TestGuild Automation Podcast

Why Your AI Tests Pass When Your Features Fail? Donovan Brady of Leapwork shows how to make AI tests traceable, cheaper to run, and safe to self-heal.
👉 Try Leapwork Play (free tier): https://testgld.link/podcastsleapwork
AI can write hundreds of tests in minutes. But can you trust what it built? In this TestGuild episode, Joe Colantonio talks with Donovan Brady, VP of Developer Relations at Leapwork, about the biggest problem in AI testing right now: trust.
Donovan shares what he's seeing across industries, from bleeding edge teams running fully agentic development to financial services and healthcare companies that run on technology but aren't tech companies. Then he demos Leapwork Play, which turns your requirements, Jira stories, source code, and design files into a knowledge base, generates standard Playwright tests, and traces every step back to the exact line of the requirement it came from.
You'll learn:
Why AI self-healing can rewrite tests to pass instead of catching real bugs
How to trace every AI generated test back to a requirement
Why generating Playwright once and running it deterministically saves time and tokens
How to find coverage gaps, prune redundant tests, and run only the tests tied to a code change
How to control self-healing so it never masks a real regression
How to run tests headlessly through an MCP server from Claude Code, Codex, or Cursor
Why exportable Playwright code means no vendor lock-in
Donovan's key lesson: when his team used Playwright plus Claude, self-healing sometimes rewrote tests to pass rather than to test the real outcome. His advice is to give your AI the full context of the business workflow you're validating, end to end.
What's your experience with AI generated tests? Have you caught one passing when it shouldn't? Tell us in the comments.
#Playwright #AITesting #TestAutomation

Running a proof of concept on test automation tools can feel like a horror movie. Vendors promise everything, your boss wants answers fast, and you don't know what you don't know. In this Halloween special, Paul Grossman, the Dark Arts Wizard, walks through his free personal automation tool POC checklist and shares the AI and Playwright tricks he's picked up this year.
Key takeaways:
Use a 177 feature checklist to compare tools side by side, saved locally so your data stays yours, with room to add any tool you find.
See how AI driven and codeless tools stack up on build time and run time using Paul's 10 step Candy Mapper challenge.
Practice on sandbox sites like Candy Mapper, Ministry of Testing's Shady Acres, and the SelectorsHub shadow DOM page.
Save tokens and time with Copilot CLI, and keep your AI on your rules with a copilot instructions file in .github.
Catch disappearing toast messages with Playwright listeners and use slowMo to stop false failures on fast loading pages.
Why AI won't replace exploratory testers, plus the AI code reviewer "death spiral" Paul ran into.
Plus a Halloween lightning round: the flakiest test ever, the scariest phrase a manager can say, and which horror monster test automation really is.

Performance testing has never been easier to start, or more important to get right. As teams ship more AI generated code, performance problems are slipping through, and they tend to surface during your highest traffic moments.
In this episode, Joe Colantonio talks with Lars Holmberg, lead maintainer of the open source Locust project, and Abhinav Premsekhar of Microsoft's Azure App Testing team. They explain how AI is changing load testing, from script creation to results analysis.
You'll see live demos of Azure Load Testing, including:
Quick URL based tests for beginners
Running existing JMeter and Locust scripts at scale
A browser extension that records user journeys and generates load test scripts automatically
Creating a Locust test from a Postman collection with the Azure Load Testing extension for VS Code
Abhinav also walks through a real failed test run, showing how client side metrics, server side metrics, and AI insights work together to locate a backend bottleneck and recommend a fix.
The conversation also covers why developers are moving from JMeter to Locust, how OpenTelemetry tracing can cut debugging time from weeks to hours, why functional testers shouldn't fear load testing, and a first look at AIO Locust, a modern async Locust alternative built on free threaded Python.
Whether you're a seasoned performance engineer or running your first load test, you'll leave with practical ways to find bottlenecks before your customers do.

Cloud device farms make it easy to get started with real device testing. But what happens when you need to scale? Costs climb fast, you wait in a queue for the devices you need, and you lose visibility into what's actually happening when a test fails.
In this episode, Nick Metcalfe, Head of Technical Sales Engineering at Cambrionix, joins Joe to explain why more testing teams are bringing their device labs in house. Nick shares how one team was quoted roughly a million dollars a month to outsource their real device testing, then cut that cost to around $300K by building their own lab. And cost is only part of the story.
You'll discover why an in house device lab can outperform a cloud lab:
Lower cost at scale. Cloud pricing works for small test runs, but at enterprise scale an in house lab can save hundreds of thousands of dollars every month.
No waiting in line. Your devices are yours, available whenever you need them, with no queue and faster turnaround.
Full control and visibility. See exactly what's happening at the hardware level, down to the millisecond a USB controller reboots, so you can tell a real bug from an infrastructure failure.
Test the devices your users actually have. Keep legacy phones, budget devices, and hand me downs in your lab, just like Snap does to support its user base across 12,000+ devices and 700,000 tests a day.
Test custom hardware. Point of sale systems, VR headsets, smart devices, and prototypes that a cloud vendor simply won't have.
Automate the hands on work. Reboot devices, recover from restore mode, and put 16 Apple devices into DFU mode at once, all through an API that plugs into your CI pipeline.
Protect your device investment. Hold batteries at a set charge level to reduce wear and extend device life for years.
Nick also covers the honest tradeoffs, including the setup and resources an in house lab requires, and why a hybrid approach of emulators first and real devices second gives you the most confidence before release. Plus, his number one tip for building a lab: do it once, do it right.
If your team is scaling mobile test automation, fighting flaky tests, or questioning your cloud device farm bill, this episode is for you.

I asked thirteen people inside AI testing one question: can AI test your software for you? Founders, CTOs, project leads, and quality leaders, many of them selling AI testing tools right now. Not one said yes.
This episode is my session from the Testear.la 2026 conference, built entirely from twelve months of interviews on this show with the people actually building this technology, including Applitools, Maestro, Bright Security, QA.tech, Cucumber, Ito, and the lead of the Selenium project.
In it you'll learn why one AI testing vendor deliberately removed AI from the part where tests run, the real productivity number teams are measuring versus the number in the case studies, the four incompatible architectural bets vendors are making and how to tell which one your tool has chosen for you, why AI generated code is producing vulnerabilities far faster than it produces code, and the one architecture rule that stops an agent from editing the test that was supposed to catch it.
You'll also get the list. Every task these thirteen people said they would never delegate, assembled from conversations that had no coordination between them. Plus two tests you can run on Monday, both free, that tell you whether you're ready to trust an agent with your suite.
If you're a tester, automation engineer, or QA leader trying to figure out where you fit in all of this, this one is for you. The people building AI testing don't trust it to test on its own. That gap is your job.
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