dev-team

Multi-agent collaboration is managed by classifying tasks into phases and assigning specialized roles like Implementer, Researcher, and QE Engineer. This orchestrates structured workflows with quality gates to ensure accountability across software engineering, documentation, and strategy tasks.

Phase-driven task orchestration is a process that sequences work through classify, design, execute, review, and iterate stages. It assigns clear ownership and blockers across distinct agent roles to execute complex tasks with enforced quality checks.

Yes, role-based AI agents can be applied across code review and technical documentation. You select specific roles such as Writer, Checker, and Security Reviewer to cover all execution phases and enforce quality gates for traceable outcomes.

You coordinate task dependencies and project conventions by aligning multi-agent execution with project rules via CLAUDE.md. The workflow manages task creation and dependencies while enforcing quality gates to capture traceable outcomes.

The best way to assign AI agent roles for quality assurance is to select a QE Engineer for testing and a Security Reviewer for vulnerability checks. These roles enforce quality gates during the review phase to capture traceable, evidence-based outcomes.

No specific framework dependency is required to orchestrate AI agent teams. You can spawn a 2-4 agent team and start Phase 1 by classifying the task, then plan execution with defined roles and QA gates within your existing environment.