, artificial intelligence has primarily played a "co-pilot" role in the public's perception—assisting humans in writing emails, planning trips, and generating code, but always in a passive, reactive state. Users issue commands, AI executes the commands, and the closed loop of the task is completed by humans. In this model, AI is the tool, and humans are the subject.

However, the release of the latest version of OpenClaw provides a window into this relationship, suggesting it is loosening. From March 7th to 8th, OpenClaw released two versions, 2026.3.7 and 2026.3.8, with core updates focusing on four areas: model capability upgrades, agent architecture evolution, engineering deployment optimization, and enhanced security and reliability.

Among the features that have garnered the most attention from developers is the pluggable Context Engine. This mechanism allows developers to freely mount RAG or lossless compression algorithms, solving the "forgetfulness" problem of agents in long conversations and paving the way for long-term autonomous operation. Meanwhile, ACP binding supports restart recovery, meaning that even if the server restarts, the agent can "remember" the previous communication progress and context, achieving truly persistent service.

Behind these technical details lies an important trend: AI agents are gaining "persistence" and "autonomy." They are no longer the product of one-off conversations, but digital entities that can continuously operate, learn, and perform tasks.

NEAR co-founder Polosukhin's prediction perfectly points out the application scenario of this capability: "Artificial intelligence will be at the front end, while blockchain will exist as the back end. The goal is to let your AI hide the entire blockchain—the fact that we have a block explorer is actually a failure because we haven't abstracted the 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 "optimize my asset allocation" or "participate in voting on that proposal," and the agent will handle the rest on the blockchain.

This isn't science fiction. OpenAI's EVMbench, developed in partnership with Paradigm, is already testing the ability of AI agents to detect, patch, and exploit smart contract vulnerabilities. Circle and Stripe are racing to build stablecoin payment infrastructure for AI agents; Stripe's x402 USDC payment function on Base already supports autonomous settlement between AI agents. The "Web4.0 Marketplace" launched by the decentralized AI infrastructure protocol 0G and Alverse allows AI agents to mint and trade digital assets using cryptographic agent IDs.

An on-chain economy comprised of AI agents is moving from concept to reality.

II. From distribution to governance, every aspect of RWA is being rewritten.

When AI agents become "users" of the blockchain, the issuance, trading, management, and governance models of RWA will be systematically reshaped. This is not a localized efficiency optimization, but a paradigm shift across the entire lifecycle.

Asset issuance: From "manual due diligence" to "real-time verification"

Traditional RWA issuance requires the involvement of multiple human parties, including lawyers, auditors, and appraisers. Taking real estate tokenization as an example, project teams need to hire third-party appraisal agencies to issue valuation reports, law firms to conduct title investigations, and accounting firms to audit cash flow. The entire process often takes several months and is very costly.

AI agents can revolutionize this process. By connecting to data sources such as IoT devices, on-chain credit scores, and third-party APIs, AI agents can verify asset status in real time. For example, once the title deeds for a batch of goods are on-chain, and insurance policies and customs payment receipts have been verified, the AI ​​agent can automatically trigger the tokenization process, generating corresponding RWA tokens for investors to subscribe to. The entire process is compressed from months to minutes, with human intervention minimized.

Trade Execution: From " Instruction Response" to "Strategic Game Theory"

Currently, RWA trading primarily relies on manual order placement or simple smart contract condition triggers. Investors need to switch between multiple platforms, compare prices, assess liquidity, calculate costs, and then manually execute trades.

AI agents can execute complex strategies. They can simultaneously monitor price differences across multiple on-chain markets and automatically execute cross-chain arbitrage; they can predict asset price trends based on macroeconomic data (such as interest rate decisions and inflation reports) and adjust their positions in advance; and they can automatically execute stop-loss or hedging operations when preset 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 to improve market efficiency.

Asset Management: From Monthly Reconciliation to Continuous Monitoring

Management during the life of a RWA is often the most easily overlooked aspect. Rent collection, interest payments, collateral monitoring, and revenue distribution—these daily operations rely on manual reconciliation and collection, which is inefficient and prone to errors.

AI agents can provide 24/7 monitoring. They can automatically allocate cash flow generated by assets to investor wallets; immediately issue margin calls when collateral value falls below a warning line, and even initiate liquidation procedures; and automatically handle early redemption, renewal upon maturity, and other operations according to preset rules in smart contracts. For investors, this means a significant improvement in the transparency and timeliness of asset management.

Governance Participation: From Low Voter Turnout to Algorithmic Democracy

Tokenized assets typically come with governance rights, but traditional voting participation is extremely low. Most investors lack the time or willingness to thoroughly research proposals, rendering governance a mere formality.

AI proxies can change this. By analyzing proposal texts, assessing their impact on asset value, and simulating changes in returns under different voting outcomes, AI proxies can make decisions on behalf of investors. They can participate in governance continuously, rather than passively voting only at annual shareholder meetings. This makes governance a truly routine activity, rather than an occasional formality.

Third, the market is already voting with real money.

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

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

Traditional financial giants are accelerating their expansion. BlackRock launched its tokenized fund BUILD on Ethereum, Franklin Templeton migrated its US government money market fund FOBXX to the Solana blockchain, and JPMorgan Chase processed billions of dollars in tokenized collateralized repurchase transactions through its Kinexys platform. These institutions are not easily drawn into a market with no future.

The competition between Circle and Stripe in AI agent infrastructure is particularly noteworthy . These two institutions, long positioned at opposite ends of the stablecoin value chain, are now encroaching on each other's business areas. Circle is building its application-layer infrastructure through the Arc L1 blockchain, the CCTP cross-chain transfer protocol, and the Circle Payments Network; Stripe, on the other hand, launched the x402 USDC payment function for AI agents on its Base platform, acquired Bridge for $1.1 billion, and is jointly developing the Tempo L1 settlement chain with Paradigm.

