Assign Tasks Across Agents with One Shared Config Object

The Agent Command Center lets you assign tasks across agents by editing one versioned config object that every agent reads at runtime. This approach keeps role prompts, tools, schedules, and approval rules consistent while you distribute work to specific agents on your agent runtime backend.

Key takeaways:

• One config object controls prompts, autonomy levels, and approvals for all agents.
• Sensitive actions always route through the approvals inbox before execution.
• Live logs and token usage appear per agent without separate dashboards.
• Changes to the config object remain traceable across every run.

Steps to Assign Tasks Across Agents

Begin by opening the config object in the control plane. Add task definitions under the tasks section and map each task to an agent identifier.

Follow these steps:

• List the tasks you need completed.
• Specify the target agent for each task.
• Set autonomy levels per task in the same object.
• Define which actions require human approval.
• Save the versioned config object and push it to your agent runtime backend.

For example, assign invoice processing to agent-invoice-01 and report generation to agent-reports-02, each referencing the same model parameters section. This process ensures every agent receives the same rules while executing different portions of the workload. You can also attach input schemas directly in the object so agents validate data before starting work.

How a Shared Config Object Controls Distribution

The single config object holds task queues, agent roles, and routing logic in one place. You edit the object once and all agents reload the updated rules on their next cycle.

Core elements stored in the object include:

• Task descriptions and input parameters.
• Agent IDs or labels for assignment.
• Schedule intervals for each task group.
• Model parameters that apply during execution.

Review version history of model parameters in config to see how prior versions affected task routing. A shared object reduces configuration drift compared with maintaining separate files per agent.

Setting Autonomy Levels and Schedules

You define autonomy levels and schedules directly in the config object so agents know when and how independently they may act.

Use these settings:

• Autonomy level 1 for read-only tasks.
• Autonomy level 2 for tool calls that need approval.
• Autonomy level 3 for fully approved sequences.
• Cron-style schedules attached to each task.

Set agent autonomy levels schedules in config object for detailed examples of schedule syntax. Trade-offs appear when raising autonomy: higher levels speed execution but increase the chance that an unapproved action reaches the approvals inbox later.

Routing Sensitive Actions Through Human Review

Every action that touches external systems must pass through the approvals inbox. The config object marks which tool calls or outputs require review. Align these markings with established controls such as those described in the NIST SP 800-53 security controls.

Approval flow checklist:

• Mark actions as sensitive in the config object.
• Route the output to the inbox before runtime execution.
• Allow edit, approve, or reject options.
• Log the reviewer identity and timestamp.
• Re-run the task only after explicit approval.

Secure agent runtime backend with config permissions shows how permissions interact with approval rules. Always require human approval for actions that touch the real world.

Monitoring Execution and Token Usage

Live visibility streams logs, intermediate outputs, and cost estimates for every agent. You track token usage across the fleet from one control plane.

Monitor these metrics per run:

• Tokens consumed by each agent.
• Execution duration against configured timeouts.
• Success or failure status of assigned tasks.
• Intermediate outputs before final results.

Track token usage multiple agents from one control plane explains how to set alerts on cost thresholds. You can also export daily aggregates to compare against budgeted token allocations for the entire fleet.

Comparison of Task Assignment Approaches

The shared object approach reduces drift between agents and keeps approval rules consistent.

Managing Task Dependencies Across Agents

When tasks depend on outputs from earlier steps, record those dependencies inside the same config object. The control plane resolves the dependency graph before dispatching work.

Dependency rules to configure:

• Output of task A becomes input for task B.
• Timeout on upstream task triggers fallback agent.
• Conditional branching based on approval status.
• Maximum depth of chained tasks to prevent runaway sequences.

This section adds traceability because every dependency reference points back to a specific config version. You can test changes by creating a new version and running a dry execution that stops before any real-world action.

Controlling Tool Permissions and Timeouts

Tool access and execution limits live inside the same config object. You grant or revoke permissions without touching individual agent code.

Permission rules to define:

• Allowed tool names per agent role.
• Maximum runtime per task.
• Token budget per execution.
• Retry count before escalation.

Control tool permissions in your agent config object and Set execution timeouts in your agent config object cover the exact syntax. Following practices from the NIST Artificial Intelligence Risk Management Framework helps maintain decision traceability when permissions change.

Audit Decision Paths After Distribution

Execution history records every decision made during task assignment. You reconstruct the path from config version to final output.

Audit steps include:

• Select the run ID from the control plane.
• Review the config version used.
• Inspect intermediate outputs and approvals.
• Export logs for compliance review.

Audit agent decision paths from execution history provides the query patterns for detailed reconstruction.

Conclusion

Assign tasks across agents by maintaining one shared config object that every agent reads from your runtime backend. This keeps prompts, approvals, and monitoring rules consistent.

Next steps:

• Open an existing config object and add three sample tasks.
• Mark one task as requiring approval.
• Deploy the new version to your agent runtime backend.
• Review the first execution logs and token counts.
• Adjust autonomy levels based on observed results.

FAQ

How many agents can read the same config object?

Any number of agents on your runtime backend can load the identical versioned object. The control plane pushes updates to all registered agents automatically.

What happens if two agents receive the same task?

The config object supports task locking so only one agent executes a given task instance. Remaining agents skip the locked task.

Can I change task assignments without restarting agents?

Yes. Edit the config object and publish a new version. Agents poll for changes at their next scheduled interval.

How do I limit token spend when assigning tasks across agents?

Set per-task token budgets and overall fleet budgets inside the config object. The control plane stops further execution once limits are reached.

Where do approval decisions appear for distributed tasks?

All sensitive actions land in the central approvals inbox regardless of which agent initiated the request. You review and approve from a single view.