A UK e-commerce company creates personalised gifts and ships them globally. Their engineering team prototypes new AI-powered services, but every new service meant manual Terraform work, infrastructure setup, and a bottleneck on the one engineer who knew how to do it.
The company runs on AWS: 15 ECS services, shared RDS databases, Terraform-managed infrastructure. Their non-technical staff and AI-assisted workflows were already generating ideas for new backend services. The problem: spinning up each one required the sole engineer to write Terraform and configure infrastructure by hand.
The goal: a workflow where a Cursor prompt scaffolds a new service, a PR merge deploys it to AWS, and no one has to write infrastructure code from scratch each time.
Monorepo with Cursor Scaffolding: A Cursor skill scaffolds each new service from the template — copying the folder structure, wiring up the database connection, and creating the Terraform module instance — from a single prompt.
PR-Merge-Triggered CI/CD: On merge, GitHub Actions applies the Terraform module, creates the ECS task definition and Fargate service, and runs database migrations. OIDC-authenticated role — no long-lived credentials.
Per-Service Schema Isolation on Shared RDS: The pipeline creates a dedicated schema for each service on the shared RDS PostgreSQL instance.
The team can now scaffold and deploy a new backend service to ECS Fargate from a single Cursor prompt, no Terraform written by hand, no infrastructure setup required. The next step is Phase 2: a fully agent-driven model where AI tools spin up and tear down services autonomously.
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