New in Rovo Chat: Where context shapes your work
Atlassian’s Rovo Chat introduces team-focused AI features to preserve shared context across tools and handoffs, enabling visible memory controls, agent mentions, shareable chats, and custom skills for reusable workflows.
Most AI tools operate in isolation, but work flows across teams through connected apps like Jira, Confluence, and Jira Service Management. Rovo Chat now bridges these gaps by making the Teamwork Graph’s context visible and editable, allowing users to review and adjust what the system remembers about their preferences and past work. This includes project timelines, formatting choices, and data handling rules, reducing the need to repeat instructions or resend context between teammates.
Teams can now @mention specialized agents—such as competitive intel or launch planning agents—directly within Rovo Chat. These agents inherit the full conversation history, decisions, and artifacts, enabling them to resume work without losing critical context that is often lost in traditional handoffs. The feature ensures continuity when expertise needs to be transferred between roles or projects.
Shared chats in Rovo preserve the entire thread, including reasoning and supporting materials, so teammates can pick up where a colleague left off without additional summaries or back-and-forth. Access remains permissions-aware, ensuring users only see information they are already authorized to view. This eliminates redundant explanations and accelerates collaboration across teams.
Custom skills allow teams to encode their repeatable workflows—such as sprint summaries or bug triage—into reusable AI processes. Described in natural language, these skills can be created, refined, and updated directly in Rovo Chat or Rovo Studio, adapting to a team’s specific standards and reducing repetitive manual work.