Agent ID Tokens in Distributed Systems

• Agent ID Tokens are specialized data constructs that uniquely, securely, and efficiently identify agents across diverse systems.
• They extend protocols like OAuth 2.0 and OpenID Connect to enable cross-domain identity sharing and robust authentication using JWTs, DIDs, and VCs.
• Advanced methods, including LLM-driven semantic token generation and zero-knowledge proofs, enhance security, efficiency, and interoperability.

Agent ID Tokens are specialized data constructs used to uniquely, securely, and efficiently identify agents—human, software, or autonomous—in distributed systems, recommender models, multi-agent architectures, and secure cloud infrastructures. They mediate authentication, authorization, and representation across domains, bridging identity protocols and application-specific needs. Agent ID Tokens encapsulate unique identifiers, metadata, claims, and cryptographic attestation, facilitating robust access management, traceability, and interoperability under varied operational regimes.

1. Protocol Extensions and Cross-Domain Identity Sharing

Agent ID Tokens in federated identity management expand upon existing standards such as OAuth 2.0 and OpenID Connect by introducing mechanisms for secure identity exchange and domain-agnostic authentication (Dodanduwa et al., 2018). Traditionally, a client is tightly coupled to a single identity provider (IDP). Cross-domain access requires redundant registration and identity duplication across multiple IDPs.

The proposed extension introduces a client-centric protocol:

• The client submits authentication requests with additional scopes (e.g., identity_share), specifying identity_share_target as the intended recipient IDP.
• Upon successful authentication, the issuing IDP returns an identity_share_token (JWT), embedding mandatory fields: issuer (iss), audience (aud), issued at (iat), expiration (exp), and subject data (sdata).
• The client presents this token to a second, trusted IDP in a token grant request (grant_type=identity_share_token). The second IDP validates the token claims and, if approved, issues an OAuth 2.0 access token for domain-specific resource access.

This method:

• Enables a single client identity to propagate across trust boundaries without duplicate registration.
• Employs signature and encryption for token integrity and confidentiality.
• Utilizes standard validation procedures per RFC7523, with token lifetime constraints expressed as $exp > t_{\text{current}} \geq iat$.

Illustration:

Step	Actor	Action
1	Client	Initiates authentication with IDP A
2	IDP A	Issues tokens incl. identity_share_token
3	Client	Requests access from IDP B using identity_share_token
4	IDP B	Validates token, issues access_token

This protocol minimizes identity duplication and supports transparent, secure multi-domain authentication, with practical applications in IoT and cloud computing infrastructures.

2. ID Token Generation and Semantic Representation

Recent advances leverage LLMs and multimodal architectures to create concise, platform-agnostic ID tokens for items and agents—often via textual, semantically-rich tokens rather than opaque numeric IDs (Tan et al., 27 Mar 2024, Lin et al., 23 Feb 2025, Zhang et al., 25 Oct 2024).

Textual ID Generation utilizes approaches such as diverse beam search to produce short, unique token sequences from item or agent metadata:

• For an item with text description $w = [w_1, ..., w_m]$, an ID generator outputs $d = [d_1, ..., d_n]$, $n \ll m$, maximizing uniqueness and semantic informativeness.
• The algorithm (cf. Algorithm 1 (Tan et al., 27 Mar 2024)) iteratively augments penalties (λ) for diversity, increases token length ($L$) as needed, and rejects colliding candidates.

Unified representation approaches (Lin et al., 23 Feb 2025) combine semantic tokens (e.g., learned via multi-layer RQ-VAE codebooks capturing shared features) and low-dimensional ID embeddings, denoted as $s_{it} = [\hat{z}_{it}, e_{it}]$.

Advantages include:

• Drastic reduction in token space (>80% vs. classical methods (Lin et al., 23 Feb 2025)).
• Improved cold-start generalization via semantic attributes.
• Superior recommendation performance, shown by HR/NDCG gains up to 40% (Tan et al., 27 Mar 2024) and 6–17% (Lin et al., 23 Feb 2025) compared to baselines.

Machine language tokens—task-oriented embeddings produced by multimodal LLMs (Xiao et al., 29 Jul 2025)—support agent-to-agent communication, encoding essential, minimal information for downstream tasks in a compact token sequence ($T_m = LLM_t[\text{cat}(e_v, e_m)]$).

3. Secure Token-Based Identity and Authorization in Distributed Systems

Token-based identity management within distributed clouds employs an authentication token (access_token) for user validation, coupled with a short-lived authorization token (perishable_token) for up-to-date permission control (Kovacevic et al., 29 Oct 2024).

IAM server roles:

• Centralizes management of user credentials, organizational hierarchies, and privilege assignment.
• Integrates an Attribute-Based Access Control (ABAC) engine for dynamic policy enforcement.
• Issues JWT for authorization per request, containing username and permission objects.

