Running Your Agency On Projects Will Put You Out Of Business

Running client accounts inside of GPT/Claude projects is a massive mistake.

You'll sacrifice quality and cause errors at scale.

A perfect example happened yesterday.

A client of mine runs a content marketing agency. They uploaded a big pile of training documents into a Claude project to "train it." The plan was simple: copywriters use that project to write hooks for social.

The COO reviewed one of the hooks recently, it sucked, told the copywriter it wasn't good and to run it through the project again.

Next version: still crap.

He knew something was broken. He just didn't know what, or how to fix it.

Here's what he didn't understand.

Context window

This is the first thing you need to understand to understand why projects dont work.

A context window is the AI's short-term memory. It's how much it can hold in its head at one time.

Picture a monitor on a desk.

Everything the AI is working with sits on that monitor — your instructions, your last message, the files it's looking at. A bigger model has a bigger monitor, but it's still a monitor. Pile too much on it and things begin to fall off.

The AI can only work with what's on the monitor right now. Anything not on the screen doesn't exist to it.

When the monitor fills up, the old stuff falls off to make room. What people call "memory" isn't really memory. It's just whatever happens to be on the monitor at that moment.

If you’re running client accounts or your operations inside of Claude or GPT projects you’re set up for failure.

Book a call here and we'll map what you need to build a scalable operation to double profit.

So what is a Claude or GPT project?

A project is the desk the monitor sits on.

You drag your files into it — brand guides, past hooks, training docs. It feels like you're teaching the AI your business.

You're not.

The AI never reads everything on that desk. It can't. The desk is bigger than the monitor.

When you ask a question, it reaches into a drawer, grabs a few pages that look related, sets them on the desk, and works off those. It never sees the rest.

It often doesn't grab the right pages. It grabs the ones that look similar. Pattern-match, not judgment. It's guessing which scraps belong, then working off the guess.

This is why uploading a stack of documents isn't training.

Training rewires the AI's brain. It changes the thing itself. You cannot do that by dragging files into a project. All you did was fill a drawer.

After you upload, the AI is exactly as smart as it was before. Same brain. You just gave it a bigger drawer to rummage through.

And here's the counterintuitive part: a bigger drawer makes it worse at any single job, not better. More paper to sort through means lower odds it grabs the right page.

The more you feed a project, the dumber it gets for the task in front of you.

So what did you actually build? A search folder. You ask for a hook, it searches the folder, grabs the closest-looking data points and blends them into an answer. Search, then blend. Every single time.

That's why running your agency's client processes inside a project falls apart.

It was never built to store your context and call on it in a way that lets your company follow a consistent procedure.

The Fix: Proper Storage

There are two steps to take to build a proper AI led operation that is not run on projects.

Step one: store information in labeled, separate files.

Client info, brand guidelines, voice of the customer — each gets its own folder.

This is your Client Bible.

We use GitHub for it right now, and there are new tools coming to market built specifically to be long-term memory for businesses like ours.

Company and client info need to be stored in a proper data warehouse that is built for AI B2B operations.

Step two: build skills.

A skill is a standard operating procedure.

A pile of old hooks only shows the AI what a hook looked like.

A skill tells it how to build one.

Take hook writing as an example.

To build the proper process for writing hooks, an agency would need to build skills for each type of hook: bold claim, curiosity, contrarian, story, authority. Each one is a clean SOP the AI runs.

Then you combine the client data with the marketing skill.

Example: “Look at the call transcript in the transcript folder from 7/1. Pull the HVAC voice file for HVAC client #1 and come up with 3 hooks using the the story skill based on the ideas shared in that transcript .”

The prompt specifically builds the context window.

The AI pulls in only the data it needs that is appropriately built.

Context managed to fit the monitor.

Then the part that compounds your result: loops

Proper infrastructure means your operation gets better over time. It learns.

A project can't do that. It has no memory of what worked.

Your skills library does — if you put a human feedback loop around it.

Someone does QA and grades the output. Good hook goes in the winners file.

Bad hook gets edited, and the feedback gets logged and folded back into the skill.

The work teaches the machine. The machine gets better. And it compounds.

Build the dream, not a prison.

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