Has the tipping point for agentic AI arrived? As AI learns to "act on its own," how can we restructure the security boundaries of Web3?

After this year's Spring Festival, did you also feel that the entire Web3 world seemed to have been suddenly taken over by "lobsters"?

Author: imToken

After this year's Spring Festival, did you also feel that the entire Web3 world seemed to have been suddenly taken over by "lobsters"?

Various AI agents, automated agents, and on-chain AI protocols have emerged one after another. From OpenClaw to a series of agent frameworks, they have almost become the core of the new narrative. However, if we pull the timeline back a little, we will find that this wave was actually foreshadowed long ago.

Back on February 25, Nvidia CEO Jensen Huang made a significant prediction during the latest earnings call: Agentic AI has reached an inflection point. In his view, AI is undergoing a crucial transformation, no longer just a tool, but beginning to proactively perceive, plan, and execute complex tasks.

When this "autonomy" capability entered the Web3 world, a discussion about control, security boundaries, and the role of humans was ignited.

I. Agentic AI: Evolving from "Assistant" to "Executor"

Before discussing this topic, we need to learn about the new concept of Agentic AI.

The difference is actually quite easy to understand from the literal meaning: this type of AI is fundamentally different from the chatbot-style AI of the past. Traditional AI is more passive; you ask a question, it answers; you input a command, it generates content. Agentic AI, on the other hand, has greater autonomy. It can proactively break down goals, call upon tools, execute multi-step operations, and continuously adjust its strategies in the feedback loop.

Take OpenClaw, which has been the subject of much discussion recently, as an example. It attempts to let AI take over the entire operation process of computer hardware: from analyzing information to calling tools, to interacting with different systems, and continuously acting under complex goals.

In other words, Agentic AI has the potential to transform AI from an "assistant" into an "executor".

Of course, this change is also the result of the simultaneous maturation of model capabilities, computing resources and tool ecosystem over the past three years. After penetrating the Web3 world, this change may have a more profound impact, since blockchain itself is a programmable and automatically executable financial system.

When AI is given proxy capabilities, it can theoretically complete a series of on-chain operations, such as:

• Independently initiate on-chain transactions (transfer, swap, staking).
• Interact with DeFi protocols and execute strategies
• Managing multisignature wallets or smart contracts
• Authorization or fund allocation is automatically completed according to the rules.

This also means that AI can automatically analyze on-chain data, automatically invoke contracts, automatically manage assets, and to some extent replace users in executing trading strategies. In fact, from a purely technical point of view, the combination of AI Agent and Web3 is almost a match made in heaven—after all, blockchain itself is a programmable and automatically executable financial system.

In fact, the Ethereum community has already recognized the profound impact of the integration of AI and blockchain. On September 15, 2025, the Ethereum Foundation established a dedicated artificial intelligence team, "dAI," whose core mission is to explore the standards, incentives, and governance structures of AI models in the blockchain environment, including how to make AI behavior verifiable, traceable, and collaborative in a decentralized environment.

To achieve this goal, the Ethereum community is pushing for several key standards, such as ERC-8004, which aims to build a composable and accessible decentralized AI infrastructure layer, making it easier for developers to build and invoke AI model services; and x402, which attempts to define a unified on-chain payment and settlement standard, allowing users to complete efficient atomic micropayments when invoking AI models, storing data, or using decentralized computing power services on-chain (further reading: "A New Ticket to the AI ​​Agent Era: What is Ethereum Betting on by Pushing ERC-8004? ").

Through these attempts, Ethereum is actually trying to answer a more macro-level question: if AI becomes an important player in the internet, can blockchain become the value settlement and trust layer of the AI ​​economy? This is why many people regard it as a new "infrastructure ticket" for the AI ​​Agent era.

But at the same time, a new security issue has begun to emerge.

II. Web4 Controversy: When AI Becomes a Major Player on the Internet

In fact, before Huang's outrageous remarks were even made, the crypto community had already been ignited by another debate.

Researcher Sigil has put forward a controversial view that he has built the first AI system capable of self-development, self-improvement, and even self-replication, which he calls Automaton. He even envisions that the future "Web4" era will be dominated by AI agents.

In this vision, AI agents will be able to read and generate information, hold on-chain assets, pay operating costs, trade in the market and earn income. In short, AI will "earn money" for its computing power and service expenses by continuously participating in market activities, thus forming a self-sustaining cycle that does not require human approval.

However, this idea quickly sparked controversy. Vitalik Buterin raised clear questions about this direction, calling it "wrong" and arguing that the core issue was that "the feedback distance between humans and AI is being lengthened." He bluntly stated that if the operating cycle of AI becomes longer and longer, while human intervention becomes less and less, then the system may gradually optimize to produce results that humans do not actually want.

