the real problemThe bottleneck moved from writing code to trusting it.
AI didn't make your team faster in a vacuum — it made individual drafting faster than your review, governance, and coordination can absorb. Gartner expects 40% of AI-augmented coding projects to be canceled by 2027 over escalating cost, unclear value, and weak risk controls. Studies put AI-generated code at roughly twice the security-violation rate of human code, and unmanaged AI output drives maintenance toward 4x by year two as debt compounds. The tooling decision isn't 'which copilot' — it's whether the extra throughput meets a structure that catches it, or hits production raw.
- AI magnifies the discipline you already have — strong controls accelerate, weak controls accelerate the mess.
- Syntactically perfect, passes the existing tests, and still wrong under the edge case nobody specified.
- The mismatch is structural: faster individual output, same-speed organizational review.
the methodThe Digital Native Method: one role encodes the rules, agents work inside them.
The fix isn't a smarter model or a stricter PR template you hope people honor. It's a division of labor. A Product Owner describes intent directly on the live product — this is broken, this should feel faster, add this. A Tech Lead encodes the standards once: architecture, conventions, security policy, your company's rules. Then every agent boots inside that encoding, and a structure verifies the output before it can land. Speed comes from the Product Owner staying in outcome-space; safety comes from the Tech Lead's rules being non-optional, not from a human re-reading every diff by hand.
- Product Owner: describes the result on the running product, no ticket archaeology.
- Tech Lead: encodes standards once; every agent inherits them automatically.
- Structure: deterministic verification stands between the model and production.
what the gates doDeterministic gates — lint, types, tests, security — before anything reaches prod.
'I never read the agent's code' is only safe if something else does, every single time, not sometimes. That's the gate. Lint, type-checks, tests, secret scanning and security checks run on every change deterministically — zero AI tokens, no model judgment, no vibes. Green or it doesn't land. This is what turns 'we let agents write code' from a liability you'll explain in an incident review into a process you can put in front of an auditor. Each task maps to one revertable change, attributable to the agent and the intent that produced it — the audit trail a CTO actually needs when someone asks 'why is this here.'
- Lint + types + tests + secrets scan run before review, not after an incident.
- Deterministic means reproducible — same input, same verdict, no model in the gate.
- One task = one atomic, revertable change with provenance you can point to.
your stack, not oursIt ships through your GitHub, on your existing AI plan.
Agentation is a layer over the tooling you already run, not a walled garden you migrate into. Work lands as branches and PRs in your own GitHub, under your existing branch protection and CODEOWNERS, on your team's own AI plan. We never hold your code. That means adoption doesn't require ripping out your SDLC — it slots the Tech Lead and the gates into the workflow your engineers already trust, so the review infrastructure scales with the throughput instead of drowning under it.
- Branches + PRs in your GitHub — your branch protection, your CODEOWNERS, your history.
- Runs on your own model plan; the code never leaves your control.
- A layer over your SDLC, not a migration away from it.
cocoricoSovereign on the tooling — the layer you can actually own.
Agentation is built by a French team. You probably won't be sovereign on the models — Claude, GPT and the frontier labs aren't French, and pretending otherwise is theater. But the model is only the engine; with just a model you don't ship much. The orchestration layer — the structure that turns raw generation into verified, governed software — is where sovereignty is real and reachable. That layer runs in the EU (Hetzner, Germany), with data in the EU (Supabase), your code in your own GitHub, and GDPR by design. You buy frontier intelligence from wherever it's best, and you keep the control plane European.
- EU hosting (Hetzner, Germany), EU data (Supabase), GDPR by design.
- Sovereignty where it's actually winnable: the orchestration, not the model weights.
- Frontier models for the intelligence; a European structure for the control.
FAQMy engineers already use Cursor and Claude. Why add another layer?
Copilots make individuals faster but hand the output straight to your review process, which doesn't scale at the same rate — that's where the chaos comes from. Agentation isn't a competing assistant; it's the structure around the models: a Tech Lead that encodes your standards and deterministic gates (lint, types, tests, security) that verify every change before it can reach production. It governs the throughput your existing tools create.
How do I get an audit trail for AI-generated code?
Every change maps to one task and one revertable PR in your own GitHub, attributable to the agent and the intent that produced it. The deterministic gates run on each change and leave a green/red record, so you get tamper-evident provenance — what changed, why, and that it passed your checks — instead of a pile of commits nobody can account for in a postmortem.
How does this control security and technical debt instead of accelerating them?
AI amplifies whatever discipline is already in place; left ungoverned it roughly doubles security violations and pushes maintenance toward 4x by year two. The Tech Lead encodes your architecture and security policy once so agents can't ship outside them, and deterministic gates (including secret scanning and security checks) block non-conforming changes before prod. What accumulates is governed code, not unreviewable sprawl.
Does adopting this mean replacing our SDLC or losing control of our code?
No. It's a layer over the stack you already run. Work ships as branches and PRs through your own GitHub under your existing branch protection, on your team's own AI plan — we never hold your code. You keep your tooling, your history, and your control plane; you add the encoding and the gates.
Is Agentation actually sovereign if it runs Claude or GPT?
We're honest about the boundary: you won't be sovereign on the model weights, and no serious vendor can promise that today. You can be sovereign on the orchestration layer — and with just a model you don't ship much, so that layer is most of the value. Agentation runs in the EU (Hetzner, Germany), stores data in the EU (Supabase), keeps code in your GitHub, and is GDPR-aligned. French team, European control plane, frontier intelligence on top.