Tag: Atlassian

  • Keeping Humans Accountable as AI Agents Take On the SDLC

    Keeping Humans Accountable as AI Agents Take On the SDLC

    Who is responsible when an AI agent finishes a development task? Ming Wu, head of engineering for Dev AI at Atlassian, puts that responsibility with people. In her interview with Alan Shimel, she separates the ability to automate work from the obligation to understand and control the result. That distinction matters as teams move beyond individual coding prompts and start assigning agents larger portions of the software development lifecycle.

    Wu describes governed agent loops as two related capabilities. Governance supplies visibility, guardrails and enforcement; loops allow agents to process batches of jobs under defined conditions instead of waiting for someone to trigger each task. The scope can include taking on an issue, carrying out the work and reviewing a pull request. Automation may handle more steps, but teams still need a way to trace activity and judge whether the output meets their expectations.

    Existing work-tracking systems offer a place to organize that activity. Wu explains why Jira is an initial interface for the approach: it already records work across teams and organizational boards. She also describes support for third-party coding agents and a shared context layer intended to give agents a consistent understanding of team priorities, organizational requirements and business intent. Coordinating those inputs is part of the engineering problem, particularly when several agents contribute to the same body of work.

    Customers are asking how to scale these workflows and demonstrate real improvements in delivery speed, according to Wu. Some software teams are already experimenting, while organizations in more traditional industries are at different stages of adoption. Her examples put visibility into work alongside automation itself: teams need to see what agents are doing, where people remain accountable and whether the investment is producing useful gains. Completing more automated tasks is only part of that assessment; understanding their contribution to the software lifecycle is the harder question.

  • Atlassian Adds CLI Option to AI Agent for Building Software

    Atlassian Adds CLI Option to AI Agent for Building Software

    Atlassian has added a command line interface (CLI) option to provide application developers with a familiar construct for invoking Rovo Dev, its artificial intelligence (AI) agent for building software

    Part of a portfolio of Rovo Software Agents that Atlassian now makes available in beta, Rovo Dev completes and debugs code, creates tests, surfaces insights and explanations of code bases, makes suggestions to improve code and generates documentation. It is also integrated with Jira project management software and Confluence collaboration tools provided by Atlassian.

    Using a set of software engineering (SWE) benchmarks maintained by researchers at Princeton and Stanford, Rovo Dev achieved a 41.98% resolve rate across 2,294 tasks.

    It can also be integrated via a Model Context Protocol (MCP) Server to access external data sources capable of providing more context that might be needed to complete a specific task.

    Shuyin Zhao, vice president of product for Atlassian, said the goal is to provide a single agent capable of performing a wide range of tasks versus requiring developers to create workflows that require them to orchestrate multiple agents to build and deploy software. The more memory that can be allocated to an AI agent the more it is able to preserve context, she added.

    Longer term, as the reasoning capabilities provided by large language models (LLMs) continue to expand, the scope of complex tasks that can be assigned to that AI agent will also increase, noted Zhao.

    Eventually, many developers will find they will be spending more time reading and evaluating code rather than writing it themselves, she added.

    It’s not clear how widely AI tools are being adopted by software developers and engineers, but a Futurum Group survey found 41% of respondents expect generative AI tools and platforms will be used to generate, review and test code. The one thing that is certain is more developers than ever are using these tools to write code, but how much of that code is making it into production environments is unknown. In some instances, an AI tool might enable a developer to write better code than they can on their own. In other instances, it might create vulnerabilities or recommend using software packages that simply don’t exist.

    Additionally, even as more code is generated, the existing DevOps pipelines might not be robust enough to deploy software at higher levels of scale than what is already being achieved.

    None of that means AI agents won’t be incorporated into DevOps workflows, but it does mean there is still a considerable amount of effort required to re-engineer those processes in a way that is optimized for AI agents rather than human application developers.

    In the meantime, however, DevOps teams would be well-advised to start creating a list of tasks they are willing to delegate to AI agents, assuming there is some level of supervision. Otherwise, it’s all but inevitable that AI agents might generate the same issues that human developers create, albeit just at a different level of scale. The issue then becomes making sure the right governance policies are in place to prevent as many of those issues from arising in the first place.

  • Atlassian Delivers on Generative AI Promise for DevOps and ITSM

    Atlassian Delivers on Generative AI Promise for DevOps and ITSM

    During a virtual Unleash event, Atlassian today announced its generative artificial intelligence (AI) capabilities are now generally available across its Jira and Confluence suite of tools for managing IT and DevOps workflows.

