Agentic Coding vs Vibe Coding

Vibe coding and agentic coding are not the same practice. One suits a throwaway prototype; the other is what production code actually requires.

Two distinct workflows have emerged for building software with AI assistants, and they produce very different outcomes. One treats whatever the model produces as a rough draft you barely look at before running it. The other treats it as a draft that has to survive a written spec, a test suite, and an independent reviewer before anyone ships it. Both workflows use the same underlying models and the same chat-driven interface, which is exactly why they get confused for each other. Confusing the two is how a weekend prototype ends up in production, and how a production incident later gets traced back to a change nobody actually read.

This post draws a clear line between vibe coding and agentic coding: what each one is good for, where the risk actually lives, and how to tell which mode a given task calls for. Neither approach is wrong on its own; both are appropriate responses to different constraints. Using the wrong one for the job, on the other hand, is a mistake that tends to surface at the worst possible time.

What vibe coding actually means

Vibe coding is the practice of describing what you want in natural language, accepting whatever code the assistant produces, and iterating by feel rather than by reading the implementation closely. You run it, see if it works, and if it does not, you describe the problem again and let the model try once more. The term was coined by AI researcher Andrej Karpathy in a February 2025 post and quickly became shorthand for a specific style of working: you stay in the loop of intent and outcome, not in the loop of implementation detail. In practice that means skimming the diff at most, trusting the model when it says a fix worked, and treating the terminal output rather than the source file as the source of truth. Used well, it is not careless by definition. It is a deliberate trade of code-level scrutiny for speed, made consciously rather than by accident.

Where vibe coding earns its keep

Vibe coding is the right tool whenever the cost of being wrong is low and the cost of moving slowly is not. That describes a fair amount of real engineering work, especially the exploratory kind that never touches a user. The point of this code is to answer a question quickly, not to be maintained, so paying for a spec and a review pass would slow down the very thing you are optimizing for.

• Throwaway prototypes built to test an idea, not to ship.
• Personal scripts and one-off tooling that only you will ever run.
• Technical spikes where the goal is learning what is possible, not shipping it.
• Hackathon and demo code with a lifespan measured in hours.
• Early UI exploration where the visual result matters more than the implementation underneath it.

What agentic coding adds that vibe coding skips

Agentic coding can look similar on the surface: an AI assistant planning and editing code across files with real autonomy. What differs is the process wrapped around it. A disciplined agentic workflow starts with a written spec so ambiguity gets resolved before code exists, not discovered after. It builds the change through mechanical, test-driven steps rather than one large unverified leap, so the agent has a tight feedback loop of tests and type checks it can use to catch its own mistakes. And, most importantly, it routes the resulting diff through review that the author did not write and cannot wave through on its own. That structure, a spec audit before any code, independent multi-agent review of both the plan and the output, and a shipping gate that blocks the merge until critical issues hit zero, is the process TLM Forge is built around; you can see how the pieces fit together on the how it works page.

Agentic workflows also tend to span more than one sitting, which surfaces a problem vibe coding rarely runs into: the assistant forgets your architecture decisions, your conventions, and your past debugging sessions the moment the context window resets. A private, persistent memory layer like MemX keeps that project context available across sessions, so the agent, and the reviewer checking its work, start from what your codebase already established instead of re-deriving it or quietly contradicting it.

That gate only does its job if it is allowed to say no. In agentic coding done properly, the review pass is adversarial by design: a reviewer, or a small fleet of them covering correctness, architecture, and security, looks for reasons to reject the change rather than reasons to approve it. Critical findings block the merge outright, and nothing ships while any remain open. That is a materially different bar than "it compiled" or "it looked right in the demo", and it is the bar vibe coding was never built to clear, because nothing in that workflow is positioned to say no in the first place.

Vibe coding vs agentic coding, side by side

The trap: vibe coding your way into production

The risk is not that vibe coding produces bad code. Plenty of vibe-coded prototypes do their actual job fine. The risk is scope creep: a prototype that worked in the demo gets a few more features bolted onto it, someone points a real user at it, and the throwaway code quietly becomes the production system, still without the scrutiny that would have caught the missing input validation, the unhandled edge case, or the authorization check that never got written. Nobody decided to skip review; the code simply never stopped being "the prototype" even after it stopped being disposable, and by the time anyone notices, the change has already shipped to real accounts. This is the pattern behind a lot of the incidents traced to AI-generated code: not that the model wrote something malicious, but that nothing independent ever looked at what it wrote before it reached users.

For more on how these gaps show up in practice, see common mistakes with AI coding tools. If the task is already heading toward production, the workflow in shipping production-ready AI code is a far better starting point than iterating by feel.

Choosing the right mode for the task

• Will this code run once, or will it run for months? Once favors vibe coding; months favors agentic coding.
• Does anyone besides you depend on it working correctly? If yes, add a spec and a reviewer.
• Can a bug in it touch user data, money, or authentication? Any yes moves it out of vibe coding immediately.
• Are you exploring what is possible, or building what ships? Exploration tolerates loose verification; shipping does not.
• Will another engineer have to read or extend this code later? If so, someone other than the author needs to have looked at it first.