the real problem“A human reviews everything” is a promise nobody keeps.
Vibe coding — describing software to a model and shipping what it returns — is exploding, and in companies it quietly becomes a mess: code no one reread, growing debt, security holes, the dreaded "why is the pipeline red" with nobody who can answer. Studies keep landing in the same band — roughly 40–60% of AI-generated code carries a security flaw, and most of those slip through because human review didn't actually happen. It didn't happen because it can't: when an agent writes a feature in minutes, a person reading every line is a dam in front of a firehose. "Keep a human in the loop" turns into a checkbox the moment the volume goes up.
- Generation is now minutes; thorough human review is still hours.
- Input validation, over-permissive roles and hardcoded secrets are the flaws that recur — exactly what a tired reviewer skims past.
- A loop only the most senior person can run is a loop that gets skipped under deadline.
which human, which momentPut the human where judgement is irreversible — not on every line.
Human-in-the-loop theory is clear: insert review where the decision is high-stakes or irreversible, and let trusted, repetitive checks run automatically. AI coding has exactly two such moments, and neither of them is "read the 400-line diff." The first is intent: what should this product do, and is the result actually good? The second is the rules: what is this codebase allowed to look like — architecture, conventions, security posture, your company's constraints. Encode those two human judgements well and the thousand line-by-line decisions in between stop needing a person at all.
- Irreversible / high-stakes → keep a human (intent, and the rules).
- Repetitive / verifiable → automate (lint, types, tests, secrets, conventions).
- Reading raw output line by line is neither high-leverage nor reliable — it's where the loop breaks.
checkpoint oneThe Product Owner stays in the loop on intent.
The first human checkpoint is the person who owns the product. In Agentation they don't open a ticketing tool — they point at the live product and describe the result: this flow is broken, this should feel faster, add this. That's the loop they're in, before work starts and again when it comes back: is the outcome right? They judge it the way a user will, by using it — not by parsing how it was typed. No engineering background required, because intent isn't syntax. This is the human checkpoint that decides whether the thing is worth shipping at all.
- Describe the outcome on the live product, in plain language.
- Approve the result by using it, not by reading a branch.
- The judgement that doesn't scale by hiring — taste — is spent here, on the product.
checkpoint twoThe Tech Lead encodes the rules once, so they hold every time.
The second human checkpoint is the Tech Lead — but it's a one-time act of judgement, not a per-PR grind. They encode the standards once: architecture, conventions, security rules, the company's specific constraints. Every agent then boots inside those rules and cannot ship outside them. This is what makes "I never read the code" honest instead of reckless: a human did read it — once, as policy — and that policy now applies to thousands of changes deterministically, instead of a person applying it to some of them, sometimes, when there's time.
- Encode the rules once; every agent is born inside them.
- The human judgement scales to every change because it's structure, not a manual pass.
- When something genuinely needs a human call, it escalates to the Tech Lead — the loop stays human exactly where it must.
between the twoDeterministic gates do the reading no human can keep up with.
Between intent and the rules sits the work humans were failing at: line-by-line verification at machine volume. Agentation runs it as deterministic gates — lint, types, tests, security scan, secrets, lock-file drift — on every change, before anything reaches production. Zero tokens, zero fatigue, zero "looks fine, ship it." Green or it doesn't land. Everything flows through your own GitHub, on your existing AI plan, so the audit trail is yours and we never see your code. That's the difference between human-in-the-loop as a slogan and as a system that actually holds.
- Gates run on every change, not on the ones someone had time for.
- Lint, types, tests, security and secrets must be green before prod.
- Lands in your GitHub with a full audit trail — verifiable after the fact, not taken on faith.
cocoricoA French team — sovereign on the tooling, where it counts.
Agentation is built by a French team, and that shapes where the data lives. We won't pretend to be sovereign on the models — Claude, GPT and the rest are American — but with just a model you can't do much: the orchestration around it, the structure that turns raw output into governed software, is most of the value, and that we can own in Europe. Agentation runs EU-hosted (Hetzner, Germany), data on EU infrastructure (Supabase), your code in your own GitHub, GDPR by design. Human-in-the-loop, and Europe-in-the-loop too.
- Sovereign on the tooling that orchestrates the models — the part that's actually most of the work.
- EU hosting (Hetzner) and EU data (Supabase); code stays in your GitHub.
- GDPR-aligned by construction — an audit trail you can show a regulator.
FAQDoesn't human-in-the-loop mean a person reads every AI-generated change?
That's the version that fails. When an agent writes a feature in minutes, a human reading every diff is a bottleneck that gets skipped under deadline — which is how unreviewed AI code reaches production. The reliable version keeps humans at the two decisions that are irreversible (the intent and the rules) and automates the line-by-line verification with deterministic gates. A human stays in the loop where judgement matters; a structure handles where volume matters.
Where exactly are the human checkpoints in Agentation?
Two. The Product Owner is in the loop on intent — describing the result on the live product and approving the outcome by using it. The Tech Lead is in the loop on the rules — encoding architecture, conventions and security once, so every agent works inside them. Between those two, lint/type/test/security gates run automatically on every change before production.
If the Tech Lead only encodes rules once, who catches new edge cases?
The gates catch the verifiable ones — types, tests, security, secrets — on every change, forever. Genuinely novel judgement calls escalate back to the Tech Lead rather than slipping through silently. So the human is in the loop continuously on policy and on exceptions, without manually reviewing every routine change.
How is this safer than just prompting an AI and reviewing the output myself?
Reviewing it yourself makes you both the bottleneck and the single point of failure — and the research shows human review at volume is where AI code defects actually get through. Agentation puts encoded rules and deterministic gates between the model and production, so verification happens on every change instead of the ones you had time for, with a full audit trail in your own GitHub.
Do I need to be a developer to be the human in the loop?
Not for the intent checkpoint. The Product Owner role is about judging whether the result is right, in plain language, on the live product — no engineering background required. The Tech Lead checkpoint is the technical one, and it's a one-time encoding of the rules rather than ongoing code review.
Where is my code and data while this runs?
Your code stays in your own GitHub and runs on your existing AI plan — we never see it. Agentation's own infrastructure is EU-hosted (Hetzner in Germany) with data on EU infrastructure (Supabase), built GDPR-aligned by a French team. You keep a verifiable audit trail of every gated change.