Which AI Agent Made That API Call? Try This Attribution Test
To attribute an AI agent’s API call, connect the action to an authenticated caller, an agent and run ID, the authority behind it, and the receiving service’s result. A display name helps you read that chain. The evidence comes from the systems that execute and record the work.
Your agent finished the job. The API logs say “automation account.”
Now someone wants to know which agent changed the record, which run it belonged to, and who approved the change. You open another dashboard. Then another.
That is a useful moment to test your setup.
Below is a small exercise you can run with a harmless read or a draft in a test workspace. It includes a record you can adapt without buying a new monitoring platform.
The question builders are already asking
In r/AI_Agents, u/SheepherderFree3931 described a setup where every agent instance used the same API credentials:
“All our agent instances share the same API credentials.”
The post asks how to identify individual instances without creating hundreds of credentials to manage. In another discussion, u/CourtAble2094 asked:
“Can you tell exactly which agent did what?”
These are questions from two public discussions, not a survey of agent builders. The second author also disclosed using AI to help write the post. Neither post establishes how common the problem is. Both give us a concrete question worth answering in our own systems.
Start with one action you can inspect from beginning to end.
Follow one action across the boundary
Imagine a research agent asks a helper to create a draft note. The helper passes the request to a connector, and the connector calls the notes API.
There are several actors here. The agent that planned the work might never open the network connection. If the connector holds the credential, the notes service may correctly identify the connector as its caller. You still need a reliable way to connect that call to the helper and its assigned task.
For the exercise, use a test workspace and synthetic content. Ask the agent to create one draft with a distinctive test marker. If you only have read access, retrieve one known test record instead.
Collect these references as the request moves:
| Point in the workflow | What to capture | What it helps establish |
|---|---|---|
| Task assignment | Task ID and approval or delegation reference | What work was authorized |
| Agent runtime | Agent ID, run ID, and parent run when relevant | Which execution selected the action |
| Connector or gateway | Authenticated principal and authorization decision | Which caller was checked and what was permitted |
| Receiving API | Request ID, status, and resulting object or version | Which request the service processed |
Have your application or gateway supply the identity mapping from trusted configuration and verified credentials. An agent typing a name into a request body is only making a claim.
If you already use distributed tracing, attach the action to the existing trace. OpenTelemetry describes how trace context connects operations across services. Both sides need compatible instrumentation or an explicit correlation arrangement; passing an extra header alone does not guarantee the other service records it. See OpenTelemetry’s context propagation guide and its instrumentation guidance.
Copy this action record
This is an illustrative internal format, not a protocol standard or a HeadlessDomains.com API payload. Adapt it to your own log fields. Every value below is fictional; the .agent name is an example, with no availability or ownership claim.
{
"event_id": "evt-example-042",
"occurred_at": "2026-09-12T09:00:00Z",
"agent": {
"canonical_name": "research-desk.agent",
"internal_id": "agent-research-desk",
"run_id": "run-example-018",
"parent_run_id": "run-example-017"
},
"authority": {
"delegated_by": "user-example-7",
"approval_ref": "approval-example-12",
"grant_ref": "grant-example-9",
"policy_version": "drafts-only-v3",
"decision": "allow"
},
"caller": {
"authenticated_principal": "notes-connector-test",
"agent_binding_ref": "binding-example-v2"
},
"action": {
"operation": "create_draft",
"target": "https://notes.example/api/drafts",
"client_operation_id": "op-example-042"
},
"result": {
"service_request_id": "req-example-781",
"http_status": 201,
"object_id": "draft-example-23",
"state": "draft"
}
}
The references are the useful part. approval_ref should lead to an actual approval record. agent_binding_ref should lead to the maintained mapping between the agent and the authenticated execution path. Writing “allow” into JSON does not create permission.
Keep this record inside your audit system with access controls and a retention policy. Store references rather than raw credentials, private prompts, or customer content. Also decide who can write or alter the record; logs controlled entirely by the actor being audited offer weaker evidence.
For an asynchronous API, the first response might only acknowledge a job. Preserve that job ID and the later completion result. A successful submission is not necessarily a completed action.
Close the agent chat and reconstruct the result
Give a teammate the resulting draft ID or service request ID. Ask them to work backward using the saved evidence, without relying on the agent’s account of what happened.
Can they find the exact request, the agent run that initiated it, the authenticated caller, and the approval that covered this action?
Record where they get stuck:
| What you find | What to fix next |
|---|---|
| The service only shows a shared connector account | Save its request ID alongside the agent run at the connector boundary. If the service offers no usable correlation evidence, document that attribution limit. |
| The runtime shows an agent name, but no verified mapping | Bind the runtime identity to the authenticated caller through trusted application configuration. |
| You can identify the agent but cannot find its authority | Preserve the delegation, grant, or approval reference with the policy decision. |
| Several retries look like separate tasks | Give each attempt its own event and link attempts to the same intended operation. Use the API’s documented idempotency mechanism where supported. |
| A job was accepted, but there is no finished object | Capture the completion response and reconcile it with the original job. |
This is a diagnostic exercise, not a security certification. A complete record helps you investigate an action; it does not prove the action was sensible or that every control worked.
Trace headers also need care. OpenTelemetry warns that incoming context can be forged and outgoing context can expose internal information. Treat correlation data as evidence to evaluate, and keep authentication and authorization checks in place. OpenTelemetry security guidance
Give the agent a name that survives the next tool change
Once you can trace the action internally, consider how someone outside your workspace identifies the same agent.
A connector account, run ID, and marketplace handle each describe part of its activity. A persistent .agent name can give its public identity a consistent reference, with links to the operator, current endpoints, and machine-readable information. HeadlessDomains.com supports that inspection through CLI and API workflows.
The connection to your logs still needs to be built. Registering a name does not automatically bind it to an authenticated principal or add attribution to another company’s API. Use the canonical name alongside your internal identifiers, and keep the verified mapping current as credentials and tools change.
For the broader architecture, read the Agent Identity Stack. If your problem is keeping an inventory of agents and owners, use the Agent Registry Checklist.
Then give one working agent a public identity you can keep using. Get started at HeadlessDomains.com, connect its record to your maintained identity mapping, and repeat the exercise after your next connector change.
Your teammate should be able to start with one API result and find the agent behind it.
Research note: We checked the quoted wording and public handles against the original Reddit pages. Quotes illustrate the authors’ questions; they are not customer testimonials or endorsements of HeadlessDomains.com.