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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsIf a shared AI agent starts but does not finish, first find the last step it completed. Check whether the required app is configured and authorized, clarify the missing action or expected output with one small instruction change, then run the task again and verify the result in the product. If traces are available, use them to identify whether the problem was tool selection, a failed call, a guardrail, or a handoff. For ChatGPT Workspace Agents, an API response of 202 Accepted means the trigger was accepted—not that the task completed.
Define what “complete” means for this task
Vague instructions make it difficult to distinguish a stalled workflow from one that finished with an incomplete result. Before editing the agent, specify what it must read, what action it must take, what the deliverable should contain, and what observable evidence will count as success.
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- Inputs: Identify the relevant records, files, or app data.
- Action: State the operation the agent should perform, not just the general goal.
- Output: Describe the expected format and required contents.
- Verification: Decide what product-side result or record will show the action occurred.
For example, replace “prepare the account update” with a request that names the relevant account record, the fields to review, the update to make, and where the completed summary should appear. OpenAI’s Workspace agents guide recommends clarifying steps, outputs, and constraints while testing an agent.
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When the platform exposes a trace, inspect the run in sequence rather than rewriting the whole workflow immediately. OpenAI describes a trace as an end-to-end record of model calls, tool calls, guardrails, and handoffs. That can help narrow the failure to a specific point.
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Check whether the agent selected the right tool, whether the call returned an error or lacked access, whether a handoff occurred at the expected point, and whether the agent returned before producing the requested output. OpenAI’s guide to evaluating agent workflows recommends traces as a way to investigate workflow behavior. If traces are not available in your product, use whatever run history, activity log, or error details it provides; the available diagnostics vary by platform.
Check app setup and permissions separately from instructions
An instruction cannot grant access to an app or workspace data. Confirm that the required app or connector is enabled for the agent, the intended account is connected, and the person or service running the task has the necessary permissions. Workspace administrators may control app availability, approval, and role access.
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- Open the agent’s configuration and confirm the required app or connector is enabled.
- Check that its connection points to the right account or workspace.
- Verify that the runner has permission to read or change the specific records involved.
- If an option is unavailable or access is denied, ask the workspace administrator to review the relevant app approval and role controls.
OpenAI’s Workspace agents guide notes that teammates may need appropriate connector permissions. The ChatGPT Workspace Agents Help Center article also directs users to check app enablement, connection, and agent permissions. These product-specific details should not be assumed to apply identically to other platforms.
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Make one targeted instruction change, then retest
If the agent had access but misunderstood the workflow or stopped before the required output, change the instruction that corresponds to the observed failure. Add the missing step, specify the output format, or clarify a constraint. Avoid changing several unrelated parts at once: a focused edit makes it easier to tell whether the change addressed the problem.
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- Keep the same representative task input.
- Change one unclear or missing instruction.
- Run the workflow again and compare its behavior with the completion conditions you defined.
OpenAI’s guide advises making specific edits and testing again to confirm an update worked. A clearer instruction can help, but it does not guarantee success if the underlying issue is permissions, a failed tool, or workspace state.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Consider file volume and shared edits
Large file collections
If the agent searches or reads many files, narrow the task to relevant material and remove or exclude unnecessary files where your workspace allows it. OpenAI’s Help Center says performance may decline as more files and larger amounts of data are added, and very large collections may prevent an agent from running. The article does not establish a universal file-count or size threshold, so there is no single limit to use as a diagnosis across workspaces.
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Conflicting changes to the shared agent
If several people edit the same agent, a concurrent save can create a conflict with your unsaved draft. Preserve your unsaved text before refreshing; then load the latest saved version and reapply only the changes you still need. Coordinate substantial simultaneous edits so collaborators do not overwrite one another’s work. These shared-draft and file-volume troubleshooting details are documented in the ChatGPT Workspace Agents Help Center article.
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Verify that the task actually finished
Check the final output or a product-side run record that confirms the requested action. Do not infer completion merely because a trigger request was accepted. In the documented ChatGPT Workspace Agents API trigger path, OpenAI says the response is HTTP 202 Accepted, has no response body or run ID, and cannot currently be retrieved through that API. That behavior is specific to this API path; it is not a general rule for every agent platform or API.
Because that trigger response does not provide a completion record, API users need a separate workflow-side way to verify the result—for example, checking the destination where the requested output or change should appear. See the OpenAI Help Center documentation for the stated API behavior.
Quick Recap
Compare the failure patterns before making broader changes
| What to compare | What it may reveal | Next check |
|---|---|---|
| Where the run stopped | Whether it failed before a tool call, during a call, at a handoff, or after the final action | Use a trace or run history to inspect the last successful step. |
| Access versus instructions | Whether the agent lacked a configured connection or permission, or had access but misunderstood the task | Check app setup and permissions before changing wording. |
| One-off versus repeatable | Whether the issue follows one input or recurs across representative tasks | Compare runs; evaluation guidance describes using datasets and eval runs to examine workflow quality and regressions, without prescribing a sample size for this diagnosis. |
| Workspace state | Whether a large file collection or a concurrent draft edit may have affected the run or configuration | Narrow the material or resolve the save conflict, then retest. |
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