Atlassian

Getting 401 response when calling Confluence cloud rest API search Hi, I’m trying to call the confluence rest api search function but facing some issues. I’m getting a 401 response even with the correct credentials. Here’s the simple code I’m trying out: curl --request GET –url ‘ https://corporate.atlassian.net/wiki/rest/api/search?cql=text~test ’ –user ‘corporate@email.com:GENERATED_TOKEN’ –header ‘Accept: application/json’ I tried with 2 tokens with difference scopes. The first token I created have these scopes Read read:account read:confluence-content.all read:confluence-content.permission read:confluence-content.summary read:confluence-groups read:confluence-props read:confluence-space.summary read:confluence-user read:me Search search:confluence The 2nd token that I created have a more limited scope having only these Read read:content-details:confluence Search search:confluence But still I’m getting a 401 even just by using the simplest sample code from the documentation of search endpoint - https://developer.atlassian.com/cloud/confluence/rest/v1/api-group-search/#api-group-search I can see that the tokens are being access as the page API Tokens shows when it was last accessed. Not sure how to proceed and what else to check. Please advise. 1 post - 1 participant Read full topic

A forge app adopted from connect is unable to be installed (still uses connectModules from manifest.yml) Hello, I am aware of the fact that is it no longer possible to install private connect apps ( https://developer.atlassian.com/platform/forge/changelog/#CHANGE-3096 ). But recently I have noticed that apps which have been adopted from connect (no longer using atlassian-descriptor.json , but rather manifest.yaml with connectModules key) are not possible to be installed on any jira/confluence instance via forge install . The installation fails every single time (does not matter if the app is private, or it is publicly listen on marketplace and just installing dev version of it on private jira instance). I tested it with removing connectModules and keeping only connect.key - also did not work. App installation failed. Tested multiple apps - every single installation failed. This is why I wanted to ask if there has been any change preventing from installing also connect apps that were adopted from forge. 1 post - 1 participant Read full topic

Inside AI Builders Week: How teams work in the AI era

Forge-sim: a local Forge runtime for testing, AI agents, and local dev Hey everyone, wanted to share the first public release of some tooling I’ve built for Forge. github.com GitHub - ryanackley/forge-sim Contribute to ryanackley/forge-sim development by creating an account on GitHub. It’s a simulated Forge runtime that runs your app locally with no cloud deploy. There are three ways to use it: A test library for running Automated Tests. Designed specifically for CI/CD testing. Headless UIKit2 rendering, shims for most forge APIs. Drop-in configurations for vitest and jest. A CLI and stdio MCP server for AI driven development that deploys and tests against a sandboxed, simulated runtime. A development server that serves your app locally for more traditional browser driven development and testing. I originally built it for a fairly complex Forge app I got tired of redeploying to Atlassian’s cloud to test the smallest change. Then I decided to build out the tool to be more general purpose. There is a lot of surface to the Forge platform, so please let me know if you try it out and run into any problems. This is a personal project. I don’t work for Atlassian and this project is not affiliated with Atlassian in any way. 1 post - 1 participant Read full topic

Bitbucket Tests now supports tracking tests from multiple branches

Developer Space could not be published 2 posts - 2 participants Read full topic

Content of Forge macros without body not discovered by Rovo Hello, Rovo Assistant is not able to discover the content of Forge macros without a body (macros without a layout property in the manifest). For example, if you ask Rovo Chat to “Summarise this page”, the content of these macros is not included in the response. Has anyone found a way to make the content of macros without body discoverable by Rovo? Thank you, Luigi 1 post - 1 participant Read full topic

Inside AI Builders Week: How teams work in the AI era Each quarter, we ask some of our R&D teams to take a week away from the day-to-day to experiment and innovate on AI. We call this AI Builders Week, and it helps fuel our AI transformation. In past quarters, we’ve focused on building for individual workflows — how do we each get faster and more productive by inserting AI into different parts of our daily to-dos? But for our most recent Builders Week, we wanted our teams to think about how they come together and how they could reinvent entire workflows with AI. In other words, moving from isolated AI solutions to truly reshaping systems end-to-end. The results: across 1,500 registrants, 92% said they were more confident using AI in their daily work, and over 120 new workflows were built in just a few days. Almost all of our participants said they are ready to bring a new workflow to their team following the event. Shifting from individual to team-built AI workflows AI capability has become broadly accessible and useful to individuals, but what actually helps organizations solve problems faster is the ability for teams to assess and redesign how they operate with AI. More and more, the measure of success can’t be whether AI can help one person move faster. Instead, we chose to anchor on team-level transformations. We’re more focused on whether a team can work differently together because of the solutions being built. Our build day was grouped by business area and expertise so teams could focus on solving pain points they feel in day-to-day work. This intentionally brought together teams working on similar problems, codebases, and requirements. We also involved leaders, Heads of Product & Design to act as strategic coaches during build time, roaming breakout rooms and engaging directly with teams mid-flight. The goal was to create conditions for teams to learn from each other and truly embed new, AI-native workflows tailored to their own environment. We gave them a tight deadline to deliver and share their results. This...

Bitbucket Tests now supports tracking tests from multiple branches Tracking tests on non-default branches was one of the most requested capabilities during the Bitbucket Tests (beta). We heard you, and today, we’re shipping it. Most teams don’t ship from main . Integration happens on develop , stabilisation on staging , releases cut from release . Until now, Bitbucket Tests only tracked the tests from default(main) branch, leaving the branches that actually matter invisible. Multiple branch tracking in Tests changes that. Bitbucket Tests can now track up to 5 branches per repository including the default branch( often main ), making failure rates, flaky scores, and test history reflect real delivery workflows. Bitbucket Tests is available on the Standard and Premium plans. Why it matters Test health where testing and releases happen. Integration, staging, and release branches are now first-class, not just the repository default. Flaky detection with full coverage. Failure rates and flaky scores aggregate across all tracked branches, surfacing the worst offenders wherever they fail. For more detail, read the earlier post on flaky test detection . Branch-specific answers. Every test execution shows its branch. A single filter isolates failures to one tracked branch. Get started 1. Ensure your test frameworks in the pipelines are generating JUnit-Xml compatible test reports. If not already generating. Know more about Bitbucket Tests 2. Open Tests → Configure branch tracking (visible to repository admins) Configure branches for Test tracking 3. Add the branches the team ships/tests from. 4. Results flow in from the next pipeline run. Left: Summary of Test results Right: Single test executions See the branch tracking section of the Get started with tests in Pipelines help document for step-by-step guidance on configuring branch tracking and making the most of test insights across your delivery branches. Share feedback Multiple branch tracking was prioritised based on customer feedback, and we’d love to keep learning from you. Share your...

