Vibe Coding Explained: What It Is, What It Is Not, and How to Work Agentically in Production (2026)
Vibe coding means describing a task in plain language and letting an AI model write the code. Andrej Karpathy coined the term on 2 February 2025, and Collins named \"vibe coding\" its Word of the Year 2025. It is a great way to build a prototype and a poor way to build a system that has to run in production a year later. We explain where a demo ends and agentic work begins, with boundaries, tests, review, and a trail, and when vibe coding turns dangerous.
Vibe coding means describing in plain language what you want built, while an AI model writes the code and the human does not read it line by line. Andrej Karpathy coined the term on 2 February 2025, and Collins Dictionary named "vibe coding" its Word of the Year 2025. It is a great way to build a weekend prototype and a poor way to build a system that has to run in production a year later. That difference is the whole point of this article.
Quick answer
"What is vibe coding" and "vibe coding explained" are one question: is this a new way of programming, or a buzzword. The short version:
- What it is: you write software by describing to the AI what you want, and you accept the result without reading the code. Karpathy called it "giving in to the vibes" and "forgetting that the code even exists."
- Who coined it and when: Andrej Karpathy, former director of AI at Tesla and a founding member of OpenAI, in a post on X on 2 February 2025.
- Recognition: Collins Dictionary named "vibe coding" its Word of the Year 2025 (announced 6 November 2025).
- When it works: prototypes, throwaway scripts, learning, quickly sketching an idea.
- When it is dangerous: code that reaches production, touches money or personal data, and that nobody has read.
- What sits on the other side: agentic work, meaning the same models driven within boundaries, with tests, review, and a trail. That is what the AI-Native course teaches.
The rest of this article unpacks those points and names the sources with dates so you can check them.
Where "vibe coding" came from and what it actually means
The term has one author and a specific date. Andrej Karpathy wrote on X on 2 February 2025: "There's a new kind of coding I call 'vibe coding', where you fully give in to the vibes, embrace exponentials, and forget that the code even exists." In the same post he described the practice without polish: "I 'Accept All' always, I don't read the diffs anymore," and he summed it up: "It's not too bad for throwaway weekend projects."
That last line is the key one, and it usually falls out of the quote. Karpathy did not announce a new engineering standard. He described a convenient mode for things you do not have to maintain.
Nine months later the term entered the language officially: Collins Dictionary named "vibe coding" its Word of the Year 2025, announced on 6 November 2025 and defined as "the use of artificial intelligence prompted by natural language to write computer code." Collins put it in one line: "programming by vibes, not variables."
Worth naming the brand, too, because it shows up in this context most often. Claude, and specifically Claude Code, is the tool most associated with agentic work on code. Not by accident: in Karpathy's original post, the example was "Cursor Composer w Sonnet," that is, Anthropic's Sonnet model, commonly called Claude. So the brand has been part of this story since the first sentence, but a tool is not a method, and that distinction decides everything.
Vibe coding versus agentic work: where the demo ends
A demo that runs is not a system you can maintain. That one sentence captures the whole difference between vibe coding and agentic work.
Vibe coding aims for speed and accepts that the code grows beyond its author's comprehension. That is a sensible trade as long as the code is throwaway. The trouble starts the moment "almost works" reaches production and someone has to read it, fix it, and answer for it later, often at two in the morning.
Agentic work uses the same models but flips the defaults. Instead of "Accept All and forget the code," it puts four things in place that vibe coding, by definition, does not have:
- boundaries the agent may act within, instead of improvising across the whole repo,
- tests that check the product's behavior before the result reaches review,
- review of the code that scales faster than the number of diffs,
- a trail you can use to reconstruct what changed and why.
This is not an argument against AI. It is the same speed, with brakes and mirrors. Models write code faster than a team can trust it, and those four mechanisms are exactly what turns speed into trust. We unpack them further in the guide on what an AI agent is.
