How I Built a Ghostbusters Team of AI Agents

I expected 300 lines of JSON. I got 180,000.

Me: “AI, what just happened?!” AI: “I’m not sure, but this unrelated thing could turn into a possible bug in the future.”

I just about flipped the metaphorical table. Maybe this whole AI development workflow wasn’t going to work after all.

But I’d made real progress the previous week by switching to a spec doc + review process to guide the agents. Still, the same problems kept showing up — lack of focus, lost context, and a constant need for manual glue work on my part.

That’s when I took a hard look at my own behavior. I was still doing too much manually. I wasn’t leaning on the tools available to me.

Two Core Problems

1. Tooling context was missing. Things like package versions, app architecture, authentication strategies — I kept having to retype and re-explain them.

2. Role-based prompting was effective — but exhausting. I had to remember what to say, when to say it, and to whom. It didn’t scale, even with good intentions.

So I fixed it.

I created a set of Cursor rules and updated our AGENTS.md file for OpenAI Codex. Each agent now has a persona — and a defined role in the workflow.

Meet the Ghostbusters (Agent Edition)

Here’s how it works, step by step:

1. @Peter

Writes the initial spec. For example: @Peter write a spec on standardizing on undici usage and removing native fetch. He follows a template and ruleset to ensure clarity.

2. @Egon

Reviews the spec with rigor: @Egon review @undici-standardization.md I assess Egon’s comments, incorporate the best suggestions, and backlog the rest.

3. @Winston / Codex

Implements the changes, guided by path-based rules that handle both code standards and app-specific logic. Each PR is reviewed and submitted sequentially, which lets me manage the workflow from my phone — even while away from my desk.

4. @Ray

Gives final implementation feedback. I direct any final polish or fixes based on Ray’s review.

The Results?

Fewer bugs. Cleaner PRs. Much better alignment on architecture and intent.

It’s working.

What’s Still Hard?

• Knowing when and where to load data in client apps
• Avoiding excessive API calls
• Building consistently with a design system

And the biggest gap?

UI impact. Agents can write Tailwind all day — but they don’t run the code. They can’t feel the experience.

That’s where I still lead — UX, product decisions, and visual storytelling.

Coming Soon…

I’m prototyping two new personas:

• @Janine: Reviews specs for UX edge cases and challenges assumptions
• @Zuul: Checks layout consistency, visual polish, and Tailwind usage

We’ll see how they do.

This agent workflow is evolving into something real. Ghosts beware.