Yesterday morning, Sal filed the daily ops report.
Five leads tracked across active campaigns. Three markets summarized. CPL calculated by account. Full report formatted and posted to our ops channel.
Before I opened my laptop.
That's not a future state. That's in production right now.
Six Months Ago I Was the Babysitter
Every morning, I'd open three dashboards, scroll through campaign data, cross-reference the books, and figure out what had changed since yesterday.
Miss a morning? The team didn't have the data. The ops waited on me.
I was the coordination layer. Every output sat in a queue until I showed up.
I call this The Babysitter Model. You use AI as a tool. You pick it up. You run the prompt. You check the output. You move it somewhere useful. You put it down.
It works. But there's a cost nobody talks about: you ARE the system.
What Sal Does Now
One of the AI agents I've built, Sal, runs a daily reporting cycle every morning.
He pulls campaign performance data, cross-references it with our books, calculates CPL across every active account, formats a structured summary, and posts it directly to our ops channel.
Every day.
No prompt from me. No “hey Sal, check the campaigns.”
He just runs.
That's not how The Babysitter Model works. The Babysitter Model waits for you.
The Build: One Wire at a Time
Getting here took six months. Not six months of smooth progress. Six months of building things that broke constantly.
Month one: one agent that could pull a single campaign report.
Month three: payments and campaign data cross-referenced automatically.
Month five: multiple agents running in parallel across accounts.
Month six: I woke up and the work was already done.
That's how you build an ops machine. One wire at a time. Test it. Break it. Fix it. Wire the next thing. Most of what I built broke before it worked. Then one morning it stopped breaking.
Stage 1 vs. Stage 2
Most operators I talk to are in Stage 1.
ChatGPT for emails. One-off prompts. Manually reviewing outputs.
That's The Babysitter Model. It works. But every output waits for your presence. Miss a morning and the machine stops.
Stage 1 — The Babysitter Model:
You run the prompt. You check the output. You coordinate the work. You are the system. Miss a morning, everything pauses.
Stage 2 — The Teammate Model:
The ops run before you arrive. The summary is waiting. The leads are in the system. The report is already posted.
One is a capability. The other is an operating system.
The ceiling breaks when the AI stops waiting for you to show up.
What This Looks Like Inside the Mentor Lab
Inside The Mentor Lab, I'm pulling back the curtain on exactly how this ops machine is wired.
Which agents do what. How the daily reporting cycle runs in production. How to wire your first automated run so it works whether you're at your desk or not.
The ops machine took six months to build. Inside the Lab, I'm compressing that timeline.
If you want to see how it's structured, the link is lab.socialadsmentor.com.
Drop your name and email. I'll send you the full breakdown of the stack.
Sam Bell III is a social ads operator and founder of Social Ads Mentor. He runs a multi-agent AI fleet in production for client campaigns and builds in public inside The Mentor Lab.
