You start by describing your infrastructure needs in detail and optionally attach any source code. The agent will clarify your requirements and either execute the task immediately or generate a plan with step-by-step instructions for you to approve. Once you’re happy with the changes, export everything to GitHub or let the agent provision it in your cloud account. Here’s a quick demo of deploying a new app to GCP / AWS: https://www.loom.com/share/4627b3cd96cc439e9981a38363b7f6f7

Why build a new coding agent when there are good ones already out there? We believe there’s room for a new agent that is specifically built for DevOps tasks since the risks are much higher – it's easy to rollback AI-related errors in a web app, but fixing a misconfigured database is not nearly as easy. By focusing specifically on cloud infra, we can provide all the visibility and checks you need to feel confident in your configuration changes.

At our previous jobs, we built an internal data / ML platform at Google Life Sciences that involved migrating off of internal Google infrastructure to the public cloud (GCP). We quickly learned how complicated it can be to configure cloud infrastructure well, even for seemingly simple tasks. Configuring an app with CI/CD requires knowledge of multiple infra tools, cloud services, and best practices. Mistakes can be costly and diagnosing issues can send you down a rabbit hole of cloud docs.

Our goal is to help engineers feel confident when making changes in their cloud. We designed the workflow to start with a prompt, a template, or a GitHub repository. After clarifying your requirements, the agent will start generating IaC, CI/CD, and other configurations using the latest docs, public Terraform Registries, and a set of best practices we dynamically load into the context window.

All changes are run through static analysis to detect hallucinations, estimate cost changes, and visualize your infrastructure components as you go. Once you’re happy with the changes, you can export everything to GitHub for review. You also have the option to deploy directly to your cloud from the workspace and let the agent diagnose any deployment issues. The deployment flow is "pseudo-deterministic" in that it follows a checklist of human-guided instructions that help it stay in bounds, but we still recommend only using this feature for dev environments and using GitOps for any changes to production.

The current plan is to continue adding support for more tools (Kubernetes and GitLab are next) and we may add a CLI that lets you bring the agent into your local workspace.

We’d love to hear your feedback and ideas!