Atlassian Corporation Plc. officially launched its new generative AI assistant, Rovo, today. This knowledge discovery product for enterprise customers aims to revolutionize the way businesses access and utilize internal information. After several months of closed beta testing, this AI assistant supported by Atlassian Intelligence is finally available to the public.
The core advantage of Rovo lies in its powerful cross-platform integration capabilities. According to Jamil Valliani, Head of AI Products at Atlassian, Rovo not only works seamlessly with the company's own products but also integrates effortlessly with mainstream enterprise tools such as Google Drive, SharePoint, Figma, and GitHub. "Rovo unlocks enterprise search functionality for the entire organization, connecting to data across various SaaS tools used in daily operations," Valliani stated.
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It is noteworthy that Atlassian has adopted an open strategy, offering some features of Rovo for free to non-Atlassian users. This means that teams not using Jira and Confluence can also enhance their work efficiency with Rovo's search and chat functionalities. Users can directly access these features through web browsers, greatly improving accessibility.
The most notable feature of Rovo is its intelligent agent system, Rovo Agents. These AI "teammates" can participate in various workflows as virtual team members. Currently, Rovo has prepared at least 20 preset agents, including user manual writers, task list assistants, error reporting assistants, release note drafters, and global translators. Users can also customize their own agents through code or no-code methods.
The practical application results are significant. Noemi Flores, a Project Manager for Employee Programs at Procore Technologies Inc., shared her experience: "Previously, it took about an hour to write a quarterly roadmap project, but now it only takes 15 minutes with Rovo."
Specifically for development teams, Atlassian has introduced dedicated developer agents. These AI assistants can automatically generate code based on task descriptions, requirements, and context from Jira issues, significantly improving the efficiency of bug fixes and feature development. They not only ensure that code changes align with issue descriptions but also provide improvement suggestions, allowing developers to focus more on challenging projects.