How to Work With AI Coding Agents: A Day Inside (Simply Explained)

Everybody says they "use AI" now. It's the new "we're data-driven." The phrase tells you nothing.

Here's the problem if you're trying to hire help or judge a vendor. You can't tell skill from chaos just by listening. The person who actually knows what they're doing and the person guessing both sound the same. Both can give you an impressive ten-minute demo.

The difference shows up later. One delivers systems that hold up when real customers use them. The other delivers a mess that breaks the moment someone touches it.

I want to show you how I actually work with AI. Not theory. The real daily rhythm I use across my own DTC fashion brand, my internal tools, and the systems I build for clients.

There are four rules I follow. They're simple. But they're the difference between AI work that ships and AI work that quietly dies in a drawer nobody talks about at the next board meeting.

Rule One: Know What Can Run at Once and What Can't

Think of AI helpers like a team of employees. Some work can run at the same time. Some absolutely cannot. Mixing those up is where people waste entire afternoons.

Research is the kind of work that runs side by side. Say I need to look into three different payment systems and review a settings file. None of those tasks touch each other. So I put three AI assistants on it at once. Twenty minutes later, I have three clean reports and I make the call.

Writing code is different. The moment two assistants change the same files, you get chaos. One assumes the kitchen is set up one way. The other assumes it's set up another way. Now you've got two versions that each work alone and break the second you put them together.

So when the work is connected, I keep it on one track. One set of assumptions. One source of truth. No exceptions.

The mistake I see constantly is people running everything in parallel because it feels productive. More assistants, more speed, right? Wrong. You get a tangled mess that takes longer to fix than if you'd just done it in order.

When you respect this rhythm, it scales. I shipped 808 code updates in 30 days running exactly this discipline. That isn't recklessness. It's knowing what to split up and what to keep in one hand.

Rule Two: Get Clear on the Idea Before Anyone Starts

A vague instruction produces vague work. Every single time. If you tell an AI assistant "add a returns flow," you'll get something that technically exists and solves none of your real problem.

So before any code gets written, I turn the one-line idea into real requirements.

"Add a returns flow" isn't a plan. It's a wish. So I sit with it and answer three questions. What does finished actually look like? What weird situations could come up? And what should this specifically NOT do?

Now the assistant has a real target. The work it produces is something I can actually use instead of something I have to throw away.

This is the most important step, and it's the one people skip when they're in a hurry. The AI does the typing. I do the thinking. That's the whole game. AI took over the grunt work. It did not take over the judgment about what to build and why. When I skip this step, I pay for it within the hour.

Rule Three: Put a Stop Sign on Anything You Can't Undo

Speed without a kill switch is a liability. This is the discipline that earns trust, especially from someone who's watched a vendor move fast and break something that mattered.

I draw a hard line around three things. Anything that deletes data. Anything that touches the live system real customers use. And anything that changes a bunch of connected systems at once.

You can't quietly undo those. A mistake there isn't a small bug. It's an incident.

So for anything in that category, the AI proposes and I approve. Nothing happens automatically. Ever.

Here's a real example. Say a change needs to update five connected projects at once. A fully automatic system would happily run all five and report success. Until the third one fails halfway through, and now everything is out of sync.

So I don't do that. I review the plan. I run it on one project first. I check the result with my own eyes. Then I give the green light for the rest. That's where I can pull the plug before the damage spreads.

The systems that brag about running completely on their own are the ones I trust least. Real control means knowing exactly where to keep your hand on the wheel.

Rule Four: Only Split Up Truly Separate Work

Extra AI assistants are not magic. They're a tool for one specific job: work that's genuinely separate.

The test is simple. If two assistants need to know what the other one did, they shouldn't be separate. They're connected. Forcing them apart just means you stitch two mismatched halves back together later.

Here's where splitting work pays off. Say I need to review four unrelated parts of a system. None of them depend on the others. So I put four assistants on it at once, each in its own corner, and pull their findings into one decision I make.

That's the right use. Fast, cheap, and the structure matches the work.

But the second those assistants start changing connected code, you've created expensive chaos. So the real skill isn't running more assistants. It's knowing when not to. One experienced person on a single track beats ten assistants on connected work every time.

When someone brags about how many AI assistants they run at once, I don't hear sophistication. I hear someone who hasn't been burned yet.

Why the Method Matters More Than the Tools

The tools change every few months. Today's best AI is next quarter's baseline. Anyone can buy access to the exact tools I use. The subscription is the easy part.

What you can't buy off the shelf is the rhythm. Split the research, keep connected work in one hand, put a stop sign on anything you can't undo, and get clear on the plan before anyone starts. That discipline worked on last year's tools and it'll work on next year's, because it's about judgment, not gadgets.

And here's the honest part. When the requirements are genuinely fuzzy, or a change is so tangled nothing can be split up, the whole thing slows to a crawl. The speed advantage mostly disappears. Anyone telling you that never happens is selling you something.

This is why most AI projects fail. The failure rate sits around 88 percent, and it's not because the tools are bad. It's because nobody applied a method. They threw instructions at the AI and hoped, then wondered why it broke.

I don't advise on this from a slide deck. I build it, every day, on real systems. My own brand and client work both.

If you've got a project stuck in that 88 percent, or you're trying to figure out who actually knows what they're doing, that's the conversation worth having.

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