Atlassian adds Jira tools for AI-native software teams

Atlassian has introduced new Jira and Teamwork Graph features for AI-native software development, aimed at giving engineering teams a single place to manage agent-based work across the software development lifecycle.

The changes target a productivity gap in software engineering, where AI tool use has risen but gains in developer speed have remained limited. Atlassian cited its own research showing a 65% increase in AI usage by engineers, while developer velocity gains stayed at about 10%.

The launch centres on Jira as a control point for planning, assigning and tracking work carried out by human developers and coding agents. Teamwork Graph provides shared context by linking work, teams, goals, code and knowledge across development processes.

In internal benchmarking, agents using Teamwork Graph context produced results that were 44% more accurate and used 48% fewer tokens than agents operating without it.

Planning tools

Among the changes is Jira for Slack, which lets teams convert Slack conversations into structured specifications and work items using the @Jira prompt. The tool can also update work items, sync discussion threads as comments and assign tasks to coding agents while teams continue collaborating in Slack.

Another addition is Jira Planner, designed for spec-driven development. It can pull information from a codebase, Jira history, Confluence records and team context through Teamwork Graph to define requirements and generate a technical specification in Confluence.

Atlassian is also adding Loom video prompts. The feature turns a screen recording and spoken instructions into structured task directions that can be shared with an agent or converted into Jira work items.

Agent oversight

Jira is also being extended so users can assign work directly to coding agents, including Claude Code, Cursor and GitHub Copilot. Work remains tied to Jira, which acts as the system of record while providing context to those tools.

A built-in Jira Coding Agent is included in every paid plan. It can turn work items into pull requests ready for review using Teamwork Graph context and code intelligence, without requiring developers to set up a local environment.

Atlassian is also introducing a single view for monitoring agent activity. Engineers can see which agent sessions are stalled, which tasks are awaiting review and which jobs have been completed across their workspaces and repositories.

That visibility is part of a broader push to move coding agents from limited trials into wider operational use. Atlassian argues that broader adoption of agent-based work will require governance, automation and measurement tools inside software already used by engineering teams.

Automation and measurement

New autonomous workflows in Jira allow engineering teams to automate processes using coding agents within Jira's automation rule builder. Teams can send bug fixes, security remediation, test generation and documentation updates to agents in the background, then notify engineers when a pull request is ready for review.

Atlassian is also launching an Agentic Engineering project template and a guided setup wizard to help teams create projects with pre-configured workflows, statuses, tracking and integrations.

For customers using Atlassian DX, a new AI cost management report will combine spending and token data from tools such as Claude, Cursor and GitHub Copilot with Jira projects and team data. This is intended to help teams link AI spending to engineering outputs and estimate cost per pull request.

Sean Joerg, Deputy CISO and Head of Corporate Engineering at Reddit, described the issue as one of coordination rather than model quality. "The bottleneck in AI-native development isn't agent capability, it's coordination at scale to keep our engineers in the flow," Joerg said. "We're partnering with Atlassian to solve that: one place where every agent action is visible, governed, and tied to a business outcome."

Industry analysts said context remains a central issue as more coding agents are introduced into software teams. Without a clear understanding of project history, technical constraints and internal decisions, faster code generation can still produce poor outcomes.

"As AI coding agents proliferate, the real bottleneck isn't model intelligence; it's organizational context. Agents operating without a deep understanding of team decisions, architectural constraints, and project history produce misaligned code more quickly, leading to technical debt and production issues," said Jim Mercer, Program Vice President, Software Development, DevOps, and DevSecOps at IDC. "By leveraging Jira and the Teamwork Graph, Atlassian is building a context layer for AI. As the system of record for agile development, it can turn tribal knowledge into a persistent, queryable data layer that can improve code quality and release velocity across the enterprise."

Products now available to paid Jira Cloud customers at no extra cost include agents in Jira for Claude Code, Cursor and GitHub Copilot, Jira for Slack, the Jira Coding Agent, Jira agent automations, agentic templates and agent sessions in Jira. Jira Planner is being offered through a waitlist for early access, while the DX AI cost management report is available to Atlassian DX customers.

"LLMs have made writing code nearly instant. The heavy lifting now is everything around it: defining what to build, governing what ships, and coordinating across humans and agents at scale," said Taroon Mandhana, CTO, AI and Teamwork at Atlassian. "Jira has been the system of record for software teams for two decades. Today, we're extending that to every agent working alongside them."