Atlassian announced new capabilities across Jira, Confluence, and DX to help engineering organisations adopt and scale governed agentic workflows across the AI software development lifecycle.
Key updates include 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.
This launch addresses a critical enterprise scaling gap. While 94% of engineering leaders report using AI, only 6% say they have the systems required to scale it across the lifecycle: “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.”
Context: Helping agents understand the work
Code Context, built on Atlassian’s Teamwork Graph, gives Rovo and coding agents secure intelligence across multi-repository codebases. This 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 let platform teams govern which agents can operate in a space and exactly what they are allowed to see.
Execution: Increasing engineering capacity with autonomous agent loops
Agentic loops in Jira automate the path from backlog to pull request 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 enable platform teams to define organizational coding standards once and map them to repositories, creating consistent guardrails for code quality.
AI review uses a dedicated agent to review pull requests against organizational standards, flagging issues before code ships.
Governance: Making 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 unifies AI Code Insights, tool and MCP tracking, model-to-task fit, and academic-validated Agent Experience (AX) research with rich software context and guardrails, closing the loop between AI observability and governance.
Jira Agent Usage Dashboard helps team leaders understand which agents are used in their workflows, correlate agent sessions with Jira work, and improve team delivery velocity with agents.
Code Context is gradually rolling out to paid Atlassian customers through open beta. Agent loops, Standards, and AI Review are available in private early access. Agent Context Controls and Agent Usage Dashboard will be generally available to paid Jira customers in the coming months. DX for Agentic Development will be generally available for Atlassian DX customers this quarter.
Learn more about Atlassian’s vision for the AI SDLC and how Jira, Confluence, and DX help teams scale governed human-agent collaboration.
𝐒𝐭𝐚𝐲 𝐢𝐧𝐟𝐨𝐫𝐦𝐞𝐝 𝐰𝐢𝐭𝐡 𝐨𝐮𝐫 𝐥𝐚𝐭𝐞𝐬𝐭 𝐮𝐩𝐝𝐚𝐭𝐞𝐬 𝐛𝐲 𝐣𝐨𝐢𝐧𝐢𝐧𝐠 𝐭𝐡𝐞 WhatsApp Channel now! 👈📲
𝑭𝒐𝒍𝒍𝒐𝒘 𝑶𝒖𝒓 𝑺𝒐𝒄𝒊𝒂𝒍 𝑴𝒆𝒅𝒊𝒂 𝑷𝒂𝒈𝒆𝐬 👉 Facebook, LinkedIn, Twitter, Instagram
