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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.
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