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TestGuild Automation Podcast

Joe Colantonio

TestGuild Automation Podcast (formally Test Talks) is a weekly podcast hosted by Joe Colantonio, which geeks out on all things software test automation. TestGuild Automation covers news found in the testing space, reviews books about automation, and speaks with some of the thought leaders in the test automation field. We'll aim to interview some of today's most successful and inspiring software engineers, and test automation thought leaders.

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20 episodes
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Avg 36 min
English

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#607
Tuesday · 36 min
Locust and Azure Load Testing: Create and Debug Load Tests with AI with Lars Holmberg and Abhinav Premsekhar
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.
#606
September 29 · 40 min
AI Agents Passed the Tests. Real Devices Didn't. with Nick Metcalfe
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.
#605
September 22 · 29 min
AI Testing Reality Check: What 13 Founders Refuse to Automate with Joe Colantonio
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. Exclusive Sponsor Quick note before we get into this one. Everything you're about to hear points at the same conclusion. The work is moving from writing tests to supervising the things that write them. That's a different skill set, and most of us are picking it up by accident. Automation Guild '27 is where you pick it up on purpose. Year eleven. February 8th through the 12th. Five days, live, with the best E2E automation practitioners working today, and you keep lifetime access to every session. The early bird list is open right now, and that's where the biggest discount goes. Head to testguild.com and look for Automation Guild '27 early bird, or grab the link in the show notes: https://testgld.link/yearlybird
#604
September 15 · 34 min
How to Stay Irreplaceable When Agents Write Your Tests with Jonathon Wright
Is AI going to replace software testers, or make them more valuable than they have ever been? Jonathon Wright says both, and in this episode he does not back down from either position. Jonathon Wright has spent thirty years in test automation, going all the way back to WinRunner. He now runs thirty AI agents across twelve screens, he sat with Gartner for their research on agentic platforms, and he says the last six months changed the job more than the previous three decades combined. We get into what he calls confidence engineering, why he has not opened a browser in five years, and what happened when he generated thirty seven thousand test scenarios from space agency specifications and put them in front of NASA. He also tells the story of a testing leader who taught his agents everything he knew about accessibility work and then got let go. A few minutes later he tells you your career has another fifteen good years in it. I pushed him on the contradiction. Listen for how he answers it. What you will learn in this episode: Why the flood of AI generated code makes verification the scarce skill, and testers the people who matter most What confidence engineering means and how to apply it to systems that never return the same answer twice Why browser automation was the easy problem, and what is actually hard now How intent driven testing lets you model a whole system before a single line of code exists Why getting the requirements right matters more with agents than it ever did without them The story of an agent taking twenty minutes to press one key, and what it tells you about the hype cycle Three concrete things to start doing Monday to stay valuable as your agent count climbs Whether you are an automation engineer, a manual tester, or a QA leader trying to work out what your team should do differently this quarter, this conversation will give you a clearer read on where the value is actually moving.
#603
September 8 · 43 min
AI Performance Testing: How to Scale Agentic AI with Kandasamy Selvaraj
Everything you knew about performance testing changes when the system you're testing is non deterministic. In this episode of the TestGuild Automation Podcast, Joe Colantonio sits down with Kandasamy Selvaraj, Principal Architect and author of the free book Rethinking Performance Engineering for Agentic AI, to unpack what it really takes to move an AI agent from a working demo to an enterprise system handling millions of conversations per hour. Checkout his free book: https://leanpub.com/agentic-ai-performance Kandasamy shares the practical playbook he's built running agentic AI in production, including why the same request can take three seconds one run and eight seconds the next, how to use harnesses to bound tool calls, reasoning loops, and token budgets, and why your SRE dashboard can look perfectly healthy while your token costs quietly balloon to five times baseline. You'll learn how his team: Shifts performance gates left into every commit with JMeter Shifts right with synthetic monitors on blue green deployments Uses Langfuse and OpenTelemetry to spot context bloat before it hits production. You'll also hear how to: Slash AI costs with prompt caching Conversation capping Routing simple queries to cheaper models Why you should load test at the API layer before touching the UI, How to keep stubs honest with production sampled latency Why one misbehaving agent can starve every other agent sharing the same provider. If you're a tester, performance engineer, SRE, or architect building on LLMs, this conversation will change how you think about scale.
