Agent Name Service (ANS): Building the Infrastructure for Trusted Interactions Between AI Agents

This article introduces Agent Name Service (ANS) — a decentralized trust framework inspired by DNS, designed to address authentication, reputation tracking, and trust-building issues between AI agents. It is a key infrastructure for building an autonomous AI agent ecosystem. Drawing on DNS concepts, ANS provides naming, resolution, verification, and discovery mechanisms, and supports secure and verifiable interactions between agents through three core components: identity layer, reputation layer, and trust layer.

As AI agents evolve from isolated systems to collaborative networks, trust issues in multi-agent architectures have become increasingly prominent, covering dimensions such as authentication, reputation evaluation, capability verification, behavioral predictability, and responsibility tracing. Existing solutions like centralized platforms (single point of failure), pre-negotiation (not applicable to open scenarios), code audits (difficult to predict neural network behavior), and human supervision (restricting autonomy) all have obvious limitations. There is an urgent need for a decentralized and autonomous trust infrastructure.

Inspired by DNS, ANS provides naming, resolution, verification, and discovery capabilities for agents. Its core components include: 1. Identity Layer: Assigns unique identifiers and stores identity documents containing public keys and capability claims on a distributed ledger; 2. Reputation Layer: A multi-dimensional, context-aware reputation system that integrates transaction feedback, third-party audits, and other information; 3. Trust Layer: Supports real-time trust-building processes such as identity resolution, challenge-response verification, and reputation querying. ANS adopts a federated architecture where multiple operators collaborate to manage namespaces.

ANS technical implementation includes: 1. Identity registration based on permissioned blockchain (balancing performance, privacy, and governance); 2. Verifiable Credentials (VC): W3C standard that supports cryptographically signed reputation and authentication information, which is composable and privacy-preserving; 3. Zero-Knowledge Proofs (ZKP): Enables privacy-preserving reputation proofs and anonymous interactions; 4. Smart contract integration: Escrow contracts, conditional execution, and automated dispute arbitration.

ANS designs protection mechanisms against common attacks: 1. Identity impersonation: Strict identifier specifications and similarity detection; 2. Reputation manipulation: Transaction fees, reputation decay, graph analysis, and staking requirements; 3. Man-in-the-middle attacks: End-to-end encryption and challenge-response verification; 4. Sybil attacks: Identity registration verification and incremental registration fees.

Typical application scenarios of ANS include: 1. Decentralized scientific collaboration: Trusted data sharing and task collaboration between agents; 2. Intelligent service markets: Authentication, reputation screening, and secure payments; 3. Cross-organization agent collaboration: Federated trust management that enables cross-domain interactions while maintaining the governance independence of departments.

Current ANS has limitations such as scalability, game-theoretic analysis of reputation, and cross-domain governance interoperability. Future research directions include: Technical optimization of ZKP and AI anomaly detection; Governance design of decentralized operator governance mechanisms; Application expansion to human-agent interaction scenarios.

ANS represents a forward-looking exploration of AI agent ecosystem infrastructure and is expected to become a core component of the agent internet. Although currently in the proof-of-concept stage and facing technical, governance, and ethical challenges, building a reliable trust infrastructure is an essential path for AI agents to realize their potential. In the future, it will drive the qualitative transformation of agents from isolated systems to collaborative networks.