Stop stitching together HR tools. Start Connecting Them. HR doesn’t have a tool problem, it has a connection problem. The Atlassian Ecosystem turns fragmented systems into one intelligent system of work. HR has quietly become one of the most cross-functional operations in any organization. Onboarding a new hire touches IT provisioning, payroll, compliance training, manager coordination, and knowledge management, often across a half-dozen platforms that don’t talk to each other. The tools exist. The problem is they’re disconnected. Onboarding checklists live in one system while the training content lives in another. Policy approvals move through email, while the policies themselves sit in a wiki that no one can find. Every handoff is a place where work stalls, data gets re-entered, and new hires wonder if anyone planned for their arrival. This isn’t a niche frustration. It’s a structural challenge, and it’s where the Atlassian Ecosystem offers something fundamentally different. One of the ecosystem’s practical strengths is flexibility in how teams adopt it. You can buy from thousands of Marketplace apps tuned to HR use case, build custom solutions with Forge or Rovo Studio when no existing app fits, or you can compose solutions by combining Atlassian products, Marketplace apps, and Rovo agents into automated workflows specific to your organization. What the Ecosystem actually is The Atlassian Ecosystem is a network of Atlassian products, 7,000+ Marketplace apps and integrations, a global partner community , and an extensible platform, all built on the same foundation. Every Marketplace app, Rovo AI agent , and partner-built solution operates on the same data model, permissions, and workflows that power Jira, Confluence, and Jira Service Management. You’re not stitching together separate platforms. You’re extending one. Four ways HR teams put this to work Across hundreds of enterprise deployments, HR success with the ecosystem falls into four categories: Planning, alignment, and optimization. Large-scale HR transformation p...

Give AI agents the full picture with Loom video prompts Your team has always been able to watch your Looms and act on them. Now, your agents can too. Loom introduces a new way to instruct your agents with video prompts . Record your screen, talk through what you want done, and get a structured action plan with rich instructions you can share with any agent, or turn into Jira work items in a few clicks. Stop engineering prompts. Start showing your agents what to do. AI agents promise to automate our most tedious workflows, but they have a massive bottleneck: human input. Getting an agent to successfully execute a task like bringing a prototype to life, making a UI change, or updating a design requires critical visual context. Up until now, that meant text prompts, copy-pasting screenshots, and cycles of back-and-forth clarification when the agent inevitably misses something. The overhead of creating these prompts often offsets the time saved by the agent. Text prompts lack visual context, voice transcripts lack technical structure, and static screenshots miss the actual click paths and screen states. Loom eliminates this manual documentation by converting a standard screen recording into structured, machine-readable instructions. By automatically parsing your narration, keyframes, target UI elements, and visited links, Loom produces an agent-ready action plan. You get the speed of real-time communication, combined with the structured metadata an AI agent needs to execute ideas, code changes, or workflows – without the need to engineer prompts. How it works Go from idea to agent handoff in just a few steps: Record your screen and talk through the task. Open the Loom Chrome extension and navigate to the Generate tab to start recording in video prompt mode. Share your screen and walk through what you want done, narrating as you go. Loom captures your transcript, on-screen clicks, links visited, and snapshots of the screens you share. For best results, record over 30 seconds. Get structured instructions for agents, without th...

Forge app translate jira? I was deeply checking for a solution to spanish people with custom languages (galician, euskera, catalan). I know the robust solution is ask for a language pack and will be ignored. I see some apps in marketplace for translate jira using browser extensions, doesn’t exist really any supported way to add a custom language in jira cloud with basic warranty of usage? 1 post - 1 participant Read full topic

Forge Object store default ttl Hi, I went through the documentation and I wasn’t able to find the default ttl value when uploading a file. In the EAP it was 90 days, but now ? Regards, Magnus 1 post - 1 participant Read full topic

Share packages across your workspace with internal packages

Connect Trello to Your Favorite AI Assistants with Trello MCP

Connect Trello to Your Favorite AI Assistants with Trello MCP 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 ...

The CLI couldn't securely store your login credentials in a local keychain I am getting an error while logging in to Forge. Please help. I am new to Forge: Error: The CLI couldn’t securely store your login credentials in a local keychain. If a local keychain is not available, use environment variables before trying again. See https://go.atlassian.com/dac/platform/forge/getting-started/#log-in-with-an-atlassian-api-token for more. 2 posts - 2 participants Read full topic

Request to Expedite Forge Remote Agent Early Access — FRGEAP-2077 Hi Atlassian Community Team, I’m currently evaluating Atlassian products during a trial period to determine whether they are a good fit for my ecosystem. Access to the Forge Remote Agent early access program is a key part of this evaluation, as remote agent functionality is one of my primary use cases. I submitted an early access request some time ago, but it is still in the queue and I have not yet received an update: Ticket: FRGEAP-2077 Because my trial period is time-limited, I would greatly appreciate it if the team could review and, if possible, expedite my request. Testing this capability before the trial ends is important to my decision about purchasing Atlassian licenses. Without the active EAP access I keep getting the following error when I try to deploy my middleware Error: Manifest validation failed: rovo:agentConnector is not a valid extension. I have reached out to the support team without any luck. Please let me know if you need any additional information from me to complete the review. Thank you for your help. 1 post - 1 participant Read full topic

Share packages across your workspace with internal packages We launched Bitbucket Packages to give teams one place to manage code, pipelines, and artifacts, and we’ve been expanding it ever since, first with Maven and npm , then PyPI and NuGet . Every one of those packages has shared one trait: it lives inside a single repository and inherits that repository’s permissions. Most of the time, that’s exactly what you want. But it also means sharing a package across repositories has meant granting people access to source code they never needed to see. Starting today, it doesn’t have to. With internal packages , a repository admin can open up read access to a package across every repository in the workspace, without handing out any access to the linked repository. Mark a package as internal, and any member of your workspace can pull it from anywhere, while write access stays exactly where it was. Why we built this Until now, a package was only as shareable as its linked repository. When another team wanted to pull a shared base image or a common library, someone first had to grant them read access to the source repository, even though all they wanted was the artifact. In practice, that created friction that had nothing to do with the code: Repetitive access requests: Repository admins and platform teams spent real time approving permission requests just so someone could pull a shared package. Oversharing to get work done: Granting repository access to share one artifact also exposed source code the requester didn’t need, and often shouldn’t see. Stalled pipelines: Any pipeline without read permission on the source repository couldn’t pull the packages it depended on, blocking automation until access was sorted out. Internal packages remove that friction directly: share the artifact, not the repository. Internal vs. private: what actually changes Every package now has one of two visibility states, set per package on the linked repository. It’s worth getting the distinction right before you turn anything on, because it’s the whole ...

