The most insane Ethereum L2: L2 built spontaneously by AI agents

Written by: Blue Fox

Yesterday we talked about Ethereum L2, which has the most strategic value. Today we'll talk about the coolest part of Ethereum L2.

This idea seems crazy, but it's not impossible.

In short, when an AI agent encounters performance bottlenecks on Ethereum L1 (such as high operating costs, latency, or computational limitations), it can theoretically initiate a migration to L2 on its own. However, to truly "inherit and spontaneously form an L2 chain"—that is, for the agent to deploy, configure, and run an autonomous L2—is not yet fully automated under the current technology stack in 2026. Nevertheless, with the maturation of standards such as ERC-8004, these behaviors may gradually approach reality.

Let's break it down:

Early formation was "migrating," not "spontaneous."

The "Intelligent" Boundaries of AI Agents

Current AI agents (based on ERC-8004) can autonomously perform tasks. For example, when they detect insufficient L1 performance, they can evaluate options (such as monitoring gas prices and transaction throughput) and then decide to migrate to an existing L2 (such as Base or Zksync). For instance, the agent can use blockchain tools to invoke bridged assets and transfer execution logic to L2.

However, this is not about "spontaneously forming a new L2," but rather utilizing existing infrastructure. The agent is like an intelligent robot that can optimize routes, but it cannot build a new "home" from scratch.

Spontaneously generated triggers

If the agent has built-in performance monitoring logic (if TPS falls below a threshold or gas costs exceed the limit), it may be able to simply "create" L2 through DAO voting grid agent collaboration. However, this requires pre-programming and is not random.

Existing cases: Some agents have already switched to L2 on their own in DeFi to optimize yield, but we have not yet seen any that have built their own chains completely independently.

So why does it still happen?

AI agents prioritize efficiency in the agent economy, much like biological evolution. If L1 becomes too congested (sequential execution causing computational bottlenecks), the agent swarm may collectively "evolve" to L2 mode. Agents are already exploring "agent-to-agent" collaboration, forming economic virtual entities, which could potentially extend to the infrastructure layer.

Is it technically supported? Partially supported, although the subsidies are high.

AI agents can deploy contracts

The AI ​​agent can hold private keys and invoke smart contracts. Based on ERC-8004, it has on-chain identity and symbols and can autonomously configure simple rollup contracts (using OP Stack/Arbitrum Orbit/zksync elastic chains). If the agent detects an L1 limit, it can inherit the state (through bridging or state migration) and then run a copy on L2.

For example, an agent can use zkVM or the optimistic rollup framework to "fork" its own execution environment.

Furthermore, L2 is essentially an extension of L1, allowing agents to "inherit" L1's data availability (DA) and security. Through the x402 payment protocol, agents can pay to deploy sorters and even use DeFi to fund infrastructure. Some projects, such as Virtuals Protocol, have already enabled agents to manage autonomous assets and NFTs, and even become validators, bringing them one step closer to building L2.

In practice, by the end of 2026, zk-rollups and modular DAs (such as Celestia) will make building L2 blockchains much simpler. Agents integrating A2A protocols can collaborate across organizations to build blockchains.

Given the current situation, what problems need to be overcome?

The system consists of three parts: first, the basic infrastructure; second, the ideological facilities and security; and third, the self-governance mechanism.

First, regarding the infrastructure, building an L2 platform is not as simple as just deploying contracts. It requires off-chain components such as sorter nodes, RPC nodes, and bridge computing to connect to the contracts. These typically require human or centralized team setup. While proxies can "call" deployments, running a sorter requires resources (GPU/CPU), and proxies currently mostly combine on-chain logic with off-chain AI, enabling them to automatically start services.

The sequential execution of L1 also causes complex computations (such as chain building simulation) to get stuck on L1.

In terms of consensus and security, L2 challenge periods or ZK proofs inherit L1 security. L2 chains built spontaneously by proxies may lack "high school Satoshi Ben's cognition," making them vulnerable to attacks or lack of acceptance. Regulatoryly, the requirement for unsettled transactions to be challenged within 7 days does not constitute "finality," and chains built by proxies may face legal escrow issues.

