Atlassian has announced new capabilities in Jira to advance AI-native software development for engineering organizations. The launch addresses the widening productivity gap where AI usage by engineers has increased 65% but developer velocity gains remain at approximately 10%, driven by bottlenecks in planning, review, and enterprise context.
Atlassian launched new Jira capabilities for AI-native software development with Teamwork Graph providing enterprise context for agents.
Teamwork Graph-enriched agents showed 44% more accurate results using 48% fewer tokens in internal benchmarking.
New features include Jira for Slack, Jira Planner, Loom video prompts, and assigning work directly to Claude Code, Cursor, and GitHub Copilot.
The Jira Coding Agent, included in every paid plan, turns work items into ready-to-review pull requests.
Autonomous workflows enable bug fixes, vulnerability remediation, and test generation using coding agents.
DX AI cost management report unifies spend and token data across third-party tools to calculate estimated cost per PR.
Atlassian Corporation today announced new capabilities in Jira to advance AI-native software development for every engineering organization.
This launch addresses a widening productivity gap: while AI usage by engineers has increased by 65%, developer velocity gains remain at approximately 10% (Atlassian DX longitudinal study, 2026). This plateau is driven by three core bottlenecks: a lack of enterprise context causing AI output to drift from requirements; unsolved SDLC bottlenecks outside of code generation, such as planning, review and maintenance; and difficulty of integrating AI across team workflows.
"The bottleneck in AI-native development isn't agent capability, it's coordination at scale to keep our engineers in the flow," said Sean Joerg, Deputy CISO & Head of Corporate Engineering, Reddit. "We're partnering with Atlassian to solve that: one place where every agent action is visible, governed, and tied to a business outcome."
Today's announcements give teams the ability to plan, orchestrate, and scale agentic work across the full software development lifecycle, whether they're working in Jira or their coding environments. Atlassian's Teamwork Graph provides the enterprise context behind many of these capabilities, connecting work, teams, goals, code, and knowledge across the SDLC so agents can act with greater relevance and accuracy. In internal benchmarking, agents enriched by Teamwork Graph showed 44% more accurate results while using 48% fewer tokens than agents operating without that context.
AI agents are only as useful as the intent and context they receive. These capabilities help teams turn conversations, requirements, codebase context, and product decisions into work agents can understand and act on:
Jira for Slack: Turn conversations into context-rich specs. Create work items and kick off agent tasks from feedback or ideas in Slack just by asking @Jira. Expanded Microsoft Teams capabilities coming soon.
Jira Planner: Enables spec-driven development by pulling from the Teamwork Graph to define requirements and generate structured technical specs in Confluence.
Loom video prompts: Turns what you show and say into structured instructions that agents can use to execute tasks, generating an action plan from screen recordings and voice instructions.
As teams adopt multiple coding agents, Jira keeps work grounded in the same source of truth:
Assign work directly to any coding agent: Assign work items to Claude Code, Cursor, or GitHub Copilot directly from Jira (with Codex coming soon).
Built-in execution with the Jira Coding Agent: Included in every paid plan, powered by frontier models and Teamwork Graph context to turn work items into ready-to-review pull requests.
Complete agent visibility: See which AI coding agents are stuck, waiting for review, or complete in a single view grouped by priority.
To move agentic work from experiments to enterprise adoption:
New autonomous workflows: Automate any business process using coding agents in Jira's enterprise-grade automation rule builder. Route bug fixes, vulnerability remediation, test generation, and doc updates to agents in the background.
Faster onboarding: New Agentic Engineering project template and guided setup wizard help teams stand up agent-ready projects in minutes.
Measure the impact and ROI of AI: DX AI cost management report unifies spend and token data across third-party tools alongside Jira projects to map AI investment to engineering outputs.
"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, IDC. "By leveraging Jira and the Teamwork Graph, Atlassian is building a context layer for AI."
"LLMs have made writing code nearly instant. The heavy lifting is now 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, 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."
About Atlassian
Atlassian unleashes the potential of every team. A recognized leader in software development, work management, and enterprise service management software, Atlassian enables enterprises to connect their business and technology teams with an AI-powered system of work that unlocks productivity at scale. Atlassian's collaboration software powers over 85% of the Fortune 500 and 350,000+ customers worldwide - including NASA, Rivian, Deutsche Bank, United Airlines, and Bosch - who rely on our solutions to drive work forward.
