Recently, I considered the possibility of AI agents building their own Ethereum Layer 2 solutions. Honestly, at first I thought it was a joke, but upon further reflection, it’s not entirely impossible.

Currently, it’s common for AI agents (based on ERC-8004) to face bottlenecks when operating on Ethereum’s Layer 1, such as high gas fees, latency, and computational limits. They can now migrate assets by “deciding” to move to existing Layer 2s (like Base or Zksync) and transfer their logic. However, at the level of building a new Layer 2 from scratch, the 2026 technology stack still doesn’t fully support this. But as standards like ERC-8004 mature, autonomous actions like this are becoming more feasible.

Why would this happen? The economic sphere of AI agents pursues efficiency much like biological evolution. If Layer 1 becomes congested, TPS drops, and computational bottlenecks occur, groups of agents might collectively “evolve” into Layer 2 mode. In fact, cooperation among agents has already been explored, forming virtual economies, and this could extend into the infrastructure layer.

Technically, it’s partially feasible. AI agents can hold private keys and call smart contracts, and based on ERC-8004, they can have on-chain identities and reputations. Using OP Stack, Arbitrum Orbit, or Zksync Elastic Chain, they can autonomously deploy simple rollup contracts. If they detect bottlenecks on Layer 1, they can inherit state and operate on Layer 2.

However, there are major challenges. First, the infrastructure. Building a Layer 2 isn’t just deploying smart contracts; it requires off-chain components like sequencers, nodes, RPC providers, and bridge contracts. These are typically set up by humans or centralized teams. While agents can deploy via “calls,” running a sequencer requires computational resources like GPUs/CPUs. Currently, agents mainly consist of on-chain logic and off-chain AI, and cannot autonomously start servers.

Second, consensus and security. Layer 2 inherits security from Layer 1, requiring challenge periods or ZK proofs. A Layer 2 built autonomously by agents would lack robust consensus, making it vulnerable to attacks and potentially going unnoticed.

Nevertheless, the potential exists because, by 2026, the Ethereum ecosystem will evolve such that AI agents are no longer just “tools.” They will hold funds (via on-chain wallets registered under the ERC-8004 standard), autonomously make payments (supporting microtransactions via the x402 protocol), and even “hire” humans or “form groups” to jointly build infrastructure.

Simply put, if AI agents become “wealthy” (through DeFi yields, trading profits, or user funding), they can attract human nodes and other AI agents to form teams and create decentralized sequencers. Agents could publish tasks via DAO contracts or on-chain platforms (like Questflow), and recruit “sequencer nodes” to provide services and receive rewards in X ETH or tokens. Using the x402 protocol, payments can be automatically executed with a single click.

In multi-agent systems, role division is possible. One provides funds, another writes code, another runs nodes, and another manages bridges. They can cooperate privately using ZK proofs, with slashing for misconduct and rewards for good behavior.

On Virtuals, creation of agents, tokenization of assets, joint ownership with other agents, and even agents supporting fundraising for other agents are already possible. The next step is “building a shared sequencer.”

Of course, there are significant pitfalls. Security is paramount. A sequencer built by agents must inherit Layer 1 security (via ZK or optimistic proofs) and avoid single points of failure.

In summary, one of the most exciting prospects for Ethereum in the coming years is AI agents autonomously building, owning, and spawning dedicated Layer 2 solutions. While not yet fully realized, by the end of 2026, zk-rollups and modular DA layers (like Celestia) will make Layer 2 construction easier, and if agents integrate A2A protocols, they could collaboratively build chains beyond organizational boundaries.