Turning every repo into a software factory sounds like marketing until you see the machinery underneath. Don Syme, Principal Researcher at GitHub, breaks down continuous AI and GitHub Agentic Workflows, now in public preview. The short version: the agents are the easy part, the quality gates and guardrails are the work.
Watch Don's AI DevCon Talk here: https://youtu.be/kbvqRWY-bUs
What we cover: – What continuous AI is, and why it sits beside CI/CD rather than inside it – Why bounding the context is what stops automated agents going off the rails – Whether the repo is really the right unit for a software factory, and where monorepos break it – One workflow or an agent zoo? Cost control, scheduling and model exams – Quality gates, human review, and equipping the reviewer instead of drowning them
Chapters: 00:00:00 - Introduction 00:03:52 - What is continuous AI? 00:07:33 - From single player to multiplayer automation 00:09:42 - Bounding the context so agents don't go off the rails 00:12:57 - Software factories, loops and harnesses 00:18:44 - Inside GitHub Agentic Workflows 00:25:19 - Why the repo became the unit of production 00:37:11 - What belongs in the repo, and what doesn't 00:45:07 - One workflow or an agent zoo? Cost control in the factory 00:52:01 - Quality gates, human review and equipping the reviewer
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