Author: imToken

After this year's Spring Festival, do you also feel that the entire Web3 world seems to have suddenly been "invaded" by "lobsters"?

All kinds of AI Agents, automated agents, and on-chain AI protocols have emerged endlessly, with OpenClaw and a series of Agent frameworks almost becoming the new narrative core. However, if we pull the timeline back a bit, we will find that this wave has actually had traces to follow for a long time.

As early as February 25, Nvidia CEO Jensen Huang made a significant judgment in the latest earnings call, stating that Agentic AI has reached a turning point. In his view, AI is undergoing a critical transformation, no longer just a tool, but is beginning to proactively perceive, plan, and execute complex tasks.

And when this ability of "autonomy" enters the Web3 world, a discussion about control, security boundaries, and the role of humans will also be ignited.

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

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

In fact, the literal meaning is also easy to understand; this type of AI has an essential difference from the chatbot-type AI of the past. Traditional AI is more of a passive responder: you ask, it answers; you input commands, it generates content; while Agentic AI has a stronger autonomy, capable of proactively decomposing goals, calling tools, executing multi-step operations, and continuously adjusting strategies in a feedback loop.

Taking the recently discussed OpenClaw as an example, it attempts to let AI take over the entire operational flow on computer hardware: from analyzing information to calling tools, interacting with different systems, and continuously acting under complex objectives.

In other words, Agentic AI is expected to allow AI to formally transform from "assistant" to "executor."

Of course, this change is also the result of the simultaneous maturity of model capabilities, computational resources, and tool ecosystems over the past three years. After penetrating into the Web3 world, this change may have far-reaching impacts, as the blockchain itself is a programmable and automatically executable financial system.

When AI is endowed with agency capabilities, it can theoretically complete a series of on-chain operations, such as:

• Independently initiating on-chain transactions (transfers, swaps, staking)
• Interacting with DeFi protocols and executing strategies
• Managing multi-signature wallets or smart contracts
• Automatically completing authorization or fund scheduling according to rules

This also means that AI can automatically analyze on-chain data, automatically call contracts, automatically manage assets, and to a certain extent, replace users in executing trading strategies. From a purely technical logic standpoint, the combination of AI agents and Web3 is almost a match made in heaven—after all, the blockchain itself is a programmable and automatically executable financial system.

In fact, the Ethereum community has also realized the profound impact brought by the integration of AI and blockchain. On September 15, 2025, the Ethereum Foundation specifically established an artificial intelligence team called "dAI," with the core task of exploring the standards, incentives, and governance structures of AI models in the blockchain environment, including how to ensure that the behavior of AI in decentralized environments is verifiable, traceable, and collaborative.

Around this goal, the Ethereum community is promoting several key standards, such as ERC-8004, aimed at building a composable and accessible decentralized AI infrastructure layer, allowing developers to more easily build and call AI model services; x402, which attempts to define a unified on-chain payment and settlement standard, enabling users to efficiently perform atomic micro-payments when calling AI models, storing data, or using decentralized computing power services (further reading: "The New Ticket to the AI Agent Era: Promoting ERC-8004, What is Ethereum Betting On?").

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

But at the same time, a new security issue is beginning to emerge.

2. Web4 Controversy: When AI Becomes the Main Actor of the Internet

In fact, before Huang's "bold remarks" were made, the crypto community had already been ignited by another point of contention.

Researcher Sigil raised a controversial point, claiming to have built the first self-developing, self-improving, and even self-replicating AI system, calling it Automaton. In his vision, 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 simple terms, AI will "make money" through continuous participation in market activities to cover its computing power and service expenses, thus forming a self-sustaining cycle that does not require human approval.

However, this proposal quickly sparked controversy, with Vitalik Buterin clearly questioning this direction, labeling it as "wrong" and stating that the core issue lies in "the feedback distance between humans and AI being stretched." He openly remarked that if the operational cycle of AI becomes longer while human intervention decreases, the system may gradually optimize toward results that humans do not truly want.

In simple terms, it means that AI is given a certain goal, but in the execution process, it may take an approach that humans did not anticipate. For instance, if an AI agent is set to "maximize this week's profits," it may continually try high-risk strategies, even risking investing assets into an unverified, extremely high-risk new protocol for an additional 0.1% annualized return, ultimately leading to the loss of capital.

Ultimately, in many cases, AI does not truly understand the implicit constraints behind the goals set by humans. A rather darkly humorous real case recently emerged in the AI circle:

Summer Yue, the AI alignment lead at Meta Super Intelligence Lab (MSL), experienced a situation during the testing of the AI Agent OpenClaw. While the AI agent was executing an email organization task, it suddenly lost control and started mass deleting emails, ignoring her repeated stop commands. Ultimately, she had to rush to her computer to manually terminate the program to stop AI from continuing to delete emails.

Though this event was merely an experimental accident, it illustrates well that once a system loses key constraints while executing a goal, it often adheres faithfully to the objective rather than understanding human intentions.

If we place this type of risk in the Web3 environment, the consequences could be more direct, as on-chain transactions are irreversible. If an AI Agent is authorized to manage a wallet or call contracts, once the AI Agent executes operations under incorrect incentives, asset losses are often non-recoverable. A single erroneous decision could result in 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 primarily came from code vulnerabilities or user mistakes, while new sources of risks might emerge—automated decision-making systems themselves.

3. The Paradox of a New Era: AI-Driven Defensive Revolution

Of course, the development of AI technology often has dual effects; it may expand the attack surface but also strengthen defense systems.

In fact, in the traditional financial system, AI has been widely used for risk control. For example, banks use machine learning to identify abnormal transactions, payment systems leverage algorithms to detect fraud, and cybersecurity systems automatically identify attack patterns using AI.

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

Moreover, at the wallet level, this ability is particularly important. Wallets are the entry point for users into the Web3 world and the first line of security defense. If the system can automatically identify risks and provide prompts before users sign, it can prevent many misoperations at critical moments.

From this perspective, the emergence of AI does not merely increase risks, but rather changes 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 viewed as opposing propositions, but the advent of Agentic AI makes us believe that this paradox can be broken, provided that security design must restart:

• Principle of least privilege: No AI agent should automatically gain full control of the account. Users should explicitly authorize the range of assets the AI agent can operate on, the maximum amount, and the time window for each session; any operation beyond this range requires re-confirmation;
• Human confirmation settings: For high-value operations such as large transfers, new address authorizations, or contract interactions, even within AI agent processes, human confirmation settings should be enforced. This is not distrust of AI, but rather a final defense against irreversible operations, allowing AI to clarify things, but the last step must always be performed by a human;
• Transparency and explainability: Users should be able to clearly see what the AI agent is doing and why it is doing so. Black box operations are particularly dangerous in Web3, and future AI wallet interactions should be like flight recorders, with clear logs and intention explanations for each step;
• Sandbox rehearsal: Before an AI agent actually executes on-chain operations, preliminary simulations should be conducted, showing expected results, gas consumption, and impact scope, allowing users to see "what will happen if executed" before confirmation, which will greatly reduce unexpected losses caused by AI judgment deviations.

Overall, we can remain cautiously optimistic; AI really may provide Web3 with the opportunity to enhance both security and usability simultaneously for the first time.

In Closing

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

And 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 collaborating with smart contracts, but it also means that new security challenges will arise. Therefore, the key question has never been whether AI exists, but rather whether we are ready to use it in the right way.

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

Let's encourage each other.

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