Securing autonomous AI agents within distributed systems is a critical challenge, driven by the imperative for robust trust, accountability, and compliance with evolving mandates like zero-trust principles. This report proposes the strategic integration of established OAuth 2.0 infrastructure with emerging Agent2Agent (A2A) protocols to support secure, scalable, and auditable agent communication. It outlines technical strategies for implementing robust machine-to-machine (M2M) OAuth flows, defining granular permissions, and enabling verifiable authenticated delegation. The report further explores advanced patterns such as Dynamic Client Registration (DCR), OAuth Token Exchange, and application-layer security, offering a roadmap for building resilient, auditable, and secure multi-agent architectures that can scale with confidence.
The landscape of artificial intelligence is rapidly evolving beyond static models to dynamic, autonomous entities known as AI agents. An agent is defined as a virtual (or physical) autonomous entity that comprehends its environment and acts upon it, often communicating with other agents to achieve common goals that a single agent could not accomplish alone.1 Unlike simpler AI assistants or bots, AI agents possess reasoning, planning, and memory capabilities, demonstrating a higher degree of autonomy to make decisions, learn, and adapt.2 These capabilities are significantly enhanced by generative AI and foundation models, allowing agents to process multimodal information and engage in complex, multi-step actions.2
The emergence of multi-agent systems, where multiple AI agents collaborate or compete towards shared or individual objectives, amplifies the need for sophisticated communication and coordination mechanisms.2 These systems can simulate complex human behaviors and leverage diverse agent capabilities to tackle intricate tasks.2 The term “agentic workflows” refers to these complex, multi-step processes orchestrated by autonomous AI agents, often involving interactions with other agents, tools, and external services.
Traditional Identity and Access Management (IAM) frameworks, such as OAuth and SAML, were primarily designed for human users or static machine identities.3 Their inherent assumption is a predictable interaction model, often involving direct user consent or pre-configured machine credentials. However, the defining characteristics of AI agents—their autonomy, decision-making abilities, learning capacity, and adaptability 2—fundamentally alter the concept of a “principal” in security. An agent’s identity is not merely a static credential but must reflect its dynamic state, evolving capabilities, and the specific authority delegated to it. This shift from human-driven or static machine processes to autonomous, dynamic agents necessitates a re-evaluation and adaptation of existing identity paradigms to support these new forms of digital actors, as traditional IAM models, while foundational, are inherently limited in addressing the unique requirements of these intelligent entities.3 The core challenge extends beyond simply authenticating a machine to authenticating an intelligent, autonomous entity that can act independently or on behalf of others, requiring a more nuanced approach to delegation and authorization.
The concept of agents performing tasks “on behalf of users” 2 introduces a critical security dimension: authenticated delegation. It is insufficient for an agent merely to prove its own identity; it must also cryptographically prove who authorized it and what it is permitted to do within that delegated authority.4 This requirement goes significantly beyond typical machine-to-machine (M2M) authentication, where the machine itself is often the primary principal. The necessity for authenticated delegation directly leads to a demand for robust auditability.4 Every action undertaken by an agent must be traceable back to its origin, including the human delegator and the precise permissions granted. This ensures accountability within highly autonomous and distributed systems, effectively mitigating risks associated with scope misalignment or resource abuse.4
This report aims to provide a comprehensive and practical guide for organizations seeking to integrate their existing OAuth services with AI agents and the emerging A2A protocol. It will detail the technical considerations, best practices, and advanced patterns necessary to establish secure, scalable, and auditable authentication and authorization for autonomous AI agents.
AI agents are sophisticated software systems that leverage artificial intelligence to pursue goals and complete tasks on behalf of users.2 Key characteristics that differentiate them from simpler bots or assistants include:
Traditional identity management systems like OAuth and SAML, while foundational for human users and static machine identities, exhibit limitations when applied to the dynamic world of AI agents.3 These protocols are primarily designed for static permissions assigned to human users and applications with well-defined scopes. However, AI agents require more granular and adaptive access control mechanisms, as their permissions may need to change dynamically based on contextual factors such as risk levels, mission objectives, or real-time data analysis.3
SAML, with its reliance on XML-based assertions and static session-based authentication, is less suited for AI agents that may require continuous authentication and real-time privilege adjustments. Its heavy reliance on user attributes also does not align well with AI-driven interactions, where contextual and behavioral factors should influence access decisions.3 A critical limitation of both OAuth and SAML in this context is their trust-based model, which often assumes that once an entity is authenticated, it remains trustworthy throughout the session. AI agents, however, introduce complexities such as adversarial attacks, evolving intent, and changing operational contexts, necessitating continuous validation rather than one-time authentication.3 These shortcomings underscore the need for dynamic identity management solutions better suited to AI-driven environments.
This challenge of continuous trust and dynamic privilege adjustment is central to securing AI agent interactions. Unlike human users who operate within relatively predictable sessions, autonomous agents can operate continuously, learn, and adapt, potentially altering their behavior or intent. This inherent dynamism means that a security model based on a “trust once, trust always” approach within a session is insufficient. Instead, there is a clear requirement for continuous validation of an agent’s identity and its authorized scope, allowing for real-time adjustments to privileges based on ongoing context and observed behavior. This moves beyond static roles and scopes to a more dynamic, contextual authorization model, which traditional OAuth and SAML, in their conventional implementations, struggle to accommodate.
Given the unique characteristics and limitations of traditional models, robust authentication and authorization for AI agents must address several core requirements:
OAuth 2.0, a battle-tested standard for authorization, provides a strong foundation for securing AI agent communications, particularly through its Machine-to-Machine (M2M) flows.
