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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →A trace can show that a tool call happened, and an approval check can stop a pending call—but neither fact alone proves that every route to a protected resource is authorized. The account behind this title needs its original event record to substantiate what was missed and what was stopped. The engineering lesson is still clear: put authorization at a boundary that can block the side effect, and verify which tools and routes that boundary actually covers.
What the log proves—and what it does not
A log and an authorization decision do different jobs. OpenAI tracing records events from a run; it can help reconstruct tool activity when tracing is enabled and configured. It does not, by itself, establish that a call was permitted by policy or that every relevant event was captured. OpenAI says tracing is configurable and unavailable under Zero Data Retention, so a missing event in a trace should be described as “not present in this trace,” not proof that it did not happen. See OpenAI’s tracing documentation.
To substantiate a particular miss or stop, preserve the trace or log and identify the tool, arguments, target resource, check or policy, decision point, and whether the tool actually executed. A record of a blocked pending call is evidence about that call and boundary—not a guarantee that other tools, direct API paths, or alternate routes are protected.
How an approval stop works
In the OpenAI Agents SDK approval lifecycle, a tool call requiring approval is recorded as an interruption rather than executed. The run returns the interruption and resumable state; the application can approve or reject the pending call before resuming that same run. This makes approval a possible enforcement point because the decision can happen before the tool’s side effect. OpenAI’s guidance is to “Add review and enforcement to your own harness.” See the Agents SDK human-in-the-loop guide.
That mechanism should not be mistaken for automatic authorization across an application. OpenAI says Responses API and Agents SDK applications do not automatically inherit Codex Auto-review; the application must provide its own review and enforcement. The useful evidence for a reported stop is therefore specific: which call was pending, which component decided, whether execution followed, and what record links the decision to the run.
Where authorization belongs
Check at the last safe boundary
Validate a proposed operation where it can still be blocked before the side effect. OpenAI recommends checking the proposed target, action, arguments, identity, and scope near tools that create side effects. A model instruction or prompt can guide behavior, but it is not a substitute for an application-enforced decision.
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In OpenAI’s JavaScript SDK, making a tool conditionally available for a request does not replace authorization that depends on its arguments or the resource it will access. Enforce those checks in execution, or use appropriate tool input guardrails and approvals. OpenAI also states: “MCP servers must authorize their own protected operations.” See the Agents SDK guardrails guide.
Keep the control plane distinct from execution
OpenAI describes the harness as the control plane around the model: it owns the agent loop, model calls, tool routing, handoffs, approvals, tracing, recovery, and run state. Sandbox compute is the execution plane for files, commands, packages, storage, and related work. Keeping authentication, audit records, human review, and recovery state in the harness can make those controls less dependent on any one execution container. The boundary still needs to cover every path that can reach the protected resource. See the sandbox and harness documentation.
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Check coverage before trusting a policy
A policy’s name is less important than its actual coverage. OpenAI documents that SDK tool guardrails apply to function tools and appropriately configured local MCP tools, while handoffs follow a different path. The documented guardrail pipeline excludes hosted MCP tools, other hosted tools, built-in computer, shell, and apply-patch tools, as well as direct agent-as-tool guardrail configuration. Those exclusions mean a policy attached to one tool path cannot be assumed to govern another. See the guardrails coverage details.
Anthropic’s Managed Agents documentation offers a useful comparison: permission events can return allow, ask, or deny; an ask pauses for a user response, and a denial prevents the tool from running. A server-side denial under automatic evaluation cannot be overridden by a confirmation response. However, custom tools are outside those managed-agent permission policies, so the application must decide whether to execute them. See Anthropic’s permission policies documentation.
A practical authorization review
For each operation that can change state or expose protected data, document these properties before relying on a harness control:
- Decision location: prompt or model-visible instructions, tool wrapper, harness, gateway, or target service.
- Timing: before the side effect, after a result, or as an asynchronous review interruption.
- Decision inputs: tool identity, arguments, target resource, caller identity, and task or engagement scope.
- Coverage: function tools, MCP, handoffs, hosted tools, shell or computer execution, and direct API routes.
- Failure behavior: allow, deny, pause, fail closed when review is unavailable, or continue only with a constrained alternative.
- Evidence: call record, policy and reason, approver, execution result, and trace linkage.
For a disputed event, follow the path end to end: identify the proposed operation, locate the component that evaluated it, confirm the decision preceded execution, and check the execution result. Then test whether the same protected operation can be reached through a different tool or route. A stop that held is meaningful evidence for the path it intercepted; broader assurance requires checking the rest of the boundary.
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