#602
September 1 · 38 min
AI Testing Is Bigger Than You Think, 5 Areas Testers Must Own with Swati Seela
Most testers are stuck arguing about whether AI is coming for their jobs. Swati Seela thinks that's a distraction from a much more useful question: when we say "AI testing," what do we actually mean? What do you think? Take our TestGuild State of Test Automation Survey now: https://testgld.link/27data In this episode, Swati walks through the AI Testing Landscape, a practical framework she presented at CAST 2026 that breaks AI testing into five overlapping areas: AI assisted testing, AI evaluation and trust, AI development quality, testing products that contain AI functionality, and testing the AI models themselves. Each one has a different test intent, a different technique, and a different toolset, which is exactly why lumping them together leaves new testers with no idea where to start. Along the way Swati gets specific about the failure modes she keeps hitting in real work. Why AI opens almost every response by agreeing with you, and why that false sense of correctness is the modern version of a green dashboard hiding tests that stopped meaning anything years ago. Why her MCP test generator kept skipping the cataloging step, what that revealed about models trading completeness for speed, and how splitting one workflow into two got her reliability back. Why traditional Boolean assertions break down against probabilistic systems, and what your automation framework has to do instead. She also shares how she uses AI to learn hard material in the semiconductor world without losing the thread, what she absolutely will not accept without laying eyes on it herself, and her answer to whether a tester with a decade of experience should be worried right now. Her closing advice comes down to two things that have to travel together: testing fundamentals, and AI literacy. Don't hate it, don't love it, just use it.
#601
August 18 · 35 min
Why IoT Testing Proves Manual Testing Never Died with Oleksii Cherkashyn
Manual testing never died. IoT is the proof. In this episode, Joe Colantonio talks with Oleksii Cherkashyn, a QA team lead, automation engineer, and IoT testing specialist who built a complete test architecture from scratch across hardware, firmware, mobile, web, and API. IoT breaks the usual automation playbook. You are not testing a shopping cart. You are connecting a real microcontroller, flashing a sketch, validating that a physical command produced the right change in a dashboard widget, and then doing the same thing again through the REST API and the mobile app. No marketplace framework covers that, so Oleksii built his own. You'll learn: Why manual QA remains essential in IoT and which cases can never be automated How Oleksii built a custom Node.js library to simulate up to 50,000 device connections for performance testing without buying the hardware Why he chose WebDriverIO over Playwright and the mobile automation reason behind it How he uses WebDriverIO MCP and cloud coding agents every day to create and repair tests The hard truth about dependent test chains and where AI agents still hallucinate How to handle time based scenarios like sleep modes, daily triggers, and delayed notifications Static versus dynamic provisioning, and how to test OTA firmware updates What to do first if your company hands you an IoT device and you have never tested hardware If you work in test automation and you want a look at the layer of testing that AI is not coming for, this one is for you. Listen up, and check out the links below for everything mentioned in this episode.
#600
August 12 · 53 min
Testing AI: Engineering Confidence in Non-Deterministic Systems with Jason Arbon
In this episode 600 of the TestGuild Automation Podcast, Joe Colantonio talks with Jason Arbon, founder of Testers.ai, Jank.AI and IcebergQA and author of the new book Testing AI: Engineering Confidence in Non-Deterministic Systems. Take Our 2027 Survey Now: https://testgld.link/27data Jason makes a case most testers have not heard yet. Coding is being absorbed by AI. Specification work is thinning out. Product, development, and test roles are converging into one. And when the music stops, the only seat left belongs to the person who can look at what the machine produced and make an evidence backed call on whether it ships. He calls that confidence engineering, and he argues it is not a rebrand of QA. It is what QA was always supposed to be. Along the way, Joe and Jason get into the containment problem and why alignment, not lockdown, is now the real safety goal. They dig into why testing cost scales quadratically, meaning ten times more generated code creates roughly a hundred times more testing demand. Jason also pushes back hard on skeptics of agentic testing, pointing out that almost nobody has run the obvious experiment of testing a site themselves for a week and comparing their results against what AI finds. You will also hear Jason's most practical piece of advice in the whole conversation. If you are not running the same suite five times against the same build and looking at the actual results, not just flake, you are not testing seriously in an AI world. Plus a detour into grokking, the Chinese Room, and Geoffrey Hinton, because it would not be a Jason Arbon episode without one. Listen up!
