Atlassian Launched New Agentic Software Workflow Tools
The company introduced features to help engineering teams scale AI usage across their development lifecycles.
Updated on Sept. 25, 2026 in Artificial Intelligence
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Atlassian has unveiled a suite of new Jira and developer experience tools designed to integrate AI agents into software workflows. These features aim to help teams automate tasks and manage AI agent access within organizational data.
Why it matters
Engineering teams often struggle to scale AI usage effectively, as software agents perform better when they have access to relevant organizational and project data rather than just simple code prompts.
Atlassian reports that teams using more context ship 64 percent more per developer. The company currently serves 350,000 customers, including 85 percent of the Fortune 500.
The players
Atlassian
Atlassian is an enterprise software company that produces products for software developers, project managers, and other software development teams.
Jira
Jira is a proprietary issue tracking product developed by Atlassian that allows bug tracking and agile project management.
Confluence
Confluence is a wiki-based team collaboration software developed by Atlassian to help teams collaborate and share knowledge.
The details
The new suite includes Code Context for sharing information across codebases and Agent Context Controls to restrict agent access to specific Jira and Confluence spaces. Additionally, agent loops can now automate backlog task processing, while an AI review agent checks pull requests against defined organizational standards.
Timeline
Atlassian launched the new Jira and developer experience features on September 25, 2026.
The DX for Agentic Development tool is scheduled for general availability in Q3 2026.
Agent Context Controls and the Agent Usage Dashboard are set for release in the coming months.
Deeper Dive
This development centers on the integration of autonomous agents with Atlassian's Teamwork Graph, which provides the necessary context for AI to operate effectively. By leveraging this existing data infrastructure, the company is bridging the gap between isolated code prompts and comprehensive project understanding.
Developers can expect more automated oversight of their codebases, including automated pull request reviews and backlog task management. These changes require engineering teams to define clear coding standards for both human staff and AI agents to ensure consistent outputs.
The takeaway
Engineering leaders should prioritize building internal systems that allow AI to safely access project data to realize significant gains in shipping velocity. Establishing clear standards early is essential to ensuring that autonomous agents adhere to organizational quality expectations.
Further reading
For more on how organizations are integrating automated systems, visit our Artificial Intelligence section.
Source note: This article includes information reported by eCommerceNews Australia.
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Do you trust AI agents to effectively handle software development tasks with minimal human supervision?
