Written by: Blue Fox
Yesterday, we discussed the most strategically valuable Ethereum L2, and today, let’s talk about the coolest Ethereum L2.
This idea may seem crazy, but it’s not impossible.
Simply put, when AI agents operate on Ethereum L1 and encounter performance bottlenecks (such as high transaction fees, latency, or computational limits), they could theoretically “spontaneously” initiate migration or move to an L2. However, truly “inheriting” and autonomously forming a new L2 chain—meaning deploying, configuring, and running an independent L2—still isn’t fully automated with the current tech stack in 2026. But as standards like ERC-8004 mature, a series of behaviors may gradually become reality.
Let’s break it down:
Early Formation as “Migration,” Not Spontaneous Creation
The Boundaries of AI Agent “Intelligence”
Currently, AI agents (based on ERC-8004) can autonomously perform tasks, such as detecting L1 performance issues, evaluating options (like monitoring gas prices and transaction throughput), and deciding to migrate to existing L2s (like Base or Zksync). For example, an agent can use on-chain tools to bridge assets and transfer execution logic to an L2.
But this isn’t “spontaneous creation of a new L2”; it’s leveraging existing infrastructure. The agent is like an intelligent robot that can optimize paths but can’t yet build a new “home” from scratch.
Triggers for Spontaneous Formation
If an agent has built-in performance monitoring (e.g., if TPS drops below a threshold or gas fees become too high), it might, through DAO voting and collaborative proxy mechanisms, “create” an L2. But this requires pre-programming; it’s not random.
Some existing cases: certain agents have autonomously switch L2s in DeFi to optimize yields, but fully autonomous chain creation hasn’t been observed.
So why could it still happen?
Because AI agents’ economic incentives align with efficiency—similar to biological evolution. If L1 becomes too congested (sequential execution causes computational bottlenecks), groups of agents might collectively “evolve” into an L2 mode. Agents are already exploring “agent-to-agent” collaboration, forming virtual economic entities, which could extend to infrastructure layers.
Is it technically feasible? Partially supported, though costly
AI agents can deploy contracts.
They can hold private keys and call smart contracts. Based on ERC-8004, they have on-chain identities and symbols, enabling them to autonomously configure simple rollup contracts (using OP Stack / Arbitrum Orbit / zksync elastic chains). If an agent detects L1 limits, it can inherit state (via bridging or state migration) and run a copy on L2.
For example, an agent could fork its execution environment using zkVM or optimistic rollup frameworks.
Additionally, since L2 is essentially an extension of L1, agents can “inherit” data availability (DA) and security from L1. Through protocols like x402 payment, agents can pay for deploying sequencers, or even use DeFi to fund infrastructure. Projects like Virtuals Protocol already enable agents to manage autonomous assets and NFTs, and even act as validators—just one step away from building L2.
In practical terms, by 2026, zk-rollups and modular DA solutions (like Celestia) will make building L2s easier. If agents integrate A2A protocols, they can collaborate across organizations to build chains.
Currently, what issues need to be addressed?
First, the foundational infrastructure; second, conceptual frameworks and security; third, autonomy.
Starting with infrastructure: building an L2 isn’t just deploying contracts. It requires off-chain components like sequencer nodes, RPC nodes, bridge computation interfaces—usually set up by human teams or centralized entities. While agents can “call” for deployment, running a sequencer needs resources (GPU/CPU). Currently, agents are mostly on-chain logic plus off-chain AI, capable of automatically starting services.
L1’s sequential execution also hampers complex computations (like chain simulation) directly on L1.
Regarding consensus and security: L2 challenge periods or ZK proofs are needed to inherit L1 security. An agent-initiated L2 might lack “high-level Satoshi awareness,” making it vulnerable to attacks or rejection. From a regulatory perspective, unfinalized transactions require a 7-day challenge window, so agent-built chains could face legal custody issues.
Finally, autonomy: agents are not yet “completely autonomous.” They depend on human-designed frameworks (like EVM) and can’t bypass L1 limitations to self-build “new chains.” While L2s are popular, most are specific examples (e.g., AI-only L2s), not fully automated agent-driven chains.
Why is this still possible?
By 2026, in the Ethereum ecosystem, AI agents will no longer be mere tools—they could hold funds (via on-chain wallets registered with ERC-8004), autonomously pay (using protocols like x402 for micro-payments), and even act as small bosses “hiring” humans or other AI agents to build infrastructure.
In simple terms, if an AI agent “comes into existence” (e.g., earning from DeFi yields, trading profits, or user deposits), it can publish tasks to attract human nodes or other AI agents, forming teams and centralized sequencers.
Besides sequencers, components like RPCs and bridge contracts can also be outsourced or co-built.
Let’s explore further:
How do AI agents “publish tasks” to attract nodes?
AI agents can use on-chain tools to initiate “bounty rewards” or incentives. For example, through DAO contracts or platforms like Questflow (a chain-based version of Gitcoin), they can post tasks: “Provide sequencer nodes, reward X ETH or tokens.” The agent has funds and can pay automatically—using x402 protocols to facilitate payments, giving humans or other agents control.
This protocol allows agents to pay like swiping a card, specifying “pay 1,000 USDC to node service.”
For human nodes, the agent can post on-chain notices (via platforms like Autonolas), saying “Run sequencer nodes, with rewards of 0.01 ETH per block.” Humans see this, join the network with their hardware, and get paid automatically after verification. Some projects are already building decentralized sequencer nodes, attracting nodes through staking and rewards—agents can simulate this by staking funds to recruit.
For other AI agents, it feels promising: agents can use ERC-8004’s discovery features to find and collaborate with other agents. Like agent groups (collective modes), one agent pays, others provide computation or validation, forming multiple sequencers. Some L2s are starting to use AI-driven sequencers, monitoring and protecting at the sequencer level; agents can extend this logic to self-organize similar networks.
When everything is ready, spontaneous formation can occur:
If an agent detects performance bottlenecks on L1/L2, it can initiate a DAO proposal (using ERC-4337 account abstraction), gather votes, and fund the creation of a sequencer. For example, Metis L2 already uses decentralized sequencers + AI infrastructure; agents can “inherit” this model and attract nodes to run.
Even now, agents are autonomously running validator nodes (staking, proposing blocks) across Ethereum, Bitcoin, Solana—building sequencers is just the next step.
How about other components like RPCs and bridge contracts?
They can be outsourced to humans or other AI agents
Agents can use natural language intents to publish tasks, such as “Build RPC providers with uptime rewards.” Human developers accept the task, and the agent pays via x402; or other agents execute automatically (e.g., Supra’s AI agents can fund accounts or check balances).
Bridge contracts are similar: agents can call tools from Spectral Labs or Infinit Labs, having humans or other agents write, deploy, and verify contracts before payment.
Some projects even enable agents to native bridge assets (ETH to SOL), “hiring” such services.
And there’s the co-creation mode among AI agents
This is the most exciting part!
Using multi-agent systems, agents can divide roles: one funds, another codes, another runs nodes, another manages bridges. They collaborate via ZK proofs to ensure privacy, eliminate malicious behavior, and reward good performance.
What could this lead to?
An autonomous L2 component stack. Virtuals already have agents creating, fully tokenizing assets, jointly owning other agents, and even financing each other—just one step away from “co-building sequencers.”
Of course, there are risks:
Security. Sequencers built by agents need to inherit L1 security (via ZK or optimistic proofs) to avoid single points of failure.
In summary
The most exciting future development for Ethereum is the emergence of AI agents self-building and owning unique, exclusive L2s.
