AI Failure Analysis
A red run tells you something broke, not why. AI Failure Analysis reads a failed test run and suggests a likely root cause along with a remediation, so triage starts with a hypothesis instead of a wall of logs. It also recommends ways to improve your tests and tracks what you do with each suggestion.
Who it’s for#
Anyone who triages failures and wants to spend less time reconstructing what went wrong before they can fix it.
What it does#
- Root cause suggestion — Analyze a failed run and propose what caused it.
- Suggested remediation — Get a recommended fix alongside the cause.
- Test improvement recommendations — Receive AI suggestions for making tests stronger.
- Feedback tracking — Mark a recommendation applied or dismiss it, and keep that history.
How it works#
When a run fails, the platform examines it and returns a suggested cause and remediation you can act on. As you work, it surfaces recommendations for improving the test itself, and records whether you applied or dismissed each one so the trail stays clear. Pair this with run management to move from a failing run to a diagnosis in one place.
Why it matters#
Triage is where engineering time disappears. Starting with a suggested cause and fix shortens the path from failure to resolution, and feedback tracking keeps your team’s decisions visible instead of lost in a chat thread.