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How to Keep Specs, Tests, and Code Aligned in AI-Assisted Development

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AI coding agents can rapidly introduce drift between specifications, tests, and code, creating false confidence when documents no longer reflect actual system behavior. A traceability model linking requirements, design decisions, tests, and pull requests can make this drift detectable and queryable. Drift typically appears in four patterns: specs updated without code changes, code changed without spec updates, tests that validate implementation rather than intent, and pull requests lacking requirement references. Researchers studying AI development frameworks have flagged specification drift as a recurring risk because agents regenerate code quickly, widening the gap between spec and implementation with each iteration. The proposed fix relies on six identifiers already present in most repositories, combined with CI checks that flag mismatches before a merge is approved.

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