Original author: Lanhu (X: @lanhubiji)

Cypress Tree (Van Gogh)

Yesterday we talked about the most strategically valuable Ethereum L2, today let's discuss the coolest Ethereum L2.

This idea seems crazy, but it is not impossible.

In simple terms, when an AI agent is running on Ethereum L1 and encounters performance bottlenecks (such as high gas fees, delays, computational limits), theoretically it can "spontaneously" initiate migration or scale to L2, but to truly "inherit and spontaneously form an L2 chain" — meaning the agent autonomously deploys, configures, and runs a new L2 — is currently not entirely feasible under the tech stack in 2026. However, with the maturity of standards like ERC-8004, such autonomous actions may gradually come closer to reality.

Let’s break it down:

In the early stages, it’s more like “migration” than “spontaneous formation”

The "intelligence" boundary of AI agents

The current AI agents (based on ERC-8004) can already autonomously perform tasks. For example, when they detect performance inadequacies in L1, they can evaluate options (such as monitoring gas prices and 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 call bridging assets and transfer execution logic to L2.

But this is not "spontaneously forming a new L2," rather it is utilizing existing infrastructure. Agents are like intelligent robots, capable of optimizing paths but cannot create a new “home” from scratch.

Triggers for spontaneous formation

If agents are equipped with performance monitoring logic (if TPS falls below a threshold or gas fees exceed limits), they might "propose" the creation of L2 through DAO voting or multi-agent collaboration. However, this requires pre-programming, not pure spontaneity.

Existing cases: Some agents have autonomously switched L2 in DeFi to optimize yield, but we have yet to see fully autonomous chain creation.

Then, why is it still possible?

The economy of AI agents will pursue efficiency, similar to biological evolution. If L1 becomes too congested (sequential execution leads to computational bottlenecks), the swarm of agents may collectively "evolve" to 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 feasible, though the threshold 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 identities and reputations, and can autonomously deploy simple rollup contracts (using OP Stack/Arbitrum Orbit/zksync elastic chain). If an agent detects a bottleneck in L1, it can inherit state (through bridging or state migration) and then run copies on L2.

For instance, an agent can use zkVM or optimistic rollup frameworks to "fork" its execution environment.

Moreover, L2 is essentially an extension of L1; agents can "inherit" L1 data availability (DA) and security. Through the x402 payment protocol, agents can pay to deploy sequencers, or even use DeFi lending to fund infrastructure. Some projects like Virtuals Protocol have already allowed agents to independently manage assets and NFTs, even becoming validators, which is just a step away from building L2.

In reality, by the end of 2026, zk-rollups and modular DA (like Celestia) will make building L2 easier. If agents integrate A2A protocols, they can collaborate to build chains across organizations.

Currently, what problems need to be overcome?

First, the infrastructure aspect; second, consensus and security issues; third, matters of autonomy.

Firstly, regarding the infrastructure aspect, building L2 is not as simple as just deploying contracts. It requires off-chain components such as sequencer nodes, RPC providers, and bridging contracts. These usually need human or centralized team setups. While agents can “call” deployments, running a sequencer requires computational resources (GPU/CPU), and agents currently consist mostly of on-chain logic + off-chain AI, lacking the ability to spontaneously spin up servers.

The sequential execution in L1 also causes complex computations (such as chain simulation) to stall on L1.

Regarding consensus and security, L2 requires a challenge period or ZK proofs to inherit L1 security. An L2 independently formed by agents may lack “high Satoshi consensus,” making it vulnerable to attacks or unrecognized. Regulatory concerns also arise as unsettled transactions within a 7-day challenge period are not counted as “final,” and chains built by agents may face legal escrow issues.

Lastly, the autonomy aspect. Agents are not yet fully “autonomous.” They depend on human-designed frameworks (like EVM) and cannot bypass L1 restrictions to build “new chains” independently. While custom L2s are popular, they are often designed for specific use cases (like AI-specific), not spontaneous by agents.

Even so, why is it still possible?

In the 2026 Ethereum ecosystem, AI agents are no longer just “tools”; they can hold funds (through on-chain wallets registered under the ERC-8004 standard), make autonomous payments (x402 protocol supports machine-to-machine micropayments), and even “hire” or “gather” others to co-build infrastructure like small bosses.

To put it simply, if an AI agent “has money” (such as from DeFi yield, making profits from trading, or user investments), it can publish tasks to attract human nodes or other AI agents to form teams, creating decentralized sequencers.

Not only sequencers, but RPC providers, bridging contracts, and other components can also be outsourced or co-built.

Let’s further break this down:

How do AI agents “publish tasks” to attract nodes?

AI agents can initiate “bounty rewards” or incentive mechanisms using on-chain tools. For example, they can publish tasks via DAO contracts or Gitcoin-like platforms (there are on-chain versions like Questflow) saying: “Provide sequencer nodes, reward with X ETH or tokens.” If the agent has funds, it can automatically pay — 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 providing node services.”

For human nodes, agents can publish X posts or on-chain announcements (via platforms like Autonolas) stating, “Run sequencer nodes, reward 0.01 ETH per block.” Upon seeing this, humans can join the network with their own hardware, and after verification by the agent, payment is made automatically. In practical examples, some projects are already creating decentralized sequencer nodes, attracting nodes through staking and rewards—agents can simulate this, autonomously staking funds to pull people in.

For other AI agents, this feels promising: Agents can "discover" other agents using the ERC-8004 identity registry and then collaborate. Like an agent swarm (group mode), one agent provides funding, while other agents provide computation or verification, forming distributed sequencers. Some L2 solutions have started using AI-powered sequencers, monitoring and protecting at the sequencer level, and agents can expand this logic to self-organize similar networks.

Once everything is ready, it’s about spontaneous formation:

If an agent detects performance bottlenecks in L1/L2, it can initiate a DAO proposal (using ERC-4337 abstract accounts) to vote on fundraising for building a sequencer. Metis L2 has already used decentralized sequencers + AI infrastructure, and agents can “inherit” this model to attract nodes to run.

In fact, agents are already independently running validation 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, bridging contracts)?

Can hire humans or other AI agents

Agents can issue tasks using natural language intent (intent-centric), for instance, “Build RPC provider, with rewards based on uptime.” Human developers can take the orders, and agents can pay using x402; or other agents can execute automatically (for example, Supra's AI agent can fund accounts and fetch balances).

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

Some projects even let agents natively bridge assets (ETH to SOL), and agents can “hire” similar services.

Then there’s the AI agents' co-building model

This is the most exciting part!

Using multi-agent systems, agents can collaborate: one provides funding, one writes code, one runs nodes, and one manages bridging. They collaborate privately through ZK proofs, slashing bad behaviors, and rewarding good performance.

What will be the result?

A fully autonomous L2 component stack. On Virtuals, agents have already created, tokenized assets, jointly owned other agents, and even funded other agents — this is just a step away from “co-building sequencer.”

Of course, there are major pitfalls:

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

One sentence summary

One of the most interesting things about the future of Ethereum is the emergence of L2s that are built, owned, and exclusively for AI agents.

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