The tech industry has spent the last two years discovering, at enormous expense and conference length, a set of principles that every restaurant manager already knows.
They call it “agent design.” They write papers about it. They’ve coined a vocabulary (orchestration, delegation, human in the loop, evaluation, guardrails) and they present it as a new discipline, because for them it is. They’ve never had to run a Saturday service with a brand new server, a hungover line cook, and a sommelier who’s brilliant with Burgundy and a hazard with bills.
I run AI agents at night and a restaurant floor at night too, technically. And I’m here to report that they are the same job. If you have ever managed staff in a high pressure service environment, you already hold an intuition for working with AI that most engineers are still acquiring the hard way. Here’s the translation table.
1. The briefing is the product
A new server doesn’t fail because they’re incapable. They fail because the briefing was bad. “Take care of table twelve” produces chaos; “table twelve is a regular, anniversary, he’s allergic to shellfish, she chooses the wine, don’t rush the dessert” produces magic. Same human, different context.
Working with AI is briefing, full stop. The people getting mediocre results from these tools are writing “take care of table twelve” prompts and concluding the staff is bad. The people getting astonishing results are writing the second kind: role, context, constraints, examples of done right, what to do when uncertain. In the industry we’d call that a pre shift meeting and an SOP. The tech industry calls it prompt engineering and pays handsomely for it. Operators have been doing it twice a day, unpaid, forever.
2. Delegate the task, never the judgment
Every manager learns this through scar tissue: you can hand off the doing but not the deciding. Not at first. The new hire runs food before they take orders; takes orders before they handle complaints; handles complaints for months before they’re allowed near a comp. Trust is granted in layers, each layer earned by verified performance at the last one.
This is exactly, exactly, the correct posture with AI agents, and it’s where smart people fail in both directions. The doomers won’t let the new hire carry a plate (”it might drop it!”) and get no leverage. The reckless promote it to manager on day one, let it email clients unsupervised, and learn in public. The operator’s instinct is the right one: tight scope, verify the output, expand scope on evidence.
I learned this the hard way with the first real tool I built: a pipeline that reads photos of the bottles I’m bringing in and drafts the tasting captions for each one, in English and Mandarin, at volume. The first batch came back like a new server reciting a script he doesn’t believe. Fast, fluent, generic. Correct notes with no soul: red fruit, a touch of spice, a smooth finish. Copy that could describe four hundred wines and sell none of them.
The fix wasn’t a smarter model. It was a better pre shift. I narrowed the brief: lead with the grower and the place, name the family that farmed it, one true sensory detail, no marketing words, no em dashes. I cast the tool to the one thing it was good at and kept the rest for myself.
It got me to maybe seventy percent. Useful, not finished. I still read every caption, and every line of Mandarin gets checked by a native speaker before a single one goes out. The machine drafts the room; it does not open the doors. It earned a lane, not a promotion. That’s the whole posture: my agents earn their autonomy the way my staff do, gradually, with me checking the work until the error rate tells me I can stop.
3. Inspect what you expect
No chef trusts a station they haven’t tasted from. The plate gets looked at before it leaves the pass, every plate, every night, no matter how senior the cook. Not because the cook is suspect, but because the standard is the standard and entropy never sleeps.
AI work needs a pass. The single biggest production mistake people make with these tools is shipping unread output. It’s the equivalent of food going to the table straight off the stove with nobody’s eyes on it. My rule at the terminal is my rule at the kitchen window: everything gets tasted. The agent drafts; I plate. The moment you stop checking is the moment something goes out cold, and with AI as with food, the guest remembers the one bad plate, not the hundred good ones.
4. Staff have shapes, work with the grain
A great manager doesn’t fight a server’s nature; they cast them. The charming one gets the celebrating tables, the precise one gets the eight top with the complicated bill, the wine mad one gets the collectors. Skill isn’t generic. Deployment is half the craft.
Models have shapes too. Some are meticulous and slow; some fast and a little sloppy; some brilliant at structure and stiff at warmth. The operator’s move (cast by strengths, cover the weaknesses with process) translates directly. I don’t ask my fastest tool for nuance or my most careful one for speed, the same way I don’t put my most poetic somm on the table that just wants the check.
5. Escalation is a designed path, not a panic
The best service systems make one thing crystal clear to every staff member: when you’re out of your depth, here is exactly who you bring it to, and you will never be punished for bringing it. Ambiguity about escalation is how small problems become Yelp reviews.
Agents need the same explicit lane: here’s what you decide, here’s what you flag, here’s the line you never cross alone. The tech world calls this human in the loop and acts like it’s novel. It’s the oldest rule on the floor: comp the dessert yourself, but a furious guest gets the manager, every time, no heroics.
6. The manager’s hours change shape, not size
Here’s the part nobody on either side tells you. Hiring great staff doesn’t reduce a GM’s work; it transforms it, from doing the tasks to designing the system: hiring, briefing, checking, coaching, improving the SOP. The leverage is real and so is the new job.
AI is identical. The fantasy is “the agent does my work while I sleep.” The reality, the productive reality, is that your work moves up a level: from producing to specifying, from doing to reviewing, from task to system. You become the GM of a staff that costs pennies, never tires, and is only ever as good as your brief. Which means the limiting factor in the AI age isn’t the model.
It’s whether you ever learned to manage.
The punchline
And there it is. The inversion I keep circling in these essays: the floor and the terminal trading places when no one was watching.
For decades, the deal was clear: tech skills were the leverage, and hospitality was the fallback, the thing you did while you figured out your real career. Now look at the actual skill stack the AI age rewards: briefing under ambiguity, layered trust, relentless quality inspection, casting by strength, designed escalation, systems management under pressure. That’s not a computer science curriculum.
That’s a GM’s Tuesday.
The operators don’t need to learn how to think about AI. They need to notice they already know, and then walk over to the terminal and start hiring.
Woody
If you run a floor, a kitchen, a hotel, a crew, or a company: what management rule have you tried on a machine? Reply and tell me. The best answers become a follow up post, with your permission and credit. I read everything.