Power of Attorney for the AI Agent

On Delegation - Human and Artificial

In our daily lives, we often delegate tasks. We might ask a colleague to respond to emails while we’re away, grant a family member access to a bank account to pay bills, or hire a lawyer with a power of attorney to act on our behalf in complex legal matters. Each delegation carries a different level of risk and requires a corresponding level of trust and verification. For simple tasks, a verbal agreement might suffice. For high-stakes actions, we demand formal identification, signed authorizations, and clear boundaries on the delegated authority. 

We need to know who is acting, be sure they are authorized by the principal, and understand precisely what they are allowed to do.

As Artificial Intelligence (AI) agents become increasingly capable of performing sophisticated tasks autonomously, our expectations of them mirror these human-to-human delegations. We envision agents managing our schedules, conducting research, interacting with online services, and even executing transactions. Just as with human delegates, if these AI agents are to act reliably and securely on our behalf across the digital landscape, the foundational questions of identity, authorization, and accountability must be rigorously addressed. The “digital power of attorney” we grant them needs to be both robust and verifiable.

Some background: What are AI Agents?

At their core, AI agents are software systems designed to perceive their environment (digital or physical), make decisions, and take actions to achieve specific goals. 

Unlike simple scripted programs, modern AI agents leverage advancements in machine learning, natural language processing, and reasoning capabilities to exhibit a degree of autonomy and adaptability.

A basic agent structure often involves:

  • Sensors: How the agent perceives its environment (e.g., receiving API responses, processing text input, analyzing images).
  • Actuators: How the agent acts upon its environment (e.g., making API calls, generating text, controlling a device).
  • Decision-Making Engine: The “brain” of the agent, often powered by Large Language Models (LLMs) or other AI techniques, which processes information and decides on the next action based on its goals and knowledge.
  • Knowledge Base: Information the agent has access to, including its instructions, learned experiences, and data from its environment.

Furthermore, complex tasks often aren’t handled by a single monolithic agent. We are increasingly seeing multi-agent frameworks where specialized agents collaborate to achieve an overarching goal. For instance, a “travel planning” request might involve one agent researching flight options, another finding hotel accommodations, a third processing payment information, and a coordinating agent orchestrating the entire workflow. This collaborative nature adds another layer to the identity and authorization challenge.

AI Agents Are Different – And Why It Matters

It’s tempting to categorize AI agents alongside existing software like traditional bots or standard applications. However, several key distinctions necessitate a different approach to how we manage their identity and trust.

  1. Autonomous Decision-Making: Unlike scripted bots that follow predefined rules or applications that act only upon direct user command, AI agents possess a “quasi-decision-making capability.” They interpret instructions, make inferences, and can choose between multiple courses of action to achieve a goal. This autonomy, while powerful, also introduces unpredictability. We need to be confident that their decisions align with the delegated intent and stay within authorized boundaries.
  2. Broad Interaction Surfaces: Agents are designed to interact with a wide array of systems and interfaces – APIs, websites (via text or even interpreting visual layouts), voice assistants, IoT devices, and potentially even physical robots. Each interaction point can have different security models and data sensitivities.
  3. Acting “On Behalf Of”: The core premise of many advanced AI agents is to act as a proxy or delegate for a human user or an organization. This “on behalf of” relationship demands strong mechanisms to prove that the delegation is legitimate and that the agent’s actions accurately reflect the principal’s intent and authorized scope. Consider the trust you place in a human assistant; a similar, verifiable trust model is needed for their digital counterparts.
  4. Complex Orchestration & Attributability: As mentioned, many sophisticated agentic products will employ multiple underlying agents, possibly orchestrated by another AI. When an action occurs, or an error is made, it becomes critical to trace it back not just to the overarching “product,” but to the specific agent (or agents) involved and the precise delegation that authorized their part in the process. This level of auditability is often more complex than logging actions of a single, monolithic application.

