Agentic coding vs vibe coding: the difference is where the review happens
What handing real work to AI coding agents looks like, and where the human review moves
tldr: Vibe coding is prompting your way to software you never read. Agentic coding is handing a defined task to an agent that plans, edits, runs your checks and hands back finished work for review. When agents do the work, the human effort moves to two places: writing the task and reviewing the pull request. The tasks that need a person afterwards are usually the ones nobody finished specifying.
This post first described FactoryKit. FactoryKit is now Buildful, and this version is updated for what it does today (October 2026).
Agentic coding is handing a defined task to an AI agent that plans, edits files, runs your tests and returns finished work for a person to review. Vibe coding is prompting your way to software you never really read. Both start with a prompt. Only one ends with somebody reading the diff.
I run coding agents against Hashnode, Bug0 and Buildful, which I'm one of the creators of. So discount the product mentions. The experience here comes from one place: the FactoryKit repository in July 2026, where the agents were Claude Code, Codex and Grok Build. Buildful now defaults to open-weight models it picks for each task, so read this as a lesson about the workflow more than about any model.
Vibe coding vs agentic coding: what's actually different
Andrej Karpathy named vibe coding in February 2025, and he didn't mean it as a criticism. He described giving in to the vibes and forgetting that the code even exists. He was building throwaway weekend projects, and for that it works.
I want to defend vibe coding for a moment, because the term has turned into an insult. It's the fastest way to find out whether an idea is worth building. I've used it to answer questions like "would this API shape feel right" in twenty minutes instead of an afternoon. The output is a prototype, and the right next step is to throw it away.
Agentic coding starts from the opposite end. You write a task the way you'd write a ticket for a colleague. The agent works somewhere else, runs your checks and comes back with a pull request, and you read that pull request. The review still happens. It just happens at the end.
| Vibe coding | Agentic coding | |
|---|---|---|
| Unit of work | A prompt in a chat | A task with a definition of done |
| Where code is written | In your editor, as you watch | In an isolated environment, without you |
| Who reads the code | Often nobody | A person, at the pull request |
| When review happens | Never, or after an incident | Before the merge |
| Failure mode | Code nobody understands | A vague task produces a vague pull request |
| Where it belongs | Prototypes, spikes, learning | Code you're going to keep |
What unread code costs you later
Vibe coding rarely fails on the first change. It fails later, when someone has to modify code no person has ever read.
Unreviewed code compounds. Every later change is made against assumptions nobody checked, by a model with no memory of why the first version looked the way it did. You don't notice the debt while the prototype works. You notice it the first time you need to change something under pressure and find that nobody knows which walls hold the house up.
Our closest brush with this shows what review is for. In July an agent built image upload for task attachments in FactoryKit. The feature worked, and it had left the uploads readable by anyone with the URL. Because every change landed as a pull request that a person read and used, we caught it the same day, and the fix merged as its own reviewed pull request before the main feature did.
Run that same afternoon as vibe coding and there's no reviewer and no follow-up task. The bucket stays public until an incident finds it. The model made the same mistake in both cases. What differs is whether anything stands between the mistake and your users.
The handoff line
The tasks that came back needing a person were usually underspecified, rarely hard.
The pull requests worth studying are the ones where I had to step in, so I went back through them.
A few were bookkeeping, like resolving a merge conflict. Some ran into sandbox plumbing we were still building that month, like signing in for the browser test or getting environment variables into the sandbox, and my fixes there were to the platform as much as to the change. The ones I learn from were tasks where I hadn't written down what finished looked like. One begins "Here is what I want to do:" and then asks for several things at once.
Compare a task made of a pasted error message. That was close to the whole specification, and the agent traced the error to its cause and came back with a small fix that merged untouched. A task like that carries its own definition of done: the error either still happens or it doesn't. A task that asks the agent to make something feel better has no such test, because the definition of done never left my head.
That's the handoff line: the boundary between work you can specify and work you still have to do yourself. Everything above it is a task. Everything below it is a conversation you haven't finished having.
The uncomfortable part is that the corrections measure me as much as the agent. When I write a sharp task, I get a pull request I can merge. When I write a lazy one, I get a plausible diff that solves the wrong problem, and I usually deserve it.
