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There is no documented universal lifetime run-count setting for every scheduled agent. A schedule’s cadence or active-task limit is not a total-run cap: manual starts, work already in progress, and automatic retries can all complicate the count. To control usage, define what counts as a run, pause future starts when the limit is reached, and check the product’s run history and applicable workspace controls.
What a run cap does—and does not—mean
For ChatGPT Scheduled Tasks, OpenAI documents plan-dependent limits on the number of active tasks and how frequently recurring tasks can run. Eligible paid plans can schedule recurring tasks as often as hourly; Free users can schedule one-time tasks or recurring tasks no more than once daily. These limits describe task quantity and cadence, not a guaranteed maximum number of executions over a task’s lifetime. The documentation also distinguishes ChatGPT Scheduled Tasks from Codex automations, so do not assume the same controls or accounting apply to both. OpenAI’s Scheduled Tasks help page describes the limits; check it and your plan or workspace settings for current terms.
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Before setting a cap, decide what it is meant to count: scheduled starts, manual starts, retries, or work still running. The official team-task guide does not specify whether every product counts manual starts or retries against a configured run budget, and it does not establish a universal lifetime run-count control. Treat those details as product-specific rather than assuming a cadence limit will enforce them.
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1. Someone starts a run manually
In shared team tasks, selecting Run now starts a run using the saved instructions. A counter that watches only scheduled occurrences would miss that start unless it also records manual runs. The team-task guide documents the action but does not say how every product applies manual runs to a cap. See OpenAI’s team-task guide for the documented controls.
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2. Work is already running when you pause
Pause suspends future scheduled and event-triggered runs; it is not a reliable way to interrupt an execution already in progress. The guide advises checking an active run separately rather than relying on pause or delete to stop it. If you reach a limit, stop future starts, then inspect the active run and handle it separately using the controls available in your product.
3. A temporary error triggers a retry
Some temporary execution errors may be retried automatically. The guide does not state the retry count or whether retries consume a configured run budget, so account for them as possible additional executions and inspect run history rather than assuming one scheduled start equals one execution.
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How to apply a practical limit
- Define the unit. Decide whether your limit covers scheduled starts, manual Run now starts, retries, and in-flight work. Record what the exact product counts; the official documentation does not resolve all of these semantics universally.
- Monitor executions. Review Previous runs to see what started and whether temporary errors or retries occurred. If a run can send a message or modify a file, check the destination for partial or completed side effects before starting it again.
- Pause future starts at the threshold. Use Pause to suspend future scheduled and event-triggered runs. Separately inspect any run already in progress; pausing is not a substitute for handling active work.
- Verify the outcome. A time-based schedule marked Completed means it has no future runs scheduled; it does not prove that its last run finished successfully. Check Previous runs and, when relevant, confirm the intended message, file change, or other destination-side effect.
- Check shared-workspace governance. For team-owned work, ask the workspace administrator which spending controls apply. Personal scheduled-task limits do not establish spending limits for shared team tasks.
Which control layer to check
| Control layer | What it addresses | What it does not establish |
|---|---|---|
| Schedule cadence | How often a recurring task is eligible to start, subject to plan and workspace settings. | A maximum lifetime run count. |
| Execution initiation | Scheduled starts and, for team tasks, manual starts through Run now. | Whether manual starts count against every product’s configured budget. |
| In-flight handling | Pause suspends future scheduled and event-triggered runs. | That pausing or deleting stops work already underway. |
| Retry behavior | Some temporary execution errors may trigger automatic retries. | A universal retry count or whether retries consume a run budget. |
| Budget governance | Workspace administrators manage applicable spending controls for shared team tasks. | That personal task limits govern shared-workspace spending. |
These control layers are documented for different parts of ChatGPT’s scheduled-task experience; they should not be read as identical behavior across ChatGPT, Codex, personal tasks, and team tasks. If you need a hard execution ceiling, verify the exact product’s counting and stopping behavior with its current documentation or workspace administrator. Do not treat frequency or active-task limits as a substitute.
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