Agentic AI Changed What We Test, And How We Test It

Quality engineering has always rested on one quiet assumption: same input, same output, pass or fail. AI-driven and agentic systems break that assumption at the foundation. The same prompt can produce different — and not necessarily wrong — outputs across runs. An agent might choose a different valid path to the same goal. The old pass/fail model doesn't just get harder to apply; it stops being the right primary framework.

This talk covers what quality engineering looks like on the other side of that shift, drawn from a team actively living it — not a finished case study, but real, in-progress work. Rather than treating AI purely as something to be tested, we'll look at AI as a tool reshaping how testing itself gets done: AI-assisted test authoring that generates cases from requirements, self-healing automation that adapts without constant manual upkeep, and where AI is starting to absorb the repetitive, low-judgment parts of QA work.

Absorbing the manual work isn't the same as absorbing the judgment. We'll talk candidly about where human oversight still has to sit in the loop — deciding what "good enough" means when outputs are probabilistic, catching failure modes that don't look like traditional bugs, and building trust in AI-assisted processes without giving up responsibility for their quality.

Attendees will leave with a framework for quality as a spectrum rather than a binary, concrete examples of AI already changing day-to-day QA work, and a grounded sense of what leadership looks like navigating this shift.

The real question isn't how do we test AI — it's what quality engineering becomes when AI is doing some of the engineering.