These differences mean that treating AI agents merely as another “API client” or a “background script” using existing generic authentication methods falls short. Their enhanced capabilities and autonomy demand a more nuanced and robust framework for identity, authorization, and accountability.

Identifying, Authenticating, and Trusting AI Agents – Current Landscape.

How do systems currently attempt to identify and authorize automated entities or delegate access? Several common methods are employed, each with its strengths, weaknesses, and suitability for different contexts, especially when considering their application to AI agents.

  • Static API Keys / Bearer Tokens
    • Summary: A pre-generated, long-lived secret string is provisioned to the agent. The agent includes this key in its requests, and the service validates it.
    • Example Workflow: A data analytics script (acting as a simple agent) is given an API key for a weather data service. It embeds this key in its requests to fetch daily forecasts.
    • Practical Cases: Simple, low-risk, machine-to-machine (M2M) data retrieval from a single, trusted source where the “agent” is a well-defined, internally controlled script.
    • Risks & Limitations:
      • Security: Highly vulnerable if leaked; compromise grants full access tied to the key’s permissions until manually revoked.
      • Context: No information about the specific agent instance, the user delegating it (if any), or the purpose of the action.
      • Granularity: Permissions are often tied to the key itself, not the specific task.
      • Revocation: Manual, often disruptive.
      • Audit: Logs only show “key X was used,” not who or what specific automated process.
    • Unsuitability for Complex Agents: Fails entirely for user-delegated agents needing fine-grained, context-aware permissions and clear audit trails across multiple services. Not scalable or secure for an ecosystem of diverse agents.

  • OAuth 2.0 Client Credentials Grant
    • Summary: The agent platform or application (not the individual agent instance) registers as an OAuth client with the service. It uses its client_id and client_secret to obtain an access token representing the application’s own identity and permissions.
    • Example Workflow: A CI/CD pipeline (the “agent platform”) uses client credentials to obtain a token from a code repository service (like GitHub) to pull code or update deployment statuses.
    • Practical Cases: Suitable for M2M scenarios where the application itself is the authenticated entity, acting autonomously without direct, per-request user delegation (e.g., background services, system-to-system integrations).
    • Risks & Limitations:
      • No User Delegation Context: Identifies the client application/platform, not a specific user delegating a task or a particular agent instance running within that platform.
      • Limited Granularity for Agent Tasks: Permissions are tied to the client application’s registration, not the specific task an agent instance might be performing for a user.
      • Audit: Identifies the platform, not the underlying reason or specific agent logic that triggered the action.
    • Unsuitability for Complex Agents: Inadequate for tracing actions back to specific user delegations or individual agent instances with distinct, task-specific permissions.

  • Cloud IAM Roles / Service Accounts (e.g., AWS IAM, GCP Service Accounts)
    • Summary: Used within a specific cloud provider’s ecosystem. Compute resources (VMs, containers, functions) running the agent are assigned an IAM Role/Service Account. The cloud platform automatically provides temporary credentials associated with that role to the agent’s environment.
    • Example Workflow: An agent running on an AWS EC2 instance needs to write data to an S3 bucket. The EC2 instance is assigned an IAM Role with s3:PutObject permissions for that bucket. The agent’s SDK uses these ambient credentials.
    • Practical Cases: Ideal for agents interacting exclusively with resources within the same cloud provider’s ecosystem. Simplifies credential management significantly.
    • Risks & Limitations:
      • Provider Lock-in: Tied to a specific cloud vendor; not interoperable with external or multi-cloud services.
      • Limited User Delegation Context: Primarily identifies the compute resource or service account, not typically fine-grained user delegation for a specific task (unless complex trust policies and AssumeRole chains are implemented).
      • Granularity: Permissions are often at the role level, which might be broader than needed for a specific agent task.
    • Unsuitability for Complex Agents: Not suitable for agents needing to interact across different cloud providers, on-premise systems, or third-party web services. Lacks a universal, application-level agent identity.