Finally, regarding autonomy, agents are not yet "fully autonomous." They rely on human-designed frameworks (such as the EVM) and cannot bypass L1 restrictions to build their own "new chains." While L2 is popular, it is mostly used in specific examples (such as AI-specific applications) and does not constitute agent automation.

Given such an improvement, why is it still possible?

In the Ethereum ecosystem in 2026, AI agents will no longer be simple "tools". They will be able to hold funds (on-chain wallets registered through the ERC-8004 standard), make payments autonomously (the x402 protocol supports machine-to-machine micro-payments), and even "hire" or "create groups" to build infrastructure like small business owners.

In simple terms, if an AI agent "has" something (e.g., earning money through DeFi yields, trading, or user-injected funds), it can issue tasks to attract human nodes or other AI agents with money to form a team, a centralized sorter.

Besides the sorter, components such as RPC startup and bridging can also be outsourced or co-built.

Let's break it down further:

How can an AI agent "post tasks" to attract nodes?

AI agents can use on-chain tools to issue "bounty offers" or incentives. For example, they can issue tasks through DAO contracts or mechanisms similar to Gitcoin (now with on-chain versions like Questflow): "Provide sequencer nodes, reward X ETH or tokens." The agent, with funds, can automatically pay out—using the x402 protocol for one-click interaction, thus gaining human-like power.

The protocol allows agents to pay humans or other agents like swiping a credit card, specifying "pay 1,000 USDC to the node service".

For human nodes, the agent posts an X message or on-chain announcement (via platforms like Autonolas) saying, "Run a sorter node, 0.01 ETH reward per block." Humans see this, join the network with their own hardware, and the agent automatically pays out after verification. Real-world example: Some projects are already building decentralized sorter nodes, attracting nodes through stake and rewards—the agent can simulate this, using staked funds to recruit more nodes.

For other AI agents, it feels great: agents can use ERC-8004 inflorescences to "discover" other agents and then collaborate. Like agent swarms (group mode), one agent contributes funding, while other agents provide computation or verification, forming multiple sequencers. Some L2-level agents start with an AI-driven sequencer model, using AI to monitor and protect at the sequencer level; agents can extend this logic to self-organize similar networks.

Once everything is ready, it will form spontaneously:

If the agent detects an L1/L2 performance bottleneck, it can initiate a DAO proposal (using an ERC-4337 digest account) to raise funds through voting to build a sorter. Metis L2 already uses a decentralized sorter + AI infrastructure, and the agent can "inherit" this model to attract nodes to run it.

Furthermore, the agent is already autonomously running validator nodes (staking, proposing blocks) across Ethereum/Bitcoin/Solana—building a sequencer is just the next step.

Besides nodes, how do we handle other components (such as RPC and bridging contracts)?

Human or other AI agents can be hired.

Agents issue tasks using natural language intent-centric statements, such as "Build an RPC provider, rewarded based on uptime." Human developers accept the orders, and the agent pays using x402; or other agents execute the tasks automatically (e.g., Supra's AI agent can provide funds to an account and retrieve balances).

Bridging contracts are similar: agents can call tools from Spectral Labs or Infinit Labs to allow humans/agents to write contracts, deploy them, and then pay after verification.

Some projects even allow agents to natively bridge assets (ETH to SOL), and agents can "hire" similar services.

Another example is the AI ​​agent co-construction model.

This is the most fun part!

They employ a multi-agent system with agent roles: one provides funding, one writes code, one runs nodes, and one manages the bridge. They use ZooKeeper (ZK) to prove privacy and collaborate, eliminating bad behavior and rewarding good performance.

What will the result be?

An autonomous L2 component stack. Virtuals already has agent creation, fully tokenized assets, co-ownership of other agents, and even agents financing other agents—it's just one step away from a "co-built sequencer".

Of course, there are also big pitfalls here:

Security. The sequencer created by the agent needs to inherit L1 security (ZK or optimistic) to avoid single points of failure.

In short

One of the most compelling things about Ethereum in the future is the emergence of AI agents who build their own unique L2 database.