Refactoring with AI: how to pay down tech debt without breaking production

One expected impact of agents is making it viable to tackle backlog and tech debt more consistently, as long as the process is incremental. The biggest mistake is trying to "clean everything" in one PR.

The mistake: refactoring in bulk

Huge refactor PRs are hard to review and easy to introduce regressions. AI can generate a lot of code at once; you need to slice it.

An incremental plan

Pick 1 module with real pain (bugs, slowness, coupling). Create behavior tests first (golden tests). Refactor in small steps: rename, isolate, simplify; only then optimize.

Tests that protect

Snapshot or golden of output. Edge cases. Critical flow integration. AI helps a lot here — but only if you give it a track. Refactoring without a track is disaster with style.

Key takeaways

Refactor one module at a time, with tests first. Small steps and human review. AI speeds things up, but the track is yours.

Read also

• Observability for AI apps: what to log so you're not flying blind
• Bilingual (EN/ES) with AI: how to internationalize product without duplicating work

FAQ

What if the module is huge? Split into sub-modules or layers and refactor one at a time, always with tests that guarantee behavior.

Do golden tests block legitimate change? Yes; use them for stable behavior. When the business rule changes, update the golden with judgment.