The OAuth 2.0 Client Credentials flow is specifically designed for M2M communication, where a client application (such as an AI agent) needs to authenticate itself and obtain an access token to interact with a server or API without direct user interaction.12 This flow is commonly used for communication between microservices, server-to-server interactions, and daemons.12
The process involves the client application sending a request to the
OAuth Authorization Server’s token endpoint, including its unique
client_id and client_secret (or other
authentication methods) along with the grant_type set to
client_credentials.12 Optionally, a scope
parameter can be included to specify the level of access or specific
resources the client needs.12 Upon successful validation of the
credentials, the Authorization Server issues an access token, typically
a JSON Web Token (JWT).12 This JWT contains claims such as the issuer
(iss), subject (sub), audience
(aud), expiration time (exp), and optionally,
the granted scopes.6 The client then validates this token and includes
it in the HTTP Authorization header as a bearer token for
subsequent requests to protected resources.12 The resource server, in
turn, validates the token before granting access.12
Table 2: OAuth 2.0 Client Credentials Flow for Agents
| Step | Actor | Action | Key Parameters/Components |
|---|---|---|---|
| 1. Request Token | Client Agent | Sends POST request to Authorization Server’s token endpoint | grant_type=client_credentialsclient_id,
client_secretscope (optional)¹² |
| 2. Validate & Issue | Authorization Server | Validates client credentials and issues Access Token (JWT) | JWT containing iss,
sub, aud, exp,
scopes⁶ |
| 3. Receive & Validate | Client Agent | Receives and validates Access Token | JWT signature
validationexp, nbf, iss,
aud claims¹² |
| 4. Access Resource | Client Agent | Includes token in API requests to Resource Server | Authorization: Bearer <access_token>¹² |
| 5. Resource Validation | Resource Server | Validates token and grants access if valid | JWT validation logic Scope checks, ACLs¹² |
Alternative Text Flow (for better compatibility):
OAuth 2.0 Client Credentials Flow:
Step 1: 🤖 CLIENT AGENT
→ POST to /token endpoint
→ Parameters: grant_type=client_credentials, client_id, client_secret, scope
Step 2: 🛡️ AUTHORIZATION SERVER
→ Validates credentials
→ Issues JWT with: iss, sub, aud, exp, scopes
Step 3: 🤖 CLIENT AGENT
→ Receives & validates JWT
→ Checks: signature, exp, nbf, iss, aud claims
Step 4: 🤖 CLIENT AGENT
→ API Request with: Authorization: Bearer <access_token>
Step 5: 📦 RESOURCE SERVER
→ Validates JWT signature, expiration, issuer, audience
→ Checks scopes & ACLs → Grants access if validImplementing OAuth for AI agents requires adherence to stringent security practices to mitigate inherent risks in automated, autonomous systems.
Protecting client IDs and secrets is paramount. Credentials should never be hardcoded into source code or committed to version control.9 Instead, secure storage services such as secret managers (e.g., AWS Secrets Manager, HashiCorp Vault) should be used to store and inject credentials securely at runtime.9 A critical practice is to assign unique credentials to each machine or service, rather than sharing one API key across multiple services.9 This reduces the “blast radius” of a compromise, as an attacker gaining access to one service would not automatically gain access to everything else.16
Access tokens issued to AI agents should be short-lived, ideally expiring within 5 to 15 minutes for most M2M use cases.9 This minimizes the impact of a compromised token by quickly rendering it useless.7 While refresh tokens are commonly used in user-based OAuth flows to obtain new access tokens without re-authentication 19, it is important to note that refresh tokens are rarely issued or used in M2M client credentials flows.18 This is because M2M clients can typically re-authenticate themselves repeatedly without requiring user interaction.18 Therefore, for most agent-to-agent or agent-to-service communication using Client Credentials, the focus should be on robust access token expiry handling and regular, automated rotation of client secrets, rather than relying on refresh tokens.18
Automated rotation of client secrets on a regular schedule (e.g., every 90 days or less) is a critical security practice.9 This process should involve generating a new secret, updating it in the secure secrets manager, and then revoking the old one after confirming the transition.9 Automation through CI/CD pipelines, cron jobs, or serverless functions can facilitate these rotations without disrupting service communications and reduce the chances of human error.18
For a higher level of assurance, OAuth providers increasingly support asymmetric client authentication, such as using Private Key JWTs or mTLS (mutual TLS), instead of shared secrets.13 In this approach, the client authenticates by signing a JWT with its private key, which the Authorization Server verifies using the corresponding public key.18 This method offers several advantages:
JSON Web Tokens (JWTs) are a highly suitable format for access tokens in agent-based systems due to their self-contained nature, allowing information about the agent and its permissions to be carried within the token itself.12 This enables stateless verification by resource servers, reducing the need for multiple database queries.21
JWTs contain a set of claims, which are statements about an entity. Several standard (registered) claims are highly relevant for agent identification:
Beyond standard claims, custom (non-registered) claims can be included in JWTs to provide more granular context about the agent and its specific capabilities.22 These claims enable richer authorization decisions and better auditability. Examples include:
It is important to be mindful of payload size limitations for custom claims, as some servers may not accept headers larger than 8 KB.22
An emerging concept, SD-JWT (Selectively Disclosable JWT), is particularly relevant for agent identity and capability advertisement through “Agent Cards”.23 SD-JWT allows for the selective revelation of specific agent capabilities, contact information, and operational metadata while maintaining cryptographic integrity and preventing correlation across different interaction contexts.23 This specification is currently under development in the IETF as draft-nandakumar-agent-sd-jwt.23 This means an agent can present only the necessary information to a requesting entity, enhancing privacy and security by minimizing data exposure. Key binding within SD-JWT provides cryptographic proof that the presenter of an SD-Card possesses the private key corresponding to the public key in the SD-Card’s
cnf claim, further strengthening identity verification.23 This is an
emerging concept and not yet a widely deployed standard.
