I try to manage AI the way I'd manage a small development team.
Hey everyone, fellow AI devs.
I've been transitioning into an AI-powered development workflow over the past few months, and I've noticed a problem —
When AI writes bad code, most of the time it's not the model being incapable. It's unclear requirements, lost context, the AI guessing on its own. This looks almost identical to what happens when a human team operates without proper management.
So I tried applying some management principles to AI development workflows and built a templated Agent architecture.
The idea is straightforward: don't treat AI as a single tool — treat it like a small outsourced team. Each stage has dedicated SubAgents handling different responsibilities, with phase checkpoints enforcing constraints along the way. Context flows between agents as highly compressed YAML, and key data persists across sessions.
In practice, the output has been fairly consistent, the whole pipeline runs automatically, and it rarely needs mid-stream intervention.
Caveats: This is based on a handful of my own projects — hasn't been tested in a real production environment. The workflow itself is on the heavy side, so it's probably not ideal for rapid iteration scenarios.
Project is open source: github.com/Saikel-Orado-Liu/Governance-Engineering
It includes five sample projects across different tech stacks and full architecture documentation. Give it a try if you're interested, and feedback is always welcome.