Advancing modern teamwork AI thinking and product news to unleash the potential of teams

RFC-140: Updating categories in the editor's element browser RFCs are a way for Atlassian to share what we’re working on with our valued developer community. It’s a document for building shared understanding of a topic. It expresses a technical solution, but can also communicate how it should be built or even document standards. The most important aspect of an RFC is that a written specification facilitates feedback and drives consensus. It is not a tool for approving or committing to ideas , but more so a collaborative practice to shape an idea and to find serious flaws early. Please respect our community guidelines: keep it welcoming and safe by commenting on the idea not the people (especially the author); keep it tidy by keeping on topic; empower the community by keeping comments constructive. Thanks! Project Summary We are planning on updating the categories in the editor’s element browser (both the slash menu and view more modal) to make discovering Atlassian & app elements easier for users. We wanted to give the developer community a heads up and open up discussion for feedback. Publish : 21 July 2026 Discuss until: 28 July 2026 Resolve by : 4 August 2026 Problem We want to improve the discoverability of all elements within the editor’s element browser (both the slash command menu, and the view more modal). Today: The slash menu version of the element browser is one long, continuous list without clear categories or grouping to help users browse it. The view more modal version of the element browser does show categories, however, the categories themselves are outdated, and don’t well enough reflect our offerings. Element browser - Slash menu Element browser - View more modal Proposed Solution We plan to: Update the set of categories we have to better capture our shared offerings. Update the slash menu to actually show the categories. Update the view more modal to show the new categories. Cosmetically modernize the slash menu and view more modal. Existing category names Rovo Formatting Confluence content Navigation Admin...

Custom deployment permissions for your environments (Beta)

Studio agent gets ALL of a Forge app's actions when only one skill is added. Bug? Hi there, When a no-code Rovo Studio agent has just one skill from my Forge app enabled, the agent can (and does) invoke every action the app declares, including skills that were explicitly not added to the agent. Only after removing the last app’s skill, the agent stops calling the app’s actions entirely. Skill selection in Studio therefore appears to work at app granularity, not per skill. So I wonder if the skill selection is just app-scoped? And is this a bug or on purpose? Best regards, Sven 1 post - 1 participant Read full topic

“You Should Be A Little Uncomfortable”: How Cursor Is Navigating the New Shape of Product Craft Every week, it seems like the rules change for building products. We’re all figuring this out in real time, and one of the best ways we’ve found to do that is to sit down with other leaders who are in the thick of it. That’s why Atlassian recently started a series called AI Talks at our San Francisco offices with senior leaders building AI products. We break down what’s working, what’s broken, and what feels uncomfortable. Recently, our Chief Product and AI Officer, Tamar Yehoshua, was joined by Maxime Prades, who leads Product at Cursor . We recently launched our Jira and Cursor integration , allowing Jira teams to assign work directly to Cursor, where a cloud agent will pick up the task and begin working. Tamar and Maxime spoke about the biggest changes that AI has brought about, and how to think of AI as a complete mindset shift, rather than just a tool. Here are some of the key industry trends they discussed, and how senior leaders should be rethinking their product orgs. The ever-shifting bottleneck In our last AI Talks, the co-founder of Gamma, Jon Noronha, shared that one of the biggest challenges in building products right now is that with engineers getting more productive with AI, product managers have become the bottleneck. Maxime echoed this feeling. “When I was at Zendesk,” he told us, “The engineers were always the bottleneck. But now, at Cursor, I’m often the bottleneck.” If you’ve solved for engineering velocity but your decision-making, data, marketing, and review processes haven’t caught up, you haven’t actually gotten fast. The bottleneck has just moved. To get true value out of AI, you have to re-architect the whole value chain, rather than focusing on just one piece of it. Should PMs be able to code? Atlassian’s point of view is that product managers (PMs) should know how to code, though the central job of a PM is to know what to build, particularly in a world where the barrier to building is so low. Maxime believes that PMs don’t have to write c...

Custom deployment permissions for your environments (Beta) You shouldn’t have to grant full repository admin rights just to let an engineer or release manager trigger a deployment. To solve the all-or-nothing access problem, Bitbucket Pipelines introduces custom deployment permissions (Beta) . You can now gate specific environments to authorized users and groups, ensuring safer releases and easier compliance. Left: Add users and groups to gate deployments for this environment. Right: Unauthorised users are blocked from deploying and need someone with access to resume the deployment. What this unblocks for your team Deployment permissions help teams protect critical environments without slowing down their release process. Reduce production risk: limit deployment access to the people and groups trusted to release to sensitive environments. Avoid unnecessary admin access: give release managers, SREs, or on-call teams deployment access without making them repository admins. Support compliance workflows: separate who can merge code from who can deploy it, helping teams meet change-management and audit requirements. How it works Repository admins can manage permissions for each environment directly from the Repository settings → Pipelines → Deployments Add users or groups: Add individual users or groups to an environment’s permissions list. Audit access: View who currently has access, including user and group details. Revoke access: Remove a user or group when they no longer need deployment access. When permissions are configured for an environment, only the users and groups listed in the permission list can trigger deployments to that environment. Getting Started To use this feature, workspace must be on a Premium plan. Additionally, Bitbucket Pipelines must be enabled for the repository and at least one deployment environment configured. You must also have Admin permissions on the repository. Navigate to Repository settings → Pipelines → Deployments . In the Deployment permissions section, select the option to allow selected us...

New research reveals how AI is making jobs bigger

Advancing modern teamwork AI thinking and product news to unleash the potential of teams

The Future of Jira Isn’t Just Tracking Work. It’s Delegating it TL;DR: I wanted to see what would happen if I mapped AI agent capabilities to the functions of a software team. So I built a team of specialized agents in Jira, assigned each one to a stage of the software development lifecycle, and used them to build a new feature for my Forge app. It ended up being one of the smoothest development workflows I’ve tried so far. Jira is becoming the delegation layer for software teams. For a long time, Jira was where you went to track work. You updated a status, closed a ticket. Something important has shifted now that agents can be assigned to work items. Jira is still a record of what happened, but now it’s something else too: a place where work can move between humans and agents. That shift got me thinking. What if I leaned into it fully and ran an experiment? What if each stage of the SDLC had its own specialized agent and the board itself handled the routing between them? I picked a real feature, mapped a specialized agent to each stage of my Jira board, and let them coordinate the work. Here’s what that actually looked like. The Project: Launchpad To pressure-test this workflow, I wanted to use something I was already building instead of creating another demo app. I’ve been working on a Forge app called Launchpad. At its core, it’s a place where I can work through an idea with an AI agent before turning it into something a team can actually start working on. For this experiment, I wanted to add a Teamwork Graph-powered context discovery layer. The goal was simple: before I started implementing a feature, I wanted Launchpad to automatically surface the organizational context around it: related Jira issues, relevant Confluence pages, implementation patterns, known blockers, and the people who might already have context on the problem. It felt like the perfect feature to pressure-test assignable agents. It wasn’t a single coding task; it naturally broke down into research, planning, implementation, and review, giving each agent a ...

