TestGuild Automation Podcast

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

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.

AI-powered testing tools promise faster automation and less maintenance, but most require teams to abandon their existing frameworks.
In this episode, we explore Alumnium, an opensource AI-native end-to-end testing solution created by Alex Rodionov, an engineer at Airbnb and a tech lead on the Selenium project.
Instead of replacing tools like Playwright or Selenium, Alumnium adds an AI layer on top, helping teams:
Reduce test maintenance by removing brittle locators
Build more resilient, self-healing tests
Write less code while increasing coverage
Run tests across web and mobile using intent-based steps
We also go beyond the hype and break down what actually matters for real teams:
Why AI-driven tests can still become flaky
The performance and cost tradeoffs of LLM-based execution
What "context rot" is—and how it impacts long test runs
How to safely introduce AI into existing test suites without rewriting everything
Check it out now: https://testguild.me/alumAI

AI is changing how we build and test software, but most teams are still struggling to turn AI-generated tests into real production value.
Use code TESTGUILD3 try for yourself free now for 3 months: https://links.testguild.com/Kobiton
In this episode, we break down what actually works when it comes to AI-powered mobile test automation, especially when running tests on real devices not simulators from Claude.
You'll learn:
How teams are generating and running Appium tests using natural language in minutes
Why AI-generated tests often fail—and how to avoid costly false positives
The real impact of AI on test automation roles and responsibilities
How real device testing exposes issues AI alone can't catch
Practical ways to reduce test maintenance while increasing coverage
We also explore a major shift happening right now: AI is making it easier to create tests—but dramatically increasing the volume of code and risk that needs to be validated.
That means one thing: Testing isn't going away—it's becoming more critical than ever.
If you're a QA engineer, automation engineer, or DevOps leader trying to keep up with AI-driven development, this episode will give you a clear, practical perspective on what to focus on next.

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