Is Vibe Coding Enough to Build and Maintain Software?

A few months ago I built a throwaway script by just chatting with an AI until it worked. I had no plan, no tests, and only a loose idea of what the code was doing. It was fun. It shipped in an afternoon. That is the magic of vibe coding, and it is genuinely useful.

But then I wanted to change one small thing, and the whole thing broke. I had no idea why. The AI had written code I did not fully understand, and now I was stuck maintaining a black box I never designed. That experience is what this post is about. Vibe coding can absolutely get you started, but keeping software alive is a different job, and the rules change once the fun part is over.

What vibe coding is good at

Vibe coding is the practice of describing what you want in plain language and letting an AI produce the code. It is wonderful for two things.

First, hobby projects. When you are just tinkering, the goal is to see something work, not to build a cathedral. Speed and delight matter more than structure. AI is a great sparring partner for that.

Second, prototyping. If you need to test whether an idea is even worth building, a fast AI-generated sketch can save you weeks. You can validate the question before you commit to the answer.

The trap is thinking those strengths carry over to production. They do not. A prototype that works once on your machine is not the same as software that has to keep working for years while requirements shift and dependencies age.

Always use a language you understand

This is the rule I broke first, and the one I now treat as non negotiable. If you let an AI write in a language or framework you do not understand, you have handed over the wheel and strapped yourself into the passenger seat with no brakes.

The reason is simple. AI will make mistakes, and you are the only safety check. If you cannot read the code well enough to spot a bad pattern, a leaking resource, or a silent failure, then you are not reviewing the work, you are praying about it. Pick a stack you could maintain by hand even if the AI disappeared tomorrow. The AI should extend your skill, not replace it.

Software engineering matters more, not less

There is a strange myth going around that AI makes engineering discipline obsolete. The opposite is true. When a human writes every line, they carry context in their head. When an AI writes the lines, that context lives nowhere except in a chat transcript you will never reread. The code is the only durable record, so the code has to be clean, modular, and honest about its limits.

Rigor is what keeps an AI-assisted project from turning into soup. That means thinking step by step instead of asking for a whole app at once. It means understanding each piece before moving to the next. And it means testing.

Test driven development keeps you honest

I used to think TDD was a ceremony for large teams. With AI in the loop, it became essential. The reason is that tests give you a ground truth the AI cannot argue with. You write the test for what should happen, the AI writes the code, and the test decides who is right.

TDD also forces small, well defined steps. Instead of a vague request for a feature, you write a failing test that pins down exactly the behavior you want. That precision is exactly what AI needs to be useful. Vague prompts produce vague code. Concrete expectations produce working software.

If you are not writing tests, you are not building software. You are collecting outputs and hoping they line up.

Git is your memory

Version control is not optional here. With an AI producing lots of code quickly, you need a history you can trust. Git gives you three things that matter enormously when working this way.

First, it lets you revert. When a change makes everything worse, and it will, you want a one line escape back to the last known good state. Second, it makes changes reviewable. A small commit that does one thing is something you can actually read and understand. A giant commit that rewrote the world is not. Third, it gives you a story. Six months later, the commit log is the only honest account of why the software is the way it is.

Commit often, in small chunks, with messages that explain intent. Treat each commit like a checkpoint you would be comfortable explaining to a teammate, because future you is that teammate.

Review every step and make the AI explain itself

A habit that changed everything for me is pausing after each change and asking the AI to summarize what it just did. Not in vague praise, but in plain terms: what functions were added, what behavior changed, what risks it sees.

This does two things. It catches the moments where the AI quietly did something you did not ask for, which happens more than people admit. And it forces the work into your head. If you cannot follow the summary, that is a signal you are moving too fast or the code is too tangled. Slow down until you understand. The summary is also a great seed for your commit message and your own notes.

Why multi agent setups can help

One subtle problem with a single long AI session is context pollution. The model starts bending toward whatever assumptions it already made earlier, even the wrong ones. It gets attached to a path because that path is already in the window.

A multi agent approach helps here because a fresh agent has fresh eyes. It has not been dragged through the earlier wrong turns, so it is less likely to defend them. You can hand it a clean brief and ask for an unbiased second opinion on a design or a bug. The lack of shared history is the feature, not the bug. It is the equivalent of asking a colleague who was not in the meeting to take a look.

Give the AI good ground to stand on

The best results come when you do some of the engineering upfront and let the AI work within those bounds. In this very repo, for example, there is an AGENTS.md file that tells any coding agent the stack, the commands, the content model, and the conventions. It is the difference between dropping someone into a dark room and handing them a map.

Write down your architecture, your build commands, your testing approach, and your style rules. Keep them where an agent will read them. The better the ground you give the AI, the less it has to guess, and the fewer surprises you will inherit later. Good documentation is not just for humans anymore. It is part of your tool chain.

The honest summary

Vibe coding is enough to start. It is not enough to sustain. The moment a project matters, the old disciplines come back, and they come back louder: understand your tools, test relentlessly, commit carefully, review constantly, and document your ground.

Used this way, AI is not a replacement for engineering. It is a force multiplier for it. The people who do well with these tools are not the ones who type the vaguest prompt. They are the ones who bring the most engineering rigor to the table and let the machine handle the parts that rigor makes routine.