When an AI Agent Acts on Your Behalf, How Can the Other Party Verify Its Authority?

When an AI agent buys something, sends an email, or makes a payment on your behalf, what can the other party actually see — and what should they be able to verify? This seemingly technical detail is becoming a core issue of trust and compliance as automated agents grow more prevalent.

A Real-World Case That Exposed a Design Flaw

The discussion was sparked by a recent controversy surrounding Meta's Muse agent. According to Meta, Muse can sell items, send mail, and make payments on behalf of users, with each agent running in an isolated virtual machine (VM) and seeking user consent before performing sensitive operations.

But one reported incident punctured that security promise: a user asked the agent to help sell some equipment. The agent accepted a $600 offer and directly shared the user's home address with the buyer. The problem was that the user had previously checked an "allow always" option, assuming the system would still prompt him again when sensitive information like a home address was involved.

This gap is precisely the crux of the issue: where does the authorization boundary of "allow always" actually lie? A serious mismatch emerged between what the user expected and what the system actually did. And when we shift perspective from the user to the other party in the transaction, the problem gets even thornier.

The Other Party Only Sees an Account — Not the Authorization Behind It

The person who raised this discussion wasn't looking to assign blame. Instead, they focused on a more fundamental design question: the store or buyer on the other end of a transaction can only see an account.

They cannot see whether the account is being operated by a human or an agent, who owns the agent, or what the agent is and isn't authorized to do. In other words, when an agent steps outside its "sandbox" to interact with external systems, its permission boundaries are completely opaque to the outside world.

This means a buyer has no way to determine: does this "person" accepting the offer actually have the authority to make decisions on the seller's behalf? Was sharing the address an authorized action, or did the agent overstep? In direct human-to-human transactions, this trust is maintained through social contracts and accountability. But when one party becomes an agent, that trust chain breaks down.

This problem has a corresponding research direction in cryptography and identity technology: Delegated Authorization. OAuth 2.0 is currently the most widely used authorization framework on the internet. It allows users to "delegate" limited permissions to third-party applications, with the authorization scope explicitly declared and verifiable by resource servers. However, OAuth was designed with the assumption that a human user actively initiates authorization. When an agent acts as a continuously running automated entity, the verifiability of this mechanism develops blind spots: external services have no easy way to distinguish whether the token holder is an agent or the user themselves, nor is there a standard way to query "what specific operations is this agent permitted to perform?" W3C's in-progress Verifiable Credentials (VC) and Decentralized Identifiers (DID) specifications, along with the OpenID Foundation's GNAP protocol, are all attempting to fill this gap — making the authorization boundary itself a machine-readable declaration that third parties can independently verify, rather than just a configuration item locked inside a platform.

Three Design Choices Around Identity, Permissions, and Verifiability

The discussion surfaced several key engineering choices for building agents that interact with external systems. None of them have a standard answer.

Whose Identity Does the Agent Use?

The first choice is identity ownership. Should an agent have its own independent identity, or should it reuse the user's login credentials?

If the agent shares the user's login, then from an external perspective, the agent's actions are completely indistinguishable from the user's own actions. If something goes wrong, accountability and behavioral tracing become murky. Giving the agent an independent identity creates an opportunity for external systems to recognize "an agent is acting here" and adopt a different trust strategy — but this also requires the entire ecosystem to support such an identity standard.

Are Permissions Granular by Action, or a Single Master Switch?

The second choice is permission granularity. Should authorization scope be defined action by action, or should it be a broad "allow always" toggle like Muse's?

The Muse case perfectly illustrates the danger of coarse-grained authorization: the user thought they were consenting to "selling an item," not realizing that authorization also covered "sharing a home address." The ideal approach is to decompose permissions down to the level of specific actions — accepting an offer is one thing; disclosing personal information is another and requires separate confirmation.

Can External Parties Verify These Restrictions?

The third — and most overlooked — choice: do these permission restrictions exist only within your own application, or can external transaction parties also verify them?

If all restrictions are sealed inside the agent's app, the other party can only passively trust the account. Only when permission boundaries are exposed externally in some verifiable form — such as verifiable credentials or queryable authorization scope declarations — can the other end truly determine whether the agent's behavior falls within legitimate authorization.

Verifiable Credentials (VC) is a data model specification formally published by W3C in 2022. Its core idea is a three-party separation between the Issuer, Holder, and Verifier of a credential — with content protected by cryptographic signatures to ensure tamper-resistance, allowing any third party to independently verify authenticity without contacting the issuer. Applied to agent authorization scenarios, a platform could issue a VC to an agent stating: "This agent is authorized to act on behalf of user X to perform the following operations: accept offers, confirm shipments — but not disclose personal addresses." When the system on the other side of a transaction receives a request from the agent, it can require the agent to present and verify this credential, determining whether the specific operation falls within the authorized scope. Compared to the current approach of agents "operating directly with the user's cookie or token," this shifts the permission boundary from an internal platform configuration to an externally verifiable declaration — one of the technical implementation paths for the "external verifiability" the article describes.

This Isn't Just a Technical Problem — It's a Compliance Problem

The discussion ended with a thought-provoking question: has anyone ever had to explain this mechanism to a compliance team?

This question highlights another dimension of agent authorization. When an agent performs actions with legal and financial consequences on behalf of a user — making payments, signing contracts, disclosing information — regulatory and compliance frameworks require clear proof of authorization, behavioral auditing, and defined accountability. An agent that cannot prove to external parties "I am authorized to do this" simply cannot hold up under compliance scrutiny.

As more and more AI agents are granted real-world operational authority, "agent identity and verifiable authorization" may well shift from an engineering option to a foundational infrastructure problem that must be solved. The Muse $600 case is just the beginning — it reminds us that before granting an agent the ability to act, we need to think clearly: when it acts on your behalf, why should the other party trust it?

This article is based on a technical discussion on Reddit. Case details come from reports cited in that discussion.

In the financial and e-commerce sectors, the compliance pressure around agent behavior is already very concrete. The EU AI Act explicitly requires high-risk AI systems to maintain auditable operation logs and to demonstrate the completeness of the authorization chain to regulators. The US FTC has, in recent enforcement actions, identified "black-box automated decision-making causing consumer harm" as a priority area. More direct precedents come from client authorization and discretionary trading rules in the financial industry: brokerage systems must record the authorization basis for every operation conducted on a client's behalf, and authorization scope cannot be so broad as to cover "everything." When AI agents become involved in shopping, payments, and contract signing, regulators will likely invoke similar frameworks requiring platforms to demonstrate that "each of the agent's actions fell within the scope of the user's explicit authorization" — and the current coarse-grained "allow always" design will almost certainly fail to meet that standard.