Learn how an Atlassian team turned noisy, overdue feature flag cleanup into a mostly hands‑off, self‑improving AI workflow using Rovo Dev, repo‑specific commands, Jira Automation, and a simple queue, boosting throughput, reducing merge conflicts, and keeping codebases healthy.

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An Atlassian team built a mostly hands-off AI workflow to automate feature flag cleanup using Rovo Dev, repo-specific saved prompts, Jira Automation, and a queue system. Generic AI prompts failed because they lacked codebase-specific context, so the team created tailored cleanup commands encoding their flag framework, class names, test patterns, and branching conventions. A self-improvement loop—reviewing failed PRs and updating the prompt—raised the success rate from 5/9 to 8/9 PRs requiring no manual edits. Jira Automation triggers Rovo Dev when a ticket is labeled, automatically creating PRs. A Jira-driven queue runs hourly, processing one cleanup at a time to avoid merge conflicts. Results: 29 of 31 flags cleaned without intervention, 12 flags cleaned in two days (85% velocity boost), and a reusable pattern applicable to other repetitive engineering tasks.

How We Turned Feature Flag Cleanup Into a Mostly‑Hands‑Off AI Workflow