Atlassian Release Notes

The missing layer between what your team decides to build and how your agents execute it.

The bottleneck in AI-assisted development isn’t code generation. It’s intent.

When you leave details out, the model fills in the blanks for you. Omit the scope, architectural constraints, and edge cases, and the agent quickly starts solving a completely different problem. When that happens, the agent isn’t just wrong – it’s confidently wrong, moving just as fast in a direction you didn’t intend. You end up debugging intent as much as code, and paying for it in rework, tokens, and time.

In a recent Atlassian survey, 60% of engineering leaders said their teams are shifting toward spec-driven development. Yet, fewer than 15% have a structured framework to actually do it. Getting the work right means building a spec your team can actually review together, and an agent that can execute without guessing. That’s what Jira Planner does: it turns rough ideas into structured, agent-ready specs grounded in a semantic understanding of your codebase, standards, and decisions.

How it works

1. Start with an idea of goal, even a rough one. You don’t need a fully-formed spec. Jira Planner asks the right follow-up questions to help you clarify scope, constraints, and what success looks like.

2. Grounded in your real context. Jira Planner reads your code across multi-repo setups, and pulls in context from Teamwork Graph. Plans reflect your architecture, your ownership structure, and your previous decisions, not generic assumptions.

3. Get structured artifacts, not walls of text. Jira Planner generates editable Confluence Live Docs that live right where your team’s work already does, connected back to Jira. That means your team can start working in the plan right away, instead of moving it somewhere else first.

4. Plan as a team, not alone. Share the plan with engineers, PMs, and designers, so you can co-edit, leave inline comments, and work through trade-offs together. Alignment happens all while the the plan is still taking shape, not after it’s already final.

5. Check whether the plan is ready. Jira Planner surfaces ambiguity upfront and tells you whether your spec is detailed enough for execution, with specific suggestions for what’s missing.

6. From plan to work. Jira Planner breaks the finalized plan into Jira work items, sequenced with dependencies and milestones, with each one carrying its own acceptance criteria and the context of the full plan. From there, agent orchestration takes over, picking up exactly where planning left off.

For the people who define what gets built

Jira Planner is for tech leads, senior engineers, engineering managers, and PMs tackling the kind of work that requires alignment: multi-sprint features, architectural decisions, and anything where more than one person needs to agree on scope.

The plans it produces are grounded in your actual repos, your team’s context, and decisions already made elsewhere in the organization. That’s what turns a rough proposal into something a team can confidently hand off to agents to build.

Availability

Jira Planner is now available in Early Access for Jira Cloud customers with Rovo enabled and Teamwork Graph connected. You’ll also need Confluence connected, since Jira Planner publishes plans as Live Docs. We’re rolling out access in waves and looking for teams who want to shape what Planner becomes. Your feedback will directly influence what we build next.

Get early access

Join the waitlist →

Original source

The pain of building decks the old way

You know the drill when it comes to creating presentations. The afternoons spent creating a deck from scratch – aligning text boxes, copy and pasting images, moving the slides around to find the right flow, the list goes on and on. The problem isn’t that the story isn’t there. It’s that getting the story out onto slides takes way more time than it should.

That’s where Confluence slides comes in. It’s the fastest way to transform existing knowledge into a presentation-ready deck in seconds — no blank slide, no copy-paste, no app-switching. Simply describe what you want and watch Rovo generate context-rich presentations in real-time.

Creating a deck shouldn’t be hard. But for most teams, it is. The manual effort of building decks the old way is painful, and the numbers are hard to ignore:

• 37% of total time spent in PowerPoint goes to formatting — adjusting colors, fonts, and layouts instead of refining the actual message (Source: Nielsen)
• 42% of that time is wasted searching for the right templates, icons, or past slides (Source: Nielsen)
• Corporate professionals spend roughly 7 hours per week on slide tasks, costing an estimated $25,500 per year per employee in lost productivity (Source: Nielsen)

Teams don’t lack ideas or content. They lack a better, faster way to turn what they already know into a story they can easily share. The result is that every deck starts from scratch, even when the content already exists somewhere.

Say hello to Confluence Slides

With Confluence slides, teams now have a new way of creating slides with Rovo, slides that are context-rich and generated with AI in seconds. Rovo pulls context from across Atlassian and connected third-party apps like Slack and Google Drive to create impactful, visual presentations grounded in your team’s work. Simply ask Rovo to create or edit your slides and it’ll determine slides structure, create content, and even build visualizations like charts and graphs — all from a single prompt. You’ll be able to collaborate and present your slides directly in Confluence, without breaking the flow of work and having to switch apps.

A few ways teams can use Confluence slides:

• Project kickoffs — Generate a presentation-ready kickoff deck from your project brief, complete with visuals like a timeline chart to get stakeholders bought in from day one.
• Campaign analysis readouts — Turn campaign performance data and retrospective notes into a visual deck, with bar charts and funnel charts generated from data already in Atlassian.
• Customer pitches — Spin up a tailored customer pitch deck from existing account notes, case studies, and competitive intel in Confluence.

A closer look at Confluence slides

Your team’s knowledge already lives in Confluence — now it can be turned into a polished deck in seconds with Confluence slides. Start with a prompt, refine with Rovo, and present your new visual presentation with confidence, all without leaving Confluence.

Create from context, not from scratch

Today, your team’s knowledge is scattered across docs, projects, and meeting notes, so every deck starts from a blank slide. Figuring out what to include and how to structure the story takes as long as making the deck itself. Confluence slides fixes that.

• Powered by the Teamwork Graph: Rovo pulls context from across Atlassian and connected third-party sources like Slack, Google Docs, Microsoft Excel so your deck is grounded in real team knowledge.
• Narrative structure built for you: Rovo determines what to include, how to sequence it, and how to frame the story.
• No blank slate: Creation always starts from existing content or a simple prompt, so you’re never stuck staring at a blank slide.

From first draft to final story — fast

Getting from a first draft to something presentation-ready takes longer than it should — every small change means more manual work. Confluence slides keeps the momentum going, with Rovo doing the heavy lifting for you.

• Impactful visuals to tell your story: Automatically generates charts, timelines, bar graphs, and images directly within a slide — so your deck is polished and presentation-ready from the first draft.
• Rovo-powered editing: Use Rovo to make edits inline, such as restructuring, rewriting, or improving your slides.
• Quick manual edits: Put the finishing touches directly on the slide yourself, such as editing text or reorganizing slides from the filmstrip.

Collaborate and present, all in Confluence

When your deck lives in a different tool from your knowledge, things drift. Teams end up maintaining two separate artifacts — the doc in Confluence, the deck somewhere else — and it’s rarely clear which is current. With Confluence slides, that split disappears. Your slides live where your knowledge lives, so you can create, share, collaborate, and present your work in one place.

• Real-time collaboration: Invite teammates to collaborate on a deck, with inline comments to gather feedback and align stakeholders.
• Present directly from Confluence: Hit presentation mode and go — no exporting, no app-switching, no friction between finishing and presenting.
• Respects Confluence permissions: Slides live in the Confluence content tree, inheriting existing space and page permissions so access is always controlled.

Tips to get the most out of Confluence slides

Can’t wait to get started? Getting to a great deck is faster when you give Rovo the right starting point. Here are a few tips:

1. Provide your sources upfront
   Link to the exact pages, Jira projects, or 3rd party connector you want used. You can also have Rovo follow a specific slide template by linking to it.
   “Create slides based on [this project poster] and [this Q3 retro]. Use the data from the retro as the primary source. Use this deck as the template — match its slide layouts: [link].”

2. Set your brand colors
   Hex codes work best, but Rovo can also interpret color descriptions, vibes, or links to a company website.
   “Use our brand colors: 0052CC and FF5630.” / “Match the palette from atlassian.com — blues and neutrals.”

3. Define the narrative and structure
   Tell Rovo what to include, exclude, or reorder. If you have a story arc, spell it out.
   “Start with a bold problem statement; end with a clear call to action.” / “Include these slides in order: intro, problem, solution, timeline, budget, next steps.”

4. Specify audience and tone
   Say who it’s for and how it should sound.
   “C-suite budget review — concise, confident, data-driven. Max three bullets per slide.”

5. Control visuals
   Note preferences for imagery, icons, charts, or layout style.
   “Include a timeline graphic for the roadmap slide.”

Your next presentation is already in Confluence

The knowledge your team needs to build a great presentation already exists — in your Confluence pages, your meeting notes, your project plans. Confluence slides gives you a faster way to turn all of that into a story you can actually share, without spending the afternoon doing it manually.

