ultrawork

To execute independent tasks in parallel, you can use an agent orchestration workflow that delegates subtasks to multiple executor agents simultaneously. This approach routes tasks to appropriate model tiers and runs them concurrently to reduce total completion time.

Model tier routing in parallel execution assigns specific tasks to different model tiers to balance cost and performance. This ensures lightweight verification runs on faster tiers while complex data-processing steps use higher-capacity models.

You can manage background execution for long-running AI tasks by applying lifecycle control policies within an agent orchestration framework. This allows independent subtasks to execute concurrently in the background while state is managed via dedicated MCP tools.

Agent orchestration for parallel execution applies specifically to workflows with independent subtasks that can be delegated and fired simultaneously. If your subtasks have strict dependencies, parallel execution will not work as intended.

To aggregate results from concurrent data-processing steps, you fire multiple executor agents simultaneously for independent tasks and then collect their outputs. The state management tools track the execution progress to ensure proper aggregation.

Yes, you need state management tools like omx_state MCP tools for parallel task delegation. These tools handle background execution policies and track the concurrent state of multiple executor agents running across different model tiers.