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.
Correctness is now a spectrum, not a binary. Non-deterministic AI systems break the pass/fail model traditional QA is built on. Attendees will leave with a framework for evaluating quality when the same input can produce multiple valid outputs.
AI is already reshaping how testing gets done. Concrete, current examples of AI-assisted test authoring and self-healing automation — not speculative future tooling, but practices teams are adopting now.
Automating the work isn't automating the judgment. Where human oversight still has to sit in the loop, and how to build trust in AI-assisted QA processes without quietly giving up accountability for quality.
New failure modes require new instincts. How to recognize issues that don't look like traditional bugs: subtle behavioral drift, technically-correct-but-off-target outputs, agents taking unintended action.
A grounded view of what's next for QA leadership. What it looks like to lead a team through this shift in real time, based on a pivot still in progress rather than a polished retrospective.

TestMu Conf
Testμ(TestMu) Conference is TestMu AI’s (Formerly LambdaTest) annual flagship event, one of the world’s largest virtual software testing conferences dedicated to decoding the future of testing and development. Built by the community, for the community, it’s a space where you’re at the center, connecting, learning, and leading together. From deep-dive sessions on emerging trends in engineering, testing, and DevOps, to hands-on workshops and inspiring culture-driven talks, every experience is designed to keep you at the heart of the conversation.

From AI Assistants to AI Coworkers: How Engineering Teams Ship Faster with Enterprise Context
TestMu 2026
Keynote: Beyond Benchmarks - Evaluating Agents Against What They Are Actually Supposed to Do
TestMu 2026
Panel Discussion: Money Moves at Machine Speed - Trust, Risk, and Quality in Agentic Finance
TestMu 2026
From Load Testing to Reliability Engineering: Making Performance Testing Predict Production Behavior
TestMu 2026
Panel Discussion: Who Tests the Machines? QE Leaders on Quality in the Age of AI-Written Code
TestMu 2026
Fireside Chat: The Economics of AI Agents: How Startups Are Rethinking Value and Monetization
TestMu 2026
Panel Discussion: Mission-Critical Priorities in Quality Engineering: The Leader's Playbook
TestMu 2026