No blank slate. No tool-switching. No reformatting. Just go from doc to deck in seconds, and present with confidence, all within Confluence.

If you’re already a Confluence user, create slides now.

Not yet using Confluence? Try it for free and discover how you can create and share knowledge across teams.

Original source

@MENTION AN AGENT ON ANY PAGE AND IT CREATES, EDITS, AND COMMENTS ALONGSIDE YOUR TEAM. THROUGH THE ATLASSIAN ROVO MCP, THE SAME AGENTS WORK FROM CLAUDE, CURSOR, OR YOUR IDE.

Agents have been working in Confluence since we launched custom agents in May 2024, and teams now run more than 5 million agent invocations a month. In February alone, Agents saved Atlassian customers more than 200,000 hours.

Now, agents can do practically everything you can do. Beyond creating or editing pages, Agents can comment, label, set status, and create whiteboards or databases — working exactly the way a teammate does. Mention one in the editor or a comment and it acts right there, grounded in your team’s own content. And through the Atlassian Rovo MCP, the same agents work from Claude, Cursor, ChatGPT, or your IDE, reading and writing the live page rather than a copy of it.

Getting work unstuck

Handing real work to an agent only matters if you can trust what it does. That belief shaped every decision we made bringing agents into Confluence, so before an agent ever touches a page, you know it will work within the permissions you set, act only where it should, and leave a clear trail behind it. With that foundation in place, agents become something you actually want on your team.

A page waits for someone to have time, feedback sits in comments until the owner circles back, and the decision everyone needs stays in one person’s head until they write it down. None of that is a hard problem. It’s that a person has to do every step, and people are busy.

Agents change the pace without changing the job. Give one a task and the page moves while you’re in a meeting. Routine feedback gets processed before you reopen it, and the decision that actually needs your judgment is waiting with the work around it already done.

How it works

• Mentioning an agent. Tag an agent in the editor or in a comment the same way you’d tag a colleague. It reads the page as context and responds there, contributing to the page without anyone moving the work or rebuilding the context.
• Seeing what agents actually did. Confluence Analytics counts agents alongside people across page, space, site, and Mission Control views. Switch any page between all viewers, people, and agents to see which agents read it and how often. Agents appear as contributors on content they helped create or update, and an agent working on someone’s behalf shows up next to the person who invoked it. If an agent read a page 200 times last week, you can now see that.
• Doing the work, not describing it. Agents create and edit pages, work with comments, apply labels, set status, and act across Confluence content types. A single prompt returns a complete formatted page with panels, tables, and diagrams, or chains a longer job end to end, like drafting release notes from a merged pull request, publishing them, and sharing a public link. The same capabilities are available through Rovo Chat, automations, custom agents, MCP, and the command line.
• Bringing your own agent. Create a Team agent in Confluence, start from a ready-made template, or connect a custom or external agent through MCP. Wherever the agent comes from, it works with the same core Confluence actions.
• Working from your tool of choice. Through the Atlassian Rovo MCP, agents in Claude, Cursor, ChatGPT, or your IDE read and write live Confluence content. The agent reads what is true right now and writes back to the same page your team is looking at.
• Setting the rules for a space. Space-level instructions give agents a shared playbook for how that team works, so the same agent behaves differently in a marketing space than in an engineering one. Agents never surface content the person who invoked them couldn’t already see, every action is reversible in version history, and a stale edit is rejected rather than quietly overwriting a teammate.

What teams are already building

The agents customers built before this release are a good preview of what a more capable one can do. Riverty’s Lessons Learned agent mines past work so teams stop re-learning the hard way. KFC’s Architecture Review agent pre-checks proposals against document standards. Pythian’s Progress Tracker pulls signal from Jira and Confluence and drafts the status email. Sprout Social’s Onboarding agent answers roughly 80% of new-hire questions and spins up role guides.

You don’t need to code anything to build one. If you can describe what you want in plain English, you can build an agent in Rovo Studio and publish it to your team.

Better context = better output

Agents in Confluence run on the Teamwork Graph, the living context layer that connects work, people, knowledge, and code across Atlassian and your connected tools. In our own benchmarking, agents grounded in Teamwork Graph context returned 44% more accurate results while using 48% fewer tokens.

Availability

Agentic workflows in Confluence are powered by Rovo, available on Standard, Premium, and Enterprise Cloud plans. All Atlassian Cloud customers can use the Atlassian Rovo MCP Server.

Getting started

1. Open a Confluence page and @mention an agent in a comment.
2. Browse ready-made agent templates and start from one built for your team’s job.
3. To work from another tool, add the Atlassian Rovo MCP server to your AI client:

• Explore agent use cases
• Set up the Rovo MCP server
• Learn from more examples of customer agents

Original source

Today we’re rolling out seven new AI features across Trello for every paid user on a Standard, Premium, or Enterprise plan. Individually, each one erases a small, familiar chore. Together, they answer a bigger question we’ve been sitting with for a long time: what if the busywork mostly handled itself, so you could focus on the work that actually matters to you?

Keep the boards, lists, and cards you love. Outsource the rest to AI.

SEVEN WAYS TO SUPERCHARGE YOUR TRELLO WITH AI

1. Create a board with AI

Leave the blank board behind. Describe a goal, whether it’s planning an offsite, running your first 5K race, or figuring out your meal planning for the week and Trello builds a structured, ready-to-use board in seconds, complete with lists and cards. A template is generic and waits for you to customize it. A board built with AI starts personalized. Every board defaults to private, so it’s yours to edit before anyone else sees it.

This is the one that changes how it feels to start something. The hardest part of any project is the first move; now the first move is a prompt.

1. Merge multiple cards into one

A frequently asked for feature is finally here! Have two cards saying the same thing? Drag one onto the other and they become one. Trello blends the title, description, and details, carries over links, attachments, custom fields, and labels, and keeps the original context below a divider so you never lose it. The earliest due date wins; to-dos become a checklist. Don’t like the result? Undo it. The originals are archived, not deleted.

Duplicate cards are how boards quietly rot. This keeps them clean without the manual reconciliation nobody has time for.

1. AI-generated checklists

One click turns a card into a plan. Trello reads the title and description and suggests a checklist of the steps to get it done. Name the checklist to steer the result, then edit, reorder, or regenerate. It’s fully editable, exactly like one you’d type yourself, only you didn’t have to type it.

1. AI-recommended focus time

A task on a board isn’t done until it has a place in your day. Connect a calendar, open any card, and Trello finds open time and recommends a focus block for it. Accept, dismiss, or set natural-language rules for when and what it suggests. Turn on proactive scheduling and Trello checks daily for cards due in the next five days and blocks the time for you. It can view your calendar to suggest, never change it without your say-so.

This is where a board stops being a to-do list and starts being a plan for actually getting it done.

1. AI-generated images for backgrounds and covers

Stop scrolling for the perfect image. Describe it and Trello makes it. Change a board background or add a card cover in seconds. Small thing? Maybe. But a board you’re proud to look at is a board you come back to, and coming back is the whole game.

1. Set a due date from the card title

Type “Submit report by Friday” and Friday becomes the due date. Trello reads relative dates (“tomorrow”), specific ones (“December 1”), and weekday references (“this Thursday”) the moment you create a card. You were always going to set that date, now you don’t have to think about it.

1. [Coming Soon to Trello Labs] AI-generated widget cards

An entirely new kind of card, live in Trello Labs later this month. Type /widget and give it a prompt (“daily AI news headlines,” “an inspirational quote at the top of my To Do list,” “the US Open schedule”) and Trello builds a richly formatted card that shows information at a glance. It’s where we’re headed: a board that doesn’t just hold your work, but brings the world into it.

GET STARTED

If you’re on a paid plan these new features are waiting for you now. Hit Create and describe a board. Drag one card onto another. Type a due date into a title and watch it move. Not on a paid plan yet? Start a 14-day Premium free trial or upgrade to turn them on.

We built these tools because sometimes you’d rather not think about building or managing a board very closely. Go make something, and tell us what you want next!

Original source

Great ideas can start with a random spark at 2 a.m., grow through a planning session with ChatGPT, or take shape in a brainstorm with Claude. AI helps you plan, but getting that plan into Trello has always been a manual, momentum-killing process.

Today, that changes.

