Vera Platforms, a startup founded by researchers at the University of Michigan, believes there’s a way to make agents trustworthy enough to handle DevOps. DevOps, which focuses on how software gets deployed and operated, is a particularly unforgiving affair. While AI agents have some leeway to make mistakes and correct errors as they generate code and work through a prompt, it’s an entirely different matter when they make changes to real infrastructure running production workloads. "Live infrastructure is unforgiving," Ang Chen, CEO of Vera Platforms and an associate professor at the University of Michigan, told The Register . "An incorrect change can take down a service, open a security hole or erase data. Mistakes are often irreversible, and there’s no safe way to experiment: running code against production infra just to see what happens isn’t an option." For the past few years, Chen and his colleagues have been working on a way to benchmark agentic DevOps and a gym – a simulation environment – to help AI models get better at DevOps tasks. "From the research that we did, we’ve figured out various weaknesses of leading models," said Chen. "For instance, [Anthropic’s] Claude, although it’s very strong with Python generation, it just has some fundamental limitations that it can’t get right when it comes to DevOps." An example of that, Chen said, is that when they asked Claude or OpenAI’s Codex to decommission resources in the AWS cloud, the models didn’t look beyond the default region. "The default region is us-east-1," he explained…
