the real riskVibe coding ships fast. In a company, it ships a mess.
Describing software to an AI and watching it appear is intoxicating — and it's now how a lot of code gets written. Inside a company that becomes a liability fast. Georgetown research found nearly half of AI-generated snippets contained security bugs; manual audits put vulnerable code as high as 68–73%. The output piles up faster than anyone can read it: near-duplicate functions, undocumented dependencies, abstractions nobody chose. Nobody fully understands what shipped, so 'why is the build red' becomes a daily standup item. The bottleneck moved from writing code to trusting it.
- AI-generated code is now linked to roughly one in five security breaches.
- Developers review instead of author — they never internalize the logic or the edge cases.
- What accumulates is comprehension debt: code that works until the day it doesn't, and nobody knows why.
what review tools actually doAugment Code Review is good — at the tool level.
Augment's reviewer is genuinely capable: a Context Engine spanning 400,000+ files, deep PR analysis, blast-radius and security flags, and accuracy that leads the public AI-review benchmark at around 65% precision. Use it and you'll catch real issues. But notice where it sits. It evaluates what was already generated and writes a comment about it. That's tool-level thinking — it assesses the output, it doesn't constrain it. A 65%-precise comment is still a comment: it can be dismissed, argued with, or quietly merged past. The diff is already written by the time the reviewer speaks.
- Comments are advisory — the human still decides whether to honor them.
- Two-thirds precision means a third of the noise is yours to triage.
- It reviews after the fact; it can't stop bad code from being produced in the first place.
the missing layerCommenting is not verifying. A gate is.
The gap is architectural, not cosmetic. A reviewer answers 'is there something wrong here?' A structure answers 'this cannot ship unless it's right.' Those are different machines. Agentation puts deterministic gates between every change and production — lint, types, tests, security, secrets scan, lock-file drift — and they don't comment, they block. Green or it doesn't land. Above them sits a Tech Lead: your architecture, conventions, and company rules encoded once, so every agent boots inside the constraints instead of being reminded of them after the fact. The result is governed code by construction, not a pile of suggestions you hope someone read.
- Gates run before prod and are deterministic — zero AI tokens, no opinion, no negotiation.
- The Tech Lead encodes the rules once; agents can't generate outside them.
- You constrain what can ship, instead of reviewing what already did.
the methodThe Digital Native Method: intent in, verified result out.
A tool doesn't fix this on its own — it needs a method. A Product Owner describes the intention directly on the live product: this flow is broken, make this feel faster, add that. A Tech Lead encodes the standards once. Agents implement inside a structure that verifies everything before it reaches production — through your own GitHub, on your existing AI plan. The Product Owner judges the outcome the way users will; the structure judges the code so no human has to babysit a 65%-precise comment thread. That's the difference between accelerating the mess and actually governing it.
- Describe outcomes on the real product, not tickets full of specs.
- Rules encoded once, applied to every agent, every change.
- Verified work lands in your GitHub — we never see your code.
cocoricoFrench software, EU sovereignty on the layer that matters.
Agentation is a French company, built by a French team. We're honest about sovereignty: nobody in Europe is sovereign over the frontier models yet — Claude and GPT are American. But with just a model you don't do much. The leverage is in the orchestration layer — the tool that turns a model into governed, verified, production software — and that layer can be European, and ours. Code stays in your GitHub, data lives in the EU (Supabase), hosting runs in the EU (Hetzner, Germany), and the whole thing is GDPR-aligned. You keep the productivity of frontier models without handing the keys of your delivery pipeline to a US platform.
- Sovereign where it's achievable: the orchestration tooling, not the model weights.
- EU hosting (Hetzner, Germany), EU data (Supabase), code in your own GitHub.
- GDPR-aligned by design — your code is never our asset.
FAQIs Agentation a replacement for Augment Code Review?
They solve different problems. Augment Code Review is an AI reviewer that comments on pull requests at high precision — useful if your team still writes and merges code by hand. Agentation is the structure that produces and verifies the code in the first place: agents implement inside encoded rules, and deterministic gates block anything that isn't green before it reaches production. One advises; the other guarantees. If your goal is fewer humans babysitting diffs, you want the gate, not just the comment.
Augment's reviewer is ~65% precise — why isn't that enough?
65% precision is strong for an AI reviewer, but precision describes a comment, not a guarantee. Two-thirds of comments being real still leaves a third as noise, and every comment is advisory — someone can merge past it. A deterministic gate has no precision number because it isn't guessing: lint, types, tests, and security checks either pass or they don't, and 'don't' means the change can't land. You want the model's judgment to assist and the structure's verdict to decide.
Doesn't an AI PR reviewer already protect us from vibe-coding risk?
It reduces it; it doesn't close it. A reviewer operates at the tool level — it assesses what was already generated and posts feedback after the fact. The enterprise risk of vibe coding is upstream: code generated outside your conventions, with no one constraining it before it exists. Closing that needs system-level governance — rules encoded once, gates that block, context carried from intent to deployment — not a smarter comment at the end of the pipeline.
We have a big, long-lived codebase. Does a gate-based approach scale to that?
That's exactly where it earns its keep. On a large codebase, comprehension debt and dependency drift are the killers — and they compound precisely because review is advisory and inconsistent. Encoding standards once and enforcing them deterministically on every change is the only thing that scales without scaling headcount. The Tech Lead carries your architecture across the whole repo; the gates apply the same bar to every agent, every time, instead of relying on whoever happened to read the comment.
Where does our code and data live with Agentation?
In your GitHub and in the EU. Agentation runs on your existing AI plan and ships verified work into your own repositories — we never store or own your code. Data sits in the EU (Supabase), hosting is EU-based (Hetzner, Germany), and the platform is GDPR-aligned. We're a French team and we're candid about it: the frontier models are American, but the orchestration layer that turns them into governed software is ours, and that's the part you can keep sovereign.