The messy runs teach something too. The image upload feature from earlier turned into a chain: while its pull request was open, follow-up tasks fixed the public uploads, adjusted the storage bucket and disabled the start button mid-upload, each as its own reviewed pull request. My only change in the whole chain was a small configuration fix. In that chain, correction looked like a conversation held in follow-up tasks, with no wall of review comments.
A few tasks I abandoned altogether. All of this comes from one repository, worked on by the people who wrote the tool, so treat it as one builder's experience.
What agentic coding needs to work at all
The tasks above the handoff line share one property: the agent can tell whether it succeeded. That depends on your setup more than on the model.
The loop isn't complicated. A task arrives. An isolated environment is created and your repositories are cloned into it. The agent makes the change, runs your checks and gets a fixed number of attempts to fix what it broke, 3 in our case. Then the change is verified, and a pull request opens for a person to review.
Three things have to be true before any of that produces work you'd merge.
You need an isolated environment, because an agent that can reach your laptop or your production credentials is a security question before it's a productivity one. You need checks you actually trust, because the agent's sense of "done" is only as good as the signal your repository gives it. And you need a review surface that carries evidence, because a diff tells you what changed and not whether it works. For anything with a UI, the evidence I trust most is a recording of the feature being used in a real browser.
Plenty of tools now share this shape. Devin from Cognition, Google's Jules, the GitHub Copilot coding agent, OpenAI's Codex and Cursor's cloud agents all take a task and return a pull request. They differ in what surrounds that loop: what checks run, what evidence comes back, and what it costs.
What changed in my week
By July I'd stopped opening a local development environment. I wrote tasks, queued them and reviewed pull requests as they arrived. Several ran at once, which changed my throughput more than any single agent capability.
What got harder is the part nobody warns you about. Writing a good task is a real skill, and it's the skill the handoff line measures. I spend more time on the first two sentences of a task than I used to spend on a small commit, and it pays for itself every time.
Where Buildful fits
Vibe coding gets you something that runs. Agentic coding with a review step gets you code you can ship.
The handoff line isn't a skill only senior engineers have. Describing what finished looks like is something a student, a freelancer or a first-time builder can do, often better than an engineer who's used to filling the gaps in their head. What most of them lack is the setup around the agent: the sandbox, the checks, the browser test and a pull request to review.
That's what we built Buildful to give people. You describe a change in plain words, the way you would when vibe coding. You get back a pull request on your GitHub repo that has run your checks, and if the change touches the UI, it's been tested in a real browser and recorded, so you can watch it work before you merge. Buildful picks the right open-weight model for each task, and you can choose a frontier model yourself whenever you want. 1,000 tasks cost $10 a month. If you're coming from a vibe coding tool, here's how Buildful compares with Lovable.
FAQs
What is agentic coding?
Agentic coding is handing a defined task to an AI agent that plans the work, edits files, runs your tests and returns finished code for a person to review. The agent works on its own instead of suggesting lines while you type, and the review happens at the pull request.
What is the difference between vibe coding and agentic coding?
Vibe coding skips the review: you prompt your way to working software and never read the code. Agentic coding moves the review to the end: an agent completes a defined task and a person reviews the result before it merges. What separates them is whether anyone reads the diff.
Is vibe coding bad?
Only when it's used for the wrong job. Vibe coding is a fast way to build prototypes, spikes and throwaway tools, and it's useful for deciding whether an idea is worth pursuing. It becomes a problem when the prototype quietly becomes code people depend on and nobody has read.
Is agentic coding safe for production code?
It's as safe as its isolation and its checks. Run every task in an isolated environment, keep production credentials out of it, keep a person approving at the pull request, and test user-facing changes before merging. Without those, you're vibe coding with extra steps.
How is agentic coding different from GitHub Copilot or Cursor in the editor?
In the editor, Copilot and Cursor help while you work, so you supervise in real time. An agentic coding tool, sometimes called a background coding agent, takes a task away and returns a pull request. You review its output instead of watching it type. Both Copilot and Cursor now also offer background agents of this kind.
What kinds of tasks can I hand to an AI coding agent?
In our runs, the work that came back ready to merge had a checkable definition of done: bug fixes with a reproduction, changes covered by tests and well-specified additions. The work that needed me was product decisions where the definition of done existed only in my head, and changes that ran into infrastructure we were still building.
Do I still need to review code an agent wrote?
Yes. Agentic coding concentrates review at the pull request, and that only works if the pull request carries evidence that the change behaves correctly: passing checks, and for UI changes, a recording of it working.
