Assign Scheduled Work from the Control Plane

Assign Scheduled Work from the Control Plane

You need one place to set recurring tasks for every agent you run on your runtime backend. The Agent Command Center lets you define schedules directly in the config object so work starts on time without manual triggers.

Key takeaways:

• Schedules live inside the single versioned config object
• Every scheduled step can route through the approvals inbox
• Token usage and cost estimates stream live for each run
• Changes to prompts or autonomy levels stay traceable

Define Schedules in Your Single Config Object

Place schedule rules inside the config object alongside role prompts and tool lists. This keeps every parameter versioned so you can audit when a task moved from hourly to daily. The object also records the original author and timestamp of each change, which supports traceability across multiple team members.

Use these fields when building the object:

• Cron expression or interval value
• Target agent identifier
• Maximum autonomy level allowed
• Required approval flag
• Model selection override
• Resource limit in tokens per run

See how to structure the full object in the guide on define role prompts in your single config object. Follow the POSIX cron specification for reliable interval syntax.

Set Execution Triggers for Autonomous Work

Choose triggers that match the cadence of your runtime. The control plane supports both time-based and event-based starts while preserving the human in the loop requirement. Time-based triggers suit regular reporting tasks, while event-based ones react to incoming data feeds.

Follow these steps to activate a schedule:

1. Open the config object editor
2. Add the schedule block with start time and repeat pattern
3. Set the approval rule for any real-world action
4. Save the new version and deploy to your agent runtime
5. Confirm the first run appears in the execution queue
6. Verify the initial token estimate against historical runs

Test the trigger in a staging runtime before production to catch syntax errors in the cron string.

Route Scheduled Tasks Through the Approvals Inbox

Sensitive actions from scheduled agents must land in the approvals inbox. You review output, token usage, and cost estimates before the step touches production systems. This step prevents unintended writes to external services or databases.

Checklist for inbox routing:

• Flag every external API call or data write
• Require explicit approval for model parameter changes
• Allow edit of generated content before execution
• Log the approver identity and timestamp
• Reject runs that exceed per-execution cost thresholds
• Require justification text for every approval decision

Compare this flow with manual methods in the article on agent command center vs manual approvals for control. Align rules with OWASP LLM security guidelines when agents generate external content.

Monitor Token Usage and Cost Estimates

Live visibility shows token counts and projected spend as each scheduled run progresses. You can set alerts that notify when usage crosses limits defined in the config object. Historical trends help forecast monthly spend across all agents.

Configure alerts in the control plane by following the steps in configure per execution cost alerts in control plane.

Track Execution History with Audit Trails

Step-level logs capture every intermediate state change for scheduled agents. You can inspect state transitions without leaving the control plane. These logs include prompt versions, tool calls, and final outputs.

Review history using these methods:

• Filter runs by schedule identifier
• Compare actual token counts against estimates
• Export logs for compliance checks
• Identify drift between config versions
• Search by approver name or decision timestamp

Further details appear in control plane audit trails for step-level agent tracking. Use the NIST AI Risk Management Framework to map logs to governance requirements.

Handle Priority and Resource Conflicts

When multiple schedules compete for the same runtime, the control plane applies priority rules stored in the config object. Higher-priority work preempts lower tasks while still requiring approval for sensitive steps. Resource contention appears as queued runs rather than dropped executions.

Priority handling checklist:

• Assign numeric priority in the config object
• Define fallback schedule if resources are busy
• Log preemption events with original timestamps
• Notify operators of any skipped runs
• Set maximum concurrent executions per agent

Handle Execution Failures and Retries

Scheduled runs can fail due to transient runtime errors or exceeded limits. The control plane records each failure with the exact error message and allows retry policies defined in the same config object.

Apply these retry rules:

• Maximum retry count before permanent failure
• Exponential backoff interval between attempts
• Alert on repeated failures for the same schedule
• Preserve original approval status on retry
• Log all retry attempts with cumulative token usage

Edge cases include network timeouts during long-running tool calls and model rate-limit responses from the provider.

Validate Outputs Before Real-World Actions

Scheduled agents often produce content that must meet business rules. Validation occurs before the approvals inbox so you only review compliant results. Invalid outputs are rejected automatically and logged for later review.

Apply these validation rules:

• Schema checks on structured output
• Keyword or pattern filters for compliance
• Length and format constraints
• Cross-reference against approved data sources
• Reject outputs containing disallowed entities

See the process in validate agent outputs rules before approvals inbox.

Conclusion

Assign scheduled work from the control plane by storing every rule in one versioned config object and routing actions through the approvals inbox. Start by creating a test schedule on your agent runtime and review the first run in the execution log.

Next steps:

• Add a schedule block to an existing config object
• Enable the approvals inbox for all external actions
• Set a cost threshold alert for the new schedule
• Inspect the first three runs for token usage patterns
• Compare against open-source alternatives if needed

Visit the main site at Run Agents to begin.

FAQ

How do I add a schedule to an existing agent?

Edit the config object, insert the schedule block, and deploy the new version to your agent runtime.

Does every scheduled run require human approval?

Yes. Any step that touches the real world routes to the approvals inbox before execution.

Can I change the schedule after the first run?

Update the config object with a new version. All prior versions remain available for audit.

Where do I see token usage for scheduled agents?

Token counts and cost estimates stream live in the control plane for each execution.

What happens if a scheduled run exceeds cost limits?

The run pauses and enters the approvals inbox for review or cancellation.