Table 3: Recommended JWT Claims for AI Agent Identity and Capabilities
| Claim Type | Claim Name | Description | Example Value | Relevance for Agents |
|---|---|---|---|---|
| 📋 Registered | iss |
Issuer of the JWT | https://auth.example.com |
Identifies the trusted authority that issued the agent’s token⁶ |
sub |
Unique identifier of the agent | agent-id-12345 |
Establishes the agent’s unique digital identity⁶ | |
aud |
Intended recipient of the token | https://api.example.com/payments |
Ensures token is used only by intended service/API⁶ | |
exp |
Expiration time of the token | 1701388800 (Unix
timestamp) |
Enforces short-lived tokens, reducing compromise window⁶ | |
iat |
Time the token was issued | 1701385200 |
Useful for auditing and determining token age²¹ | |
jti |
Unique JWT ID | uuid-v4-token-id |
Prevents replay attacks for single-use tokens²² | |
| 🎛️ Custom | agent_type |
Categorization of the agent | PaymentProcessor,
DataAnalyst |
Enables role-based or type-based authorization policies |
capabilities |
List of specific functions/tools agent can use | ["process_payment", "refund_transaction"] |
Granular authorization, aligns with agent’s advertised functions | |
delegated_user_id |
Human user ID on whose behalf the agent acts | user_abc@example.com |
Critical for authenticated delegation and human accountability⁴ | |
organization_id |
Organizational context of the agent | org_xyz |
Essential for multi-tenant SaaS environments and organizational policies⁷ | |
policy_version |
Version of authorization policy applied | v1.2 |
Supports dynamic policy updates and revocation tracking | |
| 🔬 Emerging | SD-JWT Claims | Selective disclosure of agent metadata/capabilities | _sd, _sd_alg,
cnf |
Enhances privacy and controlled capability advertisement in Agent Cards²³ |
Quick Reference for JWT Claims (text format):
📋 STANDARD CLAIMS:
• iss (Issuer): Who issued the token → https://auth.example.com
• sub (Subject): Agent's unique ID → agent-id-12345
• aud (Audience): Intended recipient → https://api.example.com/payments
• exp (Expiration): When token expires → 1701388800
• iat (Issued At): When token was created → 1701385200
• jti (JWT ID): Unique token identifier → uuid-v4-token-id
🎛️ CUSTOM CLAIMS:
• agent_type: Agent category → PaymentProcessor, DataAnalyst
• capabilities: What agent can do → ["process_payment", "refund_transaction"]
• delegated_user_id: Human who authorized → user_abc@example.com
• organization_id: Organizational context → org_xyz
• policy_version: Policy version applied → v1.2
🔬 EMERGING CLAIMS:
• SD-JWT: Selective disclosure → _sd, _sd_alg, cnf (privacy-preserving)The principle of least privilege dictates that agents should only be granted the minimum necessary permissions to perform their designated tasks.7 OAuth scopes are an effective mechanism to achieve this, allowing an application’s access to be limited to specific areas and functionalities.7
When defining scopes for AI agents, it is highly beneficial to align them with existing permission models within the organization.7 For instance, if an application already has “reader” and “editor” roles, corresponding OAuth scopes should be created (e.g.,
data:read, data:write).7 The practice of creating a single, all-encompassing “allows-everything” scope should be avoided, as it leads to over-provisioning of access and increased risk.7 Scopes should follow existing role inheritance patterns, avoiding the creation of a separate hierarchy solely for OAuth purposes.7
A crucial aspect of managing agent permissions is the distinction between user-level and organizational-level permissions.7 If an agent requires access to a specific user’s calendar, that permission should be tied to that user’s identity. Conversely, if an agent needs broader access to organizational resources, such as a shared knowledge base, the permission should be tied to the organization’s context.7 This allows for precise control and ensures that sensitive or broad access is granted only when appropriate and auditable. Furthermore, it is advisable to restrict the ability to generate OAuth tokens with the most sensitive or permissive scopes to administrative users only.7
The interplay of scopes, agent capabilities, and delegated authority creates a complex authorization matrix for AI agents. Granular scopes are not merely about limiting access; for agents, they must dynamically align with the agent’s advertised capabilities (often published in Agent Cards 3) and the specific authority delegated by a human user.4 This means that the scopes in an agent’s access token should reflect not only what the agent
can do (its inherent capabilities), but also what it is allowed to do (its assigned permissions), and critically, what it is authorized to do on behalf of someone (the scope of human delegation). This multi-faceted authorization requirement implies a need for sophisticated policy engines capable of evaluating these combined claims in real-time.
Finally, every action taken with an OAuth token by an agent should be meticulously logged with both the token identifier and the associated user or organizational context in audit logs.7 This ensures a clear audit trail and accountability for autonomous actions.
