TestGuild Automation Podcast

AI coding tools are helping teams move faster than ever, but there's a hidden cost.
In this episode, we break down new insights from a DevOps industry report revealing a growing "velocity paradox": teams are shipping more code, but experiencing more failures, rollbacks, and burnout.
You'll discover why AI adoption is heavily skewed toward coding, but not testing, pipelines, or observability, and how that imbalance is creating fragile systems that break under pressure.
More importantly, you'll learn what high-performing teams are doing differently to maintain quality while scaling speed.
What You'll Discover:
✔️ Why AI is increasing deployment failures (and how to stop it) ✔️ The "velocity vs quality" trap hurting modern DevOps teams ✔️ How to reduce flaky tests and pipeline instability ✔️ Why observability and feature flags are now critical, not optional ✔️ Practical ways to improve your CI/CD pipeline for AI-driven development ✔️ The role of QA engineers in the age of AI (and why it's growing, not shrinking)
If you're a tester, automation engineer, or DevOps leader trying to keep up

AI is changing how we build and test software, but most teams are struggling to turn that promise into real results.
In this episode, we break down what it actually takes to scale quality engineering across global teams without creating bottlenecks, burnout, or broken processes.
You'll learn:
why most test automation and transformation initiatives fail
how to separate AI hype from reality
what high-performing teams are doing differently to ship faster with confidence
Today's expert, Sunita McCoy, a Global Engineering Leader and Transformation Specialist, shares practical insights from leading large-scale engineering transformations, including:
how to build a culture that supports AI adoption
why "quality as a phase" is dead
how to shift toward treating quality as a product
If you're a QA leader, automation engineer, or DevOps professional trying to improve reliability, reduce risk, and future-proof your skills in the age of AI, this episode gives you a clear path forward.

Mobile test automation is still one of the biggest bottlenecks in modern software delivery. In this interview, QApilot's Co-founder Aditya Challa explains why most AI testing approaches fail and how to fix them.
Learn more about QApilot: https://links.testguild.com/flutterqa
If your mobile tests are flaky, slow, or hard to trust, you're not alone.
Most teams are trying to apply LLM-based AI to problems that actually require deterministic reliability—and that's where things break down.
In this video, you'll learn:
Why mobile test automation breaks at scale
The real issue with "99% accurate" AI in testing
LLMs vs deterministic AI (and why it matters for mobile apps)
How flaky tests destroy confidence in your pipeline
How QApilot approaches mobile testing differently
What reliable, scalable mobile automation should look like
What this means for you:
Fewer false positives, faster releases, and mobile tests you can actually trust.
00:00 Why Mobile Test Automation Is Still Broken
01:10 QApilot Overview
01:51 Why Mobile Testing Tools Fail
03:13 Why Appium Isn't Enough
05:09 QApilot's Approach to Mobile Testing
07:10 Scaling Mobile Testing Across Devices
08:02 Autonomous Testing + Human in the Loop
10:55 How QApilot Works (Architecture + Agents)
13:45 Real Example: Mobile App Crawling in Action
16:31 Finding Bugs Automatically (Performance + Accessibility)
18:52 Device Farms & Real Device Testing
21:50 Future of Mobile Testing (SRE + AI + Quality Layer)
27:06 Real Customer Results & Case Study
31:02 Why QApilot Focuses Only on Mobile
34:04 Where QApilot Fits in CI/CD
36:00 How to Try QApilot + Final Advice

Are you the only tester on your team—and expected to ensure quality across everything?
In this episode, we break down the growing challenge of solo QA testing in the age of AI-driven development—where code is generated faster than ever, but confidence hasn't caught up.
Christine Pinto shares real-world insights from her experience as a solo tester and now as a founder building tools designed to help testers reduce risk, collaborate better, and make smarter release decisions.
You'll learn:
Why "all tests passing" doesn't mean your product is safe
The hidden risks of AI-generated code and test automation
How to shift from test coverage to risk-based testing
Practical ways solo testers can avoid burnout and isolation
How to bring collaboration back into QA—even if you're the only tester
Why better requirements still matter more than better AI

What if your production logs could automatically generate new test cases?
In this episode, Joe Colantonio sits down with Tanvi Mittal to break down how AI-powered log mining is changing the way teams approach software testing, quality engineering, and DevOps.
Most teams ignore production logs or use them only for debugging. But those logs contain real user behavior, real failures, and real edge cases—the exact scenarios your test suite is probably missing.
👉 Learn how to:
Convert production logs into automated regression tests
Use AI to detect real-world failure patterns
Apply shift-right testing to catch bugs earlier (and smarter)
Handle the challenge of testing non-deterministic AI systems
Reduce flaky tests and automation debt with real data
If you're working with Playwright, Selenium, Cypress, or AI-driven testing tools, this episode will give you a completely new way to think about test coverage.