AI coding assistants improve velocity metrics but can increase production incidents when delivery processes are weak. The DORA 2024 report confirms teams with high AI adoption saw higher change failure rates. Three failure patterns explain this: AI-generated code fools reviewers with polished style, AI marks its own homework when writing both code and tests, and AI lacks awareness of the broader system context. The fix is a quality-first operating model with three principles: write a spec before prompting the model (breaking the self-grading loop), tier changes by risk and enforce contract tests against live APIs, and treat observability as a release gate with canary rollouts and automated rollback. Teams adopting these controls typically see incident frequency drop by roughly a third within two quarters.
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