Set practical run limits for recurring OpenAI agent work

Define a complete run

State the trigger, scope, and expected finish. A run might prepare one case or process a bounded batch of records. Avoid an open-ended instruction to keep improving operations without a clear stopping condition. For a Tennessee regional team, identify whether each location has separate work and limits or shares a central queue with an accountable coordinator.

Bound the permitted effort

Set limits on duration, tool attempts, and batch size appropriate to the workflow. Decide what happens when a limit is reached. The limits should be enforced in the application rather than existing only as a request in the prompt. Our proposed OpenAI design treats the agent’s tool access as a bounded business capability with observable progress and explicit termination states.

Reference: OpenAI: Using tools

Prevent overlapping work

Define how a new scheduled trigger behaves when the prior run is still active. Use a stable work identity and ownership rule so two runs do not process the same case independently. Test a slow dependency and a restarted worker. The system should preserve pending work while avoiding duplicate updates or repeated requests to the same employee.

Make limits useful to operators

Show whether the run completed, stopped at a limit, or needs intervention. Include the affected business references and a safe next action. Agentix can connect recurring agents to monitoring and support processes. Review limit events as evidence: frequent stops may indicate a poorly scoped task, a broken dependency, or a workflow that should be divided into smaller units.

Reference: Agentix (publisher): Agentix services

Common questions