Conclusion

In this lesson, you took a written feature plan and turned it into something an AI agent could actually execute. You prompted the AI to work out the implementation sequence — the files involved, the phases or waves of work, and the dependencies between them — and used that sequence to decide what could run in parallel and what couldn’t. From there, you built a workflow of subagents that carried out the plan.

The key takeaway is that a plan only becomes useful once it’s been turned into a sequence AI can act on. The quality of the resulting implementation depends less on the plan’s wording and more on how clearly its steps, files, and dependencies have been laid out before execution starts.

The implementation you just produced is a first pass, not a finished feature. In the next lesson, you’ll use Conductor to run further review tasks against it in parallel, then use Conductor again to open, review, and merge the resulting pull request.