How I Stopped AI Workflows From Sprawling

By mid-2024 I was juggling several AI tools at once. The work was useful, but the coordination started to eat the time I was trying to save. Every extra prompt, every follow-up, every half-finished idea added friction.

So I changed the shape of the work. Ideas became GitHub issues. Research happened first. Code changes went through review. Nothing moved forward without a human looking at it.

The workflow

Idea -> GitHub issue -> research -> code -> review -> deploy

The important part was not the diagram. It was the discipline around it.

Every item carried a priority and a budget.
The review step stayed human.
The system ran overnight so I could review the output in the morning instead of reacting in real time.

What it changed

The main win was not speed for its own sake. It was clarity. I spent less time babysitting the process and more time making decisions that mattered.

A rough summary of the difference:

| Metric | Before | After | |--------|--------|-------| | Code hours per week | 20h | 4h | | Monthly token spend | £120 | £12 | | Projects shipped per month | 1 | 4 | | GitHub commits per month | 45 | 200+ |

What made it work

Keep the routing local when you can.
Keep write access scoped.
Treat the repository as the source of truth.

That combination is what made the setup durable. Not the tools, really. The rules.

I still think that is the part people miss. AI does not remove the need for process. It makes process more obvious.