parallel-agents

Multi-agent orchestration coordinates specialized AI agents like security auditors and backend specialists to handle complex code review and feature implementation tasks in sequential or parallel execution. It enables comprehensive analysis by passing context between agents and synthesizing their findings.

Multi-agent task decomposition breaks down large software engineering problems into smaller, manageable parts handled by different specialized agents. This approach allows diverse domain expertise to be applied to complex tasks that a single agent could not accomplish alone.

Yes, you can orchestrate specialized agents like security auditors, backend specialists, and test engineers to work together. The orchestration supports both sequential and parallel execution, allowing an explorer-agent to map codebase structure before specialists implement and a test-engineer verifies quality.

To implement a new feature, use an explorer-agent to map the codebase, then deploy a backend-specialist and frontend-specialist to implement components in parallel, and finally use a test-engineer to ensure quality. Context passing and finding synthesis streamline the workflow.

The best approach is using multi-agent orchestration to first explore the codebase structure with an explorer-agent, then have a security-auditor review for vulnerabilities. This sequential execution pattern with context passing ensures comprehensive security analysis and feature implementation.

Use multi-agent orchestration when complex tasks require diverse domain expertise or multiple perspectives that exceed a single agent's capability. It is ideal for comprehensive analysis and feature implementation where specialized agents like security auditors and test engineers need to collaborate.