¶ Service · AI coding adoption
AI agents in your team, without lowering the bar.
For CTOs and heads of engineering whose teams want to work with Claude Code, Cursor or Codex. We help you decide how: a pilot, a policy, agent permissions and a review process that keeps AI-written code to the standard of everything else you ship.
Pilot · policy · permissions · review
Starting point
Adoption can start bottom-up: a few developers use their own accounts, and the company finds out later. A structured start puts four things in place before anything scales.
- Pilot
- A small group, a real part of your codebase and success criteria agreed in advance. No company-wide license purchase before you know what changes.
- Policy
- A short written policy: which tools are approved, what code and data they may see, who is accountable for AI-written code. Short enough that developers actually read it.
- Permissions
- What an agent may do on its own: read files, run tests, run shell commands, open pull requests, touch production. Each permission is a decision, not a default.
- Review process
- AI agents can produce more code than a team can review. Review rules, pull request size limits and ownership decide whether quality holds.
Planned content – draft
How a pilot could be structured
- Before the pilot would start, the team would pick one or two well-understood areas of the codebase and record the current metrics.
- The pilot group would work with the selected tools under the draft policy and permissions, with short, regular reviews of what worked and what did not.
- Policy and permissions would be adjusted from what the pilot shows, not from vendor claims.
- At the end, the team would decide with the evidence in hand: roll out, extend the pilot, or stop.
This outline is a starting point. The actual scope, group size and criteria would be agreed with you.
Data · secrets · agents · CI
Guardrails
Guardrails are ranked here by the damage a gap could cause. The highest-ranked ones should be in place before the first agent touches your main repository.
- SEV-1
Data handling
Which repositories, customer data and internal documents may be sent to which provider, under which contract settings. Separate rules for regulated data.
- SEV-1
Secrets
Agents read files and run commands. Keys in .env files, shell history or config can end up in a prompt or a log. Secrets belong in a vault, with scoped, short-lived credentials.
- SEV-2
Agent permissions
Allow-lists for commands and tools, no production credentials on developer machines, approval for MCP servers and third-party extensions, and a plan for prompt injection through issues, docs or dependencies.
- SEV-2
CI as the referee
Tests, linting, type checks, dependency and secret scanning run on every pull request, whoever or whatever wrote it. The pipeline, not the author, decides what merges.
No promised productivity numbers
How success is measured
Vendor claims and lines of code are not a measure of success. These are, and they are measured on your work, before and during the pilot.
- Lead time for changes
- From first commit to production. Measured before and during the pilot, on comparable work.
- Change failure rate
- How many deployments cause an incident or need a rollback. If speed goes up and this goes up too, nothing was gained.
- Review load
- Time pull requests wait for review and how large they are. AI tools can move the bottleneck from writing to reviewing.
- Developer experience
- Short, regular surveys of the pilot group: what got easier, what got harder, what they would not give back.
Imagine a 12-person agency where pull requests double after the rollout, but review time triples and incidents rise. On a "lines written" metric the pilot looks like a success. On the four metrics above it is a warning, and a reason to fix the review process first.
Questions
Which tool should we choose: Claude Code, Cursor, Codex or Copilot?
It depends on your stack, your security requirements, how your developers already work and what your contracts allow. We help you compare them against your criteria during the pilot, rather than picking a winner up front.
Do you train our developers?
This engagement sits at the decision level: policy, permissions, review process and pilot design. Developers learn the tools best by using them on your codebase within those guardrails. If you need structured training as well, we can tell you what to look for.
Can you promise a productivity gain?
No, and we would be suspicious of anyone who does. Published research on AI coding productivity points in different directions. The point of a pilot is to measure what happens in your team, with your code.
We work in a regulated industry. Can we still use AI coding tools?
It can be possible, with the right settings: provider contracts, data residency, what is excluded from context and what is logged. We map your constraints to concrete tool settings. Final compliance decisions stay with your legal and compliance team.
Is this only for English-speaking teams?
This site is written for teams that work in English. Our German-language practice, focused on reviews and code rescue, is at vibecodeberatung.de.
¶ Intro call
Book a free intro call
30 minutes, free of charge. Tell us about your app or your team, and we will tell you honestly whether and how we can help. We reply within one business day.
- You describe the app or the team setup.
- We ask about the stack, the data and what is at stake.
- You get an honest view of whether a review, a rescue or a rollout makes sense.