Architecture drift reduction with LLMs

Increased use of AI coding agents can accelerate drift from the intended codebase and architecture designs. Left unchecked, this drift compounds as agents and humans replicate existing patterns, including degraded ones, creating a feedback loop where poor code begets poorer code. Some of our teams are now addressing architecture drift reduction with LLMs.

This approach combines deterministic analysis tools (such as Spectral, ArchUnit or Spring Modulith) with LLM-powered evaluation to detect both structural and semantic violations. LLMs are then used to help fix these issues. Our teams have applied this to enforce API quality guidelines across services and to define architectural zones that guide agent-generated improvements.

A few lessons learned: like with traditional linting, initial scans can surface a large number of violations that require triage and prioritization, and LLMs can assist with this process. Keeping agent-produced fixes small and focused makes review easier, and an additional verification loop is essential to confirm changes improve the system rather than introduce regressions.

This technique extends the idea of feedback sensors for coding agents into the later stages of the delivery lifecycle. As one team at OpenAI describes it, drift reduction acts as a form of "garbage collection," reflecting the reality that entropy and decay emerge even in systems with strong early feedback loops.