A vibe coding agency is a development shop that uses AI coding agents — Claude Code, Cursor, Codex — as the primary authoring layer, with engineers reviewing, architecting, and hardening rather than typing every line. The good ones ship 2–4x faster than a traditional agency at similar quality. The bad ones ship a demo that looks finished and collapses the first time real users touch it. The difference is almost never the model they use. It's whether they have an engineering process wrapped around the AI, or whether the AI is the process.
We build this way ourselves, and we also get hired to clean up after teams that did it badly — enough that we run a dedicated vibe code audit for founders who inherited a codebase they can't ship from. This post is the checklist we'd hand a non-technical founder or a VP of Product who has three proposals on the desk and no way to tell them apart.
What is a vibe coding agency, actually?
The term started as a joke about "coding by vibes" — describing what you want in natural language and accepting whatever the model produces. As a solo prototyping technique it's genuinely great. As a way to build software other people depend on, unsupervised, it's how you end up with 14,000 lines of code, no tests, three competing state management patterns, and secrets in the repo.
A serious AI-assisted agency looks different from the inside. The AI writes most of the code. A human owns the architecture, the data model, the security boundary, and the definition of done. Code review is stricter than at a traditional shop, not looser, because the volume of generated code is higher and the failure modes are subtler — plausible code that compiles, runs, and is quietly wrong.
Is AI-assisted development actually faster?
Yes, on a specific slice of work. Greenfield CRUD, API integrations, admin panels, form-heavy flows, test scaffolding, and migrations get dramatically faster — we see 3–5x on that class of work. Novel algorithms, performance tuning, gnarly concurrency, and anything requiring deep domain judgement get slower if you let the model lead, because you spend the saved time reviewing subtly wrong output.
The honest number for a full product build, blended across both kinds of work, is closer to 2–2.5x. Any agency quoting you 10x is selling the demo, not the product.
The 9 checks to run before you sign
1. Ask to see the test suite from a real client project
This is the single highest-signal question, and most vibe coding shops fail it. AI writes tests happily when asked, so an absent test suite means nobody asked. Look for tests that assert behaviour, not tests that assert the implementation exists. If they can't show you a CI run with a green suite on a project they shipped six months ago, walk.
2. Ask who reviews the AI's output, and what they reject
You want a specific name and a specific answer. "Our senior engineer reviews every PR" is a non-answer. A real one sounds like: "We reject anything that adds a new dependency without justification, anything that touches auth without a test, and anything over 400 lines in a single PR." Process specificity is the tell.
3. Ask how they handle secrets, auth, and PII
Generated code leaks credentials into repos more often than hand-written code, because the model happily inlines whatever it sees in an example. Ask what secret-scanning runs in their CI. Ask whether they've ever had to rotate a key after a commit. The right answer to the second question is often "yes, once, and here's the pre-commit hook we added after."
4. Ask for a codebase walkthrough, live
Not a demo of the running app — a screenshare of the repo. Have them open a file you pick and explain why it's structured that way. Teams that genuinely own their architecture answer instantly. Teams that shipped whatever the model produced will scroll, squint, and narrate what the code does rather than why it exists.
5. Check whether the estimate has a hardening phase
A build plan that goes scope → build → launch is a plan written by someone who has never operated the thing they shipped. Real AI-assisted builds need an explicit hardening block — error handling, rate limits, observability, load testing, access control review — and it's usually 20–25% of the total timeline. If it's missing from the proposal, it will be missing from the project, and you'll pay for it later at emergency rates.
6. Ask what happens when the model is wrong in production
You're looking for evidence they've operated software. Do they have structured logging? Error tracking? Can they roll back in one command? For AI features specifically: do they log the prompt and the response so a bad output can be reproduced? Teams that have run AI products in production have opinions here. Teams that have only built them don't.
7. Ask about handover before you talk about price
Who owns the repo? Is the CI/CD in your cloud account or theirs? Is there a runbook? Can another team pick this up in a week? An agency that has a crisp answer has done handover before. An agency that gets vague is planning on you being unable to leave.
8. Look for one non-trivial thing they said no to
Every good engagement has a moment where the agency pushed back on a feature, a deadline, or an architecture the client wanted. Ask for one. A partner who has never disagreed with a client is a vendor who bills hours, not a team that owns outcomes.
9. Reference-check on the second project, not the first
First projects go well — everyone is on best behaviour and the codebase is clean. Ask for a reference from a client they've worked with for more than a year. That's where you find out whether the code they wrote in month two was still workable in month fourteen.
What should a vibe coding engagement cost?
Rough US/AU market rates as of mid-2026, for a competent AI-assisted team with real engineers on it:
| Scope | Timeline | Typical range | | --- | --- | --- | | Clickable prototype / design partner demo | 2–3 weeks | $15k–$35k | | Production MVP, single platform, real auth + payments | 6–10 weeks | $60k–$150k | | MVP + AI feature (RAG, agent, or copilot) | 8–12 weeks | $90k–$220k | | Dedicated squad, ongoing | monthly | $25k–$60k / month | | Vibe code audit + remediation plan | 1–2 weeks | $6k–$15k |
Two things to notice. First, the AI discount is real but it's roughly 40–50%, not 90% — you're paying for engineering judgement, and that hasn't gotten cheaper. Second, anything under about $40k for a production MVP with authentication and payments is a proposal that has quietly descoped something you'll discover in week nine. Our full AI development cost breakdown goes component by component if you want to sanity-check a quote you've already received.
Fixed-price or time-and-materials?
For a scoped MVP with a clear definition of done, take fixed-price or milestone-based — it forces the agency to do real scoping work up front, which is where most projects are actually won or lost. For open-ended platform work or anything where the requirements will genuinely move, a dedicated squad on monthly retainer is more honest and usually cheaper than endless change orders.
Be suspicious of a fixed price quoted without a paid discovery phase. Nobody can price a product they haven't scoped; if they do it anyway, they've either padded 40% or they're planning to argue with you about scope in month two.
What are the biggest failure modes in AI-assisted builds?
Plausible-but-wrong code. The model produces something that reads correctly, passes a happy-path test, and fails on an edge case nobody enumerated. This is why review discipline matters more here, not less.
Architectural drift. Each feature gets built in whatever pattern the model reached for that day. Six weeks in, the codebase has three ways of fetching data and two auth patterns. Prevented by a human owning the architecture and writing it down before the build starts.
Dependency bloat. Models reach for a library where ten lines would do. We've audited MVPs with 90+ direct dependencies where 25 would have covered it — every one of those is a supply-chain surface and an upgrade tax.
Missing negative paths. Generated code is optimistic. It handles the success case beautifully and the timeout, the 429, the partial write, and the concurrent edit not at all.
No observability. The app works until it doesn't, and then nobody can tell you why. Structured logs, error tracking, and a trace on every AI call are non-negotiable for anything a customer touches.
How we'd approach it at CodeLamda
We run AI-assisted builds with a fixed shape: a scoping week that produces a written architecture and a data model, build sprints where the AI writes and a named engineer owns review, an explicit hardening block before launch, and a handover package with a runbook. It's the same methodology behind our 8-week MVP process, just with the authoring layer swapped out.
If you've already got a codebase and you're not sure what you're sitting on, the vibe code audit gives you a written assessment — security, architecture, test coverage, and a remediation plan you can hand to any team, including one that isn't us.
If you're still choosing a partner, run the nine checks above on every proposal on your desk. And if you'd like a second opinion on the shortlist, book a 30-minute call — we'll tell you honestly if the answer is someone else.