Rovo vs Linear Agents: AI Agents Inside Your Tracker, and Where a Lighter Board Fits

Rovo and Linear’s agents answer the same question, how to hand tracker work to AI, from two different shapes of product. Rovo is Atlassian’s AI layer across Jira, Confluence and other apps: in Jira you assign a work item to a Rovo agent, a custom agent or a third-party agent, mention one in a comment, or trigger one when a work item changes status, and the output comes back privately to you until you share it. In Linear, agents are installed into the workspace as app users; you delegate an issue to one while a person stays the assignee, and a built-in Linear Agent chats, updates work, runs recurring loops and can start coding sessions. If your team already lives in one of these trackers, use its agents; the choice between them is really the choice between Jira and Linear. If what you need is a place where people and several AI apps share tasks, rules and progress without adopting a full tracker, a lighter shared board fits better.

Rovo agents in Jira

Atlassian’s overview of what Rovo is↗ (opens in a new tab) lists Search, Chat, Agents and Studio, the builder where custom agents are made. Rovo comes with paid Jira, Confluence, Service Collection and Teamwork Collection cloud subscriptions, and the overview says it respects user permissions, so people only see what they already have access to. A site admin has to turn on Rovo and AI features for the organization first.

Atlassian’s guide to collaborating on work items with AI agents↗ (opens in a new tab) describes four ways to give an agent work:

• Assign it: open a work item, select Agents, and pick from recommended, favorite or coding agents, or create a new agent from the picker.
• Mention it: type @ and the agent’s name in a comment, and it works on that comment’s context.
• Put it on a workflow transition: a space admin adds a “Trigger agent action” rule, and the agent runs whenever anyone moves a work item into that status.
• Put it on a board column: in team-managed spaces, an agent added to a column works on items as they enter it.

The agents can be Rovo agents by Atlassian, custom agents built in your organization, or third-party agents such as GitHub Copilot. Atlassian also documents Claude Agent for Jira, a beta integration with Anthropic’s Claude Managed Agents that writes code, pushes branches and opens draft pull requests from a work item. Coding work shows up under “My agent sessions,” sorted into needs input, working and finished.

Two details shape how a team uses it. First, the output is private: only the person who assigned the agent or triggered it can interact with it, while anyone who can see the work item can see that an agent was triggered. To share the result you turn it into a draft comment and post it. Second, each agent run uses Rovo credits from the organization’s allowance, and how many depends on the agent’s configuration and thinking mode.

Linear’s agents

Linear’s documentation on agents in Linear↗ (opens in a new tab) treats an agent as an app user: installed by a workspace admin, given access to chosen teams, and then able to be mentioned, delegated issues, comment, and work on projects and documents, depending on the permissions granted. Delegation is the key idea. Assigning an issue to an agent delegates it while the human teammate stays the primary assignee and owner. Agents are not billable seats, and they cannot sign in to the app, reach admin functions or manage users.

Linear also ships its own agent. The Linear Agent↗ (opens in a new tab) is on by default (an admin can turn it off) and works within your existing permissions. It answers questions about workspace data, creates and updates issues, projects and initiatives, summarizes work, and posts its own comments. It keeps a history of chats, can save a good conversation as a personal or team skill, and can connect to outside MCP servers configured by admins. Loops, on paid plans, run that kind of work on a schedule or when an issue changes.

For code, Linear’s coding sessions↗ (opens in a new tab) start Claude Code or Codex in a managed sandbox when you delegate an issue to Linear, then draft a pull request and put the diff on the issue for review. They are on the Basic, Business and Enterprise plans and draw on the workspace’s AI credits.

Rovo vs Linear, point by point

• Where agents come from. Jira: Rovo agents by Atlassian, custom agents from Studio, and third-party agents a site admin installs. Linear: Linear Agent built in, plus third-party agents a workspace admin installs.
• How you hand over work. Jira: assign, mention, a workflow transition or a board column. Linear: delegate, mention, chat with Linear Agent, or a loop.
• Who owns the work. Jira: the agent is added to the work item and its output is yours to review. Linear: a person stays the assignee and the agent works on their behalf.
• Who sees the output. Jira: only the person who invoked the agent, until it is posted as a comment. Linear: agents post comments and changes in the issue, and the activity feed shows assignment and delegation changes and who made them.
• Standing instructions. Jira: each agent’s configuration in Studio. Linear: agent guidance written at workspace and team level, in a Markdown editor with full history, passed to every agent.
• Memory. Rovo memory keeps facts about each person, scoped to one site, and only that person can see them. Linear Agent keeps chat history and saved skills.
• Paying for it. Jira: each agent run uses Rovo credits from the organization’s allowance. Linear: agents are not billable seats, and coding sessions draw on the workspace’s AI credits.

On memory, Atlassian’s page on Rovo memory management↗ (opens in a new tab) is explicit that memories belong to the individual: other users, including organization admins, cannot view them, and memories on one site are separate from another. That is good for privacy. It also means what Rovo learns about how one person works is not a shared record for the team.

What both have in common

Both put agents inside one tracker, and both keep what the agents know there: Linear’s guidance and skills, Jira’s agent configurations and Rovo’s memory. Agents that work outside the tracker reach it over MCP; Linear and Atlassian each run an MCP server, compared in Linear vs Jira. Both assume the team has adopted the tracker, with its workflows, spaces or teams, and admin roles, and that someone administers which agents are installed and what they cost.

Where a lighter shared board fits

Not every team that wants AI on its work wants Jira or Linear. A two-person studio, a founder with a contractor, a product manager working with Claude, ChatGPT and Cursor at once: the need is a place where the work, the rules and the progress live, and where every AI app they use can read and update them.

That is what fenbs is for. It is a web app with one kanban board, four fixed lanes (To Do, Next Up, In Progress, Completed) and three kinds of task: feature, enhancement and bug. People and AI apps read and update it over MCP; each assistant connection acts as the person who approved it, under that person’s role. People write the rules on the Decisions and rules page, and every assistant reads them first; assistants cannot sign off decisions or pre-approve work. “Save progress” records where each task stands and the project’s Where we left off, so you can switch models or apps without starting over. History records who changed what, a person or which assistant.

fenbs has no built-in agent and does not run agents, write code or open pull requests; your assistants do the work and record it on the board. It has no sprints, workflows, custom columns or epics. If you need those, or your company already runs on Jira or Linear, use Rovo or Linear’s agents, where the work already is.

Which to pick

• Your company runs on Jira and Confluence: Rovo, because its agents work on the work items and pages you already have.
• Your engineering team runs on Linear: Linear’s agents, especially delegation and coding sessions, since the human stays the owner of each issue.
• You are not on either and want a shared list that people and several AI apps can work from: a lighter board such as fenbs. See the Jira alternative and the Linear alternative pages for the full comparison.