The harness is all you need (mostly) - The GitHub Blog

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Burke Holland·@burkeholland

July 27, 2026

Updated July 28, 2026

13 minutes

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If you’re feeling overwhelmed by AI right now, you’re not alone.

Every day it seems there is a new tool, new MCP, new model, new skill, new workflow, new feature, new social post that is some form of “Hey look! I have completely figured out AI with this one weird prompt.”

I…don’t believe you.

I work with AI every single day, and what I’m finding is that less is way more. It’s not about what I install or configure or trick the agent into doing that makes any real difference. That stuff is interesting, but at the end of the day it feels like gimmicks.

I see the biggest gains in my productivity from how I use the harness and how well I understand it.

So in this post, I’m sharing you a simple workflow that you can use to drastically improve your effectiveness with AI just by using existing features of GitHub Copilot. No weird prompts. No skill everyone else seems to know about. Just the harness. The harness is all you need—mostly.

Disclaimers

I’m using the term “harness” interchangeably with “GitHub Copilot.” The point of this post is to keep things simple, so just know that GitHub Copilot is an agent harness.

I don’t mean to insinuate that you won’t ever need any skills or MCPs or instructions or custom agents, etc. In fact, those things will become quite important as you progress and need to define complex workflows and automate things for your teams. In fact, I use a few throughout this blog post!

What I am pointing out here is that you do not need any of those things to be highly successful with AI.

Also, there is a lot of slop out there. If you don’t believe that, ask the agent to create a skill to do anything at all. It will happily oblige. Whether or not that generated skill actually works, it can be easily published to any number of skill or MCP registries.

1. Pick a tool, any tool

This is an obvious one, right? Pick a tool! It’s so easy!

But even within the GitHub Copilot family, there are a lot of options. These include the CLI, the new GitHub Copilot app, VS Code, Visual Studio, and JetBrains, just to name a few.

The good news is that these experiences are increasingly being centralized on the same harness. The details can differ by tool, but the core workflow is consistent. Learn the harness once, use it everywhere.

That said, I do believe that learning the harness is key, and the best way to learn it is to be as close to it as possible. So if you are just starting out, I’d recommend beginning with the GitHub Copilot CLI. It’s a terminal interface, which means it’s just text. There isn’t much UI to learn. You enter a prompt. The agent does things. But the interaction is more direct, immediate, and, frankly, very satisfying.

For this demonstration, I’ll be using the new GitHub Copilot app. But the harness that app uses is the exact same thing you’ll be using if you are using the GitHub Copilot CLI, Visual Studio Code and many other places you can find GitHub Copilot.

2. Turn on YOLO mode

YOLO mode is also known as “Allow All.” This lets the agent execute any command without asking permission. This can vary depending on the tool you are using, but for most it is simply an /allow-all command in the chat. Otherwise, the agent is going to stop and wait for your approval every single time it needs to do some work.

Agents need autonomy for you to see an increase in productivity. If you have to approve everything the agent does, you might as well just do it yourself. Besides, that’s a miserable user experience. Nobody wants to be relegated to sitting at a desk pressing the “Approve” button all day. And pressing “Approve” over and over just trains you not to read what you are being asked to approve, which defeats the purpose.

You want to be safe with agents, though. Bad things happen to good people. When using YOLO mode, you don’t want to run the agent on your local machine. This is especially true when you are using them at work—data is private on your organization’s systems, and mistakes can be costly.

Fortunately there are a bunch of options for running agents in sandboxes. An easy one to get started with is GitHub Codespaces or development containers.

3. Start with a prototype

One of the most magical things about AI is that you can easily prototype anything and everything up front. Historically, this was not the case. Prototyping was a full phase of a project, and were often a luxury. Now, you can make one with a prompt.

Let’s look at a few examples.

Let’s say we want to build a date picker web component. That seems straightforward, but it’s actually quite complex. Think of...