On this period of AI-assisted software program growth, builders must know what to construct and the best way to govern it, whereas coding brokers want context to grasp the best way to execute appropriately.
To assist organizations navigate and succeed with AI-native growth and supply, Atlassian right now is releasing a brand new set of capabilities in Jira that the corporate stated successfully create a context-rich orchestration layer for autonomous coding brokers
Atlassian added these capabilities to handle the hole between how a lot code AI is producing and the dearth of productiveness beneficial properties by builders. Among the many points the trade faces with implementing AI efficiently are an absence of context that causes brokers to float from necessities, prompts that don’t have any reminiscence so prior work must be redone, and an absence of governance over autonomous brokers.
“When the client doesn’t really feel like they should study a totally new set of issues, however slightly with their data of the prevailing Jira, and that we put these new options within the place the place they will simply uncover and use them, the idea needs to be intuitive,” Ming Wu, Head of Engineering, DevAI, at Atlassian, defined to SD Instances.
Among the many new capabilities in Jira are Jira for Slack, which permits groups to create context-rich specs from conversations, suggestions and concepts utilizing @Jira. In response to Atlassian’s announcement, “the agent updates work objects, syncs conversations as feedback, and assigns work to coding brokers whereas your staff collaborates in Slack.”
WIth this launch, the corporate is introducing Jira Planner for spec-driven growth. Jira Planner gathers up code pulls, the staff’s Jira and Confluence historical past in addition to staff context to create necessities. Then, it will probably generate a spec in Confluence that builders or brokers can construct on. Additional, work objects may be assigned to fashions and brokers equivalent to Claude Code, Cursor or GitHub Copilot instantly from inside Jira, offering the context to get higher responses from coding brokers.
Moreover, video conferences may be turned by Atlassian’s Loom video messaging software program into directions and motion plans brokers can use to work on duties. It’s these contextual property that enable the agent to carry out effectively, Wu stated. “Context engineering is not only providing you with the uncooked knowledge. It’s the environment friendly solution to retrieve the precise context on your agent,” she stated. “Extra context doesn’t essentially imply higher. With Jira Planner, you may go begin from Jira and do the planning work together with your staff. And through the planning part, one of many key issues is placing all of the contacts collectively from all over the place. We’re tryingto bare that course of tremendous handy and likewise efficient, ensuring the precise context surfaces through the starting stage.”
To get whole visibility into agent habits, Atlassian’s Teamwork Graph collects session data accessbile from anyplace in Jira, the corporate introduced, together with new hooks within the Teamwork Graph CLI that may hyperlink native agent classes on to work in Jira, updating context repeatedly to keep away from agent drift.
In response to Atlassian, Jira for Slack, Jira Coding Agent, Jira agent automations, agentic templates, and agent classes in Jira can be found right now for paid Jira Cloud prospects at no further price. Jira Planner is on the market in early entry, and Codex in Jira is coming quickly. DX AI price administration is on the market for Atlassian DX prospects.
