We built BugBrain because software teams are shipping faster, but QA still requires a lot of manual work and test maintenance.
Today, BugBrain can explore a web application like a real user, identify verified bugs, test important workflows, check pull requests, and evaluate AI features for issues such as hallucinations and prompt injection.
Now we are deciding what to improve next, and I would rather ask real users than make assumptions.
Which capability would be most valuable for your team?
One challenge we faced while building an AI testing agent was deciding what it should do when the evidence is incomplete.
A binary PASS or FAIL result looks simple, but it can create false confidence.
For example, a page may display an error message that is expected in one workflow but is a real defect in another. If the agent does not have enough context, forcing a binary result can create a false positive.
I have worked in software testing for more than seven years, and one problem keeps appearing across different teams.
Writing the first automated test is rarely the hardest part.
The real cost appears later.
Selectors change. Test data becomes outdated. Environments behave differently. Flaky failures take time to investigate. Eventually, the team stops trusting the test suite.
