Test automation teams often face the daunting task of triaging dozens of failing scripts after a test run. This article presents a practical framework that leverages AI to automate failure diagnosis and even suggest or apply fixes. The core idea is to move beyond simple reruns and instead systematically categorize failures by root cause—environment issues, script bugs, data problems, or genuine product defects. The approach integrates with Playwright to automatically collect evidence like screenshots, logs, and network traces, feeding this context to an AI model for analysis. This reduces the time spent on manual investigation and helps teams focus on actual product bugs rather than test infrastructure issues. The methodology is particularly valuable for teams scaling their automation suites, where the volume of failures can quickly overwhelm manual triage efforts.
A practical framework for using AI to diagnose and auto-repair Playwright test failures, reducing manual triage time.