Does 99.8% represent a good success rate in end-to-end test automation of a web application? What about 99.9%? Maybe yes, but when you have a test suite that has up to 1,000 scenarios, it means ...

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Trivago's QA team built an AI-powered failure analysis job inside their GitHub Actions CI pipeline to automatically triage end-to-end test failures. The system collects structured failure reports from a Selenium-based framework, cross-correlates failures across desktop, mobile, and SSR platforms, enriches context with git diffs and 24-hour test history from Elasticsearch, then invokes the Cursor CLI with Gemini Flash in stateless ask mode. The prompt instructs the model to prioritize flakiness signals over PR regressions ('flaky until proven guilty'). The analysis appears in the GitHub Actions Step Summary within 90 seconds at a cost of roughly $0.02–$0.03 per run, reducing the need for manual log triage across 40+ daily workflow runs.

My 2 Cents: I'll gladly spend them to stop staring at tes...