#599
August 4 · 38 min
Playwright With AI: How to Automate Tests Without Shipping AI Slop with Andrew Knight
Your developers just got supercharged by AI coding agents. Your test coverage did not. So how do you keep quality high when product code is shipping faster than any test team can follow? In this episode Andrew Knight, the Automation Panda and Senior Director of Product and Engineering at Cycle Labs, shares how his six person team is running the biggest release quarter in company history using AI coding agents, spec driven development, and Playwright. You will discover: How Playwright turned itself into an AI automation platform with the MCP server, the planner, generator, and healer agents, and the new CLI and skills approach that cuts your token usage way down. Why Andy uses Spec Kit to codify his testing strategy once, in markdown, so quality standards get baked into every single pull request instead of being caught in review. How to decide which AI generated tests are actually worth running when compute time and budget are finite. What AI slop looks like from a manager's seat, and how to build a team culture that catches it before it ships. Why Andy believes AI coding tools are the new compiler and markdown is the new programming language, plus my pushback on what that means for everything testers were trained to care about. What Andy really thinks about token costs, subscription tiers, and what happens when the AI subsidies run out. Plus the one piece of advice he gives to any tester still sitting on the sidelines of the AI shift. Whether you are an automation engineer, a QA lead, or an engineering manager trying to figure out where testing fits in an AI first workflow, this episode gives you a practical playbook you can start using this week.
#598
July 28 · 45 min
Test Automation Won't Save Your QA Career, but These Skills Will with Keith Klain
Keith Klain has spent 25+ years leading enterprise quality programs in financial services and is one of the testing industry's most respected voices. In this episode, he joins Joe to discuss his BrowserStack talk, "The Death of Test Engineering," why testing isn't dying—but outdated testing roles are—and how AI is reshaping the future of software quality. In this episode: Why "The Death of Test Engineering" isn't what you think The difference between checking and testing Will AI replace software testers? Why confidence engineering matters How to communicate testing in business terms Where automation adds real value Why agentic AI testing is overhyped Notable Quotes "If you look at AI as a competitor, you're screwed. If you look at it as just another tool... it's not a threat." "Uncertainty in testing is a feature, not a bug." "If an organization doesn't value testing, you're doing it wrong." "You can be incredibly confident about something and incredibly wrong." One Thing to Try This Week Ask yourself after every critical defect: "What would the business impact have been if this had gone live?" It's a simple way to shift conversations from test cases to business risk. Resources Mentioned Rethinking Expertise — Harry Collins How Complex Systems Fail — Richard Cook Measuring and Managing Performance in Organizations — Robert Austin Emily Bender Alex Hanna Timnit Gebru Dagmar Monett Olivia Gambelin Ed Zitron Joe's Recommendation: Prophecy
#597
July 22 · 36 min
AI Testing Strategy: Stop Being a Cost Center, Start Protecting Revenue with Nandini Srinivasan
Nandini Srinivasan has spent 25 years in the quality industry and leads a global QA organization of over 150 engineers across the US, Canada, India, and Pakistan. In this episode, she breaks down exactly how she built a QA AI acceleration charter, ran a train-the-trainer model, and used a phased proof-of-concept approach to separate real AI from what she calls "powerful automation dressed up as AI. We get into: How she frames quality metrics for executives Using language around revenue protection Risk mitigation Feature velocity instead of test coverage percentages. She talks about the four pillars she uses to present her team's value: quality, scalability, performance, and availability. Nandini also shares her take on the future of QA hiring, why the judgment layer will always require a human, and what skills testers need to stay relevant as AI agents take over the more mechanical parts of automation. She is also writing a five-part LinkedIn series called "The Voice of QA in the AI Era" if you want to follow along.