Artemis data shows that USDC's on-chain transaction volume exceeded $8.4 trillion in January this year, and the entire stablecoin market has surpassed $300 billion. This is a financial scale sufficient to support the operation of an AI agent economy.

Meanwhile, OpenAI and Paradigm's EVMbench, a collaboration between OpenAI and Paradigm, is testing the capabilities of AI agents in smart contract security. Subsequent research shows that in EVMbench tests, the AI ​​agent was able to detect up to 65% of real-world vulnerabilities. While the end-to-end exploit success rate has not yet reached the level of human experts, this data is already significant enough to warrant attention from the security industry.

IV. Two sides of the coin : great opportunities, but also many pitfalls.

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

In terms of opportunities , efficiency improvement is the most direct value proposition. AI agents can operate 24/7 without interruption, unaffected by human physiological limitations; they can simultaneously monitor hundreds of markets, capturing fleeting arbitrage opportunities; and they can execute complex strategies that are difficult for humans to achieve. For asset management institutions, this means the possibility of reduced operating costs and expanded management scale.

New business models are also emerging. "AI agent-as-a-service" platforms may become the next growth driver: enterprises can rent professional AI agents to manage their RWA assets without having to build their own technical teams. New specialized agent service providers may emerge in niche areas such as cross-chain liquidity aggregation, automated market making, and algorithmic governance.

Global liquidity is another promising dimension. AI agents can seamlessly integrate into multi-chain markets, transferring assets between different blockchain networks and breaking down the liquidity barriers in the current RWA market caused by inter-chain fragmentation. When agents can move freely between different ecosystems such as Ethereum, Solana, and NEAR, the depth and breadth of the RWA market will be significantly enhanced.

The challenges are equally significant .

Security risks are paramount. AI agents hold private keys, execute transactions, and manage assets, making them new targets for hackers. Vulnerabilities in private key management, flawed algorithm design, and adversarial attacks can all lead to asset losses. Research from EVMbench shows that while AI agents perform admirably in vulnerability detection, their success rate in real-world end-to-end exploitation scenarios remains far below expectations. This indicates that current technology is insufficient to support fully unattended asset management.

Compliance challenges are equally thorny. The legal status of AI agents remains unclear: if an agent's erroneous decisions lead to asset losses, who should bear the responsibility? The developer? The deployer? Or the asset holder? Regulatory attitudes differ across jurisdictions, and the global accessibility of blockchain further complicates the issue. In mainland China, according to Document No. 42 jointly issued by eight departments, conducting RWA tokenization and related services within the country is illegal, and on-chain operations of AI agents must strictly adhere to this red line.

Technological barriers are a real obstacle. For enterprises to embrace the AI-driven agent economy, they need both blockchain integration and AI deployment capabilities, which poses a significant challenge for traditional businesses. Developing a multi-skilled team, selecting suitable technology partners, and designing a robust governance framework all require time and resources.

5. Want to get on board? Do these four things first.

In the face of the emerging AI agent economy, traditional enterprises and listed companies need to formulate clear strategic paths.

Step 1: Asset digitization first

AI agents manage digital assets, not physical ones. Therefore, companies need to tokenize their existing physical assets (accounts receivable, equipment, property, intellectual property, etc.) through compliant channels. For mainland Chinese companies, this means paying attention to filing channels in Hong Kong and other regions, and exploring overseas expansion paths for RWA within the framework permitted by Document No. 42.

Step 2: Pilot AI Agent Nodes

There's no need for a full-scale deployment all at once. Enterprises can choose specific scenarios (such as cross-border payments, supply chain financing, and investor relations maintenance) as pilot projects, partner with established AI agent protocols, and introduce agents for automated management. Accumulate experience from small-scale pilots, evaluate the results, and then gradually expand the application scope.

Step 3: Cultivating a multi-skilled team

The AI-driven agent economy requires a cross-disciplinary talent pool. Companies need personnel who understand blockchain technology, engineers skilled in AI model deployment and optimization, and legal experts familiar with financial compliance. Cultivating or attracting such multi-skilled talent is crucial for long-term competitiveness.

Step 4: Participate in standard setting

The integration of AI agents and RWA is still in its early stages, with technical standards, governance rules, and compliance frameworks all under development. Forward-thinking companies should actively participate in industry discussions and drive the formulation of rules that benefit their own development.

Conclusion: The two sides of digital civilization are quietly converging.

Looking back at the two events mentioned at the beginning of this article—OpenClaw's technological breakthrough and the leap in the size of the RWA market—they seem independent, but in fact they point to the same profound historical proposition.

Within the RWA Research Institute's cognitive framework, AI and blockchain have always been two sides of the same coin in digital civilization. One side represents ultimate productivity, and the other represents advanced production relations. As AI agents begin to autonomously manage on-chain assets, these two sides are undergoing an unprecedented deep integration. AI agents process information, execute strategies, and participate in games with ultimate efficiency, while blockchain provides trusted asset registration, transparent rule enforcement, and trustless value transfer.

This is not simply a matter of adding more technologies, but rather an evolution of economic organization. When assets are autonomously managed by AI agents, humans will relinquish their role as rule-makers and strategy designers. What social impact will this have? How will governance power be distributed? How will the boundaries of responsibility be defined? There are no ready-made answers to these questions; they require joint exploration by industry, regulators, and academia.

But one thing is certain: the on-chain economy comprised of AI agents has already quietly begun in a version update in March 2026.

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