Atlassian's Jira Now Lets AI Agents Work Alongside Humans

Atlassian announced "agents in Jira" on Wednesday, a feature that lets teams assign tasks to AI agents exactly the same way they would assign work to human colleagues. The update, now available in open beta, allows users of the company's project management software to assign and manage work for digital agents from the same dashboard they use for their human employees.

AI agents now show up as assignees with the same fields and patterns teams already know, with boards clearly indicating which tasks are with an agent, what state each work item is in, and how it fits into the sprint, release, or incident timeline.

For the millions of teams already using Jira for project management, this means they can start experimenting with AI agents without ripping out their existing infrastructure. The AI agent becomes just another resource, one that can handle repetitive tasks, process data, or flag issues that need human attention.

Atlassian frames the update around a core problem. Agents firing off work for individuals with no way to tie it back to the team's broader plans and goals. Tamar Yehoshua, Atlassian's new chief product and AI officer, described the challenge directly saying, "You've been hearing in the zeitgeist lately that all of these agents are creating more work for people, and in some ways, more chaos." "What we're really good at is putting order to that chaos."

"10x the work, without 10x the chaos" captures Atlassian's framing perfectly. With agents in Jira, teams can assign work to agents, @mention agents in comments for further iteration, and cement agents directly into workflows.

Admins decide which agents are available to whom, where they can be used, and what "done" looks like. Jira keeps track, so agents become accountable members of the team. The exchange lives in the work item for everyone with permission to see.

Agents Within Jira’s Existing Structure

Since agents operate inside Jira's existing structures, they respect permissions, project configurations, workflows, and audit trails. Approved agent updates are captured alongside the rest of the work item history, so teams can adopt AI confidently and admins can govern usage centrally.

Teams can also run agents in Jira workflows, adding agents directly to workflow steps so they can take on discrete jobs. For example, an agent can be configured to automatically kick in at an assigned status, draft an end-to-end user onboarding flow, then hand off for review and approval.

The ability to compare the work of agents versus humans on the same project could help enterprises figure out where to deploy agents and what tasks should remain human-led.

One of the biggest barriers to enterprise AI adoption is change management. Teams resist new tools that require learning new interfaces or restructuring their processes. But if AI agents show up in the same Jira board where work already happens, adoption friction will drop.

Atlassian's open toolchain approach already lets teams plug their favorite tools into Jira and see work from across the stack in one place. With agents in Jira, that now includes agents themselves.

Jira becomes the surface where teams can see the work and history for Rovo agents (tuned for teamwork and powered by Atlassian’s Teamwork Graph), and third-party and MCP-enabled agents with skills from Amplitude, Box, Canva, Figma, GitHub Copilot, HubSpot, Intercom, and more available through Atlassian's gallery of MCP servers.

Because Model Context Protocol gives agents an open, standardized way to talk to the tools and data teams already use, users can bring their own models and agents, wire them up to their unique tools and data, and shape how those agents behave in their workflows.

All of this runs through Jira's existing permissions, context, and guardrails. Teams get deep customization and powerful agents while staying in the flow, without giving up control.

The feature arrives as competitors race to embed AI throughout their enterprise platforms. Microsoft has been pushing Copilot across its productivity suite, Salesforce launched Agentforce for CRM automation, and ServiceNow is building AI agents into workflow automation. Atlassian still has the advantage of owning the territory where developers and technical teams already live.

Yehoshua framed the update as the beginning of a longer journey. "The goal is to enable people to work more productively with AI and I think this is a step," she said. "It's only the beginning of the journey. It's a long journey, but this is a really important step of how to integrate AI into the workflows that you already have."