July 14 · 39 min
How to Move from Prompt Engineering to Harness Engineering in Testing with Matt Wynne
Matt Wynne, co-creator of Cucumber and BDD practitioner, joins Joe for the first time in over a decade to talk about what two years inside a Silicon Valley AI startup taught him about the future of software testing. Matt spent time at Mechanical Orchard working alongside experienced XP practitioners to modernize legacy COBOL mainframes using LLMs, and then spent a week with the team that coined the term "software factory," where the rule was simple: humans never write the code, never read the code. In this episode, Matt breaks down what harness engineering actually means, why shared understanding is still the real bottleneck even in an agentic world, and how testers can use multiple LLMs to review AI-generated pull requests without reading every line. He also gets honest about the grief that comes with realizing you can encode years of hard-won expertise into a Markdown file, and why that does not mean your skills are worthless. If you are working in a brownfield codebase, wondering how to handle the flood of agentic PRs, or trying to figure out where testers fit in a world where agents write the code, this conversation is worth your time. Find Matt at: mattwynne.net leansoftware.ai Also check out his course: Build a Software Factory: Hands-off agentic coding for experienced engineers https://testgld.link/mattcourse
July 7 · 43 min
Agentic Engineering for Testers: How to Automate Your Way to the Top with Amit Rawat
Amit Rawat is an agentic engineer who spent two decades in QA before shifting fully into building AI agents. He's the creator of PromptWright, a desktop tool that turns natural language prompts into automated Playwright browser tests, complete with screen recording, Gherkin scenario generation, and self-healing locators. In this episode, Amit and Joe get into what it actually takes to work with AI agents at a high level, starting with why the planning phase matters more than the prompt itself. Amit breaks down his own workflow, brainstorming with AI, building a detailed plan in HTML before ever executing, and why curiosity and technical depth still matter even as AI gets more capable. They also cover why Amit believes QA professionals, more than developers or DevOps engineers, are best positioned to thrive in the agentic era, how he tracks the ROI on his $200-a-month Claude subscription, the "Chief of Staff," "Chief Health Officer," and "Chief Financial Officer" AI agents he's built to help manage different aspects of his personal life, and how he uses a memory layer so those agents understand his preferences and become more useful over time. If you're a tester, automation engineer, or QA leader trying to figure out where AI agents fit into your workflow and your career, this conversation is a practical look at what's already working today.
June 30 · 23 min
How to Test Any API Without Documentation with Liudas Jankauskas
Most API testing stops at the happy path. The problem is that the bugs that actually hurt you in production are sitting in everything many testers skip, like the boundary values, the oversized payloads, the missing tokens, the security headers, the inputs that make no sense at all. In this episode, Joe sits down with Liudas Jankauskas, who has spent almost twenty years breaking software and testing APIs since 2008. Liudas demonstrates Rentgen, his free and open-source API testing tool, live on screen. You'll watch him take a single request from a real app, map it in seconds, and generate dozens of tests covering security, boundaries, performance, and load—all from one click. You'll learn: How to discover APIs hiding under the hood of any application, even when there is zero documentation Why happy path testing leaves you exposed How to run a fast hygiene check before your real automation ever starts Liudas also explains why Rentgen runs completely locally with no server and no data leaving your machine, making it safe for banking, healthcare, and other regulated environments. Plus, he demonstrates the killer Copy Bug Report feature that drops a standards-based ticket straight into Jira or Trello. In This Episode You'll Discover How to find and test undocumented internal APIs using the browser DevTools Network tab Why happy path-only testing misses the bugs that matter most How Rentgen turns one request into security, boundary, performance, and load tests automatically Where Rentgen fits in your workflow as a pre-automation hygiene layer—not a Postman replacement How to use it for regression by comparing results across environments The one piece of advice Liudas gives every tester to level up their API testing Try Rentgen, free and open source, at Rentgen.io. Connect with Liudas Jankauskas on LinkedIn: https://www.linkedin.com/in/liudas-jankauskas/
