Vibe coding is a development style where you lean on high-level prompts and intuition instead of technical rigor — describing software to an AI rather than writing it by hand.
The "YOLO" approach promised to democratize software creation, and adoption climbed fast as people used Large Language Models (LLMs) to generate prototypes. But data from early 2025 shows the wave of uncurated, vibe-based code generation is receding in professional environments.
As a prototyping tool, vibe coding works. As a way to ship production systems, it does not. The early enthusiasm for building products without an engineering team is being replaced by disciplined frameworks. The shift is from "giving in to the vibes" to agentic engineering: AI handles implementation, human architects own quality and security.
What are the levels of vibe coding?
Vibe coding spans four tiers, ranked by how much control the user keeps over the code — from Type A (you still read and review every line) to Type C (a non-coder hands everything to the AI).
The term vibe coding was coined by Andrej Karpathy in early 2025 to describe a shift where developers "forget that the code even exists," leaning on the exponential capabilities of LLMs to bridge intent and execution. For a fuller primer, see the vibe coding explainer. These are the four tiers:
- Type A: Plain-English prompts, but you still read and review the generated code.
- Type B: Ignore the code entirely and rely on prompting alone to reach a result.
- Type C: Non-developers shipping production code via AI with no foundational knowledge.
- Type D: Standard autocomplete inside an IDE.
Vibe coding favors rapid iteration over authorship — a trial-and-error loop where you paste error messages back into the AI until the software appears to work. That serves familiar prototypes well, but it fails catastrophically in complex production environments where system interactions and architectural precision are non-negotiable.
Why did vibe coding fail to scale in production?
The failure at scale is rooted in the "productivity paradox." AI tools dramatically increase the volume of code generated, but volume does not equal system value. High-speed generation without architectural oversight produces "AI slop" — code that is either unhelpful or actively breaks existing systems. It is a fast path to losing money and trust, as teams spend more time debugging slop than building features.
Three failure modes killed the "vibe" in professional settings:
- Security holes at scale. Agents generate code with vulnerabilities. An agent that opens 1,000 pull requests (PRs) a week at a 1% error rate introduces 10 security holes weekly, and vibe coding offers no gate to catch them.
- Unmaintainable architecture. Skipping the design phase leaves no recorded reasoning, so the codebase becomes a black box that neither human nor AI can reliably refactor three months later.
- Context collapse. As sessions lengthen, agents lose track of earlier architectural decisions and produce code that contradicts itself — a failure routinely missed by anyone who "YOLOs" through the diffs.
Together these compound into "cognitive debt": the cumulative cost of managing unreliable agent behavior and lost context. Sentiment among investors and engineers has shifted from hype to skepticism toward AI-built apps that skip engineering discipline.
How does agentic engineering differ from vibe coding?
By early 2026, Karpathy and other industry leaders had pivoted to "agentic engineering," a strategy built on human-in-the-loop systems. The distinction is not the toolset — it is the discipline. AI is the implementation engine; the human stays the authoritative owner of the architecture.
| Feature | Vibe coding | Agentic engineering |
|---|---|---|
| Philosophy | YOLO (you only look once) | AI builds, human owns |
| Oversight | Little to no code review | Rigorous PR review |
| Testing | "Looks like it works" | Relentless, verified testing |
| Architecture | Organic and unplanned | Spec-driven and architected |
Production-grade agentic engineering follows a four-step workflow:
- Write the spec first. A design document defines the data model and edge cases before an agent touches a file.
- Break work into scoped modules. Large prompts give way to scoped, reviewable tasks (e.g. "implement password-reset tokens in Redis").
- Review every PR rigorously. A human reads and understands every line of AI-generated code.
- Test relentlessly. Software is "done" only once human-reviewed tests pass.
Real-world results at TELUS (500,000 hours saved), Zapier, and Stripe show AI scales safely only when governed by these structures. The developer becomes an "architect" who defines the what and the why, not a "typist" focused on syntax.
What is "Term Coding" and why does it matter?
"Term Coding" is the use of specialized technical vocabulary as a capability multiplier when working with generative models. It differs from prompt engineering: it applies domain expertise to constrain the model. Generative AI runs on "garbage in, garbage out" — the precision of the output is tied to the precision of the terminology you use.
Compare two people building an authentication system:
- Expert. Uses terms like Argon2id, CSRF, salted hashing, and OIDC. The AI returns a professional-grade, secure framework.
- Amateur. Asks for a system that "lets people sign in and works with any errors." The AI returns a generic, potentially insecure solution.
Apply Josh Kaufman's 20-hour rule — 20 hours to learn the 90% of terminology a task needs — and you can coach an AI "team" effectively. Without that vocabulary, a prompter cannot judge the result or specify a resilient solution, which leaves the system open to exploitation.
What are the security and architectural trade-offs?
The core risk of AI-assisted development is the model's difficulty generalizing outside its training distribution. AI handles well-known tasks like JSON validation efficiently, but it lacks the judgment to anticipate non-obvious failure cases in complex, multi-component systems.
For non-engineers, several critical security steps get overlooked:
- API authentication. Don't leave endpoints exposed as open ports.
- Password hashing. Never store plain text.
- SSL certificates. Secure data in transit.
- Infrastructure hardening. Lock down unused ports and limit file uploads to specific types.
AI can hand you a FIX execution-report endpoint for a forex trading platform, but it cannot architect the whole high-stakes system. Financial systems demand extensibility, performance trade-offs, and scalability that stay beyond vibe-based prompting. AI excels at syntax; human oversight builds the resilient, production-ready architecture that does not collapse under technical debt.
FAQ
What is the main difference between vibe coding and agentic engineering? Discipline and ownership. Vibe coding follows a "YOLO" approach, accepting AI output without thorough review. Agentic engineering uses AI for implementation but keeps a human in the loop for architecture, rigorous review, and relentless testing so the system stays secure and maintainable.
Can a non-coder build a secure app using only AI? For production-grade apps, it is unlikely. AI can build simple prototypes, but non-coders routinely miss protocols like password hashing, SSL, and upload limits. Without foundational knowledge you ship code you cannot explain, leaving vulnerabilities attackers exploit once the system is live.
Why are AI tools called "capability multipliers"? They magnify existing expertise. Skilled engineers use AI to automate grunt work — data translation, unit-test stubs — and spend their energy on high-level architecture. Less skilled users use it to mask knowledge gaps, generating technical debt and architectural problems faster than they can solve them.
What is the most important habit for a developer using AI agents? Rigorous oversight: write a detailed spec before prompting, read every line of a diff before merging, and ship nothing until human-reviewed tests pass. Act as an architect, not just a prompter.