CI/CD in the Era of AI and Platform Engineering: A Deep Dive into Dagger CI (Part 4)

Part 4: The AI-Native CI/CD Stack: Agents, Modules, and Spec-Driven Development Fixed pipelines for speed and reliability. AI agents to write them and fix them when they break. In Part 1 we built pipelines as real code. In Part 2 we decoupled them from infrastructure. In Part 3 we built AcmeCorp's private module library (acme-backend, acme-frontend, and acme-deploy) that wraps public daggerverse modules with organization-specific compliance, naming, and security. Now let's talk about where AI actually belongs in CI/CD, and where it doesn't. The thesis is simple: AI doesn't replace the pipeline. It writes the pipeline and fixes it when it breaks. The pipeline itself stays fixed, deterministic, and fast. What Is a Dagger Agent? Just as container primitives allow us to build CI pipelines, Dagger introduces an LLM() primitive that lets you create agents the same way you'd call any other pipeline function. Under the hood, dag.llm() connects to any supported model (Claude, GPT, Gemini) and g