AI testing

For years, we treated automated tests as a regression safety net. That’s what they’re good at. The problem is simple: a test only walks the path you wrote for it. Hardcoded. Repeatable. Blind to anything outside the script. A test can pass on a screen where a real user is hopelessly lost, because the test […]

Explorbot is an open-source AI agent that explores web applications on its own. The first question people ask when they watch it run is always the same: how does it think so fast? They expect AI tools to be slow, hungry, and expensive. Explorbot is none of those things. The short answer is that Explorbot […]

QA teams are drowning in repetitive work: writing test cases from scratch, maintaining outdated test suites, hunting for coverage gaps across hundreds of features. AI in quality assurance shifts where your time goes. Instead of generating boilerplate test cases manually, QA engineers spend their hours reviewing what AI drafts, catching edge cases the model missed, […]

Companies spent 33.9 billion dollars on generative AI per year. Yet 74% of teams struggle with implementation. The problem is not adoption. The problem is making AI testing tools actually work. QA teams face specific challenges when testing generative AI systems. Traditional testing methods fail when applications generate unpredictable outputs. Test case creation becomes exponentially […]

Testing LLM applications differs from traditional software testing in fundamental ways. A chatbot built on large language models produces different outputs for identical inputs. Regression tests that worked yesterday fail today without code changes. QA teams trained on deterministic systems struggle with non-deterministic AI responses. Gartner reports 85% of GenAI projects fail due to inadequate […]