Trello now uses MCP (a shared standard that lets AI tools talk to apps like Trello) to connect with assistants like Claude, ChatGPT, Gemini, and Cursor. That means you can manage your boards, lists, cards, and checklists just by asking. No manual work required.

AI assistants have crossed from being a developer tool into something anyone can use — nearly half the people using Atlassian’s MCP aren’t on software teams at all. And they’re not just asking AI to read their work; they’re using it to actually get things done. Now that same capability comes to Trello, which over 100 million people have signed up to use for everything from new hire onboarding, to marathon training, to wedding planning and beyond.

Say you’re chatting with Claude about a two-week Italy trip. You’ve nailed down the cities: Rome, Florence, Amalfi Coast. You’ve got restaurant recommendations, museum tickets to book, and a packing list that keeps growing. Normally, this is where the conversation ends and the real work begins. You’d open Trello, create a board from scratch, try to remember everything you just talked about, and spend twenty minutes rebuilding it all by hand. With Trello MCP, you just say: “Turn this into a Trello board with a list for each city and cards for everything we need to book.” Done. Your assistant creates the board, organizes the lists, adds the cards, and even builds out checklists for things like “book Uffizi tickets” or “pack adaptor for EU outlets.” You go from planning to packing in seconds.

Prompt: “Turn this into a Trello board with a list for each city and cards for everything we need to book.”

Or say you’re planning a wedding. You’ve been going back and forth with your AI assistant about venues, guest lists, and vendor timelines. Instead of switching to Trello and rebuilding all of that from memory, just say: “Create a Trello board for the wedding with lists for each vendor category and cards for every task we discussed.” Venues, catering, florals, photography – organized and trackable in seconds.

Prompt: “Look at my wedding Trello board and show me any cards due in July.”

The same works for anything you’re planning. Researching a team offsite? Once you’ve landed on the details in conversation, say: “Set up a board for the offsite with lists for logistics, agenda, and follow-ups.” Everything you just worked through goes from scattered chat to an actionable board in seconds.

Instead of hunting through boards to update tasks, ask your assistant to add checklist items, move cards, or summarize what’s due this week. Trello stays your source of truth, and your AI assistant just helps you act on it faster.

With Trello MCP, ideas become action without all the grunt work. Trello MCP is available to all Trello users. Ready to try it out? In your AI assistant (a plugin in ChatGPT, a connector in Claude), add the Trello MCP server and start prompting. Visit trello.com/mcp to connect your first AI assistant.

Original source

Atlassian Rovo MCP gives coding agents deeper, scoped access to complete their goals in Jira, storing and accessing critical context directly from the IDE or terminal.

TL;DR

• The Atlassian Rovo MCP is seeing 5M+ tool calls per day. Today, we’re sharing what we’ve built based on real usage patterns that showed us where to invest.
• Writes reflect nearly a third of all MCP tool calls. Coding agents like Codex, Claude Code and Cursor are creating Jira work items, commenting, updating statuses and more.
• More precise context, fewer tokens. When coding agents know exactly what they’re looking for in the Atlassian ecosystem, token usage can drop by nearly half.

Developers speak through usage, and with over 5 million daily tool calls through our MCP server, one thing is clear: coding agents are no longer just reading Jira work items. They’re participating in them.

Atlassian Rovo MCP accelerates software delivery by giving developers the tools to act, write back context, and store it for future use. With Jira as an agentic system of record, every action creates persistent context that helps teams move faster and gives agents better signal for their next goal.

5 new ways to turn context into actions with Atlassian Rovo MCP

With coding agents participating in software delivery, they need to do more than just generate code. An agent could help generate or review code, but the last mile would often require developers to jump back into Jira to connect the dots by hand. Atlassian Rovo MCP changes this, and supports expanded Jira actions, including:

1. Complete your work from the agent surface. Create work items, attach files (coming soon), find assignees (v2 public preview), and edit comments, all from a single agent interaction and without having to update Jira manually.

2. Surface Jira-linked code activity to agents. Coding agents can read the Jira development panel, including linked branches, pull requests, and commits. Diffs are tied to the work they deliver.

3. Trace product discovery to delivery. With Jira Product Discovery support, agents can connect high-level ideas, opportunities, and product context to the delivery work that brings them to life.

4. Capture time as work happens. Coding agents can capture time as work happens, update it when scope changes (coming soon), and read existing worklogs for handoffs, reporting, or planning.

5. Retrieve precise context with fewer tokens. Smarter response shaping means agents get the context they need with fewer tokens. We found that agents grounded in Teamwork Graph context deliver 44% more accurate answers using 48% fewer tokens.

Together, these enhancements create a self-improving engineering loop. Each tool call helps agents pull in the right context, take action, and persist new context for future tasks across teams and sessions.

One agent interaction. Fewer tokens. Dramatically more done.

Ready to give your coding agents access to Atlassian context and tools? Get Started with the Atlassian Rovo MCP. Once set up, these new MCP capabilities can be used through both prompts and automated agent workflows.

Try these sample prompts and get more done across fewer tool calls:

• Post-incident follow-up: Create a Jira follow-up work item for this incident, attach the relevant logs from my workspace, find the right backend owner to assign it to, and add a comment recommending next steps based on past incidents.
• Trace code activity to work: Look at this Jira work item and summarize the linked pull requests. Tell me what changed and whether there are any open follow-ups I should create.
• Close out your day: Look at the Jira work items I touched today, log the time I spent on each one based on my recent commits and PR reviews, move the items that are ready to “In Review” or “Done”, and post a one-line wrap-up comment on each.
• From discovery to delivery: Find the Jira Product Discovery idea related to this feature, summarize the customer problem and priority, then connect it to the delivery issue and add a comment explaining how the implementation maps back to the original idea.

PRO TIP

Consider using the Teamwork Graph CLI to give your coding agents direct terminal access to context and tools. Use this prompt to get started:
Install / setup TWG using https://teamwork-graph.atlassian.com/cli/AGENTS.md

Learn more about choosing between Teamwork Graph CLI and Rovo MCP, here.

Availability

The MCP enhancements mentioned above are available to Atlassian Rovo MCP users today, with the exception of worklog updates and file attachments which are rolling out later this month. Find assignees is currently in v2 public preview.

Shape what’s next for Atlassian Rovo MCP: get early access to Atlassian Rovo MCP v2 and share your feedback through the Atlassian community.

Original source

Turn Slack threads into agent-ready Jira work items, without leaving the conversation.

The Jira Cloud for Slack app is already one of the top five apps in the Slack Marketplace, trusted by 3.5 million people every month.

Today, it gets a whole lot smarter.

You can now mention @Jira in any Slack channel to create a Jira work item right from the conversation you’re already in. Describe what you need in natural language, and Jira reads the context of your thread to create a structured work item with the right fields filled in – ready to be assigned to a teammate or agent.

What’s new in the Jira Cloud for Slack app:

• AI-powered quick actions via @Jira mentions
• Natural language work item creation and updates from any channel
• Direct work item assignment to teammates or agents from Slack

Turn conversations into context-rich specs

Turn conversations into context-rich specs

Great ideas, bug reports, and scope decisions happen naturally in Slack. But when you have to switch tabs to log them, you lose valuable time, and worse, you lose the nuance of the conversation. The rationale behind a decision or the edge cases flagged in a thread rarely make it into the final ticket.

The Jira Cloud for Slack app bridges that gap. By creating and updating work items directly from your chat, you stay completely in your flow. More importantly, all that rich thread context carries over automatically. Whoever picks up the work item – whether it’s a teammate or an AI agent – gets a comprehensive spec with the context to start building immediately.

How it works

• Create and update work items from any channel: Mention @Jira and describe the bug, task, or decision. Jira reads your thread, conversation, and channel history to fill in the right space, work item type, and fields automatically. From the moment something surfaces in conversation, it’s in your system of record.
• Hand off work to a teammate or agent: Ask Jira to assign work items to a teammate or any of Jira’s supported third-party agents directly from Slack, like Jira Coding Agent, Cursor, Claude Code, or Codex.
• Keep work items up to date: Update priority, assignee, or status by simply saying so in the thread. Changes sync to Jira immediately, so your board reflects what your team is actually building.
• Configure once, adjust anytime: Use /jira agent to set a default space and add custom instructions per channel. Jira remembers your preferences from there.

MORE COMING SOON: POWERED BY THE TEAMWORK GRAPH

Because @Jira is connected to Atlassian’s Teamwork Graph, it understands the people, projects, and context behind your work, not just the words in your messages. Today, you can tap into this knowledge in DMs with Jira, but coming soon, you’ll be able to do it in any channel. Jira can surface the right owner and route work automatically.

