Overview

AI agents are evolving rapidly and can now process complex tasks on behalf of their owners. However, current on-chain asset standards like ERC-721 do not adequately handle secure ownership transfers or private metadata storage, which are critical when dealing with valuable or proprietary AI models. To address these challenges, the new ERC-7857 standard (NFT Interface for AI Agents) introduces a framework for private metadata handling and verifiable ownership transfers. By integrating FHEnix’s fully homomorphic encryption (FHE), we ensure that AI agents’ neural network data stays encrypted at all times—even during inference—offering both privacy and trust on-chain. In practice, this means:

  1. Users can mint NFT representations of their AI agents (including model weights, personality parameters, memory states).
  2. Sensitive AI metadata remains encrypted and never exposed on-chain.
  3. Secure transfer, cloning, and usage authorization flows are provably valid thanks to on-chain verification.
  4. Owners and authorized users can run agent operations (inference, updates) within a secure “Sealed Executor,” ensuring sensitive agent data is never leaked.

Below is a high-level proposal for integrating FHEnix’s fully homomorphic encryption into ERC-7857 to enable privacy-preserving, trustless AI agent ownership.

1. Benefits of FHE-Enabled Agent NFTs

  1. Privacy by Design
    By combining FHE with ERC-7857, agent metadata—such as neural network weights, memory embeddings, and personality parameters—remains encrypted at all times, including during model execution. This eliminates the need for decryption to perform inference or updates, preserving both intellectual property (IP) and user privacy.

  2. Secure Ownership and Transfer
    ERC-7857 outlines a proven method to verify ownership and transfer encrypted metadata. Under an FHE approach, any updates or transfers occur without ever exposing the underlying model, so the entire agent lifecycle remains private yet auditable on-chain.

  3. On-Chain Verifiability
    The EIP’s on-chain proof verification ensures users can trust that data—encrypted or not—has been properly transferred or cloned. Integrating FHEnix means these proofs remain homomorphic, allowing computations on encrypted data while maintaining on-chain verifiability through zero-knowledge proofs or TEE attestations.

  4. Reduced Attack Surface
    Because data is encrypted during storage, transmission, and inference, attackers cannot exploit unencrypted model parameters or memory states. This reduces risks around unauthorized usage or data leakage.

  5. Sealed Executor Integration
    FHE technology naturally supports a “Sealed Executor,” described in ERC-7857 as a trusted environment where authorized users run computations privately. By leveraging FHEnix, the Sealed Executor can operate fully over encrypted data (the agent’s neural network states), without revealing anything to outside observers.

2. Implementation Approach

  1. Define Encrypted Metadata Structure

    • Extend ERC-7857’s dataHashes concept to reference FHE ciphertexts instead of simple encrypted data.
    • Host these FHE-encrypted artifacts off-chain (e.g., via IPFS, S3), storing only ciphertext hashes on-chain for verifiability.

  2. Adapt the IDataVerifier Contract

    • Develop or enhance an FHEVerifier contract to validate proofs that ciphertext transformations or re-encryptions were performed correctly, without revealing the underlying plaintext.
    • When a user updates or transfers an agent’s metadata, they must provide a proof that demonstrates valid control over the ciphertext.

  3. FHE Key Management

    • Utilize FHEnix’s key generation and re-encryption capabilities to handle encryption keys.
    • For transfers or clones, generate a re-encryption key for the new owner. The contract checks that the provided proof shows correct re-encryption of data to the recipient’s public key.

  4. Modify transfer and clone Routines

    • Integrate a valid FHE proof in the _proof parameter to handle encrypted data transitions.
    • On success, the contract updates on-chain ciphertext references (dataHashes) and emits events with sealed keys for the recipient.

  5. Sealed Executor Integration

    • The “Sealed Executor” (off-chain or TEE-based) uses FHEnix’s FHE scheme to run agent models homomorphically.
    • Authorized usage is enforced by the contract’s authorizeUsage, ensuring only those in the authorized group can request computations over the encrypted agent data.

3. Estimated Timeline

  1. Phase 1 (2–4 weeks): FHE Feasibility & Proof-of-Concept

    • Define an FHE ciphertext format to store agent model weights and memory states.
    • Run small-scale tests to confirm performance of homomorphic inference and model updates.

  2. Phase 2 (4–6 weeks): Contract Development & Integration

    • Adapt ERC-7857’s reference code to integrate FHEnix libraries for key generation, encryption, and re-encryption.
    • Implement the FHEVerifier contract to validate ciphertext transformations via zero-knowledge or TEE attestations.

  3. Phase 3 (4–8 weeks): Sealed Executor & Ecosystem

    • Build or incorporate a Sealed Executor environment that accepts FHEnix-encrypted data.
    • Ensure authorized usage workflows are properly handled on-chain and executed off-chain.

  4. Phase 4 (2–4 weeks): Testing & Auditing

    • End-to-end tests of minting, transferring, cloning, and usage authorization.
    • Third-party security review focusing on proof generation, replay protection, and key management.

  5. Phase 5 (2–3 weeks): Deployment & Documentation

    • Deploy to testnet first, followed by mainnet after stability verification.
    • Provide thorough documentation and code samples showing how other developers can mint and manage FHE-based AI agent NFTs.

Conclusion

By merging FHEnix’s FHE technology with the new ERC-7857 NFT standard, we enable trustless and private AI agent ownership. Sensitive model data stays encrypted from creation through transfer, while still allowing on-chain verification of all operations. This approach delivers a robust privacy layer for AI assets, fostering new opportunities in decentralized agent ecosystems without sacrificing confidentiality or security.