I built a pipeline orchestrator to improve code quality beyond single-shot prompts

Despite improvements in coding agents, single-pass prompting rarely produces production-quality results. There’s still a lot of manual steering involved. My workflow lately has looked like this: Ask Claude to plan first (no code) Ask it to implement with constraints Ask it to review its own work Repeat reviews until no new issues are found Do a final human review before committing The problem Getting strong results from coding agents is already a multi-step process: Planning Implementation Iterative review But this workflow is not enforced — it lives in: Ad-hoc prompts Manual iteration As a result, it becomes: Inconsistent Hard to reproduce Time-consuming to babysit The solution I turned this into an automated pipeline. Instead of one agent doing everything, a manager coordinates multiple agents across stages: Plan → Implement → Verify → Review → Fix → Repeat Each step gets targeted prompts, not a generic instruction blob. For example: Planning → focuses on root-cause solutions, not wo