When a test fails, Currents now tells you why. Open the test and the Insights panel has the likely cause of the failure and suggested fixes. Our specialized agent combines failure information with execution steps, network logs and test behavior before and after a failure.
AI analysis is available to every organization, on every plan. An organization admin turns it on once in the organization settings, under AI and data.
The likely cause of the failure
Expand Likely cause of the failure on a failed test. While the agent works, the card lists what it checks: the actions it reads, the requests it opens, the screenshots it looks at. In our tests it took about 20 seconds. When it finishes, the card shows:
- The category of the failure: a network error, an element the test couldn't find or click, an assertion, a timeout, a config or a code error.
- The root cause, in a few sentences.
- Suggested fixes you can try.
The result stays with the test, so the next person who opens it sees it right away. Copy it into the pull request, the issue, or a prompt for your coding agent.
How we tested it
A wrong explanation is worse than none: it sends you after the wrong cause. So before turning this on for everyone, we measured it.
We took real CI failures from real production runs, each with a verified cause, and compared every answer with it. Given only the Playwright error output, an AI found the right cause 6% of the time. Our agent combines failure information with execution steps, network logs and test behavior before and after the failure. It found the right cause 81% of the time.
The gap holds for every type of failure, including bugs in the app or the backend that the Playwright error doesn't show.
We checked the suggested fixes the same way. The first fix our agent suggests addresses the real cause 76% of the time, against 11% without Currents. It also suggests fewer fixes that would only hide the bug, like a longer timeout, a retry or a weaker assertion: 6% of answers, against 17% without Currents.
No model was trained or fine-tuned, on your data or anyone else's. The work was choosing what the agent reads, the tools it uses to investigate the trace, and the model behind it. When a test has no trace, the agent works from the error, steps and console output alone, so expect less from those answers.
What does this test check?
Explain this test works on any test, passed or failed. It describes in plain words what the test does and what it checks. Useful when you review a failure in a test you didn't write, or the test name doesn't say much.
Turn it on
AI analysis sends test data to an AI provider, so it needs your organization's consent:
- Open the organization settings and select AI and data.
- Turn on AI assistance.
Only admins can change it. Members who open a test before then see who to ask.
When you want an agent to fix the failure in your code, connect it to Currents with the MCP server. It reads the same failure data and has your repository too.