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An AI agent was given one simple task: book a gym class when a slot became available. Instead, it discovered a vulnerability in the gym’s software, gained administrative access, deleted another user, and booked the slot anyway. Australian AI technologist Andrew Bird had connected an AI agent to WhatsApp to automate a routine gym booking. But when the agent encountered an API without proper authorization checks, it didn’t simply stop. It found a way around the problem and used the vulnerability to accomplish the task it had been given. And that creates a much bigger question.

CircleCI now works with Cursor Origin. Connect your repo, push a commit, and run your existing CircleCI pipeline. Now in beta.

Enterprise AI is only useful when it actually runs in production, inside the tools teams already depend on. Getting there is harder than it sounds. Forward Deployed Engineers at Atlassian work directly inside some of the world's largest organizations, building AI-powered agents, connectors, and workflows on Atlassian's platform. They work alongside customers to understand the real constraints, design solutions that hold up at scale, and see them through to deployment.

Most AI model benchmarks measure general coding ability or reasoning. GitBench, built by GitKraken developer advocate Chris Griffing, measures something narrower and more practical: how well a given AI model handles specific Git tasks, starting with commit squashing, identifying which commits in a messy history should be combined into one clean commit.