From CI failure to verified fix: how AI agents can run, diagnose, repair, and retest automated tests.
Your test fails at 2 AM in the CI pipeline. You wake up, open the logs, scroll past 400 lines of stack trace, squint at a screenshot, and mutter "is this a locator issue or did the button actually move?" Twenty minutes later you find it — a data-testid changed during a refactor. You fix it, push, and go back to your actual work.
If that paragraph felt uncomfortably familiar, you already understand the problem agentic testing is trying to solve.
This isn't another "AI will replace QA" post. It's a practical breakdown — from one SDET to another — of what agentic testing actually is, what it can realistically do today, and where it still needs you.
Why Agentic Testing Is Different Now (Not Just Rebranded RPA)
