AI challenge

Every new AI code change breaks something else

You ask for one small change and three other things stop working. That is usually not bad luck. It points to missing boundaries, tests or technical coherence. We find out where that fragility comes from.

Does this sound familiar?

The pattern often becomes visible once the codebase grows and several parts depend on each other.

  1. A simple feature causes unexpected bugs

    Functionality on other screens or flows suddenly stops working.

  2. The same bug comes back later

    A bug gets fixed, but nothing prevents it from reappearing.

  3. Refactors are almost impossible

    Too many parts turn out to depend on each other implicitly.

  4. You mostly rely on clicking through by hand

    After every change someone has to walk through the whole application to see whether everything still works.

Where do you want to be?

You want to be able to make changes again without discovering afterwards, every time, what collateral damage they caused.

Changes stay local

A change only has effects in the places where you expect them.

Regressions are found quickly

Tests and checks flag problems before users discover them.

Development becomes truly fast again

Less repair work means AI-assisted development saves time once more.

Why does this happen?

AI coding tools are strong at local changes. They don't automatically know all the implicit agreements that run through a growing product.

See how we approach this

Tests don't cover critical agreements

The AI gets little feedback when a change breaks existing functionality.

Parts are too tightly coupled

A local change affects state, data or behaviour in unexpected places.

Interfaces are not clear enough

The codebase doesn't show well enough which contracts must stay stable.

Why 9to5 for this?

The AI doesn't need to write less code. The codebase needs enough feedback and structure to make errors visible quickly.

View cases

Tests where they add value

We test critical contracts and flows, not every detail for a percentage.

Clear technical boundaries

Fewer hidden couplings means fewer unexpected regressions.

Practical AI guardrails

The development workflow helps coding agents stay within the architecture.

What else fits your situation?