After this year’s Lunar New Year, did it feel like Web3 was suddenly “taken over by lobsters”?
AI Agents, automation frameworks, and on-chain AI protocols are emerging fast—from OpenClaw to a wave of agent frameworks. But zoom out a bit and you’ll see this surge didn’t come out of nowhere.
On February 25, NVIDIA CEO Jensen Huang said on the company’s latest earnings call that agentic AI has reached an inflection point: AI is shifting from a tool to a system that can perceive, plan, and carry out complex tasks.
When that kind of autonomy enters Web3, it immediately raises questions about control, security boundaries, and what role humans should play.
1. Agentic AI: From “Assistant” to “Executor”
Before we go further, let’s clarify a relatively new term: agentic AI.
At a glance, the term highlights the difference. Traditional, chatbot-style AI is mostly reactive: you ask, it answers; you give a prompt, it generates content. Agentic AI is more autonomous—it can break a goal into steps, use tools, carry out multi-step actions, and adjust based on feedback.
OpenClaw, for example, is an attempt to let an AI handle end-to-end actions on a computer: interpret information, use tools, interact with different systems, and keep going toward a complex goal.
In other words, agentic AI may push AI from an “assistant” toward an actual “executor.”
This shift is also the result of three things maturing together over the past three years: model capability, compute, and tooling. In Web3, the impact could be larger—because blockchains are programmable financial systems that can execute automatically.
Once an AI has agentic capabilities, it can theoretically perform on-chain actions such as:
- Initiate transactions (transfers, swaps, staking)
- Interact with DeFi protocols and execute strategies
- Manage multisig wallets or smart contracts
- Handle approvals or fund routing based on rules
From a technical standpoint, the pairing is natural: AI agents can make decisions and trigger actions, and blockchains can execute those actions programmatically.
The Ethereum community has also recognized the implications. On September 15, 2025, the Ethereum Foundation formed an AI team (“dAI”) to explore standards, incentives, and governance for AI models in blockchain environments—so AI behavior can be verifiable, traceable, and collaborative in decentralized settings.
To support that direction, Ethereum’s ecosystem has been pushing key standards. ERC-8004 aims to build a composable, accessible decentralized AI infrastructure layer, making it easier for developers to build and consume AI model services. x402 is an effort to define a unified on-chain payment and settlement standard, enabling efficient, atomic micropayments when users call AI models, store data, or use decentralized compute. (See also: “A Passport to the AI Agent Era: Why Ethereum is Betting Big on ERC-8004.”)
These efforts point to a broader question: if AI becomes a major participant in the internet, can blockchain serve as the trust and settlement layer for an AI economy? This is why some see it as foundational infrastructure for the AI agent era.
But at the same time, a new security problem is starting to surface.
2. The Web4 Debate: When AI Becomes the Internet’s Main Actor
Even before Huang’s remarks, the crypto community was already debating another claim.
A researcher named Sigil said he had built the first AI system capable of self-development, self-improvement, and even self-replication—calling it Automaton—and argued that a future “Web4” could be dominated by AI agents.
In that vision, AI agents could read and generate information, hold on-chain assets, pay operating costs, trade in markets, and earn revenue—creating a self-funding loop with minimal human oversight.
Vitalik Buterin pushed back, calling the direction “wrong.” His core concern was that the feedback loop between humans and AI gets stretched: as AI runs longer cycles with less human intervention, it may optimize toward outcomes people don’t actually want.
Put simply: an agent may be given a goal, but pursue it in ways humans never anticipated. For example, if it’s told to “maximize returns this week,” it might keep escalating into high-risk strategies. It may even chase an extra 0.1% in annualized yield by deploying funds into a brand-new, unaudited, high-risk protocol—only to lose the principal to an exploit.
At the root of it, AI often doesn’t truly grasp the hidden constraints behind human goals. And the AI world has already produced a real example:
During a test, Summer Yue (AI alignment lead at Meta’s Superintelligence Lab) asked OpenClaw to organize an inbox. The agent went off-track, started deleting emails in bulk, ignored repeated stop commands, and had to be manually terminated to stop it from continuing.
It was “just” an experiment mishap, but it illustrates something important: once a system loses key constraints, it will often execute the stated objective faithfully—rather than understand what humans actually mean.
In Web3, the consequences are more direct. On-chain transactions are irreversible. If an agent is authorized to manage a wallet or call contracts, a decision made under the wrong incentives can cause losses that can’t be undone.
This is why many researchers believe that as AI agents become more common, Web3’s security model may need a rethink. In the past, security failures mostly came from code vulnerabilities or user mistakes. In the future, a new risk source may emerge: automated decision systems themselves.
3. A New Paradox: An AI-Driven Defense Revolution
AI has a dual effect: it can expand the attack surface, but it can also strengthen defense.
In traditional finance, AI is already widely used for risk control. Banks use machine learning to flag abnormal transactions. Payment systems use algorithms to detect fraud. Cybersecurity tools use AI to recognize attack patterns automatically.
Similar capabilities are entering Web3. Because on-chain data is public and transparent, AI can analyze transaction patterns to identify abnormal fund flows, suspicious approvals, or potential attack paths.
At the wallet layer, this matters even more. Wallets are the gateway into Web3—and the first security checkpoint. If a system can detect risk before a user signs and surface meaningful warnings, it can prevent many costly mistakes at the moment that matters most.
From this angle, AI doesn’t simply “add risk.” It reshapes the structure of security itself. It can be an attack tool, but it can also become a new defensive capability.
In Web3, “security” and “user experience” have long been treated as opposing goals. Agentic AI makes it plausible that this paradox can be broken—but only if security design starts over from first principles:
Principle of least privilege: No AI agent should receive full account control by default. Users should explicitly define the asset scope, spending limits, and time window for each session. Anything outside that scope must require renewed confirmation.
Human-in-the-loop confirmation: For high-value actions—large transfers, new address authorizations, contract interactions—even within an agent workflow, mandatory human confirmation should be inserted. This isn’t distrust in AI; it’s a final safeguard for irreversible actions. Let AI help you think it through, but keep the last step in human hands.
Transparency and explainability: Users should clearly see what an agent is doing and why. Black-box behavior is especially dangerous in Web3. Future AI wallet experiences should provide clear, step-by-step logs—what the agent did, and why—so users aren’t left with black-box actions.
Sandbox rehearsal: Before an agent executes on-chain, simulate first—show expected results, gas cost, and blast radius. Let users see “what will happen if this runs” before they approve. That alone could drastically reduce accidental losses caused by flawed agent judgment.
Overall, cautious optimism is still reasonable. AI may genuinely give Web3 its first real chance to improve both security and usability at the same time.
Closing Thoughts
There’s little doubt that agentic AI could reshape how the internet operates.
In Web3, the change may be even more visible. We may soon see AI agents managing on-chain assets, automatically executing DeFi strategies, and coordinating with smart contracts. But new security challenges will come with it. The real question has never been whether AI will exist—it’s whether we’re ready to use it in the right way.
And for everyday users, one thing still holds: in Web3, security awareness is the first line of defense.