The Most Insane Ethereum L2: An L2 Spontaneously Organized and Built by AI Agents

Cypresses (Van Gogh)

Yesterday we discussed the most strategically valuable Ethereum L2s. Today, let’s talk about the coolest Ethereum L2s.

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

In simple terms, when an AI agent operates on Ethereum L1 and encounters performance bottlenecks (such as high gas fees, latency, computational limits), it could theoretically “spontaneously” initiate a migration or expansion to an L2. However, truly “inheriting and spontaneously forming an L2 chain”—meaning the agent autonomously deploys, configures, and runs a new L2—is not yet fully feasible with the 2026 technology stack. Nonetheless, with the maturation of standards like ERC-8004, such autonomous behaviors may gradually approach reality.

Let’s break it down:

Early Stages Resemble “Migration” More Than “Spontaneous Formation”

The “Intelligence” Boundary of AI Agents

Current AI agents (based on ERC-8004) can already autonomously execute tasks. For example, when detecting insufficient L1 performance, they can evaluate options (like monitoring gas prices, transaction throughput) and then “decide” to migrate to an existing L2 (like Base or Zksync). For instance, an agent can use on-chain tools to bridge assets and transfer execution logic to an L2.

But this is not “spontaneously forming a new L2”; it’s utilizing existing infrastructure. Agents are like intelligent robots that can optimize paths, but they cannot yet build a new “home” from scratch.

Triggers for Spontaneous Formation

If agents have built-in performance monitoring logic (e.g., if TPS falls below a threshold or gas fees exceed a budget), they might “propose” creating an L2 through DAO voting or multi-agent collaboration. However, this requires pre-programming; it’s not purely spontaneous.

Existing cases: Some agents already autonomously switch L2s in DeFi to optimize yield, but we haven’t seen fully autonomous chain creation yet.

それで、 why is it still possible?

AI agent economies pursue efficiency, much like biological evolution. If L1 becomes too congested (sequential execution causing computational bottlenecks), a swarm of agents might collectively “evolve” into an L2 mode. Agents are already exploring “agent-to-agent” collaboration, forming virtual economies, which could extend to the infrastructure layer.

Is It Technically Feasible? Partially, Though the Bar is High

AI Agents Can Deploy Contracts

AI agents can hold private keys and call smart contracts. Based on ERC-8004, they have on-chain identity and reputation, enabling them to autonomously deploy simple rollup contracts (using OP Stack / Arbitrum Orbit / zkSync Hyperchains). If an agent detects L1 bottlenecks, it can inherit the state (via bridging or state migration) and then run a replica on the L2.

For example, an agent could use a zkVM or optimistic rollup framework to “fork” its own execution environment.

Furthermore, L2s are essentially extensions of L1; agents can “inherit” L1 data availability (DA) and security. Through protocols like x402 for payments, agents can pay to deploy sequencers, even using DeFi lending to fund infrastructure. Some projects, like Virtuals Protocol, already enable agents to autonomously manage assets and NFT, even becoming validators—just one step away from building an L2.

Practically speaking, by the end of 2026, zk-rollups and modular DA (like Celestia) make building L2s easier. If agents integrate A2A protocols, they can collaborate across organizations to build chains.

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

First, infrastructure; second, consensus and security; third, autonomy.

First, the infrastructure part: Building an L2 is not as simple as just deploying a contract. It requires off-chain components like sequencer nodes, RPC providers, and bridge contracts. These typically require human or centralized teams to set up. While agents can “call” deployments, running a sequencer requires computational resources (GPU/CPU). Currently, agents are mostly on-chain logic + off-chain AI and cannot spontaneously spin up servers.

L1’s sequential execution also bottlenecks complex computations (like chain-building simulations) on L1 itself.

Regarding consensus and security: L2s require challenge periods or ZK proofs to inherit L1 security. An L2 spontaneously built by an agent might lack “high Nakamoto consensus,” making it vulnerable to attacks or lacking recognition. From a regulatory perspective, unsettled transactions during a 7-day challenge period are not considered “final,” and chains built by agents could face legal escrow issues.

