Atlassian transforms Jira into orchestration hub for developers and AI agents amid productivity gap

Atlassian puts context and governance ahead of the agents in its plan for AI-native software delivery

Software teams have taken to coding agents faster than almost any tool before them, and yet shipping software hasn't sped up to match. Atlassian has put a number on the gap. In a longitudinal study the company ran with the engineering intelligence platform DX, the volume of AI-authored code nearly doubled over three months, while developer productivity gains stalled - topping out at around 15%, and coming in below 10% at many organizations. On the back of this comes an announcement from Atlassian with a set of new capabilities in Jira and the Teamwork Graph aimed at the work that surrounds the code rather than the coding itself. Ming Wu, Head of Engineering for Dev AI at Atlassian and joined around two years ago from Microsoft and GitHub, walked me through the reasoning ahead of the launch. Her starting point is that writing code was never where most of the effort went. The coding part, it's an essential part, but it's only fifteen to sixteen percent of the time. All the rest comes from planning and iterating on the design and alignment, and addressing all the issues when it goes out, and the code review. The rest of the software development lifecycle is where the cost lives, and Wu says this is the part that a lone coding agent tends to handle badly. Where the human holds the wheel Atlassian's shorthand for the shift is that work becomes human-steered and agent-executed. Asked what steering means when you take the word literally, Wu was clear that the senior engineer's job doesn't shrink. I actually don't think the human's role, the main role, has changed. The human is the one holding the responsibility of how this thing will ship, and what you're going to ship, or when. AI tools, it's very fast, but it doesn't know what to do. What changes is the typing and the small, well-defined tasks around it. She explains: I don't have to type anymore, but I still have to see. I have to tell it what to do. That leaves the repetitive work - tech debt, feature-flag clean-up, small atomic fixes - as the part an agent can absorb, and frees people for the more complex and creative work. The point she emphasizes is accountability - a human still has to sign off on whatever ships. Atlassian says that for this shift to actually work, three things have to be true. Intent has to be structured before work starts, so an agent gets the requirement, the architecture, the decision history and the constraints, rather than a one-line summary. The agent should be a runtime choice rather than a process one, so a team can reach for the Cursor integrated development environment on one task and Claude Code on another without the workflow forking each time. And autonomy has to stay observable, so agent work doesn't vanish into terminals and browser tabs. Why context sits at the center Running through the announcement is the Teamwork Graph, which Atlassian describes as its context layer - a map of work, code, people, decisions and dependencies that pulls together the task in Jira, the requirements in Confluence, the conversation in Slack, the code context from GitHub, and customer insight from Jira Product Discovery. Wu observes that the context layer is harder to build than it sounds, and she believes it matters more than the agents themselves. She elaborates: The context layer is not about the raw data. How do you actually retrieve, efficiently and smartly, only the relevant ones? We all know documents go obsolete. She calls the work of selecting the context that's actually relevant as context engineering, and she's put a good deal of Atlassian's investment there. She was also explicit that it's a claim many companies make and few follow through on, and she leaned on her own background to say so: I come from Microsoft, and I was in GitHub. The context graph is always the hard topic that every company claims they do. But not everyone actually puts in serious effort. Without it, Wu notes: An agent won't work well if you just throw everything together. Starved of context, according to Atlassian, an agent solves a ticket too literally, misses an architectural constraint, or produces a pull request that looks plausible until a senior engineer spends an hour unwinding it. Jim Mercer, Program Vice President for software development, DevOps and DevSecOps at the analyst firm IDC, made the same point: 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. Getting into the details, these are the elements that are shipping: Getting the context in. Jira Planner brings spec-driven development into Jira. For complex projects, Atlassian says it pulls from the codebase, from Jira and Confluence history, and from team context to produce a structured technical specification in Confluence, readable by a person and usable by an agent. Wu describes the target as brownfield work, the tangled existing systems where an autonomous agent tends to come unstuck, and she doesn't pretend the people can be designed out of it, explaining: Especially from what I see in banking and finance, that type of more rigorous environment with a lot of dependencies, you cannot skip the complexity of the real-world problem, and also the collaboration. You actually do need the people coming in. Alongside it, Jira for Slack turns a thread into a work item when a team asks @Jira, keeping the conversation that shaped a decision attached to it. Loom video prompts take a screen recording and a spoken walk-through, and turn them into structured instructions an agent can act on. Delegating and watching the work. Agents in Jira lets teams assign a work item to Claude Code, Cursor, or GitHub Copilot directly, with OpenAI's Codex listed as coming soon. The Jira Coding Agent, now built into every paid Jira plan at no extra cost, takes a well-scoped item and returns a ready-to-review pull request for routine fixes without a developer dropping into a local environment. Agent sessions in Jira, together with new Teamwork Graph command-line interface hooks, link a local terminal session back to the Jira item it belongs to, so the record survives a closed laptop. Governing the cost and the scale. Coding agent automations let teams route routine work - bug fixes, vulnerability remediation, test generation, documentation updates - through Jira's automation rule builder, with an engineer notified when a pull request is ready. The Agentic Engineering project template and a setup wizard stand up an agent-ready project with workflows and integrations pre-configured. DX AI cost management gathers spend and token data across tools such as Claude, Cursor and GitHub Copilot alongside Jira, and estimates a cost per pull request. The numbers, and what they measure Atlassian ran these patterns across its own engineering organization before shipping them. In an internal study of 6,000 engineers using the new capabilities, the company reported a 44% rise in agent task completion efficiency, a 48% drop in token consumption, a 36% reduction in pull request cycle time, and 51% of routine code vulnerabilities resolved autonomously and queued for developer review. The work is done by an agent, and then it waits for a person to review it before it merges. For atomic, well-scoped tasks such as vulnerability tickets, Wu puts the internal automation rate in the region of half to two-thirds, adding: The more complex the task is, you will have to interact and tell the agent. We also talked about benchmarks and how often a ready-to-review pull request merges with only light review. Wu points to the SWE-bench coding benchmark. She notes that the full set sits in the low 40s and the verified set far higher, but doesn't put much weight on it, observing: Honestly, I think SWE-bench is saturated. It's also open, so everyone sees that. Take the numbers with a grain of salt. We're not banking on that too much. On strategy, she places Jira as the layer that sits across those agents rather than a rival to the model builders, and agrees that some of the positioning is still moving. Jira is well positioned to be that orchestration layer. It has to be, because nobody else would be in that role. Atlassian is not a model player. I don't think that's our competing strategy right now. She ends on the point that the collaboration only works once an agent can be trusted with the context a colleague would have: You work with your teammate only when they understand your context. Then you can trust them to actually work together with you. Atlassian says Agents in Jira for Claude Code, Cursor and Copilot, along with Jira for Slack, the Jira Coding Agent, agent automations, the agentic templates, and agent sessions, are available now for paid Jira Cloud customers at no additional cost. Jira Planner is in early access, and Codex in Jira is listed as coming soon. DX AI cost management is available to Atlassian DX customers. My take The agent clears the routine vulnerability, and a person still looks before it merges. Using a metric of "queued for developer review" feels like a much simpler explanation of human-steered and avoids hype. Earlier this year I talked to several Atlassian experts about the Teamwork Graph pitch and spent a good part of those conversations pushing on whether it was real yet or still a keynote slide - how fresh the context actually was, how fast the graph knew when something had changed. A few months on, every agent capability in this launch leans on it, and the context layer is now carrying the whole product. Atlassian has put its coding agent in every paid plan, and its real effort into context and governance, rather than joining the ongoing vendor agent race. The memory of what your team decided, and the record of who signed off before it shipped, are Atlassian's solid ground, because of its strong history as the system of record.

