parallel-agents

You coordinate multiple AI agents by orchestrating discovery, analysis, and synthesis across a cohort of domain-expert agents to deliver holistic project insights. It supports both sequential and parallel execution patterns to fit different tasks.

The best way to orchestrate AI agents for synthesized outputs is using built-in agent definitions and orchestration patterns that enable context sharing and centralized synthesis. This reduces manual coordination across diverse expertise areas.

Multi-agent orchestration handles context sharing by coordinating a cohort of agents through built-in patterns, funneling diverse domain expertise into a centralized, single synthesized output for actionable insights across codebases.

No specific dependencies are required to run parallel multi-agent workflows. The skill operates independently with built-in agent definitions and orchestration patterns, needing only project context to start the workflow.

Use parallel execution patterns for AI agent tasks when you need to fit diverse, independent domain analyses into a single workflow, and switch to sequential patterns when agent discovery steps depend on prior analysis outputs.