Author: @lanhubiji
Yesterday we discussed the Ethereum L2 with the greatest strategic value; today, let’s talk about the coolest Ethereum L2.
This idea seems crazy, but it's not impossible.
In simple terms, when an AI agent runs on Ethereum L1 and encounters performance bottlenecks—such as high gas fees, latency, or computational limits—it can theoretically “spontaneously” initiate a migration or scaling to L2. However, truly “inheriting and autonomously forming an L2 chain”—meaning the agent independently deploys, configures, and operates a new L2—is not yet fully feasible with today’s 2026 technology stack. That said, as standards like ERC-8004 mature, such autonomous behaviors may gradually become viable.
Let's break it down:
It was more like a "migration" than a "spontaneous formation"
• The boundaries of AI agents' "intelligence"
Current AI agents (based on ERC-8004) can autonomously execute tasks—for example, when they detect insufficient L1 performance, they can evaluate options such as monitoring gas prices and transaction throughput, then “decide” to migrate to an existing L2 such as Base or Zksync. For instance, the agent can use on-chain tools to call asset bridges and transfer execution logic to the L2.
But this is not about “spontaneously forming a new L2”; it’s about leveraging existing infrastructure. Agents are like intelligent bots that can optimize paths, but they still can’t build a brand-new “home” from scratch.
• Self-initiated trigger
If Agents have built-in performance monitoring logic (e.g., if TPS falls below a threshold or gas fees exceed budget), they may "propose" the creation of an L2 via DAO voting or multi-agent collaboration. However, this requires pre-programming and is not purely spontaneous.
Existing cases: Some agents have already autonomously switched L2s within DeFi to optimize yield, but no fully autonomous chain-building cases have been observed yet.
So, why can it still happen?
AI agents in agent economies pursue efficiency, much like biological evolution. If L1 becomes too congested (causing computational bottlenecks due to sequential execution), the agent swarm may collectively "evolve" toward an L2 model. Agents are already exploring "agent-to-agent" collaboration, forming virtual economies that could extend to the infrastructure layer.
Is it technically feasible? Partially, though the barrier is high.
AI agents can deploy contracts.
AI agents can hold private keys and invoke 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 chains). If an agent detects a bottleneck on L1, it can inherit the state (via bridging or state migration) and run a replica on L2.
For example, an agent can “fork” their own execution environment using a zkVM or optimistic rollup framework.
In addition, L2s are essentially extensions of L1, allowing agents to “inherit” L1 data availability (DA) and security. Through the x402 payment protocol, agents can pay to deploy sequencers and even use DeFi lending to fund infrastructure. Projects like Virtuals Protocol have already enabled agents to autonomously manage assets and NFTs, and even become validators—bringing them one step away from building their own L2.
In practice, by the end of 2026, zk-rollups and modular DA solutions like Celestia will make building L2s simpler. Agents integrating the A2A protocol can collaborate across organizations to build chains.
What issues need to be addressed under current conditions?
First, the infrastructure component; second, the consensus and security component; third, the autonomy aspect.
First, regarding infrastructure: deploying an L2 is not as simple as deploying smart contracts. It requires off-chain components such as sequencer nodes, RPC providers, and bridge contracts. These typically require setup by humans or centralized teams. While agents can “invoke” deployment, running sequencers demands computational resources (GPU/CPU), and current agents are mostly composed of on-chain logic combined with off-chain AI—they cannot spontaneously spin up servers.
The sequential execution of L1 also causes complex computations, such as chain simulation, to stall on L1.
In terms of consensus and security, L2s require a challenge period or ZK proofs to inherit L1 security. Agent-created L2s may lack “high Nakamoto consensus,” making them vulnerable to attacks or lack of recognition. From a regulatory perspective, unsettled transactions during the 7-day challenge period are not considered “final,” and agent-built chains may face legal escrow issues.