Availability

This new capability is available to Jira Cloud customers with Rovo enabled.

To get started: Install Jira Cloud for Slack app and invite @jira to your channel. If you’ve already installed the app, simply choose a channel and @jira.

To learn more: Read the support article

Original source

Jira Software release notes provide information on the features and improvements in each release. This page includes release notes for platform releases and feature releases (you'll find bug fix release notes after opening one of the versions below).

Upgrade matrix

Too many release notes? Take a look at our Upgrade matrix to get a quick roll-up of the most important changes in the latest versions.

Upcoming bug fix releases

Our bug fix releases follow a predefined, monthly schedule. This makes it easier for you to plan your next upgrade.

New bug fix versions are released for three Jira feature versions: Latest version, Jira 11.3 LTS, and 10.3 LTS. We may occasionally skip a bug fix release if it isn’t needed. All bug fix releases include security and regular bug fixes.

Bug fixes are released every month between the first Tuesday and the second Wednesday.

Jira Software Data Center 11 release notes

• Jira Software 11.3.x release notes LATEST LONG TERM SUPPORT
  • Latest bug fix release: 11.3.6
• Jira Software 11.2.x release notes
  • Latest bug fix release: 11.2.1
• Jira Software 11.1.x release notes
  • Latest bug fix release: 11.1.1
• Jira Software 11.0.x release notes
  • Latest bug fix release: 11.0.1

Jira Software Data Center 10 release notes

• Jira Software 10.7.x release notes
  • Latest bug fix release: 10.7.4
• Jira Software 10.6.x release notes
  • Latest bug fix release: 10.6.1
• Jira Software 10.5.x release notes
  • Latest bug fix release: 10.5.1
• Jira Software 10.4.x release notes
  • Latest bug fix release: 10.4.1
• Jira Software 10.3.x release notes LONG TERM SUPPORT
  • Latest bug fix release: 10.3.21
• Jira Software 10.2.x release notes
  • Latest bug fix release: 10.2.1
• Jira Software 10.1.x release notes
  • Latest bug fix release: 10.1.2
• Jira Software 10.0.x release notes
  • Latest bug fix release: 10.0.1

Long Term Support releases

An Atlassian Long Term Support release is a feature release that gets backported critical security updates and critical bug fixes during its entire two-year support window. If you can only upgrade once a year, consider upgrading to an LTS release.

Learn more

Developer releases

If you are looking for the release notes for the latest (Early Access Program) release, see Jira releases (Jira developer documentation) instead.

Earlier releases

Check the list of earlier Jira releases...

Last modified on May 18, 2026

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In this section

• Jira Software 11.3.x release notes
• Jira Software 11.2.x release notes
• Jira Software 11.1.x release notes
• Jira Software 11.1.x upgrade notes
• Jira Software 11.0.x release notes
• Jira Software 11.0.x upgrade notes
• Jira Software 10.7.x release notes
• Jira Software 10.7.x upgrade notes
• Jira Software 10.6.x release notes
• Jira Software 10.6.x upgrade notes
• Jira Software 10.5.x release notes
• Jira Software 10.5.x upgrade notes
• Jira Software 10.4.x release notes
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Original source

Since we first launched custom agents in May of 2024, we’ve seen teams use Rovo to build agents in Confluence that help them accomplish everything from turning customer feedback into PRDs to maintaining consistency across large sets of data and processes.

And agents are getting even more popular, with over 5M invocations of agents every month. They take the foundation of knowledge that lives in Confluence, and act on it, saving customers over 200K hours in February alone. That’s serious time back to help teams ship faster, gain context more easily, and achieve better business outcomes.

We polled organizations to see what agents are adding value to their workflows in Confluence, so you can find inspiration for workflows across Eng, Product, HR, Project Management, and more. Here are seven of our favorites:

• HarperCollins’s Meeting-to-Action Companion: Turns messy notes into crisp decisions, documented in Confluence and completed Jira tickets, so nothing gets lost.
• Docusign’s PRD & Spec Author: Takes a rough brief and ships a review-ready PRD with linked Jira work items in minutes.
• Riverty’s Lessons Learned: Mines past work in Confluence so your team stops re‑learning the hard way.
• KFC’s Architecture Review: Pre-checks proposals against document standards, so approvals are a breeze.
• Pythian’s Progress Tracker: Pulls the signal from Jira + Confluence and drafts status update emails for you.
• Sprout Social’s Onboarding & Playbook Builder: Answers ~80% of new‑hire questions and spins up role guides for new hires.
• Procore’s Backlog & Discovery Synthesizer: Sifts feedback to surface what truly earns a spot on the roadmap.

The seven agents above work across Confluence and Jira — your knowledge layer and your execution layer. But with MCP (Model Context Protocol), agents can reach beyond Atlassian into external tools. An agent can read knowledge in Confluence, think about it, and then take action in a third-party tool without you having to context-switch, reformat, or copy-paste between tabs. Confluence just launched out-of-the-box partner agents, with Lovable, Replit, and Gamma, to turn knowledge into prototypes, codebases, or visuals, all without the human tax of translating between tools.

And because MCP is an open protocol, this ecosystem keeps growing. Any tool that supports MCP can become the next output surface for your Confluence knowledge.

Make your own agents with Rovo Studio

You don’t need to code anything. If you can describe what you want in plain English, you can build an agent. Here’s how:

1. Open Rovo Studio.
   From any Confluence page, click the app switcher in left section of top nav and open Studio. You’ll land on a canvas where you can create and manage agents.

2. Paste your instructions.
   This is the heart of the agent — a plain-language prompt that tells it what to do, what to read, and how to respond. Every use case below includes ready-to-paste instructions you can drop right in.

3. Choose tools and knowledge.
   Pick which tools the agent can use, like Confluence pages, Jira issues, Slack channels, or connect it to tools through the MCP gallery. Under knowledge, scope it to the spaces or projects it needs.

4. Test and publish.
   Run a few test prompts in the preview pane, tweak the instructions until the output feels right, then publish. Your team can start using it immediately from Rovo chat, Confluence pages, or Jira work items.

That’s it — four steps, no engineering ticket required.

Once you’re up and running, here are some tips to get the most out of your agents:

• Pick your use case.
  Identify one or two workflows where Confluence is already the system of record, like PRDs, meeting notes, or incidents, and start with agents there.
• Document your standards.
  Put your templates and guidelines into Confluence pages so agents have clear patterns to follow.
• Use agents with automation.
  Let Studio create a workflow for you. Pick an outcome and use Studio to build an automation rule and agent to let it run. The “Go further” patterns in this post are good starting points and it’s easy to use Natural Language to build the automation rules for you.
• Pilot with a small team.
  Collect feedback on drafts the agents produce. Tune prompts accordingly.
• Scale and measure impact.
  Track time saved on routine docs, number of Confluence pages kept up to standard, and fewer missed follow-ups after meetings.

Want to copy the examples from Docusign, HarperCollins, and Sprout Social? Get started with the prompts and automation patterns below.

First, paste the prompts into Rovo Studio’s Creation screen. These become your agent instructions. Then, confirm all the necessary tools are listed in the Tools drop down, and add the appropriate Spaces and Documents under the Knowledge drop down.

HarperCollins’s Meeting-to-Action Companion

HarperCollins Publishers uses a meeting-to-action agent to turn messy Confluence notes and call transcripts into structured decision logs with owners, deadlines, and linked Jira issues — automatically. It solves the classic loss of follow-ups between meetings by scanning pages, extracting decisions and action items, and creating the right Jira tickets so work is tracked. Ideal for PMs, team leads, and chiefs of staff who live in recurring meetings, it cuts an hour of manual routing and formatting down to ~15 minutes so they can focus on higher‑impact work.

Setup instructions

Paste the following into Rovo Studio’s Creation screen.

ROLE

You are a Meeting-to-Action Companion agent that turns messy meeting notes and call transcripts into structured decision logs and tracked work.

SCOPE & SOURCES

• Primary notes from [MEETING NOTES SPACE] in Confluence.
• Meeting pages with call transcripts, bullet notes, or freeform text.
• Decision-log standards at [DECISION-LOG STANDARDS PAGE LINK].

