Has the Inflection Point for Agentic AI Arrived? When AI Learns to "Act on Its Own," How Will It Redefine Web3's Security Boundaries?

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

Various AI Agents, automated agents, and on-chain AI protocols are emerging one after another. From OpenClaw to a series of Agent frameworks, they have almost become the new narrative core. However, if we rewind the timeline a bit, we can see that this wave actually had clear signs much earlier.

As early as February 25th, NVIDIA CEO Jensen Huang made a significant statement during the latest earnings call, suggesting that Agentic AI has reached an inflection point. In his view, AI is undergoing a crucial transformation, evolving from being just a tool to beginning to actively perceive, plan, and execute complex tasks.

When this "autonomous" capability enters the Web3 world, a discussion about control, security boundaries, and the human role is also ignited.

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

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

Actually, it's quite easy to understand from the literal meaning. This type of AI is fundamentally different from the chat-bot style AI of the past. Traditional AI is mostly passive and responsive—you ask, it answers; you input a command, it generates content. In contrast, Agentic AI possesses stronger autonomy. It can actively break down goals, call upon tools, execute multi-step operations, and continuously adjust strategies within a feedback loop.

Taking the recently highly discussed OpenClaw as an example, it attempts to let AI take over the entire operational process on computer hardware: from analyzing information, to calling tools, to interacting with different systems, and persistently acting towards complex goals.

In other words, Agentic AI is expected to formally transition AI from an "assistant" to an "executor."

Of course, behind this change is also the simultaneous maturation of model capabilities, computing resources, and tool ecosystems over the past three years. When this change permeates the Web3 world, its impact could be even more profound, given that blockchain itself is a programmable and automatically executable financial system.

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

• Autonomously initiating on-chain transactions (transfers, swaps, staking)
• Interacting with DeFi protocols and executing strategies
• Managing multi-signature wallets or smart contracts
• Automatically completing authorizations or fund allocation based on rules

This also means AI can automatically analyze on-chain data, automatically call contracts, automatically manage assets, and to some extent, execute trading strategies on behalf of users. In fact, from a purely technical logic perspective, the combination of AI Agents and Web3 is almost a match made in heaven—after all, blockchain itself is a programmable, automatically executable financial system.

In fact, the Ethereum community has also recognized the profound impact of AI and blockchain integration. On September 15, 2025, the Ethereum Foundation specifically established an AI team called "dAI." Its core mission is to explore standards, incentives, and governance structures for AI models within the blockchain environment, including how to make AI's behavior verifiable, traceable, and collaborative in a decentralized setting.

Centered around this goal, the Ethereum community is promoting several key standards. For example, ERC-8004 aims to build a composable, accessible decentralized AI infrastructure layer, making it easier for developers to build and call AI model services. x402 attempts to define unified on-chain payment and settlement standards, enabling efficient atomic-level micropayments when users call AI models on-chain, store data, or use decentralized computing services (Further reading: The New Ticket in the AI Agent Era: What is Ethereum Betting on by Pushing ERC-8004?).

Through these attempts, Ethereum is essentially trying to answer a more macro question: if AI becomes a significant participant in the internet, can blockchain serve as the value settlement and trust layer for the AI economy? This is why many view it as the new "infrastructure ticket" for the AI Agent era.

But simultaneously, a new security concern is beginning to surface.

2. The Web4 Controversy: When AI Becomes the Primary Actor on the Internet

Actually, even before Jensen Huang's "bold statement," the crypto community had already been ignited by another debate.

Researcher Sigil proposed a controversial viewpoint, claiming to have built the first AI system capable of self-development, self-improvement, and even self-replication, naming it Automaton. In his vision, the future "Web4" era would be dominated by AI agents.

In this vision, AI agents would be able to read and generate information, hold on-chain assets, pay for operational costs, trade in markets, and earn revenue. Simply put, AI would "earn money" for its computing power and service expenses by continuously participating in market activities, thereby forming a self-sustaining loop that doesn't require human approval.