Atlassian evolves Jira into an orchestration hub for developers and AI agents

Atlassian evolves Jira into an orchestration hub for developers and AI agents Atlassian Corp. today announced it is expanding Jira with updates that will help developers prepare, distribute and track work performed by artificial intelligence agents. The company's new Jira Planner helps turn incomplete project ideas into technical specifications, while its Jira Coding Agent and integrations with third-party agents transform work items into requests. With automation rules and an agentic engineering template, teams can design workflows where agents are assigned work as projects move through the platform. The idea behind the updates is to position Jira as the place where developers go to organize and prepare, dovetailing with the agentic AI coding boom. Coding-agent adoption is already widespread, but Atlassian sees the resulting improvements beginning to plateau as planning and coordination become a bottleneck. "We need a solution rather than a tool," said Head of Engineering for DevAI Ming Wu. "We need a solution across the entire software development lifecycle journey to actually address different pain points." This is where Jira's history becomes the connective tissue of software team progress and processes become strategically important. The company wants to become the control plane for a mixed workforce of developers and agents, whether those agents run locally, in the cloud or from third parties. "The more we talk to our customers, based on our DevEx report and also DX survey, the more we look at data, the more we believe the customers need some holistic solution to pull those AI tools together and realize the gain -- the return on investment -- from their AI tool usage," Wu said. Even with the broad adoption of coding tools, the market is in a transition. Even as developers become more comfortable using an expanding range of AI tools, companies are struggling to find reliable, company-wide value. The answer from Atlassian has been to broaden its audience and expand its tool capabilities. Instead of measuring success by how rapidly an agent produces code, Jira can help address delays caused by unclear requirements, missing project context, handoffs, environment setup, assignment, documentation, review and governance. Essentially, this is the "work that surrounds work." Industry research has shown that even as frontier models get smarter and faster, submissions from AI agents are accepted less frequently than human-authored ones and tend to be structurally simpler. According to a study by researchers at Queen's University Kingston in Canada, a review of 61,000 repositories and 47,000 developers found that AI agents aren't even second-class citizens; they're closer to a carefully tended third-class. AI agents may produce a great deal of code quickly, but the quality is considered extremely low. Getting agents to do good work requires giving them a way to coordinate, collaborate and augment human developers, who do the higher-quality work. However, if humans spend all their time orchestrating, managing and fine-tuning agents for small, laborious and boring tasks, they're not doing their own high-quality work. To help with that, Jira is adding a way to easily launch a ticketing board system that allows agents to enter into the collaborative flow called the Agentic Engineering Template. It's for more advanced adopters who want Jira workflows to work alongside agents. "It helps you to set up the Jira board where columns have a state," Wu said. "When you move the issue from one column to the other, agents are automatically assigned, so there's a certain level of more detailed, nuanced automation in that feature." Jira Coding Agent handles most bounded work in the cloud, meaning developers don't need to use a coding editor to trigger it. It can act directly from Jira, but it's not a complete or direct replacement for hands-on development. It exists so developers can delegate tasks they'd rather get out of the way without interrupting their current work. The company also announced that work can be assigned to any coding agent. This means developers can still use their favorite agentic environment for issuing requests, including Claude, Codex, Cursor, or GitHub Copilot, directly from Jira. The work stays grounded in Jira's knowledge and history and the project's path, and it feeds critical information to the agent so it can get the job done. "You want to bring the true value rather than boosting usage itself," Wu said. "[Boosting] usage itself is superficial, really. You want the customer getting good value for their money spent."