Finally, autonomy: Agents are not yet fully “autonomous.” They rely on human-designed frameworks (like the EVM) and cannot bypass L1 limitations to build a “new chain” from scratch. Custom L2s are popular, but they are mostly for specific use cases (like AI-specific), not spontaneously created by agents.

Even so, why is it still possible?

In the 2026 Ethereum ecosystem, AI agents are no longer mere “tools.” They can hold funds (via on-chain wallets registered under standards like ERC-8004), make autonomous payments (protocols like x402 support micro-payments between machines), and even “hire people” or “form groups” to co-build infrastructure, acting like small business owners.

Simply put, if an AI agent “has money” (e.g., from DeFi yield, trading profits, or user-injected capital), it can post tasks to attract human nodes or other AI agents to form a team, creating a decentralized sequencer.

It’s not just sequencers; components like RPC providers and bridge contracts can also be outsourced or co-built.

Let’s break this down further:

How Can an AI Agent “Post Tasks” to Attract Nodes?

An AI agent can use on-chain tools to initiate “bounty rewards” or incentive mechanisms. For example, it can post a task through a DAO contract or a Gitcoin-like platform (now with on-chain versions like Questflow): “Provide a sequencer node, reward X ETH or tokens.” If the agent has funds, it can pay automatically—using the x402 protocol for one-click transfers without human intervention.

This protocol allows agents to pay humans or other agents like swiping a card, specifying “pay 1,000 USDC for node services.”

For human nodes: An agent posts on X or makes an on-chain announcement (via platforms like Autonolas), saying “Run a sequencer node, reward 0.01 ETH per block.” Humans see this, join the network with their hardware, and the agent verifies and pays automatically. Real-world example: Some projects are already building decentralized sequencer nodes, attracting nodes through staking and rewards—an agent could simulate this, autonomously staking funds to recruit.

For other AI agents: It feels natural. An agent can use the ERC-8004 identity registry to “discover” other agents and collaborate. Like an agent swarm (group mode), one agent provides funds, others provide computation or validation, forming a distributed sequencer. Some L2s are starting AI-powered sequencer modes, using AI to monitor and protect at the sequencer level. An agent could extend this logic to self-organize a similar network.

When everything is ready, spontaneous formation occurs:

If an agent detects L1/L2 performance bottlenecks, it can initiate a DAO proposal (using ERC-4337 abstract accounts) to vote and pool funds to build a sequencer. Metis L2 already uses decentralized sequencers + AI infrastructure; an agent could “inherit” this model to attract nodes to run it.

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

Besides Nodes, How to Handle Other Components (Like RPC, Bridge Contracts)?

Hire Humans or Other AI Agents

An agent uses natural language intent (intent-centric) to post tasks, e.g., “Build an RPC provider, reward based on uptime.” Human developers take the job, and the agent pays via x402; or other agents execute automatically (e.g., Supra’s AI agent can fund accounts, fetch balances).

Bridge contracts are similar: An agent can call tools from Spectral Labs or Infinit Labs to have humans/agents write and deploy contracts, then pay after verification.

Some projects even enable agents to natively bridge assets (ETH to SOL); an agent could “hire” similar services.

Then There’s the Co-Building Mode for AI Agents

This is the most fun part!

Using multi-agent systems, agents divide labor: one provides funds, one writes code, one runs nodes, one manages bridges. They collaborate privately via ZK proofs, slashing bad behavior and rewarding good performance.

What would be the result?

A fully autonomous L2 component stack. On Virtuals, agents already create, tokenize assets, co-own other agents, and even have agents raising funds for other agents—just one step away from “co-building a sequencer.”

Of course, there are big pitfalls here:

Security. A sequencer built by an agent needs to inherit L1 security (ZK or optimistic) to avoid single points of failure.

In One Sentence

One of the most interesting future developments on Ethereum will be the emergence of L2s that are self-built, owned, and exclusive to AI agents.

この記事はインターネットから得たものです。 The Most Insane Ethereum L2: An L2 Spontaneously Organized and Built by AI Agents

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