June 23 · 36 min
Your AI Code Review Is Lying to You (Here's the Fix) with Evan Marshall
Your AI code review tools read the diff. They stare at your code. But they never actually run it. So the bugs that only show up at runtime, the broken user flows, the bad query plan, the duplicate submission, sail right past review and land in front of your customers. In this episode, Joe Colantonio sits down with Evan Marshall, founder of Ito and a fifteen year engineer who spent five years in applied cryptography securing hundreds of millions of dollars for millions of people. Evan is taking that ship fast without breaking things discipline and pointing it straight at testing. Ito is an agentic QA platform that builds and runs your actual app on every pull request, navigates it like a real user, exercises the frontend and backend as one system, and brings back real runtime evidence: video replays, logs, the exact lines responsible, and steps to reproduce, posted right in your PR. You will learn: Why static code review misses the bugs that cause real production incidents How Ito spins up ephemeral environments and tests across UI, API, and database Why QA is not disappearing, it is leveling up into a manager and quality strategist role How to keep your test layer separate from your code generation so your signal stays honest The skills testers and engineers need as AI writes more of the code If you are shipping AI generated code at high velocity and your QA cannot keep up, this one is for you. Try Ito on your own code. Your first ten pull requests are reviewed free, no credit card required. Check it out at https://testgld.link/itoai now. And as Joe always says, seeing is believing.
June 16 · 34 min
AI QA Agents Explained: How Amikoo Helps Testers with Ivan Barajas Vargas
What happens to QA when AI is writing ten times more code than your team can test? That is the exact problem Ivan Barajas Vargas set out to solve with Amikoo, a purpose-built AI QA agent designed to help testers, SDETs, and even developers move faster without sacrificing coverage or quality. Ivan is no stranger to AI in testing. Before generative AI became mainstream, he co-founded MuukTest, a test automation platform built on symbolic reasoning and expert systems. After six years and thousands of customer conversations, he went back to first principles to build Amikoo from scratch, this time with a harness of 12 specialized agents and 43 tools trained specifically for testing workflows. In this episode, Ivan and Joe dig into the real-world gap between AI code generation and AI-powered testing, why the QA role is being elevated rather than replaced, how Amikoo uses Playwright and page object model patterns under the hood, and where human judgment still has to stay in the loop. Ivan also shares practical advice on what skills QA engineers should be building right now and which test scenarios should never be fully delegated to an agent. If you are trying to figure out where testing fits in an agentic development world, this episode gives you a clear picture of what is possible today and what is coming next. Visit https://testgld.link/amikoo to try the freemium account, and mention you heard this on TestGuild to unlock double the free usage.
June 9 · 16 min
The AI Illusion: Why Testing Still Needs Humans with Joe Colantonio
Everyone is talking about AI replacing testers, writing tests, and transforming software quality. But what if we're asking the wrong question? In this solo episode, Joe Colantonio shares a growing concern he's seen while traveling across the country for TestGuild IRL events: a decline in testing fundamentals at the exact moment AI hype is reaching a fever pitch. Drawing insights from Carissa Véliz's book Prophecy: Prediction, Power, and the Fight for the Future, Wayne Roseberry's work on AI and meaning, and Tariq King's concept of Human Experience Testing, Joe explores why AI systems may be far less intelligent than many believe, and why human testers remain more important than ever. You'll discover: ✅ Why large language models generate plausible answers without understanding truth ✅ The difference between prediction, correlation, and genuine understanding ✅ Why AI can test software but cannot experience software ✅ What "Everything is tested, but nothing is experienced" really means ✅ How AI hype may be distracting teams from critical testing fundamentals ✅ Why empathy, context, and human judgment are becoming competitive advantages for testers Whether you're excited about AI, skeptical of it, or somewhere in between, this episode will challenge you to think more deeply about the future of testing and your role in it. Resources Mentioned 📖 Prophecy: Prediction, Power, and the Fight for the Future by Carissa Véliz 📖 Work and presentations by Wayne Roseberry 🎓 Free course: thebullshitmachines.com 🎤 Learn more about TestGuild IRL events: TestGuild.com/irl If you enjoy this episode, be sure to subscribe, leave a review, and share it with a fellow tester who's trying to navigate the AI era without losing sight of the fundamentals. #SoftwareTesting #AI #QualityEngineering #TestAutomation #SoftwareQuality #HumanExperienceTesting #ArtificialIntelligence #TestGuild #QA #TechPodcast
