Platform-Engineering on cloudplz

No-Code Infrastructure Platform

Mon, 01 Jan 0001 00:00:00 +0000

The bottleneck in database infrastructure was never the databases — it was the queue in front of them. This platform removed the queue: product teams provision production-ready Aurora clusters through a web form, no Terraform required.

Challenge

Infrastructure as code made provisioning repeatable, but it traded one bottleneck for another:

Terraform expertise required. Not every team has it, and shouldn’t need it to get a database.
Lead times. A routine cluster request waited on the platform team’s queue.
Inconsistent patterns. Left to hand-write HCL, teams produced infrastructure that drifted in quality and shape.
The platform team as critical path. Every database request flowed through a small group — that doesn’t scale.

The goal: democratize provisioning without democratizing misconfiguration.

Terraform Module Library

Mon, 01 Jan 0001 00:00:00 +0000

Self-service infrastructure only works if the self-service path is the easiest path. This module library made the compliant, production-hardened way to provision AWS infrastructure also the fastest one.

Challenge

Seventy application repositories, each capable of provisioning its own infrastructure. Without a shared library, every team reinvents VPC wiring, database parameter tuning, and IAM boundaries — and every reinvention is a future incident and an audit finding.

Database Canary Applications

Mon, 01 Jan 0001 00:00:00 +0000

When a platform team changes a Terraform module, bumps an engine version, or upgrades a connection library, the blast radius is every application built on top. Canary applications turn that blast radius into a tripwire.

Challenge

Platform-level changes fail in ways unit tests don’t catch: a parameter group default that breaks connection pooling, a provider upgrade that drifts state, an engine version with different driver behavior. The first signal used to be a product team’s pager.

Treating AI Agents Like Infrastructure: Provisioning Hermes Agent with Ansible

Thu, 10 Sep 2026 00:00:00 +0000

I’ve spent 18 years making cloud infrastructure reproducible. Terraform modules for databases. Ansible roles for VM configuration. Packer templates for golden images. When I started running an AI agent on a Linux VM, I didn’t think about it — I reached for Ansible.

Three days and 15 commits later, I had a fully idempotent playbook that provisions Hermes Agent from bare Ubuntu to production-ready. And the patterns I used? They’re the same ones I use for databases and caching clusters. That’s not a coincidence — it’s the point.