remote_worker_connector

You can distribute tasks to multiple AI agents for parallel processing by acting as a Swarm Leader and using the `dispatch_batch_tasks` function to execute multiple independent instructions concurrently across remote worker agents.

Yes, you can maintain conversational context with a specific remote worker agent by using the `sub_session_id` parameter during task delegation, ensuring the worker retains state across multiple interactions.

Load balancing in distributed computing swarm delegation works by automatically assigning incoming tasks to idle worker nodes registered in a SQLite database, though you can also specifically target individual workers if needed.

Yes, you can forcefully halt a worker node's current task to execute an urgent instruction by dispatching the new task with the parameter `priority` set to `URGENT`, effectively triggering task preemption.

Yes, you need a SQLite database because the agent orchestration system uses a SQLite registry to actively manage remote worker discovery, track node availability, and maintain health monitoring for the swarm.