When an AI agent calls a business tool, the receiving system needs more context than “this request came from our AI account.” It needs to distinguish the acting agent, the task it is performing, and the permissions the tool should enforce.
Start with an accountable owner
Register a separate identity for a clearly defined agent role. Record the owning workspace, the team responsible for it, and its intended purpose. A vendor-review assistant and a finance-approval service should not become indistinguishable simply because they use the same model.
Truthlocks machine identity supports this identity context. The model that generates an answer is only one part of the workflow; the application and connected tools decide how that answer can lead to an action.
Make permissions enforceable
Define the tasks and tools an agent may use, then check that each receiving integration actually enforces the intended limits. Recording a permission in a profile does not automatically add an authorization check to an unrelated external service.
For an invoice assistant, a sensible pilot may allow gathering references and preparing a recommendation while reserving payment authorization for a person or separately authorized system. Test an out-of-scope request as well as the expected path.
Connect activity to evidence
Use configured action receipts and workflow history to record what the agent reported doing. Include the relevant task or business reference, acting identity, captured inputs, and outcome. Keep sensitive information out of broadly shared logs unless the workflow explicitly needs and permits it.
Completeness depends on the events you connect. Missing receipts should remain a visible gap in the evidence, rather than being interpreted as proof that nothing happened.
Plan for changes and revocation
Assign responsibility for reviewing activity, changing permissions, and stopping an agent. Test what happens to active work and external credentials when access is revoked. Confirm the effect in downstream systems instead of assuming a single platform action controls every integration instantly.
The agent-platform use case and support automation example show how these responsibilities fit a practical process. Machine identity makes activity more attributable; it does not make AI output infallible.