Does using AI in QA testing increase risk for software companies?

While this sounds promising, it’s also where some of the biggest risks are emerging. Agentic testing introduces unpredictability into a process that depends on clear pass-or-fail results. While good prompting and careful temperature settings can guide agents toward more deterministic behavior, they remain sensitive to subtle changes in context — a slightly delayed loading state, a minor UI shift or even dynamic content reordering. These variations can cause the agent to behave differently across runs, leading to test flakiness that stems not from actual bugs, but from the AI’s inconsistent interpretation of the interface. This fragility makes agentic testing difficult to rely on, especially at scale.

With traditional test scripts, a human tester or developer can read through the code, understand the intent and verify whether the test is sound. Agentic testing doesn’t offer that same transparency. While agentic testing tools often provide video recordings, logs or DOM snapshots of their actions, relying on these outputs for validation creates a new kind of bottleneck. Reviewing even a few minutes of footage to understand what the agent did — and why — is dramatically slower than scanning through a deterministic test script. You lose the quick readability and repeatability of traditional test code, and instead inherit a review process that feels more like a forensic investigation. Over time, this erodes confidence in the system and adds significant overhead every time a test misfires or fails without a clear reason.

This lack of visibility is a major red flag for any experienced QA professional. Testing isn’t just about the outcome — it’s about traceability, repeatability and accountability. Without those things, QA becomes a black box. And black boxes are dangerous, especially in tightly regulated industries like finance, healthcare or infrastructure.