At Tabnine, we firmly believe that the future of software development is AI-driven: AI agents working hand in hand with human engineers to accomplish tasks, with AI overseeing and managing every aspect of the software development life cycle. Earlier this year, we shared a preview of what we’re building at Tabnine to make this future a reality — an AI agent that generates code automatically from Jira issues. Today, we’re excited to announce the general availability of this capability by introducing two new AI agents — our Jira Implementation Agent and Jira Validation Agent.
Now with just a single click, Tabnine can implement a Jira issue, generating code from the requirements outlined in those issues. In addition to generating code for issues, you can also use Tabnine on either human- or AI-generated code to validate and review your implementation. The Jira Validation Agent will verify that your code accurately captures the requirements outlined in the Jira issue, offering guidance and code suggestions if it doesn’t.
Tabnine is the first AI code assistant to offer an integration with Jira and the first to offer a complete “issue-to-code” AI agent. This represents a step change in how developers use AI in software development. You can now accomplish macro-level tasks using Tabnine with a single click and can directly ask Tabnine to implement a story, bug, task, or subtask. There’s no longer a need to decipher requirements in a Jira issue, break it down into tasks, and feed specific prompts into an AI chat window. Today’s announcement expands our AI agent offerings, building on the recent innovations we’ve introduced with our AI Test Agent and onboarding agent.
Over the years, Atlassian Jira has become the heart of the agile engineering process for many teams. It’s now commonplace to create epics for large projects and then break them down into smaller, executable chunks by creating stories, bugs, tasks, and subtasks, which are then assigned to individual developers.
Even though there’s no technical limitation on the type of Jira issues that Tabnine can implement, to ensure the most relevant and accurate output, we recommend using Tabnine for issues like stories, bugs, tasks, and subtasks (i.e., issues that represent defined and specific units of work). This keeps the communication with AI concise and makes it easier to check the responses. During implementation, you can then ask Tabnine to refine the output (as needed), review the reference files used to generate the output, and work through the follow-up questions suggested by Tabnine.
Tabnine currently implements parent issues in Jira — for example, if a story (parent issue) has three subtasks (child issues), and you ask Tabnine to implement the story, Tabnine will only implement the story, not the subtasks. This limitation will go away over time, but as of today, you’ll need to implement each subtask separately.
Context is everything in AI. Without it, AI code assistants deliver output that might be textbook accurate, but is typically generic and not aligned with your codebase and company’s coding standards.
With the launch of these new agents, Tabnine now leverages the information contained in the Jira issues as context as you work on a project. As of this first release, Tabnine only uses the text in the Jira issue’s title and the description as context. Comments and other data will be incorporated over time. As such, to get optimal results, we recommend ensuring there’s detailed information in the issues’ title and description.
For example:
In addition to using the information in Jira issues, Tabnine continues to leverage locally available code and data in the developer’s IDE as context. This includes runtime errors, imported libraries, other open files, current file, compile/syntax errors, noncode sources of information, currently selected code, connected repositories, conversation history, Git history, project metadata, and other project files. Enterprise users also have the option to connect Tabnine to their organization’s code repos to provide more comprehensive context.
To use the new agents, you must first connect Tabnine to Jira — simply click the Jira icon in Tabnine, review the connection request, and hit Accept to establish the connection.
Once the connection is established, all the Jira issues assigned to you as an individual user are available in Tabnine. Tabnine respects the existing Jira user permissions, ensuring that only the issues assigned to you are available. As with all other Tabnine AI agents, we have a zero data-retention policy with any information we’re exposed to when we connect to Jira.
Once you’re connected to Jira, you can select a specific issue and ask Tabnine to implement it. Once you hit Enter, Tabnine generates code to meet the requirements of the Jira issue. From there, you can review and revise the code as needed before inserting it.
In addition to code generation, you can validate code with this functionality. Simply select the code you’ve written and ask AI chat if it aligns with the requirements outlined in the Jira issue.
Tabnine’s Jira Implementation Agent and Jira Validation Agent are now available to Tabnine Pro and Enterprise customers. There’s no additional cost to use this feature — individual users can simply update their IDE to get access.
For our Enterprise users, this functionality is disabled by default and Tabnine administrators have the control to enable it — once enabled, it’s available to all Tabnine users at the company. Contact us and we’ll assist you with getting started.
Check out our Docs and attend our Tabnine Live session on October 10 to learn more. Not yet a customer? Sign up for free today.
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