The Agent2Agent (A2A) Protocol is an open standard designed to enable secure, scalable, and seamless collaboration between autonomous AI agents across different frameworks, vendors, and domains.24 It provides a standardized, vendor-neutral protocol for agents to discover each other, share capabilities, delegate tasks, and coordinate complex workflows.24
A2A leverages common web standards such as HTTP, JSON-RPC requests, and Server-Sent Events (SSE) to ensure reliable communication while providing essential security, auditing, and compliance guardrails for enterprise use.24 It supports rich data exchange and asynchronous push notifications, enabling AI agents to manage long-running tasks efficiently.24 The protocol establishes a common language and framework, allowing agents to communicate effectively across diverse systems and enabling them to negotiate interaction modalities.24 A2A’s goal is to scale enterprise-level agentic systems, allowing coordination of hundreds or thousands of autonomous agents across distributed environments without bottlenecks.10 It enables dynamic composition and optimization of workflows by allowing agents to share real-time task updates and intermediate artifacts.10
Security and authentication are critical components of the A2A protocol, ensuring that agents can communicate securely and trust each other’s identities.24 A2A treats each agent as a first-class, HTTP-based enterprise application, leveraging existing infrastructure rather than inventing new protocols.10 At its core, A2A requires that every interaction happens over HTTPS with modern TLS versions (1.2 or higher) and strong cipher suites, ensuring data in transit cannot be snooped or tampered with.10
A2A incorporates enterprise-grade authentication and authorization mechanisms, supporting OpenAPI authentication schemes.26 Agents advertise their authentication requirements in their Agent Cards, including support for OAuth2, mTLS, API keys, or OpenID Connect.26 Clients obtain and present these credentials out of band, never embedding secrets inside the JSON-RPC payload itself.10
The design of A2A as an “identity-aware” and “secure by default” protocol 26 is a significant advantage for organizations with existing OAuth infrastructure. Its direct support for OAuth2 and other standard authentication schemes means that an existing OAuth service can directly fulfill A2A’s authentication requirements. This architectural choice simplifies integration, allowing organizations to extend their current identity management capabilities to the emerging agent ecosystem. This alignment positions A2A as a de facto standard for secure AI agent interoperability on the web.10
Beyond authentication, A2A provides centralized security and governance capabilities, enforcing mTLS, Role-Based Access Control (RBAC), quotas, and Data Loss Prevention (DLP) policies at the gateway level, shielding agents from direct exposure.24 It also supports schema and payload validation, redaction of sensitive data, and normalization of API versions to prevent schema drift and data leakage.24 For auditability, A2A offers end-to-end observability, utilizing OpenTelemetry-powered tracing to connect every agent interaction into a unified span for audit and fast incident response.24
A cornerstone of the A2A protocol is the concept of “Agent Cards.” These are standardized, machine-readable JSON documents that every A2A-compliant agent exposes.26 Agent Cards detail an agent’s identity, capabilities, endpoints, supported message types, authentication requirements, and operational metadata.26 The process of an agent publicly declaring its functions and security requirements through these cards is referred to as “capability advertisement.”
Agent Cards serve a dual purpose: they function as a service discovery mechanism (describing what an agent can do) and as an advertisement of the agent’s identity and security posture (how to authenticate and authorize it).26 By explicitly detailing authentication requirements, Agent Cards become a critical component for establishing trust and enabling secure communication
before interaction. This moves beyond simple endpoint discovery to a security-first discovery mechanism, where agents can programmatically understand the security expectations of their peers.26 This dynamic discovery, coupled with explicit authentication requirements, enables dynamic task negotiation and collaboration between agents.24 The emerging SD-Card format, based on SD-JWT, further refines this by allowing granular, privacy-preserving selective disclosure of agent capabilities, ensuring that only necessary information is shared in specific contexts while maintaining cryptographic integrity.23
In multi-agent systems, A2A facilitates secure task delegation and communication between a “client” agent (user-facing, receiving tasks from a user) and one or more “remote” agents (A2A servers, best suited to perform a delegated task).10 Through a shared protocol and discovery mechanisms, agents can:
A2A maintains state across multi-step interactions, allowing each stage of a task (sending inputs, receiving partial or final outputs) to be correlated correctly.10 This is crucial for complex workflows involving multiple back-and-forth exchanges between agents.
It is important to understand A2A’s role in the broader landscape of agent communication protocols, particularly in relation to Model Context Protocol (MCP) and Agent Communication Protocol (ACP). These protocols address different layers or aspects of the agentic AI stack and are often complementary rather than competing.27
Table 4: Comparison of Agent Communication Protocols (A2A, MCP, ACP) Security Features
| Feature/Protocol | A2A (Agent-to-Agent Protocol) | MCP (Model Context Protocol) | ACP (Agent Communication Protocol) |
|---|---|---|---|
| Primary Focus | Agent-to-agent communication, collaboration, task delegation 27 | Agent-to-tool/data access, providing external context to models 27 | Local-first agent orchestration, intra-runtime communication, workflow delegation 28 |
| Communication Model | HTTP, JSON-RPC 2.0, Server-Sent Events (SSE) 24 | JSON-RPC for tool calls 30 | REST-based communication, HTTP conventions 30 |
| Security Mechanisms | OAuth 2.0, API keys, mTLS, OpenID Connect; HTTPS/TLS 1.2+; capability-scoped endpoints; centralized governance; schema validation 24 | Compatible with API gateways, OAuth2, mTLS, enterprise authentication standards 28 | Designed for local sovereignty; less explicit detail on specific external identity integration in provided material 28 |
| Identity Advertisement | Agent Cards (JSON documents detailing capabilities, auth requirements) 26 | Implicit via tool/API definitions; context-aware responses 11 | Offline discovery via embedded metadata 30 |
| Scalability Vision | Scales enterprise-level agentic systems, coordinates thousands of agents across distributed environments 10 | Enhances individual agent capabilities by providing access to external functionalities 11 | Orchestrates workflows, delegates tasks, maintains state across multiple agents; supports scale-to-zero 29 |
| Interoperability | Framework-agnostic, vendor-neutral 24 | Universal integration with APIs, databases, tools 11 | Allows agents to communicate across different frameworks/stacks 30 |
Alternative Protocol Comparison (simplified view):
🌐 A2A (Agent-to-Agent Protocol)
Focus: Agent-to-agent communication & collaboration
Transport: HTTP, JSON-RPC 2.0, Server-Sent Events
Security: OAuth 2.0, mTLS, OpenID Connect, TLS 1.2+
Discovery: Agent Cards (JSON capability documents)
Scale: Enterprise-level, thousands of distributed agents
🔧 MCP (Model Context Protocol)
Focus: Agent-to-tool/data access & external context
Transport: JSON-RPC for tool calls
Security: API gateways, OAuth2, mTLS compatible
Discovery: Implicit via tool/API definitions
Scale: Enhances individual agent capabilities
🏠 ACP (Agent Communication Protocol)
Focus: Local-first orchestration & intra-runtime communication
Transport: REST-based, HTTP conventions
Security: Local sovereignty design (limited external identity detail)
Discovery: Offline via embedded metadata
Scale: Workflow orchestration, scale-to-zero supportAs agentic systems mature and scale, more sophisticated authentication and authorization patterns become necessary to manage their lifecycle and interactions securely.
