the trapVibe coding gets you a prototype. It does not get you to production.
You described what you wanted, an AI wrote it, and it works on your screen. That feeling — steering software by vibe instead of syntax — is real and it's fast. The problem is what's underneath. Independent audits of AI-built apps keep finding the same things: hard-coded credentials, missing CSRF protection, server-side request forgery, queries that fetch a thousand rows to show one. AI-assisted commits leak secrets at more than twice the rate of human ones. Around 45% of AI-generated code ships with a vulnerability. None of that shows up in the demo. All of it shows up the week you have real users. Most vibe-coded apps never cross that gap — they stall the moment 'works for me' has to become 'safe for everyone.'
- It runs in the demo — that tells you nothing about auth, inputs, or scale.
- The fast generation is paid back as a long tail of debt nobody documented.
- In a company it's worse: code no one relits, no owner, 'why is it red,' impossible to maintain.
the shiftDon't 'review the code.' Run it through a structure that verifies everything.
The instinct after generating an app is to read the diff yourself and hope you catch the problems. That doesn't scale and it isn't your job. The only thing that reliably ships AI code is the Digital Native Method: a Product Owner describes the intent on the live product, a Tech Lead encodes the rules once (architecture, conventions, security, your company's standards), and agents deliver inside a structure that checks every change before it reaches production. You stop being the safety net. A deterministic gate becomes the safety net — and it runs every single time, not just when you have the energy to review.
- Product Owner: describes the outcome on the real product, in plain language.
- Tech Lead: encodes the rules once so agents can't ship outside them.
- The structure: lint, types, tests, security run before anything lands.
step 1Annotate — describe the change on the live product, not in a ticket.
Open your product and point at what's wrong or missing: this button should do X, this flow is broken, add this screen, this feels slow. The annotation becomes a task — the intent captured exactly where it makes sense, against the real UI, with the context already attached. No spec document, no translating a feeling into Jira fields. This is where you spend your judgement: on whether the result is right, not on how it gets typed.
- Point at the live UI and say what you want in your own words.
- Each annotation becomes a tracked task with its context attached.
- Bug, feature, or design — the intent is captured where it actually lives.
step 2Dispatch — the Tech Lead hands the task to an agent, in isolation.
You don't open a terminal and run a model freehand. The task goes to a Tech Lead that knows your standards and dispatches a worker agent onto an isolated git worktree — its own branch, its own sandbox, no risk to your main line. The agent reads your conventions first, then implements. Several tasks can run in parallel without stepping on each other. This is the difference between 'prompt, paste, deploy' (how prototypes die) and a real pipeline: every change is born governed, on a branch, inside the rules.
- Agents boot inside your encoded conventions — they can't freelance.
- Each task gets an isolated worktree; main stays clean.
- Parallel work is safe because every agent is sandboxed.
step 3Gate — lint, types, tests, and security run before you ever see it.
This is the step that separates shipping from shipping a mess, and it's the one solo vibe coding skips. Before any change is allowed forward, deterministic gates run with zero AI judgement involved: linting, type-checking, your test suite, a secrets scan, a dependency/lock-file check, a security pass. Green, or it doesn't move. No model is asked to grade its own homework. The questions you'd otherwise ask by hand — 'is the login secure,' 'will this crash at 100 users,' 'did it hard-code a key' — are answered by checks that run identically every time. That's how 'I never read the code' stops meaning 'nobody did.'
- Lint + types: the change compiles and matches your conventions.
- Tests: your critical paths still pass before merge, not after an incident.
- Security + secrets scan: no hard-coded keys, no known-vulnerable deps.
step 4Merge — it ships through your own GitHub, as a reviewable PR.
Verified work doesn't get pasted into production. It lands as a pull request in your repository, with a conventional-commit history and a green check, ready to merge into your existing CI/CD. You own the code, the history, and the deploy. Nothing happens in a black box you can't audit. If you have engineers, they review a clean PR instead of untangling a vibe-coded blob; if you don't, the structure is your reviewer. Either way the result is the same: production code, in your repo, that you can maintain.
- Output is a clean PR on your GitHub — not a copy-paste into prod.
- Conventional commits and a passing gate make the history legible.
- It plugs into the CI/CD and deploy you already trust.
cocoricoBuilt in France — sovereign on the tooling, hosted in the EU.
Agentation is a French company, built by a French team. We can't make you sovereign over the models — Claude, GPT and the rest are American — but the model is only a fraction of the system. With a raw model alone you can't do much; the value is in the orchestration around it, and that's exactly the layer you can own in Europe. Agentation runs on EU infrastructure (Hetzner, Germany), keeps data in the EU (Supabase), works entirely through your own GitHub, and is built for GDPR. You bring your existing AI plan; we never see your code. Sovereign on the tool, even when the model isn't yours.
- Hosting in the EU (Hetzner, Germany), data in the EU (Supabase).
- Your code stays in your GitHub on your AI plan — we never see it.
- GDPR by design — the orchestration layer is the part you can actually own.
FAQHow do I ship an app with AI without shipping a security mess?
Don't rely on reading the diff yourself — that's where solo vibe coding fails. Put the work through a structure: a Tech Lead encodes your rules once, agents implement on isolated branches, and deterministic gates (lint, types, tests, secrets scan, security) run before anything merges. Green or it doesn't ship. That replaces 'I hope I caught the problem' with 'a check caught it, every time.'
Can AI actually produce production-ready code?
AI produces code that looks production-ready and often isn't — audits routinely find hard-coded secrets, missing CSRF protection, and unscalable queries that never surface in a demo. The fix isn't a smarter model, it's a structure around it: encoded conventions, isolation per task, and automatic gates before merge. With that in place, AI-written code reaches production reliably; without it, most builds stall after the prototype.
Do I need to be a developer to ship an app this way?
No. The whole point of the annotate-dispatch-gate-merge loop is that you work in outcome-space: you describe the result you want on the live product, and the structure handles correctness. Founders, PMs, designers and operators drive it. If you can tell when the product is good, you can ship it.
How is this different from just using Cursor, Lovable or Bolt?
Those tools generate code and hand it to you to review, fix and trust. You're still the bottleneck and the only safety net. Agentation puts a Tech Lead and automatic gates between you and the model, dispatches work onto isolated branches, and delivers a verified pull request to your own GitHub — so you receive checked results, not raw output you have to babysit.
Where does my code and data live?
Your code stays in your own GitHub, on your existing AI plan — Agentation never sees it. The orchestration runs on EU infrastructure (Hetzner in Germany) with data in the EU (Supabase), built for GDPR. You can't be sovereign over American models, but you can be sovereign over the tooling that orchestrates them — and that's the part that does the real work.