Throw Your IDE Away? Why CLI Coding Agents Are Changing Everything

The title sounds dramatic, I know. Throw your IDE away? Really?

To be clear: I’m not literally telling you to uninstall VS Code and go live in a cave with Vim and a solar-powered ThinkPad. I still open my IDE sometimes. But I’ve also now shipped multiple real projects — full web apps, production code — without ever opening it at all.

Something fundamental has changed in how software gets built. And, whether you’ve noticed it yet or not, your job as a developer has already changed because of it.

This isn’t a crisis. It’s actually kind of exciting, but only if you learn how to adapt.

So, Is AI Going to Replace Your Job?

Short answer: Yes.

But also: You will still have a job.

Your job used to be writing code. Today, your job is designing and guiding systems. The AI writes the code, but you are the one who shapes intent, correctness, architecture, priorities, tradeoffs, and strategy.

If that sounds unsettling, just remember this: the thing that made you good at programming was never typing. It was thinking, experience, intuition, and adaptability. Those qualities are more valuable than ever — you are more valuable than ever.

The First Domino: GitHub Copilot

Back in 2022, when Copilot first landed, a bunch of us crowded around a monitor at work and watched it autocomplete real code like it understood what we meant. I remember saying:

“Oh. This is the beginning of the end of how we all write software.”

At the time, the future of coding was unclear. Copilot was mostly one-line autocompletion. Helpful — but not transformative yet.

Then Cursor came along and changed everything. Cursor didn’t just assist the line you were typing. It started understanding the entire file. It saw where you were going and hopped ahead. It felt like someone was coding with you.

Most recently, of course, are agents — multi-file, multi-step reasoning systems that you can give goals to. Now you can just say:

“Add user login to my app.”

And it would go off and do it — not perfectly — but unsettlingly well.

The center of gravity of coding has shifted from writing code to describing behavior.

Vibe Coding Is Just Coding

Earlier this year, Andrej Karpathy described vibe coding — the experience of building software by:

Describing what you want
Letting the model generate code
Testing by feel
Fixing things by conversation
Skimming, not analyzing

The idea spread instantly because it wasn’t just theory — it captured what people were already doing. The code is no longer the main object. The workflow is, and if that’s true, then something else follows:

Your tools no longer need to revolve around the IDE.

The CLI Agent Revolution

This part still surprises people. All of the coding agents released this year have come as flavorful command-line tools:

One after another — every major lab made the same move. Why? Because the terminal is:

IDE-agnostic
Fast to iterate
Familiar to developers
Easy to run remotely or in CI
Easy to script and automate

But there’s also a more radical reason — one that took a while to accept:

What if we just don’t need IDEs for most development anymore?

The agent is the environment. It’s the interface between you and the code.

So How Does This Work Without an IDE?

Let’s walk through the actual workflow — not as bullet points, but as something you can feel yourself doing.

AI agent workflow diagram: Goals > Architecture > Feature > Manually Test > Codify with Tests > Update Rules > Deploy

1. Start by Describing the Project

Before any code exists, you tell the agent what you’re building. Not just stack choices — the actual intent of the product. Why it exists. Who uses it. What the experience should feel like.

The agent generates the initial repository structure:

Framework
Directory layout
Config files
Environment setup
All the boring stuff

It handles the scaffolding so you can skip to actual work.

2. Establish the Architecture

Now you discuss the shape of the system:

What database to use
How to deploy
How authentication works
UI vs server rendering
Any integrations you’ll need

You talk through this with the agent — and it wires things together accordingly.

Then you tell it to update the rule file with a description of the system and how it works. This is the agent’s memory — the shared understanding of how your project works.

3. The Core Loop: Feature → Test → Teach → Commit

Now the development rhythm begins, and it looks like this:

You choose one meaningful feature — not a tiny task, but something that moves the product forward. You describe it clearly and the agent writes the code.

When it says it’s done, you test by using it — not by reading code. If the feature behaves correctly, you ask the agent to write end-to-end tests to codify the behavior. Then you have it update the rule file again so it remembers how this feature fits into the system.

Finally, you commit, and repeat.

Why This Works

This workflow ensures that the agent is always responsible for the changes it makes. Each step forward is backed by tests, and a terse synopsis of the feature in the rule file ensure’s future agent instances are aware of what the intention was.

So, no, you aren’t writing the code, but your role is just as important. You’re defining the intent, guiding the system, ensuring correctness, and deciding direction.

You are designing software — no longer whittling it out of nothing line by line.

The Real Skill to Learn Now

The thing that matters most is not knowing the right model or the right tool.

It’s model intuition — the ability to:

Explain what you want clearly
Notice where the model misunderstood
Guide it back on course
Teach it, progressively, how your system works

This is the new literacy.

The sooner you start building this intuition, the easier the future feels.

Where This Goes Next

The next big shift is parallel agents. With shared memory and coordinated roles, building large systems the way teams of people do today.

This is coming faster than most realize. So now is the moment to get good at guiding one agent — because soon you’ll be orchestrating many agents at a time. You’re being promoted.

If You Want to Go Deeper

I’m teaching a course on exactly this:

Parallel agent workflows
Practical coding with CLI agents
Rule-based agent memory systems
How to ship real production software with this approach

You can get early access here: https://aiddcourse.com

And you can follow me on Twitter — @jpschroeder — for ongoing updates.