Parallel Agent Swarm — High-Speed Distributed Task Processing

I will build a parallel agent swarm for high-throughput task processing — spinning up multiple identical agent instances to process a large task set simultaneously, with work distribution, progress tracking, partial failure handling, and result aggregation. Parallel agent swarms reduce processing time proportionally with swarm size — a task that takes a single agent 10 hours is completed by a 10-agent swarm in approximately 1 hour, making swarm architecture the right approach for any task set too large for sequential processing to meet your time requirements.

The swarm covers work queue distribution across agent instances, concurrent execution with configurable worker count, partial failure handling (failed items redistributed or logged for retry), progress tracking across the swarm, and result aggregation from all workers.