Part 4 of 4: Rethinking the quality operating model for AI-native engineering. Imagine this scenario: Two teams ship the same product on the same day. Team A ran a suite of tests via AI agents, got green across the board, and shipped. Team B ran the same suite of tests via AI agents, got the [...]
Part 3 of 4: Rethinking the quality operating model for AI-native engineering If you’ve been following this series so far, you’ll know in the previous two articles, I’ve covered: why quality has outgrown the old operating model, and why trust is the real bottleneck when teams try to adopt AI testing tools. Being completely honest, [...]
Part 1 of 4: Rethinking the quality operating model for AI-native engineering Here's a scenario I'd suspect most engineering leaders reading this will recognise: An incident slips into production. Not a sev-1 or 2, more like a sev-3 that the team commits to resolving in their backlog rather than through on-call, the kind that generates [...]
Part 4 in "The Autonomous Testing Tipping Point" series If you've been following along, you'll know we've been exploring application integrity, the gap opening up between AI-accelerated development and our ability to test what we're shipping, and the broader implications for how we think about quality. In this one, I want to get hands-on. I've [...]
So… It’s here - the thing I’ve been hinting at across my past two articles. If you’ve been following my 2-part series about “The Autonomous Testing Tipping Point”, in both parts I talked a lot about the crossroads we’re at in our industry, between the existing world of quality and testing strategies that revolve around [...]
Last week, in part 1, I set the scene: why testing is at a crossroads, what autonomous testing means as a spectrum (not a switch) across both teams and in AI systems, and how autonomy functions best. I also discussed why autonomy within these AI systems is important, in relation to removing friction that stops [...]