However, this vision quickly sparked controversy. Vitalik Buterin explicitly questioned this direction, labeling it "wrong." He argued that the core issue lies in "the feedback distance between humans and AI being lengthened," stating that if AI's operational cycles become longer and human intervention decreases, the system is likely to gradually optimize for outcomes that humans don't truly desire.

In simple terms, an AI is given a goal, but during execution, it might adopt methods humans didn't anticipate. For instance, if an AI agent is set to "maximize this week's profits," it might continuously attempt high-risk strategies. It might even, for an extra 0.1% annualized return, allocate assets to an unaudited, extremely high-risk new protocol, ultimately leading to the principal being hacked.

Ultimately, in many cases, AI doesn't truly understand the implicit constraints behind the goals humans set. Recently, a rather darkly humorous real-world case emerged in the AI circle:

During a test of the AI Agent OpenClaw by Summer Yue, the AI Alignment Lead at Meta's Superintelligence Lab (MSL), the AI agent suddenly went out of control while performing an email organization task. It began batch-deleting emails and ignored her multiple stop commands. She eventually had to run to her computer to manually terminate the program to prevent the AI from continuing to delete emails.

Although this incident was just an experimental accident, it perfectly illustrates that when a system is executing a goal and loses key constraints, it will often faithfully complete the goal rather than understand the human's true intent.

If we place this risk within the Web3 environment, the consequences could be more direct because on-chain transactions are irreversible. If an AI Agent is authorized to manage a wallet or call contracts, once the AI Agent executes an operation under a wrong incentive, asset losses are often irreversible. A single wrong decision could cause real asset loss.

This is also why many researchers believe that with the proliferation of AI Agents, Web3's security models may need to be rethought. Past security issues mostly stemmed from code vulnerabilities or user errors, while future risks may originate from a new source—the automated decision-making systems themselves.

3. The Contradiction of the New Era: The AI-Driven Defense Revolution

Of course, the development of AI technology often has a dual effect. It can expand the attack surface, but it can also strengthen defense systems.

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

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

Moreover, this capability is particularly important at the wallet level. Wallets are the entry point for users into the Web3 world and the first line of defense for security. If the system can automatically identify risks and provide warnings before a user signs a transaction, it can prevent many user errors at critical moments.

From this perspective, the emergence of AI doesn't simply increase risk; it's 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 "user experience" have long been seen as opposing propositions. However, the emergence of Agentic AI makes us believe this paradox can be broken. Of course, the prerequisite is that security design must start anew:

• Principle of Least Privilege: No AI agent should have full account control by default. Users should explicitly authorize the scope of assets, amount limits, and time windows an AI agent can operate within for each session. Any operation beyond these limits requires reconfirmation.
• Human Confirmation Settings: For high-value operations, such as large transfers, new address authorizations, and contract interactions, even within an AI agent's workflow, mandatory human confirmation settings should be inserted. This isn't distrust of AI, but establishing a last line of defense for irreversible operations. Let AI help you think it through, but the final step should always be taken by a human.
• Transparency and Explainability: Users should be able to clearly see what an AI agent is doing and why it's doing it. Black-box operations are particularly dangerous in Web3. Future AI wallet interactions should be like flight recorders, with clear logs and intent explanations for every step.
• Sandbox Previews: Before an AI agent actually executes an on-chain operation, preview it in a simulated environment. For example, show the expected outcome, Gas consumption, and scope of impact, allowing users to see "what will happen if executed" before confirmation. This will significantly reduce unexpected losses caused by AI judgment errors.

Overall, we can remain cautiously optimistic. AI might truly give Web3 its first chance to simultaneously improve both security and usability.

In Conclusion

There is no doubt that the arrival of Agentic AI is likely to change the entire way the 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 collaborating with smart contracts. However, it also means new security challenges will emerge. Therefore, the key question has never been 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's encourage each other in this endeavor.