There’s a big difference between building one AI agent in a lab and putting an entire team of them to work inside a real company.
Most of what you read about AI online ignores that difference. I can’t afford to ignore it.
My two pizzerias are paying the bill.
Dáme Pizza and Lov Pizza aren’t hypotheticals. There are suppliers to pay, teams to manage, menus to review, ads to run, customers to answer. All of it, every day, without overheating.
Over the last few months I’ve started replacing parts of my brain with agents: finance, marketing, operations, customer service, data. The result is strange to explain to anyone who hasn’t seen it working.
The first one in production is an agent that watches my WhatsApp, wired to the Digital Cortex that serves as shared memory for everything else.
Why public, not private
I could write this just for myself, in an Obsidian notebook. It would make more sense in some ways: less pressure to polish, less risk of a competitor copying, less exposure.
But three things change when you document in public:
- Writing quality forces thinking quality. Every time I try to explain a decision to a reader who isn’t inside my head, I find holes in the reasoning. I’ve thrown out entire agents just by trying to write about them.
- Each post becomes a case study. I don’t need to convince anyone it works. The whole page here is the proof. Whoever shows up already shows up convinced.
- The audience becomes a waitlist, not a sales funnel. People who want to deploy agents in their own company find me with a documented track record, instead of me having to prospect.
What you’ll find here
Real architecture decisions, context formats that worked (and the ones that didn’t), metrics from both pizzerias, token costs, failures in production, and the why behind every choice.
No get-rich-quick promises, no theory from someone who’s never run anything.
If you’re a small or mid-size business owner, you’ll leave here with a map to do this in your own business, even if you decide not to hire me.
For those building something similar in another context: let’s talk.
Next post opens the architecture: which agents exist today, what each one does, and why the orchestrator was the piece that took the longest to get right.