parallel-agents

Multi-agent orchestration coordinates specialized AI agents to perform cross-domain analysis within a single cohesive workflow, passing context between agents and synthesizing findings into a unified report. It supports sequential and parallel execution patterns for complex tasks requiring diverse domain expertise.

Running a security assessment across frontend, backend, and QA domains is best handled by native Claude Code agent orchestration, which invokes multiple domain experts in a single session to evaluate end-to-end features and synthesize findings into one harmonized report.

Context passing between AI agents during parallel execution works by sharing intermediate findings and domain-specific insights across specialized agents in a deterministic execution flow, ensuring each agent receives relevant context to perform its designated analysis task.

Yes, multi-agent orchestration is specifically designed for architecture reviews requiring input from multiple domains. It maps the project with an explorer-agent, invokes domain agents per orchestration patterns, and synthesizes results into a unified report covering cross-domain requirements.

No external dependencies are required to run parallel AI agent patterns. The orchestration leverages built-in Claude Code agents with a deterministic execution flow, implementing context passing, resume support, and unified synthesis without additional components or libraries.

Orchestration patterns for multi-agent analysis are limited to tasks that benefit from cross-domain synthesis, such as architecture reviews and feature implementations. They follow a deterministic execution flow, which may not suit highly dynamic or unpredictable analysis scenarios requiring real-time agent adaptation.