Dynamic Client Registration (DCR) is an extension of the OAuth 2.0 specification that allows client applications, including AI agents and microservices, to register themselves programmatically with an authorization server at runtime, without manual human intervention.20 This eliminates the need for pre-provisioning clients and is particularly beneficial for managing ephemeral clients, such as AI agents that spin up temporarily.20 DCR fits seamlessly into modern automation pipelines like GitOps and CI/CD, enabling efficient, automated workflows and reducing credential reuse by isolating each client with unique credentials and permissions.20
The DCR process involves the client sending a JSON payload containing its metadata (e.g., client_name, redirect_uris, grant_types, token_endpoint_auth_method) to the authorization server’s client registration endpoint.20 The server then responds with a unique
client_id, potentially a client_secret, and other metadata, including a registration_access_token for self-service management.33
For secure DCR, especially for autonomous agents, strong authentication methods are critical. Mutual TLS (mTLS) can validate the client at the network layer, ensuring only trusted workloads can register.20 Additionally, clients can prove their identity using signed JWTs as “software statements” during registration.20 While DCR can support “open registration” for any client, in enterprise environments, it is a necessity to enforce initial access tokens to protect the registration endpoint.33 These initial access tokens are typically obtained via the Client Credentials flow or a user interactive flow, and they signal that the registration request comes from an authorized entity.33
Table 5: OAuth 2.0 Dynamic Client Registration (DCR) Security Best Practices for Agents
| Practice | Description | Rationale for Agents |
|---|---|---|
| Enforce Initial Access Tokens | Require clients to present an initial access token (e.g., obtained via Client Credentials flow) to register.33 | Prevents unauthorized or malicious agents from freely registering, maintaining a secure perimeter for automated onboarding. |
| Require Strong Client Authentication | Utilize mTLS or Private Key JWTs for client authentication during registration.18 | Replaces vulnerable shared secrets with cryptographically stronger methods, enhancing trust and auditability for agent identities. |
| Validate Client Metadata | Rigorously validate the JSON payload submitted by the client during registration (e.g., client_name, redirect_uris, grant_types).31 | Prevents misconfigurations or malicious inputs that could lead to security vulnerabilities or operational issues for agents. |
| Implement Rate Limiting on Registration Endpoint | Apply rate limits to the DCR endpoint to prevent abuse and Denial of Service (DoS) attacks.9 | Protects the authorization server from being overwhelmed by rapid, potentially malicious, agent registration attempts. |
| Monitor Registration Events | Log all DCR requests, including client metadata, initial access token usage, and registration outcomes.9 | Provides an audit trail for agent onboarding, enabling detection of suspicious activity or unauthorized registrations. |
OAuth 2.0 Token Exchange (RFC 8693) is an advanced grant type that allows a system to exchange an existing security token (the “subject token”) for a new one with different attributes, such as altered scopes or audience.13 This mechanism is particularly valuable in multi-agent systems for enforcing the principle of least privilege and managing complex delegation scenarios.13
The token exchange flow enhances security by ensuring tokens are scoped appropriately, reducing risks from over-permissioned tokens.13 It is useful when an existing token is invalid or insufficient for accessing a specific resource or service.13 Key use cases for agents include:
The token exchange protocol categorizes tokens into three types: the subject_token (the existing token being exchanged), the actor_token (an optional token representing the entity requesting the exchange on behalf of the subject), and the requested_token (the newly issued token).13 Best practices for implementation emphasize robust policies on the authorization server to prevent privilege escalations and the use of the
scopes parameter to restrict the context in which the resulting access token is valid.13
Table 6: OAuth 2.0 Token Exchange (RFC 8693) Use Cases for Multi-Agent Systems
| Use Case | Description | Agentic Scenario Example |
|---|---|---|
| Delegation of Authority | An agent acts on behalf of a human user or another agent, with the new token reflecting both the original subject and the acting agent. | A “User Assistant Agent” exchanges its user’s token for a new token that identifies itself as the actor, allowing it to call a “Payment Agent” on the user’s behalf. |
| Principle of Least Privilege | An agent requests a new token with a more granular scope for a specific sub-task, limiting its access to only what is required. | A “Data Analysis Agent” with broad read access exchanges its token for one with a specific financial_data:read_only scope before accessing sensitive financial APIs. |
| Cross-Domain Access | An agent needs to interact with services residing in different security domains or identity providers. | A “Logistics Agent” from Company A exchanges its token for one valid in Company B’s domain to access Company B’s shipping APIs. |
| Impersonation | An agent needs to perform actions as if it were a specific user or another agent within the system. | A “Customer Service Agent” exchanges its token to temporarily impersonate a customer’s identity to troubleshoot a specific account issue. |
| Workflow Chaining | Sequential tasks where each step requires a refined or specialized token for the next stage of the workflow. | An “Order Processing Agent” receives an initial order token; it exchanges this for a “Payment Authorization Token” to interact with a payment gateway, then for a “Shipping Request Token” for a logistics service. |
While Transport Layer Security (TLS/HTTPS) provides essential encryption for data in transit 10, for highly sensitive agent interactions, there is a growing necessity for additional application-layer security mechanisms. This approach ensures end-to-end protection of messages, regardless of intermediary hops or potential decryption at network layers.