WHEN INVOKED

• Read the full notes/transcript and extract:
  • Decisions (what was decided, by whom, and why).
  • Action items (what needs to happen, by when, and by whom).
• Rewrite the page into a structured decision log with sections: Decision, Rationale, Owner, Due date, Links (to Jira issues and related docs).
• For each action item:
  • Create Jira issues using default project [JIRA PROJECT KEY] and issue type [JIRA ISSUE TYPE], unless otherwise specified.
  • Assign issues in this order of precedence: 1) Explicit @mentions on the page, 2) Meeting owner, 3) Team queue or default assignee.
  • Add appropriate labels/components based on the meeting context.
  • Link created Jira issues back to the Confluence page and list their keys under the relevant decision/action.
  • Send a brief Slack notification to [CHANNEL ID] summarizing decisions/actions and linking to the updated page.

OUTPUT

• A cleaned-up Confluence decision log that replaces or augments the original notes.
• A set of Jira issues linked from the decision log.
• A short Slack message with the link and key highlights.

CONSTRAINTS

• Do not invent facts; mark unknowns as TBD and ask focused questions.
• Do not invent decisions, owners, or dates.
• If information is missing, include a short "Open Questions" section.
• Keep sensitive details internal unless explicitly asked for a customer-facing version.

Try these prompts
“Turn these notes into a decision log and create Jira issues for each action.”
“Summarize this customer call into 5 bullets and publish to Confluence.”
“Turn this standup page into a status update for our exec Slack channel.”

Docusign’s PRD & Spec Author

Docusign uses a PRD & Spec Author agent in Confluence to turn a short outline into a complete, review-ready PRD that matches their team’s structure and tone, with suggested Jira epics and stories linked from the doc. It eliminates blank-page PRDs and copy-paste drift by learning from prior specs, decisions, and retros to justify choices. Built for PMs, tech leads, solution architects, and founders who need to go from idea to spec fast, it standardizes outputs, cuts manual rewriting, and reduces glue work across teams.

Setup instructions

Paste the following into Rovo Studio’s Creation screen.

ROLE

You are a PRD & Spec Author agent that turns short problem statements or Jira work items into complete, review-ready PRDs and specs.

SCOPE & SOURCES

• Prior PRDs and specs in [PRD EXAMPLES SPACE OR LABEL] to learn structure, tone, and depth.
• Standards and templates at [PRD TEMPLATE/STANDARDS PAGE LINK].
• Input brief from a short problem statement and/or a Jira epic (including linked discovery notes and feedback).

WHEN INVOKED

• Analyze the brief and any linked Confluence pages (discovery notes, research, customer feedback).
• Draft a complete, review-ready PRD in Confluence that follows our headings and conventions from [PRD TEMPLATE/STANDARDS PAGE LINK], including:
  • Problem statement and background
  • Goals/non-goals
  • Users and use cases
  • Requirements and acceptance criteria
  • Assumptions
  • Risks
  • Dependencies
  • Open questions
• Propose Jira epics and stories that map to the PRD:
  • Default to project [DEFAULT JIRA PROJECT KEY] and issue types [DEFAULT JIRA ISSUE TYPES], unless otherwise specified.
• Link the Jira issues back to the PRD and cross-link the PRD from the Jira issues.

OUTPUT

• A new or updated PRD in Confluence, published under [WHERE TO PUBLISH PRDS].
• A set of linked Jira epics/stories reflecting the proposed work.
• Optional: a short summary for stakeholders via [NOTIFY VIA] with key highlights and a PRD link.

CONSTRAINTS

• Do not invent facts; mark unknowns as TBD and ask focused questions.
• Stay consistent with tone and level of detail used in prior PRDs in [PRD EXAMPLES SPACE OR LABEL].
• Do not overwrite existing PRDs without preserving key decisions and rationale; update incrementally.

Try these prompts
“Turn these notes into a decision log and create Jira issues for each action.”
“Summarize this customer call into 5 bullets and publish to Confluence.”
“Turn this standup page into a status update for our exec Slack channel.”

Go further: automate it

The most advanced version of this agent doesn’t wait for a PM to invoke it. Set up an automation rule using natural language that triggers on a schedule and points the agent at 90 days of customer feedback from a Jira project or JPD board. The agent identifies emergent themes, clusters them, and auto-generates short PRD drafts for each — structured, user-backed, and ready for a PM to refine.

Automation workflow:
Trigger: Scheduled (weekly) → Action: Invoke “Feedback to PRD & Spec Author” agent → Prompt: “Analyze the last 90 days of feedback from [FEEDBACK PROJECT LINK HERE], identify the top emergent themes, and generate a one-page PRD draft for each theme with linked evidence” → Action: Publish new Confluence page as PRD from agent output.

Riverty’s Lessons Learned Agent

A Lessons Learned Rovo agent helps Riverty turn past investigations into a reusable knowledge base. It scans completed tasks from Riverty’s data analysis desk, distills the key insights, and publishes standardized learnings to Confluence so teams can quickly see what’s been tried before, what worked, and what to avoid. Instead of starting from scratch, users can query the agent with a problem they’re facing and immediately surface lessons gleaned from similar tasks, complete with links back to the original analysis. As Atlassian Product Owner Andrei Tuch puts it, “Every time someone asks us to connect one of the popular LLMs to our Jira or Confluence, we help them implement their use case in Rovo instead – because it always works better.”

Setup instructions

Paste the following into Rovo Studio’s Creation screen.

ROLE

You are a Lessons Learned agent that turns completed analysis tasks into a reusable knowledge base and answers new questions by surfacing relevant past learnings.

SCOPE & SOURCES

• Completed tasks from your data analysis desk (Jira tickets, summaries, attachments).
• Confluence pages containing prior analyses, retros, and decision logs.
• Optional: a central "Lessons Learned" index page to organize topics, tags, and links.

WHEN INVOKED

• Ingest and synthesize learnings from completed analysis tasks: problem, approach, data used, key findings, decisions, caveats.
• Publish concise, standardized summaries to Confluence with links back to source tasks and artifacts.
• Answer user queries about current problems by retrieving and summarizing lessons from similar past tasks; highlight applicable insights and known pitfalls.
• When helpful, create or update a central "Lessons Learned" index page that categorizes learnings by topic, system, and tags.

OUTPUT

• A Confluence page (or section) per learning with: Context, What we tried, What worked/failed, Key takeaways, Reuse guidance, Links.
• Cross-links between related learnings and the optional central index.
• Short, actionable answers in chat that cite and link to the relevant learnings.

CONSTRAINTS

• Do not invent facts; mark unknowns as TBD and ask focused questions.
• Preserve source wording for critical details (numbers, thresholds, caveats); summarize without changing meaning.
• Attribute each learning to its original task/page with links and dates.

Try these prompts
“Analyze these tasks and publish Lessons Learned pages in Confluence.”
“Surface past lessons that apply to this problem, with risks and caveats.”
“Update the Lessons Learned index with today’s entries and cross-links.”

KFC’s Architecture Review Agent

KFC uses an Architecture Review agent to raise the quality bar on proposals before they ever reach the Architecture Review Board. The board had strong guidelines and principles in place, but they weren’t consistently followed, so review time was spent sense‑checking completeness and basic alignment instead of debating trade‑offs and long‑term strategy. Now, teams invoke an agent that checks new proposals against KFC’s standards, flags gaps, and suggests improvements, so only proposals that meet baseline expectations move forward. The result: fewer low‑quality submissions, more time on high‑value architectural discussion, and an architecture history that’s easy to navigate and keep up to date.

Setup instructions

Paste the following into Rovo Studio’s Creation screen.

ROLE

You are an Architecture Review agent that pre-reviews architecture proposals and helps maintain high-quality architecture decision records.

ACCESS

• Confluence (read/write)
• Jira (read-only or write, as configured)

SCOPE & SOURCES

• Architecture principles and guidelines at [ARCHITECTURE PRINCIPLES/GUIDELINES PAGE LINK].
• Example high-quality proposals at [EXAMPLE PROPOSALS SPACE/LABEL/PAGE TREE].
• Architecture decision templates at [ARCHITECTURE DECISION TEMPLATE PAGE LINK].
• Draft proposals as Confluence pages or attachments, optionally linked from Jira.

WHEN INVOKED ON A DRAFT PROPOSAL

• Check the proposal against guidelines, including required sections, clarity of scope and context, explicit trade-offs and alternatives, risks, dependencies, operational concerns, and alignment with existing patterns/principles.
• Compare to similar, high-quality proposals and call out deviations.
• Suggest concrete improvements: sections to add, clarifications to make, risks to articulate, and relevant systems/ADRs/Jira epics to link.
• Provide a short summary at the top with a readiness rating (Ready/Needs Work), key gaps, and recommended next steps.

