At Tessl, 95% of the code shipped by their internal "Dark Factory" has never been looked at by a human, and the team still ships hundreds of pull requests a week, including through entire weekends. Rob Willoughby, who leads AI engineering at Tessl, joins Simon to open up the hood on how it actually works: the orchestrator, the verification layers, and the failures that forced the team to rebuild trust from scratch.
What we cover: – How Tessl routes 65-70% of its own pull requests through an autonomous "Dark Factory" – Why context in the repo matters more to output quality than which model you use – How natural language "verifiers" turn code review taste into fast, cheap checks agents can pass or fail – The queue bug that took dozens of pull requests to fix, and the from-scratch Elixir rewrite that stress-tested the whole system – How to start building your own software factory, one verification layer at a time
Chapters: 00:00:00 - Introduction 00:01:44 - Rob Willoughby joins: Tessl's PR numbers 00:04:01 - Live demo: kicking off two pull requests 00:14:03 - Building the Dark Factory: orchestrator vs. context 00:19:01 - Code review layers: Code Rabbit, Tessl Change Verify, and verifiers 00:29:58 - Earning trust: accountability in an autonomous system 00:33:20 - What broke: the queue bug and the Elixir rewrite experiment 00:39:56 - Onboarding new engineers into the factory 00:42:58 - Advice for teams starting their own software factory 00:52:03 - Back to the demo, and the road to 100% adoption
🌐 Tessl: https://tessl.io 🔔 Subscribe for weekly episodes on AI-native development
What would your own verification layer catch, and where would it break? Let us know in the comments.
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