Most cloud services follow the same architectural patterns, yet engineers still hand‑assemble Terraform code for each of them. In this session, based on real work done in production, you’ll see how documented architecture can become a machine‑readable contract, how opinionated modules act as guardrails, and how AI can handle the repetitive translation work without replacing human judgment.
We’ll walk through a live, end‑to‑end demo that starts with a documented service architecture and ends with Terraform generated through a constrained module library. You’ll see what the AI gets right, where it’s intentionally limited, and how engineers review and refine the output to ensure standards, governance, and intent are preserved.
You’ll leave with a practical model for encoding architectural decisions so AI can reliably interpret them, a repeatable pattern for building and using opinionated modules that enforce consistency, and a human‑in‑the‑loop workflow that keeps engineers accountable while freeing them from low‑value assembly work. This isn’t “AI writes Terraform.” It’s a shift in how platform teams standardize infrastructure, reduce repetition, and focus engineering effort where it matters most.
Learning Objectives
- Architectural contracts: Learn how to encode architectural intent into machine‑readable contracts that AI can reliably interpret.
- Opinionated module guardrails: Understand how opinionated Terraform modules enforce consistency, governance, and platform standards.
- Human‑in‑the‑loop AI workflows: See how constrained AI generation plus engineer review creates a safe, repeatable workflow that eliminates low‑value Terraform assembly while preserving intent.