    In addition, Atlassian also committed to adding generative AI capabilities to its Bitbucket continuous integration/continuous deployment (CI/CD) platform to enable DevOps teams to review pull requests and leave comments automatically with suggested changes around syntax and code conventions via a natural language interface. Developers will also be able to autogenerate pull request descriptions from commit messages.

    Matt Schvimmer, head of products for the Agile and DevOps division of Atlassian, said the capabilities provided by Atlassian Intelligence will reduce the level of toil and stress DevOps teams experience at a time when the velocity at which applications are being developed and deployed only continues to increase.

    Atlassian reported nearly 10% of its more than 265,000 customers have already leveraged Atlassian Intelligence since the launch of the beta program. Organizations, for example, can instantly create user stories within Jira Software tickets, change the tone of a customer response within Jira Service Management, create summaries, launch workflow requests via prompts and generate a starting point for a test plan in Confluence.

    Natural language capabilities, currently available for Confluence, will shortly be generally made available for Jira. An ability that promises to help demystify company-specific concepts, jargon or acronyms is also available now in beta, with support for Jira Software and Jira Service Management to shortly follow.

    There is little doubt at this juncture that AI will be pervasively applied across both ITSM and DevOps workflows. As those advances are made, it should also become feasible to reduce the level of toil these teams regularly encounter. At the same time, the level of scale at which ITSM and DevOps workflows can be effectively managed should also increase substantially.

    Each organization will need to decide how quickly to incorporate AI into those workflows, but hopefully, the level of burnout experienced by IT personnel will be sharply reduced as more tasks are automated. Less clear is the impact AI will have on the size of IT teams required to provide those services, but for the foreseeable future, there will always be a need for some level of human supervision.

    In the longer term, the war for IT and DevOps talent that has been waged now for multiple decades should diminish as managing these workflows becomes more accessible, noted Schvimmer. Organizations will not need to rely so much on the expertise of a handful of specialists, he added.

    Organizations, however, would be well-advised to make sure AI is embraced from both the bottom up and the top down to ensure optimal adoption, said Schvimmer.

    In the meantime, IT teams should take an inventory of the processes that are likely to be automated by AI today with an eye toward restructuring teams as more tasks are automated. Ultimately, the goal should be to let machines handle the tasks they do best to enable the IT staff to provide services that deliver more value to the business.

  • Atlassian Strengthens Jira Navigation and Integration

    Atlassian Strengthens Jira Navigation and Integration

    Atlassian today made it simpler to navigate the Jira Software and Jira Work Management project management applications as part of an ongoing effort to make it simpler for DevOps teams to collaborate with other stakeholders of application development projects.

    Megan Cook, head of product for Atlassian, said the ability to share release dates across multiple calendars, overviews that aggregate views of and summarize multiple projects along with command palette and importer tools make it simpler for, for example, marketing teams and other individuals outside of a DevOps team to keep track of application delivery schedules.

    In addition, Atlassian has tightened integration between Jira and Confluence, a wiki-based collaboration application, and a previously launched Atlassian Intelligence capability that infuses generative artificial intelligence (AI) capabilities will also soon be added to Jira Work Management alongside existing support for Jira. There will also be connectors for Jira Work Management to integrate with Atlassian Admin, Atlassian Analytics, Access and Data Lake offerings, along with advanced security features.

    Finally, Atlassian is adding support for a design tool developed by Figma that can now be directly installed in Jira.

    Collaboration between application development teams and the rest of the business is becoming more crucial as organizations increasingly realize how much they depend on software to drive revenue. One of the primary reasons Atlassian is investing in generative AI is to make it simpler for anyone to understand the status of any given project and the downstream impact any change to a delivery schedule is likely to have.

    Generative AI, of course, has already moved from being considered advanced technology to a set of capabilities that most end users will soon expect to be embedded within every application. One of the primary benefits generative AI provides is it makes a range of applications originally designed for specialists, such as a software engineering team, more accessible to a wider range of end users.

    It’s not clear how much generative AI will change the relationship between DevOps teams and the rest of the business, but with increased visibility, there should be a greater appreciation for the challenges developers and DevOps teams face. Any member of an organization should be able to plainly see, for example, why more resources need to be allocated to a specific project to ensure that an application is delivered on time.

    At the same time, application developers will find project management processes are about to become less disruptive as it becomes simpler to automatically import relevant data from the tools and platforms they use directly into a project management application such as Jira.

    One way or another, software engineers should expect that as generative AI continues to evolve, the divide between them and the rest of the business will narrow. There may be times when that level of additional scrutiny may feel burdensome, but on the whole, it should make it easier for DevOps teams to get the resources required to achieve any given goal. After all, the root cause of any given delay to a project will soon be plain for anyone to see.