Set Agent Autonomy Levels Schedules in Config Object

You need one place to define how much independence each agent receives and when it runs. The Agent Command Center stores these choices inside a single versioned config object that applies to your agent runtime backend.

Start by opening the config object for the agent you want to adjust. Every change to autonomy levels or schedules stays traceable because the object versions automatically.

Key takeaways

• Autonomy levels control which actions run without review.
• Schedules determine when autonomous work begins.
• All settings live in one config object.
• Sensitive steps always reach the approvals inbox.

Define Autonomy Levels for Each Agent

Autonomy levels decide which tool calls and outputs execute immediately. Lower levels require approval for every external action. Higher levels allow routine steps while still routing money or data changes to the approvals inbox.

Create levels that match your risk tolerance. Common settings include:

• Read-only mode with no external calls.
• Internal tool use only.
• External calls after human review.
• Full execution with post-run audit.

Review the current level in the config object before any production run. Align these choices with established practices such as those outlined in the NIST AI Risk Management Framework for maintaining human oversight on autonomous systems.

Additional considerations when defining levels:

• Map each level to specific tool permissions in the config object.
• Document the rationale for each autonomy tier.
• Test boundary conditions where an action crosses from approved to unapproved.
• Update levels whenever new tools are added to the agent.

Set Scheduled Triggers in the Runtime Backend

Schedules tell your agent runtime backend when to start autonomous work. You define them with cron expressions inside the same config object.

Use these steps to add a schedule:

• Locate the schedule key in the config object.
• Enter a valid cron string such as 0 9 * * 1-5.
• Specify the work item or prompt the agent receives.
• Save the version so every future run follows the new timing.

Test the schedule on a non-production runtime first. Verify that the cron expression matches your operational calendar and that overlapping schedules do not exceed available runtime resources.

Edit Model Parameters Alongside Autonomy Rules

Model parameters such as temperature and max tokens sit next to autonomy settings in the single config object. Choose model parameters agents on your runtime to keep prompt behavior consistent with the chosen autonomy level.

Adjust parameters using this checklist:

• Set temperature between 0.0 and 0.7 for deterministic output.
• Limit max tokens to control cost estimates.
• Choose top-p only when you need diversity.
• Record the version after each change.

Lower temperature often pairs with stricter autonomy levels. Review parameter impact on intermediate outputs before committing the version.

Route Sensitive Actions Through the Approvals Inbox

Human in the loop remains mandatory for any step that touches production systems. Configure approval rules in the config object so the agent pauses and waits for your decision.

Typical approval triggers include:

• Database writes.
• External API calls with payment.
• Email or message dispatch.
• File uploads to shared storage.

Route agent actions to approvals inbox for controlled execution to maintain this gate. Extend the rule set to cover new action types as your agent capabilities grow.

Compare Autonomy Configurations

Different autonomy levels produce different operational patterns. Use the table below to select the right balance for your workload.

The comparison shows that higher autonomy increases token usage and still requires some oversight. Revisit the table whenever you introduce new tools or change the underlying model.

Monitor Live Execution Logs and Cost Estimates

After you set autonomy levels schedules, watch each run through streamed logs. Stream agent execution logs token usage per run gives visibility into intermediate outputs and cumulative cost.

Track these metrics per execution:

• Start time and schedule trigger.
• Tokens consumed at each step.
• Number of approvals requested.
• Final status and any errors.

Compare daily totals against your budget before scaling schedules. Export logs to an external monitoring system when you need longer retention than the runtime backend provides.

Version the Config Object for Traceable Changes

Every edit to autonomy levels or schedules creates a new version of the config object. Version agent config object for traceable runs explains how to roll back if a schedule produces unexpected behavior.

Follow this sequence when updating:

• Clone the current version.
• Adjust autonomy or schedule values.
• Add a short change note.
• Deploy the new version to your agent runtime backend.
• Verify the first run stays within expected token counts.

Maintain a changelog outside the config object for team review.

Test Schedules Before Production Use

Run a short test cycle on an isolated runtime backend. Confirm that the chosen autonomy level and schedule produce the expected logs without triggering unnecessary approvals.

Validation checklist:

• Confirm cron expression fires at the intended time.
• Verify autonomy level matches approval rules.
• Check token usage stays under the set limit.
• Review one full execution log for accuracy.
• Record the config version used in the test.

Document test results so future operators understand the baseline behavior.

Manage Token Budgets Across Multiple Scheduled Agents

When several agents run on independent schedules, token consumption can accumulate quickly. Place per-agent token caps inside the config object and monitor aggregate usage across all scheduled executions.

Apply these practices to stay within budget:

• Assign daily or weekly token quotas per autonomy level.
• Pause new scheduled runs when the quota is reached.
• Review cost estimates before raising any quota.
• Reallocate unused tokens only after examining prior run logs.

Track cross-agent totals in a shared dashboard view so one high-volume schedule does not starve others.

Conclusion

Update the config object with your chosen autonomy levels schedules today. Start with a single agent, apply the settings, and review the first three runs through the approvals inbox. Then expand to additional agents only after you confirm stable token usage and clear logs.

Next steps

• Open the config object for your highest-volume agent.
• Set one autonomy level and one schedule.
• Route all external actions to the approvals inbox.
• Monitor the first scheduled execution.
• Version the object and document the change.

FAQ

How do autonomy levels affect scheduled runs?

Autonomy levels decide whether a scheduled run can complete actions without human review. Lower levels still create entries in the approvals inbox even when the schedule triggers.

Can I change schedules without touching autonomy settings?

Yes. Both values live in the same versioned config object. Edit only the schedule key, save a new version, and the autonomy level remains unchanged.

What happens if a scheduled run exceeds the token limit?

The agent runtime backend stops the run and logs the token count. You receive an alert and can adjust the max tokens parameter in the next config version.

Do all external actions require approval regardless of schedule?

Yes. Human approval for actions that touch the real world stays mandatory. The approvals inbox receives every sensitive step even on fully scheduled runs.

How do I roll back a schedule change?

Select the previous version of the config object and redeploy it to your agent runtime backend. All future scheduled runs then follow the earlier autonomy levels and timing.