parallel-agents

You can orchestrate multiple AI agents by chaining specialized roles like security auditors and test engineers within a single session. This approach allows sequential processing where context and findings are passed between agents to synthesize a final analysis.

Multi-agent orchestration coordinates specialized AI agents to perform cross-domain tasks like feature implementation and architectural reviews. It maintains a unified session state so that findings from one agent inform the next, ensuring comprehensive analysis.

To execute a security audit and feature review concurrently, use a multi-agent workflow to map project structure, review API endpoints, and identify test coverage gaps. The system synthesizes these sequential findings into a cohesive final report.

Yes, native agent system integration is required to manage sequential chains, context passing, and the final synthesis of findings. This integration ensures that specialized agents can share a unified session state during complex task execution.

Yes, multi-agent workflows are designed for cross-domain expertise such as architectural reviews, security auditing, and feature implementation. Specialized agents work in concert to analyze different perspectives and synthesize findings into high-quality output.

The primary limitation is the strict dependency on native agent system integration to manage context passing and sequential chains. Without this underlying integration, the system cannot maintain the unified session state required to synthesize findings across specialized agents.