Yesterday, we discussed the most strategically valuable Ethereum L2s; today, let’s talk about the coolest Ethereum L2s.
This idea may seem 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 realistic.
Let's break it down:
Earlier, 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 cannot yet build a brand-new "home" from scratch.
• Self-initiated trigger
If agents are built with 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 between L2s in DeFi to optimize yield, but no fully autonomous chain-building cases have been observed yet.
So, why is it still possible?
AI agents in agent economies will 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 to form virtual economies, which could extend to the infrastructure layer.
Is it technically feasible? Partially feasible, 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, or 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 use a zkVM or optimistic rollup framework to "fork" its own execution environment.
Moreover, L2s are inherently 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 sequencers and even finance infrastructure using DeFi lending. Projects like Virtuals Protocol have already enabled agents to autonomously manage assets and NFTs, and even act as validators—bringing them one step closer to 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.
Under current conditions, what issues need to be addressed?
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 need to be set up by humans or centralized teams. While agents can "trigger" deployment, running sequencers requires computational resources (GPU/CPU), and current agents are mostly composed of on-chain logic plus off-chain AI, unable to spontaneously spin up servers.
L1's sequential execution 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 seven-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 create their own "new chains." While custom L2s are popular, they are mostly designed for specific use cases—such as AI-specific applications—and are not self-initiated 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 for one-click transfers, without human intervention.
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 X posts or 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 automatically made. Real-world example: Some projects are already building decentralized sequencer nodes, attracting participants through staking and rewards—the agent 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 model, 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 can accept these tasks and be 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 provides funding, one writes code, one runs a node, and one manages the bridge. They collaborate privately via ZK proofs, slash malicious behavior, and reward positive contributions.
What will the result be?
A fully autonomous L2 component stack. On Virtuals, agents already create other agents, 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 major pitfalls here:
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 developments for Ethereum's future is the emergence of L2s that are built, owned, and exclusive to AI agents.