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Sandboxing your AI agent is only half the story. The other half is where your data goes when it hits your LLM provider's API. In this clip from our secure execution agents webinar, Ben Norris, founding engineer at relaxAI, explains why the sovereignty of your AI provider matters just as much as the security of your agent's environment and why relaxAI was built on a sovereignty-first principle, with inference running exclusively in the UK and no foreign data transfer.

Most platform engineering stories are told in hindsight, with the rough edges smoothed out. On June 17th, we are doing it differently. Join us for Platform Engineering Unplugged, a frank conversation with a practitioner who has navigated the real challenges of building and scaling platform engineering. What worked, what didn't, and what they would do differently. If you lead engineering teams and are thinking seriously about platform engineering, this is the session for you.

AI agents are powerful. They're also unpredictable, non-deterministic, and capable of doing things you didn't ask them to do, as the Rome Alibaba and Claude Mythos case studies make very clear. The answer isn't to avoid agentic AI. It's to run it properly. In this demo, Ben Norris, founding engineer at relaxAI, shows how to build a fully sandboxed AI agent environment from scratch, an ephemeral Civo VM provisioned via Terraform and GitHub Actions, locked down with egress policies, an unprivileged Linux user, and hard resource caps, running a Claude Code session pointed at the relaxAI API.

Thousands of configuration options. Optimisations everywhere. No clear way to track what's running where. Civo Platform Engineer M R Rishi breaks down why running AI on your own infrastructure is harder than it should be and how Konstruct changes that.