Get your engineering team working with AI coding agents
Coding agents no longer just complete a line: they read a repository, change several files, run the tests and propose a complete change. Well framed, they change a team’s pace. Poorly framed, they quickly produce a lot of code nobody understands.
The problem
Most teams adopted these tools developer by developer, with no shared rules. Gains are real but uneven, code review becomes the bottleneck, and nobody can say what a human actually checked.
An agent is only effective if the repository is legible to it: tests that run, written conventions, well-split tasks. These are the same conditions as onboarding a new developer — and they are often missing.
How we work
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1
Assess the ground
State of tests, continuous integration, documentation, conventions. We identify what prevents an agent from working properly, which is usually what already slows the team down.
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2
Set a working agreement
What is handed to an agent and what is not, how a task is described to it, who reviews what, which parts of the code require a second review. An instruction file versioned in the repository replaces individual habits.
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3
Tool up verification
The faster code gets written, the more verification must be automatic: tests, static analysis, assisted code review, dependency and secret checks. The agent goes through the same gates as humans, in the same pipeline.
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4
Measure over time
Lead time from request to production, review bounce rate, incidents. We compare before and after on your own indicators rather than repeating vendor figures.
What makes these projects fail
Confusing writing speed with delivery speed
If review and tests do not keep up, code piles up at the door. The gain is measured at production, not in lines of code.
Handing agents the keys to production
Limited access, isolated environments, secrets out of reach, human approval before any irreversible action. Treat an agent like a very fast external contractor.
Leaving juniors alone with the tool
An agent amplifies the judgement of whoever drives it. Without review by an experienced developer, technical debt settles in very fast.
What you receive
- Diagnosis of the repository and the integration pipeline
- Working agreement for coding agents, versioned in your repositories
- Verification chain: tests, static analysis, assisted review
- Team training on your own tasks
- Delivery indicators before and after
Frequently asked questions
Is our code sent to a third party?
That depends on the tool and the contract. We help you choose a setup that fits your constraints: plans with a no-reuse commitment, European hosting, or open models run on your machines for the most sensitive repositories.
Does this work on a legacy codebase?
Yes, provided you start with what makes the repository verifiable: tests on critical parts and reliable continuous integration. Agents are in fact a real help for writing those tests and documenting inherited code.
Will agents replace our developers?
That is not what we observe. The work shifts: less typing, more design, task splitting and review. The teams that gain the most are those whose experienced developers drive the agents.
Is your situation close to this one?
Describe it in a few lines. We will tell you whether AI is the right answer — and we will also tell you when it is not.
Talk about your project