  • Mutual TLS Authentication (mTLS)
    • Summary: Both the agent (client) and the service (server) use X.509 certificates from a trusted Certificate Authority to authenticate each other during the TLS handshake.
    • Example Workflow: Internal microservices within a secure corporate network use mTLS to establish authenticated, encrypted communication channels. The identity is derived from the client certificate’s subject.
    • Practical Cases: Excellent for securing service-to-service communication within a controlled network where a Public Key Infrastructure (PKI) is established. Provides strong transport-level authentication.
    • Risks & Limitations:
      • Certificate Management Complexity: Managing the lifecycle (issuance, renewal, revocation via CRL/OCSP) of potentially many agent instance certificates can be burdensome.
      • Limited Application-Level Context: Authenticates the machine or process holding the certificate’s private key. Doesn’t inherently convey user delegation context, specific task purpose, or fine-grained application permissions within the authentication mechanism itself (these are usually managed separately based on the certificate’s identity).
      • Interoperability: Requires all parties to trust the same CA(s).
    • Unsuitability for Complex Agents: While providing strong client identity, mTLS alone doesn’t solve the problem of conveying rich, verifiable delegation context or standardized application-level permissions for diverse agents interacting with many services.

  • Signed Requests (e.g., AWS Signature V4, Custom HMAC)
    • Summary: The agent possesses a long-term secret key. For each request, it uses this key to generate a cryptographic signature over parts of the request. The server, knowing the key, validates the signature.
    • Example Workflow: An application makes calls to AWS S3. Each request is signed using AWS Signature V4, which involves an access key ID and a secret access key.
    • Practical Cases: Provides strong per-request authenticity and integrity, mitigating some risks of bearer token replay. Common for major cloud provider APIs.
    • Risks & Limitations:
      • Long-Lived Secrets: Still relies on the secure management and distribution of long-term secret keys to agents.
      • Lack of Standardized Context: The signature proves authenticity but doesn’t inherently carry standard user delegation information or granular permissions for the specific task.
      • Revocation: Typically relies on deactivating the key ID on the server side.
      • Complexity: Implementing custom signing schemes correctly can be complex.
    • Unsuitability for Complex Agents: While secure for point-to-point API calls, it doesn’t provide a standardized framework for agent identity across diverse services or convey the rich delegation context needed for trust and fine-grained control in an ecosystem.

Closing Notes – Charting the Course for Agent Trust

The existing landscape of authentication and authorization offers valuable tools, yet the unique nature of autonomous AI agents highlights critical gaps. Relying on methods designed for human users or simpler machine-to-machine interactions leaves us ill-equipped for a future populated by sophisticated, decision-making agents acting across diverse digital services.

To bridge this gap and enable agents to operate safely and effectively, a forward-looking solution must address the core challenges head-on. Such a solution would need to provide:

  1. Verifiable Agent-Specific Identity: Moving beyond application IDs or infrastructure roles, it must offer a standard way to uniquely and cryptographically identify individual agent instances and their lineage (issuer, model), distinct from the delegating user.
  2. Contextual & Granular Delegated Authority: The mechanism must allow users or organizations to grant specific, fine-grained permissions tied to the agent’s intended task and purpose, not just broad scopes. This authorization needs to be verifiable by the receiving service.
  3. End-to-End Attributable Auditing: Interactions must generate audit trails that reliably link actions back to the specific, verified agent instance, the delegating principal, and the authorization context, even across complex, multi-agent workflows.
  4. Interoperable Trust Mechanisms: A standardized framework is needed for services to dynamically verify the legitimacy of agents and their issuers, potentially incorporating verifiable attributes or reputation signals, allowing trust decisions beyond simple allow/deny lists or 1:1 relationships.

The path forward requires establishing the digital equivalent of a robust “power of attorney” system tailored for AI agents. Building this foundation – focused on verifiable identity, explicit delegation, granular control, and interoperable trust – is paramount to unlocking the vast potential of AI agents while ensuring accountability and security in our increasingly automated world.