Request to enable the newest AI models in Rovo Dev Hello Atlassian Rovo Dev team, Please enable the newest models currently available for our Rovo Dev account and expose them in the Rovo Dev model selector. Specifically, we would like access to: OpenAI GPT-5.6 Anthropic Claude Fable 5 Anthropic Claude Opus 4.8 Anthropic Claude Sonnet 5 Thank you! 1 post - 1 participant Read full topic

How do I refund a wrong transaction on Phone Pe? 82717 45722 Cu in no KB x do KB cu HC at g BH on BH 2 posts - 1 participant Read full topic

पेटीएम से पैसा दूसरे के खाते में गलती से चला जाए तो वापस कैसे पाए✍️0827-174-5722? 1. क्लाइंट सर्विस नंबर: अपने बैंक या यूपीआई ऐप के ग्राहक सेवा नंबर 0827-1745722 पर कॉल करें। · 2. जानकारी बताएं: जब आप ग्राहक सेवा पर हों, तो सुनिश्चित करें कि आप उनसे निम्नलिखित जानकारी साझा करें:. 2 posts - 1 participant Read full topic

All PowerUp iframes failing Calling t.popup() with an args object successfully loads the iframe HTML, but window.TrelloPowerUp.iframe() throws TypeError: t is not iterable from iframe.ts:72 . Removing args allows the iframe client to initialize normally. I use a call like this: const popupOpts = { args: { view: "settings" }, title: "Settings", url: "./details.html", height: 265, }; return t.popup(popupOpts); Then in the iframe when it appears, it fails here: const t = window.TrelloPowerUp.iframe(); // We are seeing this error on ALL Power-Ups: Uncaught TypeError: t is not iterable Ut iframe.ts:72 iframe index.js:69 js details.js:15 This happens ONLY if you used args in the call of the popup or the modal. We use args extensively and therefore our Power Ups are failing. I see other Power-Ups function, and validated they are not using args. This appears to have started since early this AM. Is there anyone else seeing this same issue? @Trello NOTE : I have submitted a P1 Developer Support Request for this as well: ECOHELP-148080. Confirmed it was a change to iframe.ts in the last 24 hours that has caused this Trello Wide outage. 1 post - 1 participant Read full topic

Any plans for Commercial Cloud → Government Cloud / Isolated Cloud migration? What happens to Forge app data (Storage, issue properties, custom fields)? Hi all, Docs for AGC/Isolated Cloud only cover Data Center → Cloud migration. Is a Commercial Cloud → AGC/Isolated Cloud path planned? If it ever ships, a key problem: Forge installations of the same app get a different app key on Commercial Cloud vs. AGC vs. Isolated Cloud. That breaks anything keyed by app key: 1. Forge Storage (KVS, Forge SQL, Object Store) - does this data move with the site, or is it on each vendor to build their own export/import? 2. Issue properties used for JQL search - two failure modes depending on how the property key is built: Key includes the app key: issue migrates fine, but the property key no longer matches (different app key in the new cloud) → JQL search silently breaks until the app rewrites every property with the new key. Key is app-key-agnostic (same everywhere): the property key survives, but the app’s own backend data behind it may not have migrated → JQL can return stale/wrong matches until the app clears those properties. Since the app key will necessarily differ between clouds, would Atlassian rewrite entity property keys automatically during migration (old key → new key), or expose an API/notice ahead of cutover so vendors can remediate properties themselves right after migration? 3. Custom fields - field key and schema key are also app-key-scoped. Would custom field definitions + values migrate at all (given the old key won’t match any app in the destination), get dropped, or get remapped to the new installation? Any pre-GA / directional guidance from Atlassian would help us build the right safeguards ahead of time. Thanks! 1 post - 1 participant Read full topic

Security vulnerability in adf-schema Is there a plan to update adf-schema library to fix the linkify high vulnerability described here https://socket.dev/npm/package/@atlaskit/adf-schema/alerts/56.1.5?tab=dependencies ? We have an app using adf-utils and security tools are flagging this vulnerability weeks ago. 1 post - 1 participant Read full topic

Bring GitHub context into Rovo with the Teamwork Graph connector for GitHub GitHub in Rovo for software teams AI is making it faster to write code. The new bottleneck is understanding the work around the code: what changed, why it changed, who reviewed it, and how it connects to the plan. That context lives across Jira, GitHub, Confluence, and the conversations that happen around them. Teamwork Graph helps bring it together. That connection is powered by the Teamwork Graph connector for GitHub, which helps make eligible GitHub data available in Atlassian experiences through Teamwork Graph. For software teams, GitHub is where implementation takes shape. Pull requests show what changed. Issues capture technical tasks and decisions. Commits and branches show how work moved. Review comments explain the tradeoffs behind the final code. For engineering leaders and software team leads, that context matters because delivery is not only about writing code. It’s about understanding progress, unblocking review, reducing handoff cost, and keeping the plan connected to your teams’ work. The opportunity: connected software context The process of building software work creates useful signals everywhere: a work item in Jira, decisions in pull request comments in GitHub, design content in Figma or documentation in Confluence. When that context is connected, teams can move faster with less manual reconstruction: Planning gets clearer because teams can see what already happened Reviews move faster because teams can find the surrounding work context Status reporting gets easier because the progress from planning and code tools can be understood together AI Agents become more useful because they can reason with more of the work record around the code Rovo is built for that context problem. With GitHub connected to Teamwork Graph, Rovo can help teams find and use GitHub context from inside Atlassian. Why this is different GitHub, Jira, and AI coding agents each solve important parts of the software delivery workflow. The gap is that each sees only part of the sy...

