What AI Gets Right and Wrong in Your Ansible and Terraform Automation

AI can write and modify Ansible Playbooks and Terraform configurations in seconds. Some of that work is genuinely good. It can suggest fixes and help bring code closer to compliance. But how consistent is it, really — and how much of what it produces can you actually trust?

In this session, we will show you the results of AI generated and modified automation through two real-world use cases. First, we look at an Ansible Playbook generated with AI from scratch. Then we examine what happened when the team used AI to upgrade and secure an existing set of Playbooks. In both cases, we break down the results: what AI got right, what it missed, and where it confidently got things wrong – including issues that could easily make it into production.

The question isn’t whether to use AI in your Ansible and Terraform workflow. It’s what happens between the moment AI produces or modifies your code and the moment that code runs in production.

We’ll look at why that verification step is crucial when using AI, what a governance layer can catch that AI may not, and how Steampunk Spotter – built to be exactly that layer – fits into the CI/CD pipeline you already have.

⚡ Bonus for Attendees: Architecture Plan ⚡

Apply for Architecture Plan and find out exactly where Steampunk Spotter fits in your pipeline, as your governance and AI guardrail – tailored to your setup.