Google’s Application-Layer Transport Security (ALTS) is an example of a mutual authentication and transport encryption system that operates at the application layer to protect RPC communications.36 ALTS provides applications with an authenticated remote peer identity, which can then be used to implement fine-grained authorization policies.36 This allows application developers to focus on functional logic while security is handled transparently at a deeper level.
Message Level Encryption (MLE) using JSON Web Encryption (JWE) and JSON Web Signature (JWS) is another robust application-layer security technique.37
The Agent Network Protocol (ANP) also highlights the importance of an “Identity and Encrypted Communication Layer” based on W3C DID standards, providing decentralized identity authentication and end-to-end encrypted communication between agents.38
The increasing emphasis on application-layer security, beyond just TLS, indicates a significant shift towards end-to-end trust for agent communication. This suggests that for highly sensitive agent interactions, merely encrypting the transport layer is insufficient. Message signing (JWS) ensures integrity and non-repudiation, while message encryption (JWE) provides confidentiality end-to-end, even across intermediaries. This is a direct response to the “never trust, always verify” principle 6 and the potential for complex, multi-hop agent interactions where data might be exposed at intermediate processing points.
When designing authorization for agent-based architectures, a key decision involves choosing between centralized and distributed authorization patterns.
The discussion of centralized versus distributed authorization highlights a critical architectural decision point for scalable agent systems. While centralized models (like an authorization service or API gateway) simplify policy management and consistency, they introduce single points of failure and potential latency. Distributed models offer resilience but complicate policy enforcement and consistency across a potentially vast number of autonomous agents. For dynamic, high-volume agent interactions, the choice will heavily influence performance, scalability, and security posture, requiring careful consideration of the trade-offs between consistency, latency, and operational complexity.
The following diagram illustrates the three main architectural approaches for agent authorization:
flowchart TB
subgraph CP ["🏢 Centralized Authorization Pattern"]
CA1["🤖 Agent A"] --> AG1["🌐 API Gateway<br/>Initial Auth"]
CA2["🤖 Agent B"] --> AG1
CA3["🤖 Agent C"] --> AG1
AG1 --> AS1["🔐 Central Auth Service<br/>Policy Engine"]
AS1 --> M1["📦 Microservice 1"]
AS1 --> M2["📦 Microservice 2"]
AS1 --> M3["📦 Microservice 3"]
end
subgraph DP ["🌐 Distributed Authorization Pattern"]
DA1["🤖 Agent A"] --> DM1["📦 Service 1<br/>Local Auth"]
DA2["🤖 Agent B"] --> DM2["📦 Service 2<br/>Local Auth"]
DA3["🤖 Agent C"] --> DM3["📦 Service 3<br/>Local Auth"]
DM1 -.-> DM2
DM2 -.-> DM3
DM3 -.-> DM1
end
subgraph HP ["⚖️ Hybrid Pattern (Recommended)"]
HA1["🤖 Agent A"] --> HG["🌐 API Gateway<br/>Auth + Routing"]
HA2["🤖 Agent B"] --> HG
HG --> HAS["🔐 Policy Engine<br/>Complex Decisions"]
HG --> HS1["📦 Service 1<br/>Simple Local Auth"]
HG --> HS2["📦 Service 2<br/>Simple Local Auth"]
end
style AS1 fill:#ffebee
style HAS fill:#e8f5e8
style HG fill:#f3e5f5Pattern Comparison:
| Pattern | ✅ Advantages | ❌ Disadvantages |
|---|---|---|
| Centralized | Strong Consistency Centralized Policy |
Single Point of Failure Latency |
| Distributed | High Availability Low Latency |
Policy Consistency Complex Management |
| Hybrid | Best of Both Scalable Consistent Policies Performance |
- |
The evolving landscape of AI agents presents several challenges and areas for future development in identity management.
