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To verify an AI agent’s permissions, define the access its task requires, test the agent with the actual identity and tools it will use, and inspect the runtime environment it can reach. A visible tool list or a successful test is not proof that every permission boundary is correct.
Define the intended access before testing
Write down the task the agent is meant to perform and the least access that can complete it. OpenAI’s platform permissions guidance says to begin with the minimum permissions required and add more only as needed. It also recommends validating expected access with a non-owner account before broad rollout (OpenAI platform RBAC documentation).
Record the principal behind each connection: an end user, a service account, the agent builder, or another credential holder. The same tool can have very different effective access depending on whose credentials it uses.
Map each permission layer separately
Do not treat an agent’s tool list as its complete authorization model. Inventory the layers that can affect access, and note which identity controls each one.
#1 Best Overall
- Platform roles: who can configure, publish, or administer the agent.
- Agent audience and enabled tools: who can run it and which actions are available to them.
- App-level controls: how a connected app may be used through the platform.
- Provider authorization and source-system access: what the external service and the connected account permit.
- Tool and API credentials: what keys or tokens the tool can use, and which principal they represent.
- Runtime resources: which files, credentials, and network destinations the execution environment can reach.
These boundaries are distinct. OpenAI’s app-permissions guidance explains that app controls govern use of an app after access exists; they do not grant provider permissions or change permissions in the connected source system (ChatGPT app permissions and connected services). Likewise, the Agents SDK security policy says model output, tool calls, remote content, and serialized state do not by themselves authorize access to host resources or credentials (Agents SDK security policy).
Assess what each tool can do
For every tool, document its operation, the account permissions it requires, and whether its effects can be reversed. OpenAI’s practical agent-building guidance identifies read versus write access, reversibility, and required account permissions as useful risk factors (agent-building safety guidance).
| Check | What to record | Why it matters |
|---|---|---|
| Access type | Read, write, or both | Writing or changing data has different consequences from viewing it. |
| Account scope | Permissions the connected principal needs | A narrowly named tool may still act with a broadly privileged account. |
| Reversibility | Whether and how an action can be undone | Hard-to-reverse actions warrant stronger limits and review. |
| Ownership | Whose connection or credential is used | Builder-owned access can extend beyond the builder if an agent is shared. |
Use this inventory to remove unnecessary capabilities and decide which actions need approval, tighter audience restrictions, or additional safeguards.
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Configuration screens show intended settings, not necessarily the access a particular user experiences. Use a non-owner account representative of the intended audience and exercise the workflows the agent will actually run. Compare what it can read, change, and reach with the baseline you documented. OpenAI’s RBAC guidance specifically recommends this kind of non-owner validation.
Rank #3
- Choose the test identity. Use an account that does not own or administer the agent, connection, or relevant source system.
- Run the real workflow. Include the tools and data paths used for the intended task, rather than checking only whether a tool appears in a menu.
- Check both allowed and denied access. Confirm required actions work and that out-of-scope data or operations remain unavailable.
- Record the result. Note the identity, workflow, configuration, observed access, and any difference from the intended baseline.
- Repeat after material changes. Retest when roles, connectors, credentials, tools, audiences, or runtime settings change.
A passing test establishes only what was exercised under that identity and configuration. It does not certify untested workflows or every permission layer.
Make workflow checks repeatable
For teams using the OpenAI Agents SDK, its testing utilities support deterministic tests of agent workflows, including workflows that use tools (Agents SDK testing documentation). Build tests around the behaviors that matter: the intended tool is invoked, required access succeeds, and disallowed actions do not succeed.
Keep authorization assertions explicit. A deterministic workflow test is not automatically an authorization test; it verifies access only if the test checks the identity and access outcome relevant to the boundary. Retain the tested workflow, principal, expected result, observed result, and configuration context so future runs can be compared.
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For workspace agents, review who can run the agent, which tools and actions are enabled, and which account owns each connection. OpenAI warns that publishing an agent with a personal connection may expose the builder’s app access to people who use that agent. Workspace guidance recommends least privilege and regular configuration audits (Workspace agents guidance).
Best Value
- Limit the audience to people who need the agent.
- Prefer appropriately scoped connections over a builder’s broad personal access.
- Review sensitive or high-impact connectors and actions before enabling them.
- Revisit the configuration as the audience, tools, or connected accounts change.
Check the runtime boundary, not just tool permissions
Generated code may be able to access files, credentials, and network routes available to its execution environment. OpenAI’s sandbox documentation describes these environment-level access paths and advises keeping credentials in the application that handles a function tool where applicable (sandbox security guidance).
Review the files mounted into the runtime, secrets available to it, and network destinations it can reach. Limit these resources to what the task requires. A tool permission review does not establish that generated code is isolated from host resources; runtime access needs its own controls and tests.
Keep an evidence trail for permission changes
Maintain a short record for each agent that captures the intended task, required permissions, principals and connection owners, enabled tools, audience, runtime resources, and test outcomes. When access changes, compare the new effective behavior against that record—not merely against the previous screen configuration. This makes permission drift easier to detect and gives reviewers a concrete account of what was tested.
Quick Recap
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