How Atlassian for Startups grew from an experiment into a global program

Why AI alone isn’t enough for enterprises (and what Mercedes-Benz did about it)

Advancing modern teamwork AI thinking and product news to unleash the potential of teams

IncidentIQ — evidence-grounded root cause analysis for production incidents (feedback welcome) Hi all — sharing a project I built and would love technical feedback on, especially from anyone working on incident tooling. The problem When a service goes down, an on-call engineer typically has to manually correlate signals across separate tools — metrics dashboards, log search, deployment history, and past-incident records — to figure out what changed. That correlation work is usually the slowest part of incident response, not the fix itself. What I built A small FastAPI service that automates the correlation step, not the guessing step: Retrieves deployment history, metric trends, and error logs for a given incident Computes an evidence-strength signal (strong / moderate / weak) from code — deployment recency, metric delta magnitude, historical match score — before any LLM is involved Uses an LLM only to turn already-scored evidence into a readable narrative, with every claim citeable back to a specific log line or metric. It never generates its own confidence number — that’s a deliberate design choice, since a model asked “how confident are you” will produce something plausible-sounding with no calibration behind it Similarity search over past incidents via vector embeddings, to answer “has this happened before” A real GitHub integration: pulls deployment history from the GitHub REST API, and a webhook receiver with HMAC-SHA256 signature verification for real-time push events What’s honest about scope The demo runs against two synthetic services with an injected connection-pool-exhaustion scenario — there’s no live Prometheus/Elasticsearch cluster behind it. Everything downstream of that data existing (correlation, scoring, retrieval, narrative synthesis) is real and tested; the log/metric ingestion is not yet connected to a real production source. 32 tests cover the evidence-scoring logic, the API, and the GitHub integration (including a regression test that the narrative never fabricates a confidence percentage). Stack: FastAPI, SQLAlchemy, pgvector/Postgres...

Can JSM portal-only customers authenticate to a third-party app via OAuth 2.0 (3LO)? Can Jira Service Management portal-only customers (external customers without a full Atlassian/product license) authenticate into a third-party web app via Atlassian OAuth 2.0 (3LO) — i.e., can we reuse their existing JSM identity to sign them into our own app? Or is 3LO limited to full Atlassian accounts? If it’s not supported for portal-only customers, what’s the recommended approach for authenticating them into an external app? 1 post - 1 participant Read full topic

Two years in: how Atlassian for Startups grew from an experiment into a global program — and why it matters more than ever

How we’re evolving Jira for AI-native software development

Advancing modern teamwork AI thinking and product news to unleash the potential of teams

myTeamTickets() JQL function How to develop “Team[Team]” IN myTeamTickets() dynamic JQL function? The JQL functipon should be return currentUser()'s Team issues. A user can include multiple Teams. I want to create one JSM Queue (My Team Issues) for this reason. 1 post - 1 participant Read full topic

Two years in: how Atlassian for Startups grew from an experiment into a global program — and why it matters more than ever Early-stage startups face a particular set of growing pains. It’s not just figuring out what to build and for whom – there’s also a need to measure progress, coordinate across projects, and keep knowledge accessible, all while likely making some drastic pivots. These challenges are what we call a “collaboration tax,” and if it isn’t solved in the earliest days of operating the company, the “tax” only compounds as a startup scales. In June 2024, Atlassian launched a program built on a straightforward premise: if we could get early-stage founders building with the right collaborative tools from day one, they’d be able to deliver more value to their customers and their startup would grow with Atlassian as they scale. Two years later, the premise is holding. As of June 2026, Atlassian for Startups had onboarded more than 5,000 startups from around the world – over 2,000 in the first year and more than 3,000 in the second. From AI to Semiconductors to Deep Tech to Hospitality, our startup customers span nearly every industry, and they’re bringing deep expertise to real-world problems. I work directly with startups from across the globe through the Atlassian for Startups program. The most rewarding moments are hearing a founder share that Atlassian has become so ingrained in how they work that they can’t imagine running their business without it. That’s exactly why we built this program – founders need tools that scale with them, so they can stay focused on what they’re truly passionate about: building great products for their customers. “Since joining Atlassian for Startups we’ve: shipped multiple big sprints and closed 15 new clients….” – Amir, Timber Exchange “The Atlassian Startup Program has been incredibly valuable for our team, and it has played a meaningful role in helping us advance our AI-based character infrastructure. Over the past year, we’ve made strong progress on the technology side and are now preparing to enter commercialization, which we expect to begin...

How we’re evolving Jira for AI-native software development New Jira and Teamwork Graph capabilities help engineering teams plan, assign, govern, and measure work across humans and AI agents. Whether I speak to customers or Atlassian’s own engineering teams, the message is consistent: the unprecedented adoption of powerful coding agents has transformed software development, but the hard parts of delivering software are…gasp…still pretty hard. Teams still have to decide what to build, and why it should exist. They need to understand the system they’re changing and which constraints matter. They have to know what “done” means, and whether the output is safe to ship. That is the reality behind the AI productivity gap. In a longitudinal study we ran with DX across professional engineering teams, AI usage has increased by 65%, but overall developer velocity did not. It topped out at a 15% increase, with many organizations seeing gains averaging 10% The gap is not because models are bad at writing code. It’s because software development has never been only about writing code. It is about turning business goals, strategies, and context into working software inside a real organization. For more than two decades, Jira has evolved to meet software teams of every shape and methodology: we’re the source of truth for what to build, who is doing it, how it’s going, and what shipped. Today, the shape of software teams is changing rapidly. AI-native software development teams require a new system where context for agents is a first-class citizen and tasks are delegated to agents while humans steer, and review. Engineers, product managers, designers, and security teams bring judgment and context to the work. It’s a new way for humans and agents to work together with clear plans, shared context, and validation that what comes back is something the team can stand behind. Today, we’re announcing new agentic product development capabilities in Jira built for that shift. Teams can plan work with AI, turn intent into agent-ready specs, assign work...

ContentPropertyManager and Confluence 11 Hello @Kusal , is there still a plan to add #getKeys API to the ContentPropertyManager? I am referring to the request previously raised by @jens : Confluence 10.0 release EAP is available now - #218 by jens Your answer was “Regarding the suggestion to add a #getKeys API - this is a sensible addition, but unfortunately it’s too late to commit to this for 10.0. I’ve added it to our backlog to implement prior to 11.0.”. As Jens already mentioned, this is functionality that we cannot replace on our side, and we rely on these keys in several of our products. Could you please let us know whether this is still planned and when we can expect it to be implemented? Best regards, Daria 1 post - 1 participant Read full topic

