Atlassian today launched a hub for tracking strategic projects in addition to making generally available its Rovo generative artificial intelligence (AI) agent framework.
Announced at an Atlassian Team ’24 Europe conference, the company is also previewing additional integrations across its portfolio for managing software development projects.
Rovo makes it possible for organizations to orchestrate tasks using either Atlassian or custom AI agents that are trained using the Atlassian Intelligence generative artificial intelligence (AI) framework. The agents can then automate a range of tasks across multiple projects that can now be centrally managed via an Atlassian hub, dubbed Atlassian Focus, that provides an overview of the various initiatives that might be using a range of Atlassian applications to manage specific individual tasks.
Mitch Ashley, a chief technology advisor for The Futurum Group, said Atlassian, in effect, is laying out a strategy for first applying AI across workflows that span an entire organization and then managing those initiatives using a knowledge graph capability that has been embedded into Atlassian.
Matt Schvimmer, head of product for agile and DevOps at Atlassian, added that in the context of a software development lifecycle (SDLC) that means making it simpler to apply AI at scale. For example, Atlassian is previewing support for an extension for GitHub Copilot that it has developed in addition to also making it possible to use its Loom AI tool for recording video messages to create issues and documentation within the company’s Jira project management application.
At the same time, Atlassian is making it possible to create code plans/recommendations and pull requests (PRS) using data stored in Jira along with the ability to review code and suggest edits within the PR.
Over time, the various silos that currently make up the software development lifecycle (SDLC) will continue to disappear as more workflows are driven by AI, said Schvimmer. In effect, software development projects will be managed within a single workflow, otherwise known as an epic, in a way that, for example, dramatically reduces the amount of time that previously might have been spent comprehending documentation, he noted.
It remains to be seen where the handoff between machines and humans will be in the age of AI as the development of software and associated business workflows becomes increasingly more automated. As AI agents take on many of the manual tasks that previously required a human to perform, there should be more time available for humans to orchestrate more ambitious projects. For example, Atlassian noted that beta users of Rovo have saved one to two hours of time per week on average by making it easier to find the right information at the right time.
Regardless of what overall level of increased productivity that might be achieved, it’s already clear most end users would prefer to automate as many repetitive tasks as possible. The challenge, and the opportunity now, is determining which of the potentially many projects that can be launched to prioritize first, given their relevance to the business.
