Atlassian expands OpenAI partnership for Rovo and enterprise agents
GPT-6 models will power Atlassian agents and Rovo
Atlassian and OpenAI have expanded their partnership to bring frontier models from the GPT-6 family to Atlassian’s platform, including its Rovo AI experience. Under the new agreement, OpenAI models will power agents across the platform and give Atlassian broader access to GPT-6 Astra and the GPT-5.6 series.
The companies say the aim is to connect model capabilities with the work context already held in enterprise systems. Rovo combines OpenAI intelligence with Atlassian’s Teamwork Graph, an enterprise context layer that connects people, projects, documents and decisions.
The partnership extends a collaboration that began in 2023. Atlassian is also increasing its own use of Codex and ChatGPT Enterprise: more than 3,000 of its developers use Codex in terminals, integrated development environments and code-review workflows.
Context from Jira and Confluence
Atlassian said its Teamwork Graph plugins can provide Codex users with relevant work items and technical documentation, supporting software writing, testing and delivery. The company also offers Atlassian and Teamwork Graph CLI plugins for ChatGPT and Codex, allowing customers to connect project information, documentation and development context to those tools, subject to appropriate permissions.
A recent Atlassian plugin extension brings Jira work items, Confluence content and people into ChatGPT and Codex prompts. Its pinned Atlassian Home can also surface assigned work, recent Looms, projects and Bitbucket pull requests.
The model rollout follows the set of ChatGPT and Codex product announcements documented in ChatGPT and Codex product announcements and gives Atlassian a route to bring new reasoning capabilities into Rovo through OpenAI APIs as the models advance.
From launch assessment to agent oversight
Atlassian illustrates the intended use with a product manager asking whether a launch is on track. Rovo can connect Jira tickets, Confluence documents and relevant discussions to identify engineering blockers, flag missed milestones and surface decisions needing attention. OpenAI models then turn that material into an assessment of launch readiness and recommended next steps.
The companies are also exploring deeper Jira integrations for assigning work to AI agents, tracking progress, capturing decisions and reviewing results. Paired with DX, Atlassian’s platform for measuring developer productivity and engineering performance, the capabilities could help engineering leaders measure effects on development speed, cycle time and developer experience while retaining human control.
For businesses, the practical implication is that AI deployment can be tied to existing work systems, but the value depends on permission-aware access to reliable organizational context and human review of agent output.