A significant challenge lies in securely managing the delegation of authority from human users to AI agents. This process often begins with a human user delegating a task to an AI agent via natural language.5 The agent must then translate these natural language instructions into a structured, machine-readable policy (e.g., using XACML or ODRL) that defines the scope and permissions for its actions.5 This translation from human intent to machine-executable policy can introduce a “semantic gap” (the disconnect between human natural language instructions and machine-executable policies), where the agent’s interpretation may not perfectly align with the user’s original meaning. A critical “human-in-the-loop” approval step is often required, where users review and explicitly approve the AI-generated policies before authorization is granted.5 This ensures alignment with the user’s intent and prevents unauthorized scope escalation. Cryptographically signed “delegation tokens” are then issued, encoding the agent’s permissions, user identity, and validity period, preventing tampering and unauthorized actions.5
The following diagram illustrates the complete delegation workflow from human authority to agent execution:
flowchart TD
U["🧑 Human User<br/>Original Authority"] --> D1{"📝 Delegation Decision<br/>What can agent do?"}
D1 --> P1["🔐 Policy Generation<br/>Natural Language → XACML/ODRL"]
P1 --> A1{"✅ Human Approval<br/>Review & Authorize?"}
A1 -->|"❌ Reject"| R1["🚫 Deny Access<br/>End Process"]
A1 -->|"✅ Approve"| S1["📜 Sign Delegation Token<br/>Cryptographically Bound"]
S1 --> CA["🤖 Client Agent<br/>Receives Delegated Authority"]
CA --> T1["🎫 Token Exchange<br/>RFC 8693"]
T1 --> ST["🔑 Scoped Token<br/>Principle of Least Privilege"]
CA --> A2A{"🌐 A2A Protocol<br/>Agent-to-Agent?"}
A2A -->|"Yes"| AC["📋 Agent Card Discovery<br/>Capabilities & Auth Requirements"]
AC --> RA["🤖 Remote Agent<br/>Task Execution"]
A2A -->|"No"| API["🔌 Direct API Access<br/>Resource Server"]
RA --> AL["📊 Audit Log<br/>Action Traceability"]
API --> AL
AL --> CHN["🔗 Chain of Accountability<br/>User → Agent → Action"]
style U fill:#e1f5fe
style CA fill:#f3e5f5
style RA fill:#f3e5f5
style AL fill:#fff3e0
style CHN fill:#e8f5e8Alternative Flow Diagram (if Mermaid doesn’t render):
Human-to-Agent Delegation Flow:
🧑 Human User (Original Authority)
↓
📝 DELEGATION DECISION: What can agent do?
↓
🔐 POLICY GENERATION: Natural Language → XACML/ODRL
↓
✅ HUMAN APPROVAL: Review & Authorize?
├─❌ REJECT → 🚫 Deny Access (End Process)
└─✅ APPROVE → 📜 Sign Delegation Token (Cryptographically Bound)
↓
🤖 CLIENT AGENT (Receives Delegated Authority)
├─🎫 Token Exchange (RFC 8693) → 🔑 Scoped Token (Least Privilege)
└─🌐 A2A Protocol Decision: Agent-to-Agent?
├─YES → 📋 Agent Card Discovery → 🤖 Remote Agent (Task Execution)
└─NO → 🔌 Direct API Access (Resource Server)
↓
📊 AUDIT LOG: Action Traceability
↓
🔗 CHAIN OF ACCOUNTABILITY: User → Agent → ActionKey Components: - 🧑 Human
Authority: Original source of all permissions - 📝
Policy Translation: AI converts natural language to machine
policies
- ✅ Human-in-Loop: Critical approval step prevents
scope escalation - 🔐 Cryptographic Binding:
Tamper-proof delegation tokens - 🎫 Dynamic Scoping:
Token exchange for least privilege - 📊 Full
Auditability: Complete traceability chain
As discussed previously, the dynamic nature of AI agents, their learning capabilities, and potential for evolving intent necessitate a shift from one-time authentication to continuous validation and adaptive access control.3 Agents require more granular and adaptive access control mechanisms, as their permissions may need to change dynamically based on contextual factors.3 This implies the need for real-time authorization systems that can evaluate context, behavior, and risk levels continuously. Monitoring and auditing token requests and usage for unexpected activity patterns are essential to detect issues such as compromised credentials or unauthorized actions.9 Machine learning-based security anomaly detection (e.g., via Azure Sentinel or Defender) can further strengthen security by flagging unusual AI-Agent behavior across systems.6
A particularly complex authorization problem arises when AI agents operate directly on a user’s device or at the edge, performing operations as if they were the user (e.g., desktop agents).7 In these scenarios, traditional network-centric OAuth flows become less directly applicable. The challenge is that the agent is not a separate user account, nor is it simply a Linux process user owner; data sharing is often intended, yet fine-grained control is needed to differentiate “who” (human or agent) is taking an action.7 This suggests a need for new, more granular permissions at the operating system or application level to manage agent access to local resources and user data.7 Protocols like ACP, with their local-first focus and minimal network overhead, are designed for such tightly controlled or offline environments.28
The challenge of on-device agents represents a fundamental shift from network-based authorization to a more localized security paradigm. When an agent acts as if it were the user on a local device, traditional network-centric OAuth flows, designed for remote resource access, become less relevant. This implies a need for novel authorization primitives at the operating system or application layer that can differentiate between human and agent actions locally. This is a frontier challenge for agent identity, potentially leading to a convergence of device security, application security, and identity management to address the unique trust boundaries of on-device autonomous entities.
Maintaining clear audit trails and accountability is paramount in autonomous multi-agent systems. All critical function calls and agent interactions should pass through identity server checks, inherently generating an audit trail.6 End-to-end observability, perhaps powered by tools like OpenTelemetry, can connect every agent interaction into a unified span for auditability and fast incident response.24 This allows for tracing actions to the specific agent, the human user on whose behalf it acted, and the validated credentials used at a specific time, thereby achieving accountability in an autonomous system.4 Comprehensive logging of all authentication attempts and authorization decisions, coupled with real-time monitoring, is crucial for detecting suspicious activity before incidents escalate.9 Data minimization practices, redaction before logging, and encryption of data both in transit and at rest with aggressive retention and purge policies are also vital for compliance and security.10
Integrating OAuth with AI agents and the A2A protocol requires a strategic, phased approach, focusing on secure foundations and scalable patterns. The following visual diagrams illustrate the key workflows and architectural patterns discussed throughout this document.
