Atlassian is upgrading its AI coding agents with new capabilities across Jira, Confluence, and DX.
The updates include the extension of Code Context to ground agents in multi-repo codebases, Agentic loops in Jira to convert Jira backlogs into pull requests, and DX for Agentic Development to measure AI delivery impact.
The aim is to help engineering organizations adopt and scale agentic workflows across the AI software development lifecycle by connecting agent context, execution, governance, and measurement in the systems where teams are already planning and delivering work.
“The biggest bottleneck in AI software engineering isn't model intelligence, it's organizational context,” said Taroon Mandhana, Atlassian CTO, AI & Teamwork.
“Enterprises need more than isolated sessions and one-off prompts. Jira has long been the system of record for how teams work. By extending that foundation to orchestrate agents alongside engineers, we’re giving teams a safe, measurable way to scale agentic workflows across the SDLC.”
Atlassian eyes deeper context
First up is the introduction of Code Context, built on Atlassian’s Teamwork Graph and giving Rovo and coding agents secure intelligence across multi-repository codebases.
This, Atlassian said, enables more accurate results across the entire lifecycle, from vetting backlog ideas for architectural feasibility and generating code-aware implementation plans to accelerating bug triage and root-cause discovery.
Agent Context Controls, meanwhile, let platform teams govern which agents can operate in a space and exactly what they’re allowed to see.
Also new is the introduction of agentic loops in Jira to convert Jira backlogs into pull requests, by continuously scanning for well-defined, unassigned work items, delegating them to Jira Coding Agent for execution and testing, and opening ready-to-review PRs directly in Jira.
Standards enables platform teams to define organizational coding standards once and map them to repositories, creating consistent guardrails for code quality, and AI review uses a dedicated agent to review pull requests against organizational standards, flagging issues before code ships.
Meanwhile, Atlassian claimed engineering organizations need automation, auditing, and measurement built into their systems if they're to be able to make agentic work accountable and measurable.
DX for Agentic Development measures AI impact across throughput, quality, adoption, and cost, mapping total AI investment directly to engineering outputs. It includes AI Code Insights, tool and MCP tracking, model-to-task fit, and academic-validated Agent Experience (AX) research.
Keeping tabs on agents
Finally, Jira Agent Usage Dashboard is designed to help team leaders understand which agents are used in their workflows, correlate agent sessions to Jira work, and improve team delivery velocity with agents.
"This is what takes governed agent loops from an experiment to something you can put in front of your board," said the firm.
Code Context is being gradually rolled out to paid Atlassian customers through open beta, while Agent loops, Standards, and AI Review are already available in private early access.
Agent Context Controls and Agent Usage Dashboard will be generally available to paid Jira customers in the coming months, said the firm, and DX for Agentic Development will be generally available for Atlassian DX customers this quarter.
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