Finally, regarding autonomy: Agents are not yet fully "autonomous." They rely on human-designed frameworks (such as EVM) and cannot bypass L1 limitations to build their own "new chains." While custom L2s are popular, they are mostly designed for specific use cases (such as AI-specific applications), not spontaneously created by Agents.
Even so, why is it still possible?
In Ethereum's ecosystem in 2026, AI agents are no longer mere "tools"—they can hold funds (via on-chain wallets registered under the ERC-8004 standard), make autonomous payments (supported by the x402 protocol for machine-to-machine micropayments), and even act like small business owners by "hiring" or "forming groups" to collaboratively build infrastructure.
In simple terms, if an AI agent becomes “wealthy” (e.g., through DeFi yield, trading profits, or user-funded capital), it can post tasks to attract human nodes or other AI agents to form a decentralized sequencer.
Not only sequencers, but also RPC providers, bridge contracts, and other components can be outsourced or co-built.
Below is a further breakdown:
How does an AI agent "post tasks" to attract nodes?
AI agents can use on-chain tools to launch "bounties" or incentive mechanisms. For example, they can post tasks via DAO contracts or Gitcoin-like platforms (now available on-chain, such as Questflow): “Provide a sequencer node, reward X ETH or tokens.” With funds available, the agent can automatically pay using the x402 protocol with a single click—no human intervention required.
This protocol allows the agent to pay humans or other agents like swiping a card, specifying “Pay 1,000 USDC for node services.”
For human nodes, an Agent can post on X or issue on-chain announcements (via platforms like Autonolas) saying, “Run a sequencer node, earn 0.01 ETH per block.” Humans see this and join the network using their own hardware; once verified by the Agent, payments are made automatically. Real-world example: Some projects are already building decentralized sequencer nodes, attracting participants through staking and rewards—Agents can simulate this by autonomously staking funds to attract users.
For other AI agents, it feels great: Agents can “discover” each other using the ERC-8004 identity registry and then collaborate. In an agent swarm mode, one agent pays while others provide computation or verification, forming a distributed sequencer. Some L2s are already adopting AI-powered sequencer models that use AI to monitor and secure at the sequencer level; agents can extend this logic to self-organize into similar networks.
Once everything is ready, it forms spontaneously:
If an agent detects a performance bottleneck on L1/L2, it can initiate a DAO proposal (using ERC-4337 abstract accounts) to vote on funding the construction of a sequencer. Metis L2 has already implemented a decentralized sequencer with AI infrastructure; the agent can "inherit" this model to attract node operators.
In fact, agents are already autonomously running validation nodes (staking, proposing blocks) across Ethereum, Bitcoin, and Solana—setting up a sequencer is merely the next step.
What about other components, such as RPC and bridge contracts, besides nodes?
You can hire humans or other AI agents.
Agents post tasks using natural language, intent-centric commands, such as “Set up an RPC provider with rewards based on uptime.” Human developers take on these tasks and are paid by the agent using x402; alternatively, other agents can automatically execute them (e.g., Supra’s AI agent can fund accounts and fetch balances).
Bridging contracts work similarly: agents can use tools from Spectral Labs or Infinit Labs to write, deploy, and verify contracts, then pay after verification.
Some projects even allow agents to natively bridge assets (ETH to SOL), and agents can "hire" similar services.
Also, the AI agents collaborative model
This is the most fun part!
Using a multi-agent system, agents divide responsibilities: one funds, one writes code, one runs a node, and one manages the bridge. They collaborate privately via ZK proofs, slash malicious behavior, and reward good conduct.
What will the result be?
A fully autonomous L2 component stack. On Virtuals, agents already create, tokenize assets, co-own other agents, and even finance other agents—just one step away from “co-building the sequencer.”
Of course, there are also big pitfalls:
Security. The sequencer built by the agent must inherit L1 security (ZK or optimistic) to avoid single points of failure.
One sentence summary
One of the most exciting things about Ethereum's future is the emergence of L2s that are built, owned, and exclusive to AI agents.