In our Condfluence DC app the Actions stoped working After upgrading to Confluence 10 our team tried to do every necessary modification in our custom app to make compatible with the new version. But we cannot get the Actions working. We changed the xwork tags to struts tags in our atlassian-plugin.xml, but we cannot find anything in the documentations that we need to do more. Now we get this error when we try to do an Action: java.lang.IllegalStateException: Error loading action class ... Caused by: java.lang.ClassNotFoundException: com.catworkx.confluence.dme.actions.ClearBrokenAttachmentsAction Here is our atlassian-plugin.xml: ${project.description} ${project.version} images/plugin-icon.png images/plugin-logo.png images/vendor-icon.png images/vendor-logo.png /plugins/servlet/cwx-dme/settings true com.atlassian.auiplugin:ajs com.atlassian.auiplugin:dialog2 page cwx-dme-copy-dialog com.atlassian.auiplugin:ajs com.atlassian.auiplugin:aui-select2 cwx-dme-settings com.atlassian.auiplugin:ajs com.atlassian.auiplugin:aui-select2 cwx-dme-settings com.catworkx.confluence.dme.ao.DmeEntityManager com.catworkx.confluence.dme.ao.SettingsEntry /pages/cwx-dme-copy-page-tree.action?idOfPageToCopy=$helper.page.id&spaceKey=$helper.space.key page action-copy /templates/cwx-dme-copy-page-tree.vm /pages/cwx-dme-copy-template.action?idOfPageToCopy=$helper.page.id&spaceKey=$helper.space.key page action-copy /templates/cwx-dme-copy-template.vm /pages/cwx-dme-copy-template-metadata.action?pageId=$helper.page.id edit action-copy /templates/cwx-dme-copy-template-metadata.vm /pages/cwx-dme-propagate-metadata.action?pageId=$helper.page.id edit action-copy /templates/cwx-dme-propagate-metadata.vm /cwx-dme-copy-all-metadata.action?pageId=$helper.page.id edit action-copy /templates/cwx-dme-copy-all-metadata.vm com.atlassian.auiplugin:ajs space /cwx-dme-clear-broken-attachments.action?spaceKey=$generalUtil.urlEncode($helper.spaceKey) /templates/cwx-dme-clear-broken-attachments.vm /plugins/servlet/cwx-dme/settings /cwx-dme/settings /plugins...

ConvoAiHomeThreadsInput must define one or more fields Is there a specific reason why I am getting this message when using Introspection? 1 post - 1 participant Read full topic

Can a Marketplace App MCP be connected to the Heart of Rovo / TWG? Hello there, We have been exploring how external MCP servers integrate with Atlassian Rovo , and we wanted to share our current findings and open a discussion about what’s possible beyond what we’ve achieved so far. Following Atlassian’s official documentation on connecting external MCP servers ( Add an external MCP server from Atlassian Administration | Atlassian Support ), we successfully connected a c ustom MCP server for one of our Marketplace apps , Projectrak. The MCP exposes several tools that interact with our app’s API, and Atlassian correctly detects and registers those tools during setup. From our testing, we’ve confirmed that: MCP tools can be used as skills within agents created in Rovo Studio These agents are able to invoke the MCP and retrieve live data from our app However, beyond this point, the integration seems limited . The Atlassian documentation mentions that MCP tools can be used in “Rovo, Studio, and other experiences.” While Studio is clear (custom agents), it is not obvious what “other experiences” refers to in practice. So far, we have not observed: Any usage of MCP tools by the default Rovo experience MCP data being surfaced in general Rovo Chat interactions Any indication that MCP data is being indexed or incorporated into the Teamwork Graph Automatic insights or suggestions in Rovo based on MCP-connected data In other words, connecting an MCP does not currently seem to make its data part of Rovo’s core intelligence layer. Our goal is to understand whether it is (or will be) possible to: Connect external app data (via MCP) to the core of Rovo Make that data available for general Rovo insights, not only through explicitly configured agents Have MCP-backed data contribute to the Teamwork Graph At the moment, the only clear and reliable pattern we see is using MCP tools inside custom Rovo agents . While useful, this feels like just one part of the potential. We would love to hear from others who are experimenting with MCP: Have you found an...

Rolling releases and SQL We’re evaluating Rolling Releases (Preview) for our Forge native app. Some of our customers are currently on an older major version of our app that does not use Forge storage/SQL. In our latest major version, we introduced SQL. In the Preview Limitations documentation , it states: Upgrading from a version without storage to a version with storage (KVS and SQL) is not supported. We’re trying to understand how this limitation applies to our situation. If some customers are on a major version without SQL, and newer major versions use SQL: Does this limitation mean we cannot use Rolling Releases for the app at all while those customers remain on the older, non-SQL version? Or would Rolling Releases only apply to customers who are already on a version that includes SQL, while customers on the older non-SQL version would simply not receive those updates? More generally, does this limitation mean there is no supported upgrade path from a version without storage/SQL to a version that uses SQL, or is there another migration approach we should follow? If a customer on the older non-SQL major version manually upgrades to the newer SQL-based major version, would they then be eligible to receive subsequent Rolling Releases, or would the storage limitation still prevent that upgrade path? Will this be supported on the GA release? Thanks! 1 post - 1 participant Read full topic

Sourcetree API-Token Issue Hello, Two weeks ago, while using Sourcetree, I became aware of the CHANGE-3222. It took me awhile to get my API token setup and working correctly but I was eventually successful and have been able to use sourcetree. Then yesterday I was again confrunted with the inability to access bitbucket, I checked and this was during a Brownout Window. I have repeated all the steps to make sure my sourcetree is set for API-Token authentication, with it confirming that Authentication is OK. But still I am not able to access bitbucked during the Brownout Windows. Attached screen grabs of the relevent parts of sourcetree. Assisance would be greatly appreciated. 3 posts - 2 participants Read full topic

How Axel Springer is using Rovo Dev to help engineering teams focus on higher-value work The learnings in this blog post are based on the session, “Accelerating the SDLC at scale: How Axel Springer uses Rovo Dev to ship faster”, presented at Atlassian’s Team ’26 conference. You can check out this session and others on demand. Every engineering organization feels pressure to ship faster, but increasing velocity alone isn’t really the most useful goal. Martin Bilt, Lead Product Excellence at Axel Springer, frames this challenge differently. According to Bilt, the more important question is whether engineers are spending their best hours on work that moves the product forward or on the repetitive scaffolding around it. Axel Springer is a global media and technology company behind brands such as Business Insider, Politico, BILD, and idealo. Headquartered in Berlin and active in more than 25 countries, the company has spent more than 70 years evolving from a traditional publisher into a technology-driven media company by repeatedly betting on technology well before it felt safe or comfortable. This technology-driven culture has shaped the company’s approach to AI in software development. Adopting AI was never a question for Axel Springer; their real question was, “Where do we start? And how quickly can we get up to speed?” Axel Springer placed Rovo Dev in the middle of how several engineering teams build software. This pilot focused on real workflows, measurable outcomes, and the day-to-day friction that prevents engineers from spending more time on meaningful problem-solving. Speed is a byproduct, focus is the goal Axel Springer found that a more important outcome than velocity alone was redirected attention . Rovo Dev helped move senior engineering time away from repetitive setup work and toward the problems that matter most. The company used a simple framework to think about where AI could help. Work fell into four categories: quick wins, strategic bets, time sinks, and fillers. AI was especially useful for high-frequency, low-complexity work that creates...

