parallel-dispatch

To run multiple AI agents in parallel, you orchestrate their concurrent execution by dispatching each with configurable prompts, timeouts, and context. This parallel processing approach allows multiple agents to work simultaneously on sub-tasks, significantly reducing overall workflow time and aggregating results efficiently.

Agent orchestration is the coordination of multiple AI agents working concurrently within multi-agent systems. You need this approach for complex workflows requiring multi-faceted reviews, such as dispatching security, testing, and documentation agents simultaneously to achieve a comprehensive analysis quickly.

Configuring timeouts and managing agent failures during parallel workflow automation involves setting explicit timeout limits and failure handling protocols for each dispatched agent. This ensures robust workflow completion by preventing stalled processes from blocking the aggregation of results from other successful agents.

Yes, parallel task processing supports comprehensive code reviews by dispatching specialized agents like a security-architect, test-architect, and requirements-analyst simultaneously. This allows you to review artifacts across different architectural domains concurrently and aggregate their holistic assessments rapidly.

The primary limitations of parallel dispatch for multi-agent systems involve managing agent failures and handling scenarios where agents take too long to respond. While it streamlines complex workflows, you must configure robust timeout management to prevent individual agent delays from impacting the overall result aggregation.