New Jira and Teamwork Graph capabilities give engineering organizations a single place to plan, orchestrate, and scale agentic work across the full software development lifecycle

Atlassian Corporation, a leading provider of AI-powered collaboration and team productivity software, 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.

Accelerating planning and spec-definition through enterprise 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: Jira for Slack closes the gap between team conversations and structured work. Now, you can turn conversations into context-rich specs. Create work items and kick off agent tasks from feedback or ideas in Slack just by asking @Jira. The agent updates work items, syncs conversations as comments, and assigns work to coding agents while your team collaborates in Slack, with expanded Microsoft Teams capabilities coming soon.
• Jira Planner: The all-new Jira Planner enables spec-driven development to modern software teams. For complex projects, Jira Planner pulls from the Teamwork Graph, including your codebase, Jira and Confluence history, and team context, to define requirements and generate a structured technical spec in Confluence, ready for a developer or coding agent to build upon.
• Loom video prompts: Now, Loom turns what you show and say into structured instructions that agents can use to execute tasks. Record your screen and talk through what you want done. Loom captures your screens, clicks, links, and voice instructions and generates an action plan you can share with any agent or turn into agent-ready Jira work items in a few clicks.

Delegate and monitor agent work more efficiently

As teams adopt multiple coding agents, Jira keeps work grounded in the same source of truth whether execution happens inside Jira, in a cloud agent, or locally in a developer environment.

• Assign work directly to any coding agent. Now you can assign work items to Claude Code, Cursor, or GitHub Copilot directly from Jira (with Codex coming soon). Work stays grounded in Jira as the single source of truth, with context feeding improved responses from coding agents.
• Built-in execution with the Jira Coding Agent, included in every paid plan. Powered by frontier models, the Jira Coding Agent uses the Teamwork Graph’s enterprise context and code intelligence to turn work items into ready-to-review pull requests, allowing rapid fixes and workflows within Jira without requiring local environment setup.
• Get complete agent visibility: See which AI coding agents are stuck, what’s waiting for review, and what’s complete. Every engineer working in Jira gets visibility into agent sessions running across their spaces and repos in a single view, grouped by what needs attention first.

Deploy coding agents more intelligently across the enterprise

To move agentic work from experiments to enterprise adoption, organizations need automation, onboarding, and measurement built into the systems teams already use.

• Grow engineering capacity with new autonomous workflows. Now, every engineering team can automate any business process using coding agents directly in Jira’s enterprise-grade automation rule builder. Teams can route bug fixes, vulnerability remediation, test generation, and doc updates to agents in the background, with engineers notified when a PR is ready for review.
• Faster onboarding for agentic engineering. The new Agentic Engineering project template and a guided setup wizard help engineering teams stand up agent-ready projects in minutes, with workflows, statuses, tracking, and integrations pre-configured.
• Measure the impact and ROI of AI: Teams can leverage the new DX AI cost management report to unify spend and token data across third-party tools like Claude, Cursor, and GitHub Copilot alongside Jira projects and teams, mapping total AI investment directly to engineering outputs to calculate an estimated cost per PR within DX.

“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. 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.”

“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.”

Availability

Agents in Jira (Claude Code, Cursor, and GitHub Copilot), Jira for Slack, Jira coding agent, Jira agent automations, agentic templates, and agent sessions in Jira are available today for paid Jira Cloud customers at no additional cost. The waitlist for Jira Planner EAP is open, Rovo for Microsoft Teams is available in early access, and Codex in Jira is coming soon. DX AI cost management is available for Atlassian DX customers. Learn more at Jira for AI-Native Software Development | Plan and Orchestrate AI Agents.

Source: BUSINESS WIRE