In this episode, Richard and Vernon explore the evolving concept of automation in quality, especially in the context of AI and Gen AI. They discuss how new technologies are blurring the lines between testing and quality, and what this means for the future of software development and testing practices.00:00 - Intro00:52 - Welcome and weekly catch-up01:11 - Vern's deep dive into the AI rabbit hole02:39 - Rich’s quit(er) work week, new threads, and dentists04:15 - Richard buys a domain and we started the pod proper06:09 - Tool idea #1: Using an LLM to evaluate user stories and acceptance criteria automatically07:35 - Is analysing a story "testing" or "quality"? The ISTQB static analysis debate10:27 - Vernon's diabetes analogy: AI is forcing us to finally do what we always said we should12:19 - Better stories = better testing: how quality work amplifies everything downstream13:11 - Tool idea #2: "If we made this change, what areas of the system would be impacted?"14:23 - Distilling years of system knowledge into 5–10 questions an agent could ask18:37 - Tool idea #3: The PR Analyser — summarising code changes through a testing and quality lens21:45 - Vernon's "1 unit of effort, 5 units of testing" — the quality multiplier effect23:29 - Comparing story analysis to actual implementation: where did understanding diverge?24:43 - Tool idea #4: Dynamic test selection — cherry-picking the right tests to run first27:05 - Tool idea #5: An agent that analyses failed builds and attempts to fix them27:28 - Why Richard's first attempt always "fixed" the test instead of the code (and what was missing)29:21 - Dan's AI agents: one thinking partner, one employee monitoring production32:42 - The documentation goldmine: why AI-generated RCA notes might matter more than the fix33:39 - Tool idea #6: A holistic quality dashboard pulling insights across stories, code, tests, and process36:43 - John Cutler on context: it's not data you pass around — it's formed through interaction40:43 - More options than ever: whether it's testing, quality, or static analysis — you can do it differently now41:56 - The real skill: spotting the opportunity to make yourself more effective42:30 - Ge Hill's Lump of Code Fallacy and why task analysis matters43:34 - Why Richard got into automation: efficiency, not because he was told to45:03 - Vernon's big question: in a world where agents can do everything, what's your performance review about?46:52 - Context, craft, and product knowledge can't be delegated to tools yet48:29 - Call to action: What are you building? What tools couldn't you build before that you can now?49:29 - Upcoming: Test Automation Days and PeerCon Live in NottinghamLinks to stuff we mentioned during the pod:04:15 - Automation in QualityRichard bought the automationinquality.com domain! The concept explored throughout this episode.05:28 - Kalpesh Sodha aka KalpsShout out to Richard's colleague who played devil's advocate on the "is it testing or quality?" question07:31 - Static analysis29:44 - Dan "The Agile Guy" ElliottHis post about how he uses AI agents as a "thinking partner" and an "employee" with different missions and capabilitiesDan’s websiteDan's LinkedIn36:52 - John CutlerJohn Cutler's piece on how context isn't just data you move around — it's formed through interaction between peopleJohn's newsletterJohn's LinkedIn42:37 - Rob SabourinMy quick Perplexity search for Rob's public material on Task AnalysisRob's Linkedin42:45 - Michael “GeePaw” HillHis Lump of Code Fallacy. The idea that coding isn't just one activity — there are three flavours of work that occur when you codeMichael’s websiteMichaels Mastadon49:35 - Test Automation DaysRichard will be keynoting at Test Automation DaysMake sure you say hi if you’re there50:10 - PeersConVernon and Richard will be recording a live episode at PeersCon!If you're there, come say hi and grab a mic 🎙️

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