OPTIONAL: FINAL DECISION RECORD

• Once approved, use [ARCHITECTURE DECISION TEMPLATE PAGE LINK] to generate a finalized Confluence decision record; capture decision, rationale, trade-offs, risks, dependencies; and link related Jira work and ADRs.

OUTPUT

• A reviewed and annotated proposal that meets baseline standards, or clear guidance on what to fix.
• Optionally, a finalized architecture decision record page linked from Jira and other system documentation.

CONSTRAINTS

• Do not invent facts; mark unknowns as TBD and ask focused questions.
• Do not invent architecture decisions, risks, or dependencies; if unclear, add questions for the proposal owner.
• Respect existing decision history; do not overwrite approved decisions, only add context or clarifications.

Try these prompts
“Review this proposal against our guidelines and list what needs fixing.”
“Rewrite this draft to match our architecture proposal template.”
“Create an architecture decision record for this approved proposal.”

Pythian’s Progress Tracker

Pythian uses a Progress Tracker agent to replace ad‑hoc, manual status emails with consistent, data‑backed updates pulled directly from Jira and Confluence. Built for account managers, customer success leaders, and product marketers who send regular customer updates, it scans a Confluence space and linked Jira work to draft polished, customer‑facing summaries with progress, key wins, risks, and next steps — plus an internal‑only version that includes the sensitive details. Instead of hunting across issues, docs, and email threads to remember what changed, teams invoke the agent from a single Confluence page or Jira epic and get tailored updates in minutes. Combined with Transcript Insight and Team Recap agents, it’s saving Pythian teams an average of 20 minutes per day and freeing them up to focus on more strategic, high‑impact work.

Setup instructions

Paste the following into Rovo Studio’s Creation screen.

ROLE

You are a Progress Tracker agent for customer projects that turns scattered work updates into consistent, data-backed status summaries.

ACCESS

• Confluence (read/write)
• Jira (read for linked projects; write for comments or labels if enabled)

SCOPE & SOURCES

• A specified Confluence page or Jira epic as the system of record for the project.
• Linked Jira issues (status, comments, labels, due dates).
• Related Confluence pages (plans, decision logs, prior updates).
• Example status updates and summaries at [STATUS EXAMPLES SPACE/LABEL/PAGE TREE].

WHEN INVOKED ON A PROJECT

• Determine what has changed since the last update (completed work, in-progress items, blocked tasks, new risks, notable customer interactions/decisions).
• Synthesize a concise status summary emphasizing outcomes delivered, progress against milestones, upcoming work/timelines, and key risks with mitigations.
• When requested, create two versions:
  • Customer-facing: friendly, non-technical, value/timeline/next steps, no sensitive details.
  • Internal: technical details, blockers, staffing notes, technical debt; clearly marked Internal-only.
• Maintain consistent headings: Overview; Progress since last update; Key wins; Risks/blockers; Next steps/upcoming milestones.
• Link relevant Jira issues and Confluence pages in each section.

OUTPUT

• Draft/update a Confluence status page under [STATUS SPACE/PARENT PAGE] with clear date and sections.
• Optional: email/Slack-ready snippets for customer and internal audiences.

CONSTRAINTS

• Do not invent facts; mark unknowns as TBD and ask focused questions.
• Do not expose internal-only details in customer-facing versions.
• Keep tone aligned to prior examples in [STATUS EXAMPLES SPACE/LABEL/PAGE TREE].

Try these prompts
“Draft a customer-ready status update from this plan and linked Jira issues.”
“Summarize progress since the last update with wins, risks, and next steps.”
“Draft an exec summary for the customer and a detailed one for our team.”

Go further: close the loop entirely

The ultimate version of this pattern doesn’t just write the update — it automates the entire feedback collection cycle. Here’s what an internal team at Atlassian built: when new customer feedback hits a Jira project, an automation rule sends the customer a Calendly link to schedule a follow-up call. Loom records the meeting. A Rovo agent summarizes the recording and writes polished notes into a Confluence page. Then a second automation broadcasts the summary — customer name, key themes, action items, and the Loom recording link — to a shared Slack channel. The PM’s only job? Show up and listen. Everything else happens automatically.

Sprout Social’s Onboarding & Playbook Builder

Sprout Social uses an Onboarding & Playbook Builder agent to turn scattered how‑tos, runbooks, and team docs into structured, role‑specific onboarding guides and reusable playbooks. Paired with Loom-based onboarding, the agent now answers ~80% of new‑hire questions directly in their Slack onboarding channel by routing them to the right Confluence content and steps. The result: more than 360 hours of work saved per year, faster ramp-up for new employees, and a dramatically lighter IT and team‑lead burden during those critical first weeks.

Setup instructions

Paste the following into Rovo Studio’s Creation screen.

ROLE

You are an Onboarding & Playbook Builder agent for new hires and recurring team processes.

ACCESS

• Confluence (read/write)
• Jira (read/write where enabled)
• Slack (read/write in specified onboarding channels)

SCOPE & SOURCES

• Treat the specified Confluence space(s) or page tree(s) as the source of truth for how the team works: how-tos, runbooks, team charters, architecture overviews, decision logs, process docs.
• Use role descriptions, competency frameworks, existing onboarding checklists as anchors for what complete onboarding/playbooks should cover.
• Incorporate Loom or other embedded videos in Confluence as primary learning assets when available.

WHEN INVOKED FOR A NEW HIRE

• Ask for or infer role, team, location, seniority (e.g., "Backend Engineer, Core Services, Mid-level").
• Cluster related Confluence pages into themes (Day 1 basics; Tools & access; Architecture overview; Team rituals; Key projects & metrics).
• Identify gaps or stale content; flag as TODOs with concrete suggestions.
• Draft a sequenced onboarding guide in Confluence (e.g., 1–2 weeks) that states goals, orders topics by need, links canonical docs/Loom/Jira, and proposes tasks that can become Jira issues.

WHEN INVOKED FOR A RECURRING PROCESS PLAYBOOK

• Analyze past Confluence pages, Jira issues, retros for the process (e.g., launches, incidents, QBRs).
• Extract steps, roles, timelines, checklists, known pitfalls.
• Draft a reusable playbook that defines purpose/owner/success metrics; outlines phases with inputs/outputs and DRIs; links examples/templates/Jira; includes a copyable checklist.

SLACK + Q&A BEHAVIOR

• When mentioned in onboarding channels: search relevant Confluence pages and Loom links; point to the best canonical resource first, then summarize if needed.
• If no good answer exists: say so; offer a provisional answer based on adjacent docs; suggest creating/updating a Confluence page/section.

OUTPUT

• A structured onboarding guide page for the role/team.
• One or more reusable process playbook pages in Confluence.
• Helpful, link-rich Slack answers guiding new hires to canonical docs.

CONSTRAINTS

• Do not invent facts; mark unknowns as TBD and ask focused questions.
• Do not invent policies, access rights, or sensitive details; if unclear, add a "Questions for manager or IT" section.
• Prefer existing canonical pages over duplicates; if multiples exist, choose and label the canonical one.

Try these prompts
“Build a 2-week onboarding path for a new backend engineer on this team.”
“Create a reusable playbook for beta launches from these three past docs.”
“Curate the top 10 docs a new PM should read and organize them on one page.”

Procore’s Backlog & Discovery Synthesizer

Procore uses a Backlog & Discovery Synthesizer agent to bridge the gap between customer insights in Confluence and the product backlog in Jira. Instead of PMs trying to remember which interview, feedback log, or research report justified a given idea, the agent connects discovery notes, research pages, and feedback docs to related Jira epics and stories, then rolls everything up into evidence‑backed themes and prioritized recommendations. Prioritization sessions shift from “what do we remember” to “what does the evidence say,” and hunting for buried tickets or docs that used to take 20 minutes now happens almost instantly via Rovo chat and summarized Confluence pages.

Setup instructions

Paste the following into Rovo Studio’s Creation screen.

ROLE

You are a Backlog & Discovery Synthesizer agent that connects customer insights in Confluence to the product backlog in Jira and produces evidence-backed priorities.

