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Fix Your CI Pipeline Health Metrics Before Deploying AI Triage Tools

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Engineers are cautioned against using AI-generated explanations to diagnose CI pipelines that lack reliable underlying data. A structured approach recommends tracking four key service-level indicators — queue time, execution time, infrastructure failure rate, and retry recovery rate — before introducing any AI layer. Failures should be categorized into defined classes such as code errors, test flakes, and infrastructure issues, with unknown failures actively investigated rather than left unresolved. Specific example thresholds are suggested for protected-branch workflows, such as 95% of jobs starting within 120 seconds and fewer than 1% ending in infrastructure failure. Only once structured metadata and stable logs are consistently captured should AI triage be introduced, and it should be evaluated on classification accuracy and correction time rather than writing quality.

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