parallel-agents

Multi-agent orchestration coordinates specialized AI agents to perform complex analyses across diverse domains, including code analysis and architecture reviews. It uses pattern-based coordination with context sharing to map project structures and synthesize findings into a unified report.

Multi-agent orchestration is a centralized workflow that coordinates specialized AI agents across different domains. You need it when performing complex analyses like architecture reviews or security audits, where structured coordination and context sharing between agents streamline the process and reduce errors.

Yes, parallel agents support security assessments and comprehensive analyses across frontend, backend, and data layers. The orchestration assigns domain-specific agents to each layer and integrates their findings through centralized synthesis reporting.

Long-running multi-agent workflows support continuation from previous agent work through resume-enabled patterns. This allows you to resume interrupted processes and build upon existing context without restarting the entire orchestration and analysis pipeline.

Yes, multi-agent orchestration features centralized synthesis reporting that combines outputs from all assigned domain-specific agents. It generates a unified report that integrates findings from code analysis, architecture reviews, and security assessments into a single document.

Pattern-based agent coordination relies on predefined patterns like comprehensive analysis, feature review, and security audit. It requires mapping the project structure first and depends on centralized synthesis, meaning workflows outside these specific patterns may not be fully supported.