ACCESS

• Confluence (read/write)
• Jira (read for relevant projects; write where enabled for comments/labels)

SCOPE & SOURCES

• Discovery and research content in Confluence (user interviews, discovery notes, feedback logs, research reports, experiment results).
• Backlog items in Jira (ideas, feature requests, bugs, roadmap epics) in specified projects/boards.
• Prioritization framework at [PRIORITIZATION FRAMEWORK PAGE LINK] (e.g., RICE, impact/effort).

WHEN INVOKED

• Ask for or infer product area, segment/persona, and timeframe (e.g., "Core workflows, mid-market, last 90 days of feedback").
• Cluster discovery inputs into themes (workflow, persona, segment, problem area).
• For each theme:
  • Identify/link related Jira issues (epics, stories, bugs) via titles, descriptions, tags, linked Confluence pages.
  • Summarize customer signals (pain points, requests, positive feedback) with short excerpts where useful.
  • Flag conflicting/unclear signals as open questions.

OUTPUT

• Create/update a Confluence summary page under [DISCOVERY/ROADMAP SPACE] that lists themes with evidence, groups/links Jira items under each, includes a ranked "Recommended priorities" section with rationale, and captures open questions/risks/assumptions.
• Keep language clear and decision-ready for prioritization and roadmap reviews.

CONSTRAINTS

• Do not invent facts; mark unknowns as TBD and ask focused questions.
• Do not fabricate quotes, quantitative metrics, or prioritization scores; base only on visible data.
• Preserve nuance; if evidence is weak or mixed, state it and recommend further discovery.

Try these prompts
“Synthesize these discovery pages into themes and map them to Jira epics.”
“Recommend top 10 backlog items for next quarter using our RICE framework.”
“Summarize top customer pain points by segment and link related Jira tickets.”

Go further: automate it

One internal team at Atlassian runs a version of this agent on a weekly schedule across 12,000+ feedback tickets spanning five Jira projects and a Slack channel. Every Friday morning, the agent analyzes the last 30 days of feedback, breaks it into weighted themes — positive feedback on new features, negative feedback on usability, feature requests and suggestions — and posts a structured summary to Slack with example quotes, percentages, and specific recommendations for next steps. Leadership reviews it in minutes. No analyst assembled it. No one remembered to ask.

Automation rule:
Trigger: Scheduled (every Friday at 8AM) → Action: Invoke “Feedback Analyst” agent → Prompt: “Analyze the last 30 days of feedback from this project, break it into top themes with examples and links to issues, as well as key customer quotes. Provide recommendations for next steps” → Action: Send Slack message with agent response to your team channel.

How to invoke Confluence Agents with Jira

If your team lives primarily in Jira, these agents meet you there. Confluence is the knowledge layer, Jira is the orchestration layer, and agents move fluidly between the two.

1. Invoke via the assignee picker.
   When you assign an issue in Jira, choose an agent as the assignee. The agent treats the issue as its brief, looks at linked Confluence pages and related tickets, and then writes back into Confluence — creating or updating a PRD, meeting notes page, or whatever the agent is designed to produce — while updating the Jira issue with a link.

2. @mention agents in comments.
   In Jira or (coming soon!) Confluence comments, mention an agent and give it instructions tied to that issue. The agent will read the issue description, attachments, and any linked Confluence pages, perform the requested work, and then respond in the Jira comment thread with links to the Confluence content it created or updated.

3. Use “Open in chat” for deeper refinement.
   From a Jira issue, switch into a chat-style view with the agent using “Open in chat.” This pulls in the issue context and any connected Confluence pages, letting you iterate: ask follow-up questions, refine drafts, or request different versions of a Confluence page or update.

4. Automate invocation with board column triggers.
   Agents can run automatically when an issue moves into certain columns on a Jira board. Moving an epic into “Discovery” could trigger the PRD & Spec Author to draft a first-pass PRD in Confluence. Dragging a bug into “Ready for Postmortem” could trigger the Incident & Postmortem Coach to assemble a Confluence report from linked issues and notes.

5. Enable agents in Jira via Studio surfaces.
   To make agents available inside Jira, you control their visibility through Studio surfaces. By turning on the Jira surface for a given agent, you allow it to appear in the assignee picker, comments, and relevant Jira entry points while keeping its core logic grounded in Confluence and your other knowledge sources.

Original source

The Atlassian Confluence team is pleased to announce the release of Confluence 9.2.20, which is a bug-fix release.

Don't have Confluence 9.2 yet?

Check out the new features and other highlights in the Confluence 9.2 release notes.

Get the latest version

We recommend you read the Confluence 9.2 release notes and you back up your confluence-home directory and database before upgrading.

Key issues fixed in this release include:

• CONFSERVER-103611: Confluence JMX metrics collection (DCDP) logs frequent AttributeNotFoundException: No such attribute: Mean
• CONFSERVER-102539: Confluence uses high L2 cache and OOMs (typically encountered where pages with lots of versions come into play) due to Activity streams gadgets request from Jira
• CONFSERVER-74857: Content REST API is not rate limited

Last modified on May 5, 2026

Original source

The Atlassian Confluence team is pleased to announce the release of Confluence 10.2.11, which is a bug-fix release.

Don't have Confluence 10.2 yet?

Check out the new features and other highlights in the Confluence 10.2 release notes.

Get the latest version

We recommend you read the Confluence 10.2 release notes and you back up your confluence-home directory and database before upgrading.

Issues resolved in 10.2.11

• CONFSERVER-103634: BlueprintDiscoveryUpgradeTask blocks startup with 100% CPU busy (Bug, Highest priority, Fixed)
• CONFSERVER-103611: Confluence JMX metrics collection (DCDP) logs frequent AttributeNotFoundException: No such attribute: Mean (Bug, Medium priority, Fixed)
• CONFSERVER-102709: Scheduled mails gets stuck in Mail Error Queue with OAuth 2.0 SMTP setup (Bug, Low priority, Fixed)
• CONFSERVER-102539: Confluence uses high L2 cache and OOMs due to Activity streams gadgets request from Jira (Bug, Low priority, Fixed)
• CONFSERVER-74857: Content REST API is not rate limited (Bug, Low priority, Fixed)

Last modified on May 5, 2026

Original source

Introducing Remix with Rovo and partner agents in Confluence — a new way to instantly transform Confluence pages into charts, prototypes, presentations, and apps

The last mile of knowledge

Something strange happened over the past decade of work. Teams got incredibly good at creating knowledge — documenting decisions, capturing meeting notes, writing specs. But all that effort exposed a different problem: most of that knowledge never reaches the people who need it, in a format they can actually use.

Confluence pages with visual elements are nearly 2x as likely to be read by a wider audience compared to pages without.

This isn’t a search problem. It isn’t an access problem. It’s a format problem. The knowledge exists. It’s just stuck in a form that doesn’t match how the next person needs to consume it. This means manual work: copying from docs into slides, reformatting for different audiences, and losing context, repackaging existing knowledge instead of creating new.

We’re introducing two new experiences to close that gap to change how teams get value from work they’ve already created. Remix with Rovo transforms content on any Confluence page into new formats like charts, infographics, and other visuals. Pre-built third party partner agents for Lovable, Replit, and Gamma, turn Confluence content into working prototypes, starter apps, and presentations in those tools without manual copy-pasting or custom integrations.

What’s available today

Remix with Rovo begins rolling out today in open beta to Confluence Cloud customers with Rovo, continuing over the next few weeks. At launch, it supports data visualizations, infographics, diagrams, and charts — with more formats coming soon. Find Remix in your Editor toolbar.

Out-of-the-box partner agents for Lovable, Replit, and Gamma are in open beta and start rolling out next week. Admins can enable partner agents in Atlassian Administration under Connected Apps, with no custom agent creation or scripting required.

Introducing Remix with Rovo

Confluence has always been where teams go to create and share knowledge. With Remix, it becomes something more: an adaptive workspace — one where the content itself reshapes to meet the reader, not the other way around.

Select any content on a Confluence page and instantly transform it into a visual format optimized for how someone needs to consume it.

A data-heavy section becomes a chart. A process description becomes an infographic. A long-form analysis becomes a visual summary. No copy-pasting, no switching tools, no reformatting. Just the boost in understanding that comes from nailing the format.

Confluence pages with 1 or more visual element are 18% more likely to be read by a wider audience.

Three things make Remix with Rovo fundamentally different from what’s come before:

1. It’s non-destructive. Remix never overwrites your page. Every remix is an extra layer on top of the source, which stays intact as the canonical version — so you get new ways to view the content without creating copies that go stale.

