AgentDID: Trustless Identity Authentication for AI Agents

• URL: http://arxiv.org/abs/2604.25189v1
• Date: Tue, 28 Apr 2026 03:50:01 GMT
• Title: AgentDID: Trustless Identity Authentication for AI Agents
• Abstract summary: We study the problem of identity authentication and state verification for AI agents.<n>We present AgentDID, a decentralized framework for identity authentication and state verification.<n>Results show that the system achieves scalable identity authentication and state verification.
• Score: 22.255618309452412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AI agents are autonomous entities that can be instantiated on demand, migrate across platforms, and interact with other agents or services without continuous human supervision. In such environments, identity is critical for establishing reliable interaction semantics among agents that may lack prior trust relationships. However, existing identity and access management mechanisms are designed for human users or static machines, assuming centralized enrollment, persistent identifiers, and stable execution contexts. These assumptions do not hold for AI agents, whose identities are self-managed, short-lived, and tightly coupled with their execution state and capabilities. We study the problem of identity authentication and state verification for AI agents and identify three challenges: (1) supporting self-managed identities for autonomously created agents, (2) enabling authentication under large-scale, concurrent interactions, and (3) verifying agents' dynamic execution state, such as whether their context and capabilities remain valid at interaction time. To address these challenges, we present AgentDID, a decentralized framework for identity authentication and state verification. AgentDID leverages decentralized identifiers (DIDs) and verifiable credentials (VCs), enabling agents to manage their own identities and authenticate across systems without centralized control. To address the limitations of static credential-based approaches, AgentDID introduces a challenge-response mechanism that allows verifiers to validate an agent's execution conditions at interaction time. We implement AgentDID in compliance with W3C standards and evaluate it through throughput experiments with multiple concurrent agents. Results show that the system achieves scalable identity authentication and state verification, demonstrating its potential to support large populations of AI agents.

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