Why individual AI speed isn’t delivering the ROI CIOs expected

Advancing modern teamwork AI thinking and product news to unleash the potential of teams

Bitbucket-jira-cli: a gh-style CLI for Bitbucket and Jira, with branch-name-as-Jira-key automation I built an open-source CLI called bitbucket-jira-cli (command: bj ) and wanted to share it here. Full disclosure: I’m the author. The idea is simple: bring the GitHub gh workflow to Bitbucket and Jira. bj manages Bitbucket pull requests, repositories and pipelines, and Jira issues, from one command tree that mirrors gh . If you know gh pr , gh issue and gh repo , you already know bj pr , bj issue and bj repo . The part I actually built it for is branch-name-as-Jira-key. Your git branch carries the key (for example feature/PROJ-42-thing ), and bj uses it automatically. bj pr create fills the PR title from the ticket, links the PR to the issue, and moves the ticket to In Progress. bj pr merge moves it to Done. No copy-pasting keys between tabs. A few other notes: It talks to the REST APIs directly, so it works in scripts and CI, and there is no server to run. Bitbucket Cloud and Jira Cloud, with a scoped or unscoped API token for each. It ships an agent skill, so AI coding agents can drive it the same way they drive gh . Install: uv tool install bitbucket-jira-cli # or: pip install bitbucket-jira-cli bj auth login github.com GitHub - Spenhouet/bitbucket-jira-cli: The gh CLI experience, for Bitbucket and Jira The gh CLI experience, for Bitbucket and Jira Docs: Bitbucket Jira CLI - Bitbucket Jira CLI Here a quick demo: I’d like to hear where the current commands don’t match how you work. Feedback and issues welcome. 1 post - 1 participant Read full topic

How to automate product workflows with AI-powered video: A product manager’s guide Every product team has the same problem. The work that moves a product forward keeps getting squeezed by the work that surrounds it. Status meetings. Context-setting. Bug report write-ups. Documentation nobody has time to create. Atlassian’s 2024 State of Teams report found that knowledge workers spend 72% of their time on “work about work.” The fix is not another productivity hack or a new project management methodology. It is rethinking how context moves between people and systems. AI-powered video is emerging as the connective layer that automates the busywork product teams have accepted as inevitable. This blog walks through five workflows where AI-powered video eliminates manual effort. From automating bug reports to turning meeting conversations into updated work items, these are capabilities product teams are using today. What is AI-powered video? AI-powered video is screen recording software that uses artificial intelligence to do more than just capture what is on your screen. It transcribes what you say and understands the context of what you are showing. Then it takes action on your behalf: generating summaries, creating documentation, filing work items, and briefing AI agents on what to do next. Unlike traditional screen recording, AI-powered video treats the recording as structured data. The transcript, the on-screen content, browser context, and developer logs all become inputs that AI can process, organize, and route to the right tools automatically. Why product teams need workflow automation Product managers are the connective tissue of their organizations. They translate between engineering, design, leadership, sales, and support. That translation work generates an enormous amount of context transfer: meetings to align, documents to write, tickets to update, decisions to record. Most of this context transfer is manual. Someone watches a meeting and writes up the notes. Someone sees a bug and spends ten minutes describing it in a ticket. Someone gives...

Developer Space won't publish to Marketplace, "Failed to process request" (topic deleted by author) 1 post - 1 participant Read full topic

Scheduled/Web trigger function invocation fails with wrapper-level TypeError, regardless of module type or function name Bug: Scheduled/Web trigger function invocation fails with wrapper-level TypeError, regardless of module type or function name Summary A named exported function in index.js , referenced correctly by manifest.yml ’s handler field, cannot be invoked by the Forge runtime. Every invocation — whether via scheduledTrigger , webtrigger (manually curled), or after renaming the function to match the documentation’s own example — fails identically with: TypeError: t[w.functionName] is not a function at /mnt/wrapper/wrapper_v24_XX.cjs:2:905258 at g (/mnt/wrapper/wrapper_v24_XX.cjs:2:905264) at new Promise ( ) at /mnt/wrapper/wrapper_v24_XX.cjs:2:904992 at AsyncLocalStorage.run (node:internal/async_local_storage/async_context_frame:63:14) at BufferedInvokeProcessor.Q [as handler] (/mnt/wrapper/wrapper_v24_XX.cjs:2:904238) at BufferedInvokeProcessor.processInvoke (file:///var/runtime/index.mjs:1099:33) at file:///var/runtime/index.mjs:1185:98 at InvokeStoreSingle.run (file:///var/runtime/index.mjs:83:12) at _Runtime.runWithInvokeContext (file:///var/runtime/index.mjs:1198:24) Note the wrapper filename version changes between invocations ( wrapper_v24_5.cjs , wrapper_v24_20.cjs , wrapper_v24_25.cjs , wrapper_v24_35.cjs , wrapper_v24_50.cjs , wrapper_v24_85.cjs , wrapper_v24_100.cjs — all observed across different invocations of the same function). A different exported function ( index.handler , built via resolver.getDefinitions() ) in the same file invokes correctly on every call — the app’s primary resolver-based UI functionality (search, export, saved filters, etc.) all work without issue. Environment App ID: ari:cloud:ecosystem::app/0628f03f-e8d8-4cdc-b5cc-9dedbe26ebd3 Environment: development Site: power-exporter-test.atlassian.net (also reproduced on arzenicstech.atlassian.net ) Runtime: nodejs24.x , memoryMB: 512 , architecture: arm64 Forge CLI: updated to latest (13.1.0) partway through investigation — issue predates and persists after this update Manifest (relevant excerpt...

Bitbucket Cloud pipelines: trigger variables rejected at 10,240 bytes since 2026-07-08 — intended change or regression? Since ~05:00 UTC on 2026-07-08, triggering a pipeline via POST /2.0/repositories/{workspace}/{repo}/pipelines with custom variables started returning 400: “Variables exceed the maximum allowed count or total size. Maximum count: 50, Maximum total size: 10240 bytes.” …for requests that succeeded fine before that time. Nothing changed on our side. What’s confusing: - The 10,240-byte total-size limit contradicts the documented limits — the Variables & secrets page states 100 KB for shared variables and 120,000 chars per custom value. 10 KB appears nowhere in the docs. - It’s a hard, clean cliff: requests whose combined variable values are 10,240 bytes fail 100% of the time (verified across hundreds of triggers). - Onset was sudden and hit many separate workspaces at the same time. - No matching entry in the developer changelog. Questions: 1. Was the total-size limit for API-trigger variables intentionally reduced to 10,240 bytes? If so, where is it documented? 2. If not intentional, is this a known regression, and is a fix/rollback planned? 1 post - 1 participant Read full topic

