CI/CD in the Era of AI and Platform Engineering: A Deep Dive into Dagger CI (Part 2)

Part 2: Decoupling Pipelines from Infrastructure In a Platform Engineering world, developers shouldn't care where CI runs. The pipeline code stays the same; only the runner configuration changes. In Part 1, we built our first Dagger pipeline: typed, testable code that runs identically on a developer's laptop and in GitHub Actions. We left off with a minimal, working CI workflow: a few lines of YAML that run dagger call on GitHub's shared runners, with zero infrastructure to manage. That setup works, but it has limits. Every job starts with a cold Dagger cache, builds share GitHub's 2-vCPU runners with the rest of the world, and there's no persistent state between runs. For a small project, that's fine. For a team shipping dozens of PRs a day, it's a bottleneck. This part is more SRE/Platform Engineer oriented. We'll dig into caching internals, runner infrastructure, and the operational tooling that makes Dagger viable at scale. We'll explore three approaches, each with different tradeo