TestGuild Automation Podcast

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.

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.

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.

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.

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!