Jira AI integration that answers questions and keeps issues up to date
With Coworker's Jira AI integration, ask about Jira in plain English and Coworker writes the JQL, reads the issues, comments and change history, and answers next to the Slack threads and calls behind them. Agents can then create the issue, add the comment or move it to the next status, and each change can wait for your approval.
How Jira context reaches your AI tools
Coworker brings Jira together with the rest of your tools in OM2, its organizational memory, then answers in Coworker's own apps, or in Claude, ChatGPT and Cursor through Coworker MCP.
What teams do with Jira AI and Coworker
Coworker's Jira AI agents file, update and report on issues next to the Slack threads and calls behind them.
Release status
Ask what is still open in a version and who owns it, and get the list from live Jira data.
Bug reports to tickets
File a bug from a Slack thread or a customer call with steps, priority and the parent epic, written as Jira rich text with headings, lists and code blocks.
Why did this change?
Get an issue's change history: who moved it, when, and which fields changed.
Updates without the clicks
Comment on an issue, move it to In Review or log time against it. Each write comes back with a View in Jira link.
Fields that fit your projects
Coworker checks issue types and fields against each project before it files, so issues match how each project is set up. Values that don't fit are flagged, and a missing required field is caught before the issue is created.
More for engineering teams: Coworker for engineering, and our Jira MCP guide.
Live in minutes
Connect your Jira Cloud site
Your Jira Cloud site is connected once for the whole workspace, then each person signs in.
Each person signs in with Atlassian
Reads and writes run on each person's own Atlassian login, so Jira's project permissions still apply.
Decide what needs approval
Tick Require approval for Jira on an agent. Each create, update, transition or comment then shows up as a card and only runs when someone approves it.
Every model needs the right context.
Atlassian's Rovo MCP Server can only see what lives in Atlassian's apps. OM2 connects your Jira tickets to the Salesforce accounts, call transcripts, support tickets, and Slack threads around them in one permission-aware knowledge graph, so Claude, ChatGPT, or Cursor starts every task with your customers, projects, and decisions already loaded.
Mosaic Financial
Series B fintech · $3.1M ARR · 47 seats
project
SOC 2 Type II audit
person
Priya Nambiar
workflow
Enterprise onboarding
team
Customer Success
source
#mosaic-impl
Mosaic Financial
Series B fintech · $3.1M ARR · 47 seats
Enterprise-grade security, from day one
SOC 2 Type II
Audited annually, report on request
Hosted locally
Your memory stays in your region. Open-source models run in the US.
Isolated per company
In our cloud or your VPC
Never trains on your data
No model provider, closed or open source
Your permissions, enforced
The access already set in Salesforce, Slack, Jira and Drive
Also included
Related Project Management connectors
FAQ
Frequently asked questions
Coworker is a context layer for enterprise AI. It connects 50+ work tools, builds a permission-aware company graph called OM2, and runs agents that act across those tools, inside Coworker or in Claude, ChatGPT and Cursor through Coworker MCP.
Atlassian's Rovo MCP Server is a natural fit if your work lives in Jira and Confluence, and it works with any MCP client, including Claude and ChatGPT. Coworker MCP is for answers that also need your CRM, call transcripts and Slack, in one connection with Jira and the rest of Coworker's 50+ connectors, on each person's own Atlassian login.
Rovo is Atlassian's AI, built into Jira and Confluence, and it can search connected apps like Google Drive and Slack. If your team lives in Atlassian, it is a natural fit. Coworker works across Jira, your CRM, call transcripts and Slack through 50+ connectors, and Jira is one of the sources its organizational memory draws on. More in Coworker vs Rovo.
Issues with their descriptions and comments, each issue's change history, projects and their fields, components, and versions with the issues in each. Ask in plain English and Coworker writes the JQL for you.
It can create issues, including subtasks and children of an epic, with custom fields. It can update fields, move an issue through its workflow, comment (optionally visible only to a group or role) and log work. It does not delete issues or manage sprints and boards.
Coworker works with Jira Cloud. Server and Data Center aren't supported.
You decide. Tick Require approval for Jira on an agent and every write from it waits for a person, who can review the issue and its fields before it runs.
Coworker checks the issue type and every field against the project before it files. Values that aren't valid options are flagged, a missing required field is caught first, and fields left off the create screen are filled in right after. Coworker then double-checks the issue and tells you if an assignee, label or priority didn't apply.
Yes. An agent can run on a schedule, when a meeting ends, or when a new message lands in a Slack channel you pick. For example, a bug report in #support can start an agent that files the issue, or a weekly digest of what is still open in the release.
Coworker MCP connects Claude to Salesforce, Jira and Google Drive in one connection. Jira and Google Drive run on each person's own login, so the permissions they already have carry over. In Salesforce, admins choose who can search, and every change runs on the person's own Salesforce login, so Salesforce decides what they can edit.
Connect Jira in minutes
Connect your Jira data and watch agents go to work in minutes.