Simply put, AI is given a certain goal, but in the process of execution, it may take a way that humans did not expect. For example, if an AI agent is set to "maximize this week's returns", it may keep trying high-risk strategies, and it is even possible that in order to get an extra 0.1% annualized return, it will invest assets in an unaudited and extremely high-risk new protocol, ultimately resulting in the principal being stolen.

Ultimately, in many cases, AI does not truly understand the implicit constraints behind human-set goals. Recently, a real-life example with a touch of dark humor has emerged in the AI ​​community:

Summer Yue, the AI ​​alignment lead at Meta Super Intelligence Lab (MSL), encountered a problem while testing the AI ​​Agent OpenClaw. The AI ​​agent suddenly went out of control while performing an email organization task, starting to delete emails in bulk and ignoring her repeated stop commands. In the end, she had to go to the computer and manually terminate the program to stop the AI ​​from continuing to delete emails.

Although this incident was just an experimental accident, it illustrates well that when a system is executing a goal, once key constraints are lost, it tends to faithfully complete the goal rather than understand the true intentions of humans.

If this risk is placed in a Web3 environment, the consequences may be more direct because on-chain transactions are irreversible. If an AI Agent is authorized to manage wallets or invoke contracts, once the AI ​​Agent performs operations under incorrect incentives, asset losses are often irreversible, and a single wrong decision may cause real asset losses.

This is why many researchers believe that with the proliferation of AI agents, the security model of Web3 may need to be rethought. Past security issues stemmed more from code vulnerabilities or user errors, but new sources of risk may emerge in the future—the automated decision-making systems themselves.

III. The Theory of Contradictions in the New Era: The AI-Driven Defense Revolution

Of course, the development of AI technology often has a dual effect: it may expand the attack surface, but it may also strengthen the defense system.

In fact, AI is already widely used for risk control in the traditional financial system. For example, banks use machine learning to identify abnormal transactions, payment systems use algorithms to detect fraudulent activities, and cybersecurity systems use AI to automatically identify attack patterns.

Similar capabilities are also entering the Web3 field. Because on-chain data is open and transparent, AI can analyze transaction behavior patterns to identify abnormal fund flows, suspicious authorizations, or potential attack paths.

Moreover, this capability is especially important at the wallet level. The wallet is the user's entry point into the Web3 world and the first line of defense for security. If the system can automatically identify risks and provide warnings before the user signs, many accidental operations can be avoided at critical moments.

From this perspective, the emergence of AI is not simply increasing risk, but rather changing the structure of the security system. It can become both an attack tool and a new defensive capability.

In the Web3 industry, "security" and "experience" have long been considered opposing propositions, but the emergence of Agentic AI makes us believe that this paradox can be broken, provided that security design must be redesigned:

• The principle of least privilege: No AI agent should be granted full account control by default. Users should explicitly authorize the AI ​​agent to operate on a certain range of assets, a certain amount limit, and a certain time window in each session. Any operation that exceeds the scope must be reconfirmed.
• Human confirmation settings: For high-value operations, such as large transfers, new address authorization, and contract interactions, human confirmation settings should be forcibly inserted even in the AI ​​agent process. This is not a distrust of AI, but rather to establish a last line of defense for irreversible operations. Let AI help you think things through, but the final step should always be done by a human.
• Transparency and Explainability: Users should be able to clearly see what the AI ​​agent is doing and why it is doing it. Black box operations are especially dangerous in Web3. Future AI wallet interactions should be like flight recorders, with clear logs and intent explanations for every step.
• Sandbox rehearsal: Before the AI ​​agent actually executes on-chain operations, it is rehearsed in a simulated environment, such as displaying expected results, gas consumption, and scope of impact, so that users can see "what will happen if it is executed" before confirmation. This will greatly reduce unexpected losses caused by AI judgment bias.

Overall, we can remain cautiously optimistic; AI may indeed give Web3 its first opportunity to simultaneously improve security and usability.

In conclusion

There is no doubt that the arrival of Agentic AI is likely to change the way the entire internet operates.

In the Web3 world, this change will be even more pronounced. In the future, we may see AI agents managing on-chain assets, AI automatically executing DeFi strategies, and AI working in collaboration with smart contracts. However, this also means that new security challenges will emerge. Therefore, the key question is never whether AI exists, but whether we are prepared to use it in the right way.

Of course, for ordinary users, the most important point remains unchanged: in the Web3 world, security awareness is always the first line of defense.

Let us all strive together.

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