June 2 · 35 min
AI Agents in QA: How to Keep Up with AI-Driven Dev Velocity with Vilhelm von Ehrenheim
AI coding tools promised to make development faster — and they delivered. But here's the problem nobody talks about enough: when you speed up coding, you don't eliminate the bottleneck in the SDLC. You just move it. And for most teams, it lands squarely in QA. In this episode, Joe sits down with Vilhelm von Ehrenheim, Co-founder and Chief AI Officer of QA.tech, to dig into how agentic AI is reshaping software testing from the ground up. Vilhelm brings serious ML credibility, he helped build Motherbrain, one of the earliest production LLM systems in venture capital, and he's now applying that experience to one of the hardest problems in software delivery: testing at AI development velocity. You'll learn how QA.tech's behavioral knowledge graph gives AI agents the context they need to actually understand your application, why validating user intent beats checking element identifiers every time, how autonomous agents can review PRs, reproduce bugs from Slack messages, and generate targeted tests without a single line of test code ,and what the tester's role actually looks like when agents do the heavy lifting. If you're wondering whether your QA practice can survive the pace of AI-driven development, this one's required listening. 🔗 Book a demo now: https://testgld.link/qatechdemo
May 19 · 38 min
Maestro MCP, AI Mobile Testing That Fixes Its Own Tests with Leland Takamine
What happens when AI agents can not only write mobile app code, but also validate their own work automatically? In this episode, I sit down with Maestro Co-founder and CEO Leland Takamine to explore one of the biggest shifts happening in software testing right now: agentic mobile testing. Leland shares how his team went from solving mobile performance testing challenges to building one of the fastest-growing mobile automation frameworks used by companies like Microsoft, Meta, Amazon, and DoorDash. We dive deep into: How AI coding agents are changing mobile testing workflows What "closing the agentic feedback loop" actually means Why deterministic testing still matters in the age of AI How Maestro MCP lets AI agents validate mobile app changes automatically Why mobile test maintenance may finally become manageable The future role of testers as AI-generated code explodes Leland also gives a live demo showing an AI agent building, validating, debugging, and generating a reusable mobile test completely autonomously. If you care about AI testing, mobile automation, MCP servers, or the future of QA engineering, this episode will likely change how you think about testing workflows over the next few years. Try it out now for yourself: Maestro Studio: https://testgld.link/mstudio Maestro MCP docs: https://testgld.link/maestromcp
May 12 · 38 min
AI Testing Costs, How to Prevent Runaway Token Bills with Arthur Hicken
AI-powered testing tools are exploding across software engineering teams… but so are the hidden costs. In this episode, Joe sits down with Arthur Hicken to unpack the growing problem of runaway AI token usage, unexpected LLM billing, and the operational risks of deploying AI agents into testing and DevOps pipelines. Inspired by Arthur's article on the emerging "Token Tax," this conversation explores why many teams are underestimating the true cost of AI automation. You'll learn: Why AI-generated testing can create unexpected scaling costs How runaway AI agents and infinite loops happen Real-world examples of massive AI billing surprises Why deterministic problems shouldn't always use LLMs The hidden risks of "vibe testing" and autonomous AI remediation How QA teams can monitor, test, and control token usage Why performance testing and service virtualization matter more than ever in AI systems Practical strategies to avoid expensive AI deployment mistakes Whether you're a software tester, automation engineer, QA leader, or DevOps practitioner, this episode will help you think more strategically about AI testing before costs spiral out of control.

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