orchestrator

To coordinate AI agents for feature implementation, you describe the task clearly and an orchestrator spawns specialized subagents to handle design, development, QA, and operations. It enforces a gate-based workflow and returns structured results with artifacts.

A gate-based workflow for multi-agent pipelines enforces strict progress checkpoints where specialized subagents must pass validation before proceeding. This ensures self-review, test-driven development, and sequential or parallel execution gating for complex tasks.

Yes, you can use test-driven development to validate tasks across parallel AI agent pipelines. The orchestrator enforces TDD validation and self-review gates during execution, ensuring subagents meet test requirements before synthesizing final structured results.

Yes, the orchestrator supports architecture decisions and bug fixes by delegating these multi-domain tasks to specialized subagents. It coordinates the necessary design, development, and QA pipelines, enforcing gates and returning a structured summary with artifacts.

You automate deployments and reviews by defining the task for the orchestrator, which then spawns specialized subagents for the DEPLOY or REVIEW pipelines. It gates progress, validates execution, and synthesizes the structured deployment or review results.

You should not use a multi-agent orchestrator for simple or single-domain software engineering tasks that do not require multi-agent coordination. The orchestrator is designed for complex, end-to-end projects needing parallel pipelines, gate enforcement, and specialized subagent delegation.