Smarter agent orchestration cuts AI coding costs by splitting planning and execution

A workflow shared on r/codex proposes reducing token costs in AI-assisted coding by dividing responsibilities across agents rather than relying on a single expensive model for every step. A high-capability model handles planning and review, while a cheaper agent executes the bulk of the implementation, limiting costly context to where it actually affects outcomes. The approach emphasizes compressing conversation history between stages, retaining only decisions, constraints, and relevant files rather than full intermediate reasoning. A critical review step checks whether the implementation followed the plan, tests cover real failure cases, and the solution is not unnecessarily complex. The article cautions that delegation only saves money when the executor receives a clear plan — poor planning leads to rework that erases any cost advantage.