one thing we never really solved was understanding test failures at scale. when youve got hundreds of tests running, figuring out if a failure is real or just flakiness takes forever. someone usually has to manually look at logs and screenshots and piece together what happened.
we started routing failed tests through an ai diagnostics tool that analyzes the logs, screenshots, and error messages to generate a root cause report. instead of “test failed”, we get something like “selector ‘button.submit’ was not found because element is inside shadow dom, or network timeout during api call to /users endpoint.”
the diagnostics take a few seconds per failure, but the report quality is legit. we can see patterns we never noticed before—like certain tests always fail on firefox at specific times, or certain api responses are inconsistent.
the next step we havent fully implemented is routing the diagnostics through different ai models depending on the type of failure. like, selector issues route to visual reasoning models, api failures route to models that understand http stuff, infrastructure issues route to models experience with logging and observability.
it feels like overkill but im curious if anyone here has actually done this and found it worth the effort. does ai-powered failure analysis actually change how you approach test maintenance?
alt question: are you still manually digging through test logs, or have you found systems to automate the analysis part?
routing failures through different models is exactly what autonomous ai teams are built for. one model excels at analyzing selector issues, another understands network failures, another sees infrastructure problems. you don’t pick one best model—you route each failure to the model best suited for that type of analysis.
with Latenode, you build an agent that classifies test failures, routes them to the appropriate diagnostic model, generates a root cause report, and even suggests fixes. all automated, all tracked.
this scales way better than manual logs analysis. teams have gone from days of troubleshooting to minutes of automated diagnostics.
ai diagnostics work best when you have structured logging. if your test logs are clean and include context, diagnostics provide real value. if logging is messy and inconsistent, the ai flails around and generates generic reports.
we set up automated failure analysis and it cut our troubleshooting time by about 60%. instead of guessing what went wrong, we have actual data about failure causes. but we still need humans in the loop—sometimes the diagnosis is right but the fix isn’t obvious.
failure diagnostics are only useful if they lead to action. good diagnostics that don’t get fixed are just noise. make sure your workflow includes remediation, not just analysis.
routing to multiple models actually helped catch edge cases. some failures looked like one thing but were actually something else—different models spotted that.