Atlassian AI Capabilities target governed agentic development
AI is already part of software engineering, but many organisations are struggling to move beyond isolated use cases. Atlassian is addressing that gap with new Atlassian AI capabilities across Jira, Confluence and DX, aimed at helping engineering teams scale governed agentic workflows across the software development lifecycle.
The company says 94% of engineering leaders report using AI, while only 6% say they have the systems needed to scale it across the lifecycle. The gap, Atlassian argues, is less about model intelligence and more about organisational context.
Giving AI agents more context
The new capabilities start with making agents better understand the work around them. Code Context, built on Atlassian’s Teamwork Graph, gives Rovo and coding agents secure intelligence across multi-repository codebases.
That context can support tasks ranging from checking whether backlog ideas are technically feasible to creating implementation plans, triaging bugs and finding potential root causes.
For enterprises, the bigger shift is control. Agent Context Controls allow platform teams to decide which agents can operate in a space and what information they can access. This puts governance closer to the point where AI work actually happens.
Jira moves from backlog to execution
Atlassian is also pushing agentic software development further into Jira through Agentic loops. The feature can scan for defined, unassigned work, delegate tasks to Jira Coding Agent for execution and testing, and open ready-to-review pull requests.
Standards and AI Review add another layer. Teams can define coding standards once and map them across repositories, while a dedicated agent reviews pull requests against those standards before code is shipped.
The direction is clear: AI agents are being positioned not just as assistants, but as participants in the engineering workflow.
Governance becomes part of the workflow
That shift also creates a need to govern AI agents and measure what they actually deliver. DX for Agentic Development is designed to track AI impact across throughput, quality, adoption and cost, linking AI investment with engineering outputs.
The Jira Agent Usage Dashboard adds visibility into which agents teams are using and how their sessions connect with Jira work.
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