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CI/CD in the Era of AI and Platform Engineering: A Deep Dive into Dagger CI (Part 3)

CI/CD in the Era of AI and Platform Engineering: A Deep Dive into Dagger CI (Part 3)

via Dev.to PythonSami Chibani

Part 3: From Scripts to a Platform: Your CI/CD Module Library Let's be clear: Dagger doesn't eliminate the complexity of modern CI/CD systems. It tames that complexity into a testable, maintainable, and reusable system suitable for modern Platform Engineering practices. In Part 1 we wrote pipelines as real code. In Part 2 we decoupled them from infrastructure. Our dagger-ci-demo module works. It builds, tests, and runs identically on a laptop and in CI. But it's a single module in a single repository. What happens when the organization grows? The Growth Problem Let's say you're at AcmeCorp . Your platform team adopted Dagger six months ago. The first project went well: a single .dagger/ module with build, test, and deploy functions. Then things accelerated. New repositories appeared. The mobile team needs CI. The data team wants to containerize their pipelines. The infrastructure team is building internal tools. Each team creates their own .dagger/ module, and within weeks, you see the

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