Security paradigms involve:

• Token decoupling: separating authentication from permission updates to avoid stale access.
• All exchanges over HTTPS; reliance on trusted third-party for secret/certificate management.
• Permission payloads reside only in transient tokens, reducing leakage risk.

Configuration dissemination tools are integrated into the architecture to vet all configuration change requests through the IAM, assessing latency and throughput metrics—a model for scalable, secure cloud deployments.

4. Agentic AI Identity: Decentralized Authentication and Fine-Grained Access Control

Emergent Multi-Agent Systems (MAS) demand agent identities beyond static tokens. The framework redefines Agent ID as a rich, verifiable construct anchored by Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) (Huang et al., 25 May 2025):

• Each agent possesses a globally resolvable DID, with associated cryptographic keys.
• DID documents list verification methods and endpoints.
• VCs attest to capabilities (e.g., tool certifications), provenance, behavioral scope, and compliance.

Decentralized authentication uses distributed registries (DLT-backed), challenge–response protocols, and portable VCs, eliminating single-point-of-trust. Fine-grained access leverages an Agent Naming Service (ANS) for discovery, dynamically evaluates capabilities and context at policy decision points (PDPs), and supports Just-In-Time (JIT) issuance of narrowly scoped VCs.

Zero-Knowledge Proofs (ZKPs) enhance privacy:

• Agents prove possession of certain attributes (compliance, capability) without disclosing raw data.
• Policies may require ZKPs for attribute verification, maintaining both privacy and auditability.

Global session management synchronizes security state across adapters/protocols, ensuring real-time revocation and traceability (cf. immutable logs, cryptographic non-repudiation).

5. Delegation, Accountability, and Auditable Agent Actions

A proposed authenticated delegation framework enables users to delegate authority securely to AI agents (South et al., 16 Jan 2025):

• Each agent receives an Agent-ID token (OAuth 2.0 native client registration), which encodes identifiers, capabilities, modality support, and links to the delegating user.
• Users issue Delegation Tokens, cryptographically referencing both their user ID token and the Agent-ID token, along with explicit scoping and expiration.

Formal symbolic representation: $$\text{DelegationToken} = \text{Sign}_{\text{user}}(\text{Hash}(\text{UserID}) \| \text{Hash}(\text{AgentID}) \| \text{Scope} \| \text{Exp})$$

The framework extends OAuth/OIDC flows to include agent-specific registration and delegation exchanges. Integration with UMA enables granular delegation and chain-of-accountability, with mutual agent verification (pairwise pseudonymous identifiers, audience restriction). Natural language permission translation—via LLM or template—yields auditable, enforceable policies.

Security measures:

• Digital signatures and reference hashes create tamper-resistant chains.
• Token lifetimes, revocation endpoints, and robust logging support accountability.
• Auditing and federated deployment across real-world scenarios (web, API, remote environments, agent collaboration).

6. Efficiency, Interoperability, and Application Scenarios

The evolution of Agent ID Tokens from simple identifiers to semantically rich, cryptographically-verifiable, context-aware constructs delivers:

• Substantial efficiency gains in storage and communication—e.g., JTCC compression in agent communication (Xiao et al., 29 Jul 2025), semantic token reduction in recommenders (Lin et al., 23 Feb 2025).
• Enhanced generalization and cross-domain operability; platform-independent textual tokens (Tan et al., 27 Mar 2024), decentralized discovery via ANS (Huang et al., 25 May 2025).
• Fine-grained, dynamic access management linked to agent capability and context, with privacy-preserving attribute proof via ZKPs (Huang et al., 25 May 2025).
• Robustness against stale permissions and unauthorized access in distributed settings through dynamic token mechanisms (Kovacevic et al., 29 Oct 2024).
• Broad applicability in areas including IoT, cloud services, recommender systems, multi-agent orchestration, personalized assistants, collaborative AI, and secure task-oriented agent communication.

7. Challenges and Solutions in Agent ID Token Ecosystems

Notable challenges—trust establishment, token security (signature/encryption), privacy, interoperability, and scalability—are addressed through:

• Standardized protocols (OAuth 2.0, OpenID Connect, JWT, SCIM, DIDs, VCs).
• Dynamic registration and validation procedures, including dynamic client registration and SCIM revalidation for enhanced trust.
• Decentralized identity models and distributed authorization architectures.
• Sophisticated tokenization algorithms supporting uniqueness, semantic richness, and minimized collision.
• Privacy-centric frameworks (ZKP-enabled) to balance auditability and information minimization.

Continuous advances transform Agent ID Tokens into foundational elements for secure, scalable, interoperable, and auditable agent-driven systems across domains and modalities.