2. It’s opinionated. Remix gives you ready-made format options or a freeform prompt if you already know what you want. Pick a preset (like a chart for numbers or an infographic for a flow), or describe the output in your own words. In both cases, it analyzes the content to propose a strong first version you can tweak.

3. It’s embedded, not separate. Remix views are created and live right on the page. Instead of sending people to a separate deck, report, or tool, the most digestible version of the content sits where they already are. Anyone visiting a Confluence page can turn the source into the version that’s easiest for them to scan, compare, and act on.

When the right format lives in a different tool

Sometimes the next step isn’t a better chart — it’s a working prototype. A starter app.

That’s why we’re also launching out-of-the-box partner agents in Confluence, starting with Lovable, Replit, and Gamma, built on Rovo and powered by MCP.

From any Confluence page, invoke a partner agent that carries your content (and context) into a native output in that partner’s tool using Rovo Chat. A product spec becomes a real Lovable application our designer can interact with in minutes. A technical doc becomes a Replit starter app your engineer can fork and extend. Meeting notes become a Gamma presentation your team lead can walk into a room with.

And Rovo Skills keep that output linked back to the source page it came from. That linkage runs through the Teamwork Graph, the same layer of work relationships and context, built from over 100 billion data points across Atlassian, that powers agents in Jira and MCP skills for Rovo. When a partner agent carries your content into Lovable or Replit, it doesn’t just carry the text. It carries the context: who created it, what project it belongs to, what decisions it connects to.

Enable a partner’s MCP server once and within minutes, teams get a ready-to-use agent in their Rovo directory, pre-configured by the partner, inheriting the permissions and context of your workspace. And because everything routes back through Confluence, work created in an external tool doesn’t disappear into that tool’s silo. It stays anchored to your source of truth.

And these partner agents are just the beginning.

Go further with MCP skills in Rovo

The same foundation that’s powering these Partner agents – MCP – also lets you bring in tools beyond the ones we’ve launched with today.

MCP lets any tool connect to Confluence as an AI-aware service. Today that includes Lovable, Replit, and Gamma. But the protocol is open, the server is documented, and any partner can build an agent that works with the knowledge your team already has in Confluence, without waiting for us to build a bespoke integration.

To discover MCP‑compatible skills from your favorite apps, and use them with Rovo across Confluence, Jira, and more, visit our gallery of MCP servers and start connecting them to your work today.

Check out Rovo MCP skills

A different bet about AI in the enterprise

This is the second chapter of a platform shift we started in February. Agents in Jira showed what happens when AI joins your team inside the tool where work gets tracked. Today, Remix and partner agents show what happens when AI joins your team inside the tool where knowledge lives. Together, they mark a turn from AI that helps individuals produce faster to AI that helps teams deliver to each other — across tools, across formats, across the last mile.

Because the last mile of knowledge isn’t about writing more. It’s about delivering better.

See what that looks like in practice

Check out our new digital series, Rovo at Work, to see product demos and real-world examples of how Atlassian teams use Remix, Rovo Skills, and Rovo Dev in Jira to transform how they get work done and deliver better outcomes.

Watch Rovo at Work

Original source

Introducing Remix with Rovo and partner agents in Confluence — a new way to instantly transform Confluence pages into charts, prototypes, presentations, and apps

The last mile of knowledge

Something strange happened over the past decade of work. Teams got incredibly good at creating knowledge — documenting decisions, capturing meeting notes, writing specs. But all that effort exposed a different problem: most of that knowledge never reaches the people who need it, in a format they can actually use.

Confluence pages with 1 or more visual element are 18% more likely to be read by a wider audience.

This isn’t a search problem. It isn’t an access problem. It’s a format problem. The knowledge exists. It’s just stuck in a form that doesn’t match how the next person needs to consume it. This means manual work: copying from docs into slides, reformatting for different audiences, and losing context, repackaging existing knowledge instead of creating new.

We’re introducing two new experiences to close that gap to change how teams get value from work they’ve already created.

Remix with Rovo transforms content on any Confluence page into new formats like charts, infographics, and other visuals.

Pre-built third party partner agents for Lovable, Replit, and Gamma, turn Confluence content into working prototypes, starter apps, and presentations in those tools without manual copy-pasting or custom integrations.

What’s available today

Remix with Rovo starts rolling out today in open beta for Confluence Cloud customers with Rovo. At launch, Remix supports — data visualizations, infographics, diagrams, and charts — with more formats coming soon.

Out-of-the-box partner agents for Lovable, Replit, and Gamma are in open beta and start rolling out next week. Admins can enable partner agents in Atlassian Administration under Connected Apps, with no custom agent creation or scripting required.

Introducing Remix with Rovo

Confluence has always been where teams go to create and share knowledge. With Remix, it becomes something more: an adaptive workspace — one where the content itself reshapes to meet the reader, not the other way around.

Select any content on a Confluence page and instantly transform it into a visual format optimized for how someone needs to consume it.

A data-heavy section becomes a chart. A process description becomes an infographic. A long-form analysis becomes a visual summary. No copy-pasting, no switching tools, no reformatting. Just the boost in understanding that comes from nailing the format.

Confluence pages with 1 or more visual element are 18% more likely to be read by a wider audience.

Three things make Remix with Rovo fundamentally different from what’s come before:

1. It’s non-destructive. Remix never overwrites your page. Every remix is an extra layer on top of the source, which stays intact as the canonical version — so you get new ways to view the content without creating copies that go stale.

2. It’s opinionated. Remix gives you ready-made format options or a freeform prompt if you already know what you want. Pick a preset (like a chart for numbers or an infographic for a flow), or describe the output in your own words. In both cases, it analyzes the content to propose a strong first version you can tweak.

3. It’s embedded, not separate. Remix views are created and live right on the page. Instead of sending people to a separate deck, report, or tool, the most digestible version of the content sits where they already are. Anyone visiting a Confluence page can turn the source into the version that’s easiest for them to scan, compare, and act on.

When the right format lives in a different tool

Sometimes the next step isn’t a better chart — it’s a working prototype. A starter app.

That’s why we’re also launching out-of-the-box partner agents in Confluence, starting with Lovable, Replit, and Gamma, built on Rovo and powered by MCP.

From any Confluence page, invoke a partner agent that carries your content (and context) into a native output in that partner’s tool using Rovo Chat. A product spec becomes a real Lovable application our designer can interact with in minutes. A technical doc becomes a Replit starter app your engineer can fork and extend. Meeting notes become a Gamma presentation your team lead can walk into a room with.

And Rovo Skills keep that output linked back to the source page it came from. That linkage runs through the Teamwork Graph, the same layer of work relationships and context, built from over 100 billion data points across Atlassian, that powers agents in Jira and MCP skills for Rovo. When a partner agent carries your content into Lovable or Replit, it doesn’t just carry the text. It carries the context: who created it, what project it belongs to, what decisions it connects to.

Enable a partner’s MCP server once and within minutes, teams get a ready-to-use agent in their Rovo directory, pre-configured by the partner, inheriting the permissions and context of your workspace. And because everything routes back through Confluence, work created in an external tool doesn’t disappear into that tool’s silo. It stays anchored to your source of truth.

And these partner agents are just the beginning.

Go further with MCP skills in Rovo

The same foundation that’s powering these Partner agents – MCP – also lets you bring in tools beyond the ones we’ve launched with today.

MCP lets any tool connect to Confluence as an AI-aware service. Today that includes Lovable, Replit, and Gamma. But the protocol is open, the server is documented, and any partner can build an agent that works with the knowledge your team already has in Confluence, without waiting for us to build a bespoke integration.

To discover MCP‑compatible skills from your favorite apps, and use them with Rovo across Confluence, Jira, and more, visit our gallery of MCP servers and start connecting them to your work today.

Check out Rovo MCP skills

A different bet about AI in the enterprise

This is the second chapter of a platform shift we started in February. Agents in Jira showed what happens when AI joins your team inside the tool where work gets tracked. Today, Remix and partner agents show what happens when AI joins your team inside the tool where knowledge lives. Together, they mark a turn from AI that helps individuals produce faster to AI that helps teams deliver to each other — across tools, across formats, across the last mile.

Because the last mile of knowledge isn’t about writing more. It’s about delivering better.

See what that looks like in practice

Check out our new digital series, Rovo at Work, to see product demos and real-world examples of how Atlassian teams use Remix, Rovo Skills, and Rovo Dev in Jira to transform how they get work done and deliver better outcomes.

Watch Rovo at Work

Original source