Vibe coding solo versus agentic work in a team: the table
These are not two levels of the same thing. They are two different things for two different goals. The first column matters most: it sets everything else.
| Dimension | Vibe coding solo (prototype) | Agentic work in a team (production) |
|---|---|---|
| Goal | See the idea working as fast as possible | A system you can trust over time |
| Who writes | The model; the human accepts without reading | The model within boundaries; the human drives and answers for it |
| Tests and review | Usually none; "it ran" is enough | Acceptance tests and review before the result lands |
| Maintenance | Code grows beyond comprehension; hard to return to | Conventions and a trail you can return to in six months |
| Risk | Hidden flaws and "almost works" surface later | Named boundaries, exceptions escalated, decisions logged |
Read this table as a warning against one mistake: using the left-column mode for a right-column goal. A prototype built with vibe coding is fine, as long as everyone knows it is a prototype.
When vibe coding is enough, and when it is dangerous
Honestly: vibe coding can be the best possible choice and the worst, depending on exactly what you are building.
It is enough, even optimal, when:
- you are building a prototype to see the idea, and you will throw it away after the decision,
- you are writing something one-off: a script that crunches data once and disappears,
- you are learning a new tool or language and want to reach a result fast,
- you are working alone, on code no one else will maintain, and the cost of a mistake is low.
In those cases, reading every diff would be a waste of time. Karpathy was right: for weekend projects, it is a great tool.
It is dangerous when the code is meant for production. Here the 2025 numbers are concrete. In Veracode's GenAI Code Security Report from July 2025, based on 80 tasks across more than 100 models, AI-generated code introduced an OWASP Top 10 vulnerability in 45% of cases; only 55% were secure. In the Stack Overflow 2025 Developer Survey (July 2025, more than 49,000 developers), the biggest frustration was "AI solutions that are almost right, but not quite," cited by 66% of respondents, and 45% admitted that debugging AI-generated code takes more time than they expected. In the same survey, about 72% of developers said vibe coding is not part of their professional work.
This is not an argument against using AI for code. It is an argument against confusing the mode. The biggest risk comes from one move: when code written "on vibes," which nobody has read, lands where it handles money, personal data, or customers. "Almost works" is the most expensive kind of bug there, because it surfaces late and does not show up in the demo.
So before you call a prototype a finished product, calculate its real cost. This is a sum you fill with your own numbers, not our promise:
Annual cost of "almost works" =
hours per week spent debugging code nobody read
x hourly rate of the people doing it
x 52If that cost grows faster than the team, the bottleneck is not writing speed. It is the absence of boundaries, tests, review, and a trail, which is exactly what vibe coding, by definition, does not have.
How to move from vibe coding to agentic work
The good news is that you do not have to choose between AI's speed and trust in the code. You only add method to a tool that already works. In practice, that is three steps:
- Leave vibe coding where it is good. Keep building prototypes, sketches, and one-off things "on vibes." Not every piece of code deserves review.
- For code that stays, add the four mechanisms. Boundaries the agent works within; acceptance tests it must pass; review that scales with the number of diffs; a trail that shows what changed and why.
- Teach it to the team on your own repo, not on slides. These are engineering habits, not general knowledge, and you practice them on real code. That is exactly what the AI-Native course is: agentic work run on your own repository, not another course about prompts.
If you build software and want the team to drive agents fast and sensibly, the right address is Claude Code training for dev teams. If you are still working out which course fits your situation, start with the overview in AI agent courses and training in Poland. And if your team does not write code day to day and the point is using AI knowingly, AI training for teams, aimed at AI literacy, fits better.
If you would rather we build and maintain the system than have your team build it, start with a free process scan: 30 minutes with an engineer and a written takeaway in two business days. We will say plainly whether it is worth it at all, before you spend anything.
Vibe coding showed how fast an idea can turn into working code today. Agentic work is the next step: the same speed, but with boundaries, tests, review, and a trail, so the demo can be maintained in production. If you have a repo and a goal, let's talk about the course.