The following diagram illustrates the complete lifecycle of OAuth integration with A2A protocol, showing how human delegation, token management, and agent-to-agent communication work together:
sequenceDiagram
participant U as Human User
participant CA as Client Agent
participant AS as OAuth Auth Server
participant AA as A2A Agent
participant RS as Resource Server
Note over U,RS: OAuth + A2A Integration Lifecycle
U->>CA: 1. Delegate task with permissions
CA->>AS: 2. Request token (Client Credentials)
Note right of AS: grant_type=client_credentials<br/>client_id, client_secret<br/>scope=delegated_permissions
AS->>CA: 3. Return access token (JWT)
Note right of AS: Contains: iss, sub, aud, exp<br/>delegated_user_id, capabilities
CA->>AA: 4. Discover agent via A2A
Note right of AA: GET /agent-card<br/>Returns capabilities & auth requirements
AA->>CA: 5. Return Agent Card (JSON)
CA->>AA: 6. Authenticate & delegate task
Note right of AA: Authorization: Bearer token<br/>JSON-RPC 2.0 over HTTPS
AA->>AS: 7. Validate token
AS->>AA: 8. Token validation response
AA->>RS: 9. Access protected resource
Note right of RS: Uses delegated permissions<br/>from original user context
RS->>AA: 10. Return requested data
AA->>CA: 11. Task completion response
CA->>U: 12. Final result with audit trail
Note over U,RS: All interactions logged for accountabilityAlternative Text Flow (if diagram doesn’t render):
Step-by-Step OAuth + A2A Integration Flow:
👤 Human User
↓ 1. Delegate task with permissions
🤖 Client Agent
↓ 2. Request token (Client Credentials: grant_type, client_id, client_secret, scope)
🛡️ OAuth Auth Server
↓ 3. Return access token (JWT with iss, sub, aud, exp, delegated_user_id, capabilities)
🤖 Client Agent
↓ 4. Discover agent via A2A (GET /agent-card)
🤖 A2A Agent
↓ 5. Return Agent Card (JSON with capabilities & auth requirements)
🤖 Client Agent
↓ 6. Authenticate & delegate task (Authorization: Bearer token, JSON-RPC 2.0/HTTPS)
🤖 A2A Agent
↓ 7. Validate token
🛡️ OAuth Auth Server
↓ 8. Token validation response
🤖 A2A Agent
↓ 9. Access protected resource (using delegated permissions)
📦 Resource Server
↓ 10. Return requested data
🤖 A2A Agent
↓ 11. Task completion response
🤖 Client Agent
↓ 12. Final result with audit trail
👤 Human User
🔍 Note: All interactions are logged for complete accountabilityThe integration of existing OAuth services with AI agents and the Agent2Agent (A2A) protocol represents a critical evolution in enterprise security. The inherent autonomy, collaborative nature, and delegated authority of AI agents necessitate a departure from traditional human-centric or static machine identity models. By leveraging OAuth 2.0’s robust M2M capabilities, particularly the Client Credentials flow, organizations can establish a foundational layer of secure authentication for their agents.
The adoption of advanced OAuth patterns like Dynamic Client Registration is essential for scaling agent onboarding, while OAuth Token Exchange provides the necessary flexibility for managing complex delegation, enforcing least privilege, and enabling cross-domain interactions in dynamic multi-agent workflows. Furthermore, the A2A protocol’s design, with its “identity-aware” principles and explicit support for standard authentication schemes, provides a clear path for secure and interoperable agent-to-agent communication. The use of Agent Cards for capability advertisement and the potential for application-layer security mechanisms like message signing and encryption further strengthen the trust framework.
While challenges remain, particularly in managing human-to-agent delegation and securing on-device agents, a proactive approach to identity management, rooted in established OAuth standards and adapted to the unique requirements of AI agents, is paramount. By prioritizing granular permissions, continuous validation, and comprehensive auditability, organizations can build scalable, accountable, and resilient multi-agent systems that securely unlock the transformative potential of autonomous AI. This strategic approach will be crucial for navigating the complexities of AI governance and ensuring the safe and effective deployment of agentic solutions in the enterprise.
Sebastian Schkudlara is a Software Architect with over 19 years of experience designing scalable, cloud-native platforms and secure, agentic AI systems. He specializes in OAuth-based identity frameworks, large language model orchestration, and event-driven architectures for enterprise environments.
Sebastian has led transformative technology initiatives across logistics, insurance, and developer tooling sectors, with recent focus on building multi-agent ecosystems and intelligent AI proxies that optimize cost and performance across diverse model providers. His expertise encompasses the intersection of backend engineering, DevOps practices, and AI strategy—positioning him uniquely at the forefront of secure AI integration in modern distributed systems.
He currently builds intelligent AI routing and optimization platforms, and develops marketplace solutions for autonomous AI agents. Sebastian’s work consistently bridges the gap between theoretical AI capabilities and practical enterprise implementation, with particular emphasis on security, scalability, and operational excellence.
🔗 Connect: LinkedIn | 📧 Contact: For enterprise AI architecture consulting and speaking engagements