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Architectural Technical Debt in AI-Assisted Codebases

Drift is useful when a team already knows that AI-assisted delivery is increasing output, but cannot yet see where structural technical debt is accumulating.

This page focuses on one narrow question: how do you detect technical debt that is architectural rather than merely stylistic?

What kind of technical debt drift is good at

Drift is strongest when technical debt shows up as cross-file coherence loss:

  • duplicated implementation shapes that should have stayed shared
  • mixed architectural patterns inside one module
  • import boundaries that erode over time
  • modules whose change behavior suggests structural instability

That is a different problem from formatting debt, typing debt, or security debt.

Why AI-assisted teams feel this early

AI coding tools make local task completion faster. They do not automatically preserve the repository's shared implementation habits.

The result is often technical debt with a structural signature:

  • more local variants of the same concern
  • more copy-modify helpers
  • more exceptions to boundaries that used to be clean
  • more review effort spent explaining why code technically works but does not fit

What drift gives you

  • deterministic detection of architectural debt patterns
  • file-level findings that teams can inspect directly
  • JSON and SARIF outputs for automated workflows
  • a rollout path that starts with observation instead of hard gating

What drift does not claim

Drift does not claim to detect every form of technical debt.

It is intentionally narrower: it helps when debt becomes visible as architectural erosion in real code structure.

  1. Run Quick Start.
  2. Look at the highest-scored findings in modules that already feel expensive.
  3. Use those findings to decide whether the debt is local cleanup or a structural pattern worth standardizing.