subagent-driven-development

AI subagents for iterative development work by breaking a feature plan into discrete tasks, dispatching a fresh subagent for each task, and enforcing a strict two-stage review process. This prevents context pollution and validates spec compliance and code quality before proceeding to the next task.

To execute an implementation plan using AI agents, you provide the plan file, such as ./my-feature-plan.md. The system automatically decomposes the plan into manageable tasks, assigns an independent AI subagent to each task, and enforces spec compliance and code quality reviews sequentially within the same session.

Subagent-driven development prevents context pollution by assigning a fresh, independent AI subagent to each task within the implementation plan. This isolated execution ensures that context from one task does not interfere with subsequent development work.

The best way to automate code review and spec compliance is to enforce a strict two-stage review process after each task's completion. The first stage checks spec compliance, and the second stage evaluates code quality, ensuring a robust final product through focused agentic execution.

Yes, this methodology is designed for detailed feature implementation plans. It ensures each part of the feature is built correctly, tested, and reviewed sequentially via dedicated subagents and a two-stage review process, leading to a robust final product.

Using independent AI subagents for software development requires a detailed implementation plan as input and operates within a single session for the two-stage review process. It relies on the quality of the initial plan decomposition and may not handle tasks requiring cross-task state maintenance.