What Is Vibe Coding and How Do You Do It Without Breaking Your Code?
Vibe coding means describing what you want in plain language and letting an AI coding agent write or edit the code, then steering it by reacting to what you see and what breaks. It works best for prototypes, scripts, UI tweaks, and throwaway tools where speed matters more than long-term structure. It becomes risky the moment you stop reading the code in a system that handles money, secrets, user data, or complex state. The difference between a productive vibe coding session and a broken codebase is almost never the model — it's the loop you run and the guardrails you keep in place.
The core loop: prompt, run, read, correct
Vibe coding is not "type once and ship." It's a tight feedback cycle:
- State intent. Describe the outcome, not the implementation: "add a search box that filters the table as I type," not "write a
filterTable()function." - Let the agent write or edit. The agent produces a diff — ideally small enough that you can read it in under a minute.
- Run it. Execute the code, open the page, or trigger the command.
- Read the failure. Paste the actual error or describe the wrong behavior. "The filter works but resets when I click a row" is far more useful than "it's broken."
- Correct and repeat. Each correction should narrow scope, not expand it.
The loop is the skill. People who struggle with vibe coding usually skip step 4 — they describe what they think is wrong instead of feeding back what actually happened.
When vibe coding works, and when it doesn't
| Situation | Vibe coding fit | Why |
|---|---|---|
| Prototypes and demos | Strong | Cheap to throw away, no downstream dependents |
| One-off scripts and data cleanup | Strong | You can verify the output directly |
| UI and styling tweaks | Strong | Visual feedback is immediate and unambiguous |
| Glue code between APIs | Moderate | Works until auth or rate limits get involved |
| Business logic with edge cases | Weak | The agent can't know your domain rules |
| Auth, payments, secrets handling | Weak | Mistakes are silent and expensive |
| Complex state management | Weak | Bugs surface far from where they're introduced |
| Production systems with real users | Weak without review | Every diff needs a human who understands it |
The pattern: vibe coding scales with how fast you can verify the result. If you can see it working in five seconds, iterate freely. If verification means "deploy and wait for a support ticket," slow down.
Guardrails that keep the code usable
These are the practices that separate "I shipped a tool this afternoon" from "I spent three days untangling AI-generated spaghetti."
- Version control from the first prompt. Commit before you start and after every working state. When the agent goes sideways, you reset instead of negotiating.
- Keep diffs small. One intent per prompt. "Add validation to the email field" beats "improve the form."
- Write tests for anything you can't eyeball. If the logic has branches, a test is cheaper than re-reading the diff five times.
- Read every line before it ships. Not to rewrite it — to confirm you could debug it at 2 a.m.
- Never let the agent touch secrets, credentials, or production config without you reading that specific change.
- Delete aggressively. Vibe-coded prototypes accumulate dead code fast. If a file isn't earning its place, remove it.
How agent tooling extends the loop
Basic vibe coding is copy-paste: the model writes code, you run it. Agent-based tools close the loop by letting the model act — running commands, reading files, inspecting errors, and editing in place. That's the difference between a suggestion engine and a collaborator.
Tool-connection standards like MCP (Model Context Protocol) matter here because they let an agent reach beyond the chat window: query a database, call an API, read a file tree. Multi-agent setups push further — one agent writes, another reviews or runs tests — which is useful for catching the class of mistake a single agent tends to repeat. The tradeoff is the same as always: more autonomy means more surface area you're not watching. Grant tool access narrowly, and keep the human review step even when the agent says it's done.
A realistic first session
Pick something you can verify in seconds — a script that renames files, a page that filters a list. Describe the outcome, let the agent write it, run it, and feed back the first thing that's wrong. Commit when it works. Then try the next small change. If you find yourself pasting the same error three times, stop prompting and read the code yourself — that's the signal the loop has stopped working and you need to understand the problem before you can describe it.