subagent-driven-development

Subagent-driven AI development executes implementation plans by dispatching a dedicated, independent AI subagent to each discrete task, followed by a two-stage review process to ensure spec compliance and code quality. This facilitates rapid iteration and high-quality code generation within a single development session.

The best way to automate task execution for a complex feature is to break down a clear plan into discrete tasks, assign a fresh AI subagent to implement each part, and enforce a rigorous two-stage review for spec compliance and code quality before moving to the next task.

Yes, you need a clear plan with discrete tasks and associated context. Effective AI-driven task execution requires this structured input so independent subagents can be properly dispatched to implement each task and undergo rigorous code review.

The two-stage code review process evaluates AI-generated code by first checking for spec compliance, and then assessing overall code quality. This happens after each independent subagent completes its task execution to ensure robust, well-tested output.

No, this AI development approach requires a plan broken down into discrete, independent tasks. Without discrete tasks and associated context, the system cannot dispatch dedicated subagents to execute the implementation plan effectively.

You should not use independent subagents for implementation plan execution when you lack a clear plan with discrete tasks and associated context. The approach relies on dispatching a fresh subagent per task, so undefined or highly coupled tasks will prevent effective task execution and code review.