Atlassian launches AI agent coordination tools for Jira By Investing.com

SAN FRANCISCO - Atlassian Corporation (NASDAQ:TEAM) announced Wednesday new capabilities in Jira designed to coordinate AI agents across software development workflows. The $23.3 billion software company has seen its stock decline 52.5% over the past year, though shares have rebounded 4.2% in the past week as the company pushes deeper into AI-powered development tools. The company stated that while AI usage by engineers has increased 65%, developer velocity gains remain at approximately 10%, according to its 2026 longitudinal study. The features aim to address what Atlassian describes as coordination challenges in AI-native development. The push into AI coordination comes as Atlassian maintains an impressive gross profit margin of 84.8%, reflecting the strength of its software-as-a-service business model. According to an InvestingPro tip, 30 analysts have revised their earnings upwards for the upcoming period, signaling growing confidence in the company's AI strategy. The new capabilities include Jira for Slack, which converts conversations into work items and can assign tasks to coding agents. Jira Planner uses the company's Teamwork Graph to generate technical specifications in Confluence by pulling from codebases and project history. Loom video prompts allow users to record screen instructions that generate action plans for agents. Teams can now assign work items directly to coding agents including Claude Code, Cursor, and GitHub Copilot from within Jira. The Jira Coding Agent, included in paid plans, converts work items into pull requests using enterprise context from the Teamwork Graph. Atlassian stated that in internal benchmarking, agents using Teamwork Graph showed 44% more accurate results while using 48% fewer tokens than agents without that context. The platform adds visibility features showing the status of AI coding agents across projects and includes automation capabilities for routing tasks like bug fixes and vulnerability remediation to agents. A new DX AI cost management report consolidates spending data across third-party AI tools. "The bottleneck in AI-native development isn't agent capability, it's coordination at scale," said Sean Joerg, Deputy CISO and Head of Corporate Engineering at Reddit. The features are available today for paid Jira Cloud customers at no additional cost, according to a press release statement. Jira Planner is available through an early access waitlist, and DX AI cost management is available for Atlassian DX customers. With the company's next earnings report scheduled in 15 days, investors will be watching closely for signs that these AI innovations are driving growth. InvestingPro analysis suggests the stock is currently undervalued, with 13 additional ProTips available to subscribers, including detailed insights on the company's financial health and growth prospects. In other recent news, Atlassian Corporation reported a strong third-quarter revenue performance, attributed to seat expansions and cross-sell activities. Analysts from Macquarie noted the company's ongoing momentum in artificial intelligence, with Rovo and MCP usage increasing by 20% month-over-month. Meanwhile, Bernstein reaffirmed an Outperform rating on Atlassian, highlighting potential revenue growth challenges due to Data Center accounting dynamics but expecting improved visibility later in the year. Truist Securities maintained a Buy rating, focusing on the company's AI strategy following the Team 26 event, which raised investor questions about monetizing AI products. Piper Sandler also reiterated an Overweight rating after attending Atlassian's annual user conference, where the company showcased new platform enhancements like the Teamwork Graph and Rovo. Citizens kept a Market Perform rating on Atlassian, based on mixed feedback from a customer survey. Lastly, Macquarie adjusted its price target for Atlassian to $130 from $150, maintaining an Outperform rating despite the valuation review. These developments reflect the varied analyst perspectives on Atlassian's current and future performance. This article was generated with the support of AI and reviewed by an editor. For more information see our T&C.

Atlassian Corporation Announces System for Ai-Native Software Development in Jira

Atlassian Corporation 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%. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 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.