the real riskThe risk isn't bad AI code. It's ungoverned AI code.
Surveys in early 2026 are blunt: most enterprise teams now let AI generate large blocks of code from plain prompts, only a minority have any formal AI governance, and AI-written code carries a materially higher vulnerability density than hand-written code. The danger compounds because people trust generated code more and scrutinise it less — exactly when volume explodes. A small error rate times 1000x the output is a lot of mistakes reaching prod. Manual review alone cannot keep up; that's not a discipline problem, it's an arithmetic one.
- AI now writes more code than your reviewers can read — the bottleneck moved.
- Generated code is trusted more and inspected less, right when volume spikes.
- Without a control layer, 'who approved this?' has no answer and no audit trail.
policy isn't enoughA policy you can't enforce is a wish.
Most 'AI governance' is a document: an acceptable-use policy, a review-tier matrix, a training deck. Useful, but none of it stops an agent from shipping outside the lines — it asks humans to catch violations after generation, in the same review queue that's already overwhelmed. Real governance sits between the model's reasoning and the act of shipping: rules compiled into checks that run every time, on every change, with no human in the loop deciding whether to bother. The control belongs in the pipeline, not in a wiki.
- Encoded rules beat written rules: agents boot inside them, can't opt out.
- Enforcement is deterministic and automatic — not a reviewer's good day.
- Every change carries evidence of what was checked, not a promise it was.
the methodThe Digital Native Method: intent in, governed result out.
There's a way to do AI development that's actually safe at volume, and it has a shape. A Product Owner describes the intent directly on the live product — no ticket-writing, no specs. A Tech Lead encodes the rules once: architecture, conventions, security boundaries, your company's standards. Approved agents implement inside those rules. Then deterministic gates — lint, types, tests, security scan, secrets, lock-file drift — run before anything reaches production. Green or it doesn't land. Humans judge the outcome; the structure judges the code. That's governance by construction, not by inspection.
- Product Owner: describes the outcome on the live product, in plain language.
- Tech Lead: encodes the rules and the maintainability bar once, for everyone.
- Approved agents only: a curated set, not whatever the model felt like calling.
- Gates before prod: lint, types, tests, security — pass or the change is rejected.
the softwareA method needs software to make it real. That's Agentation.
Encoded rules, approved agents and pre-prod gates only govern anything if something runs them every single time — which is the part you can't do by hand. Agentation is that runtime. It hosts the Tech Lead that holds your standards, spawns governed agents in isolated git worktrees, runs the gate suite deterministically with zero AI tokens, and only marks work done when it's green. Crucially, it all happens inside your own GitHub, on your existing AI plan. The audit trail is your commit history and your PRs — independent evidence, not our word for it.
- Agents run isolated; the gate suite blocks anything red from reaching prod.
- Ships through your GitHub — your repo, your branches, your PR history is the audit log.
- Runs on your existing Claude / model plan — we never store or see your code.
cocoricoFrench software, EU data — sovereign on the layer that matters.
Agentation is built by a French team. We're honest about sovereignty: nobody in Europe is sovereign on the frontier models yet — Claude, GPT and the rest are American. But the model is only raw capability; with a model alone you don't get much done. The orchestration layer around it — the rules, the gates, the agent fleet, where the code lives and who can see it — is where governance and sovereignty actually live, and that layer can be European. Ours is: hosting in the EU (Hetzner, Germany), data in the EU (Supabase), your code in your GitHub, GDPR by design.
- Sovereign where it counts: the orchestration and governance layer, not the model.
- EU hosting (Hetzner, Germany) and EU data (Supabase) — GDPR by construction.
- Your code never leaves your GitHub; we orchestrate, we don't collect.
FAQWhat is AI coding governance, exactly?
It's the set of controls that bound what coding agents can do and verify what they produce before it ships: encoded rules they boot inside, a fixed list of approved agents, and deterministic gates (lint, types, tests, security) that run on every change. Done right it's enforced by the pipeline automatically, not by a policy document or a tired reviewer.
If agents write the code, who is accountable for it?
You are — which is exactly why the structure exists. Agentation doesn't ask you to trust the model; it puts a Tech Lead and automatic gates between the model and production, and ships everything through your own GitHub. Your commit history and PRs are the audit trail, so accountability comes with evidence of what was checked, not a vendor's assurance.
How is this different from running a SAST scanner on AI output?
A scanner inspects code after it's written and hands a human a queue of findings to chase — the same overwhelmed review loop, just longer. Governance by construction prevents most violations upstream: agents work inside encoded conventions and a maintainability bar, and the gate suite (including security) is a hard pass/fail before merge, not advisory output you may or may not act on.
Does my code or data leave my environment?
No. Agentation runs through your own GitHub, on your existing AI plan — we never store or read your code. The orchestration layer is hosted in the EU (Hetzner, Germany) with data in the EU (Supabase), GDPR by design. You stay sovereign on the layer you can actually be sovereign on.
Do I need engineers to set up the governance rules?
The Tech Lead encodes the rules once — architecture, conventions, security boundaries, your standards — and from then on every agent inherits them. You can bring your existing engineering standards or start from sensible defaults. After setup, the person owning the product describes intent in plain language; the encoded structure handles the governance on every change.