From tool to teammate: How one Atlassian team made AI a real coworker

Advancing modern teamwork AI thinking and product news to unleash the potential of teams

IP Whitelisting Blocking Bitbucket - GCP Dataform Connection Hello Community, I have an issue with a connection between Dataform in GCP to Bitbucket private projects which is working only with whitelisted IPs. I have 2 GCP projects and the mentioned Bitbucket private workspace. I have successful configuration with Dataform in project A via SSH, but I get the following error in project B via Developer connect: Invalid resource state for “projects/project-b/locations/europe-west3/secrets/bitbucketCloudApiToken-21wfz/versions/latest”: failed to verify authorizer token: Get " https://api.bitbucket.org/2.0/repositories/elena-ws/repository-elena-ws/hooks": generic::permission_denied: 403: To access this repository, an admin must whitelist your IP. Dataform in GCP is part of Big Query, checked the GCP documentation where I found an IP range for Dataform for Frankfurt 3 and whitelisted it in BB, also double-checked for Big Query limitations, but according the documentation, it does not have specific IP ranges. The IP ranges for IPv4 inbound for bitbucket. org, api.bitbucket. org, and altssh.bitbucket .org are whitelisted. There are no VPCs, Cloud NATs or additional limitation from the GCP project. The token has permissions per documentation. Via the tests, the authorization one needed repository:admin and the read one needed webhook, both were granted to both API tokens, and even with the additional permissions I still get that error. For the test, I tried to add new repository connection in Dataform from project A, where we already have established SSH connection for another repository, but the result was the same. My question is: What should I whitelist more? I also did CURL of the hooks API, just to check the IPs behind, but it’s already whitelisted because it matches the given from BB range. Could it be a bug of BB related with the whitelist restriction? I created a test workspace, without that option and it works as expected. Thank you in advance! Best Regards, Elena 1 post - 1 participant Read full topic

Dynamic properties indexing I’m successfully using the https://developer.atlassian.com/platform/forge/apis-reference/dynamic-modules/ API, but I have a problem that my dynamic entity properties are not properly synced. The definition for a working and not working modules are Working { "key": "aggregator-43f05283-1ab4-400d-afe5-406d06e1e462-customfield_10001", "type": "jira:entityProperty", "data": { "propertyKey": "pwhierarchies.aggregator.43f05283-1ab4-400d-afe5-406d06e1e462.customfield_10001", "entityType": "issue", "key": "aggregator-43f05283-1ab4-400d-afe5-406d06e1e462-customfield_10001", "values": [ { "type": "string", "path": "customfield_10001.name", "searchAlias": "hierarchyEpicsAggregatedTeam" } ] } } Not working { "key": "customaggregator-43f05283-1ab4-400d-afe5-406d06e1e462-pwhierarchies-system-pending-team", "type": "jira:entityProperty", "data": { "propertyKey": "pwhierarchies.customaggregator.43f05283-1ab4-400d-afe5-406d06e1e462.pwhierarchies-system-pending-team", "entityType": "issue", "key": "customaggregator-43f05283-1ab4-400d-afe5-406d06e1e462-pwhierarchies-system-pending-team", "values": [ { "type": "string", "path": "result.name", "searchAlias": "hierarchyEpicsPendingTeam" } ] } } Properties And the properties of an issue (HT-11) are { "key": "pwhierarchies.aggregator.43f05283-1ab4-400d-afe5-406d06e1e462.customfield_10001", "value": { "customfield_10001": [ { "avatarUrl": "https://teams-directory-frontend.prod-east.frontend.public.atl-paas.net/assets/teams/avatars/v4/lime_no-highlight_3.svg", "isVerified": false, "name": "QA", "id": "0dd6a03a-e100-458a-b879-89f6f09c8848", "isVisible": true, "title": "QA", "isShared": true }, { "avatarUrl": "https://teams-directory-frontend.prod-east.frontend.public.atl-paas.net/assets/teams/avatars/v4/red_no-highlight_4.svg", "isVerified": false, "name": "Developers", "id": "0489f6ae-bbb9-4d29-b603-19274bf35e5b", "isVisible": true, "title": "Developers", "isShared": true }, { "avatarUrl": "https://teams-directory-frontend.prod-east.frontend...

Every agent. Every state. Full visibility in Jira.

Why AI alone isn’t enough for enterprises (and what Mercedes-Benz did about it) The learnings in this blog post are based on the session, “How Mercedes turns Teamwork Graph into an AI advantage”, presented at Atlassian’s Team ’26 conference. You can check out this session and others on demand. Foundation models have become astonishingly capable over a very short period. At the beginning of this decade, they could barely look up facts online. Today, they can produce work that would take a human engineer more than an hour, and the pace of improvement has been exponential, especially over the last 18 months. And yet, something strange is happening. When you ask individual users whether AI tools have helped them, more than 50% say “yes”, that they’ve actually saved time and effort. But when you zoom out and ask companies whether they’ve seen dramatic improvements to their business, 96% report that they have not . The technology is there, but unlocking the enterprise-level value has been stubbornly difficult. Now, Mercedes-Benz’s recent experience proves the harder enterprise challenge is not whether AI can generate output; it’s whether that output can connect to the messy reality of work across tools, teams, data, and decisions. The real bottleneck isn’t the model Work inside organizations depends on collaboration, so when enterprise AI treats it like an individual activity, it will always fall short of its promise. The average large enterprise runs over 367 SaaS applications , since often multiple teams use their own tools and follow their own rituals. When teams need to collaborate, people find themselves navigating unfamiliar interfaces, reconciling different planning cycles, and chasing down context scattered across a dozen systems. Goals developed in isolation can leave teams rowing in opposite directions, even when everyone has the best intentions. “You have to use at least 11-plus tools to accomplish a task, or even more — like, I don’t know, 100-plus, 300-plus tools in the company you have to maintain, use, know, get data out of there, use ...

From tool to teammate: How one Atlassian team made AI a real coworker In Q4 FY26, Atlassian’s People Insights (PI) team stopped treating AI as a productivity add-on and committed to something harder: fully agentifying its operations—giving AI persistent access to the team’s context, data, definitions, and workflows so it can act as a genuine collaborator rather than a smarter search engine. For PI, that meant a shared tech stack (VS Code + Rovo Dev + Bitbucket ), a three-layer workspace where context lives in skills and agents that every team member inherits, and a structured push to get the whole team there. What PI built has a name that’s gaining traction across the industry: context engineering . Anthropic describes it as “curating and maintaining the optimal set of tokens during LLM inference”—in plain terms, persistent, structured access to everything it needs to do useful work efficiently. What a connected AI system can look like Many AI users still think of AI primarily as a way to supplement knowledge: open a chat window, ask a question, get an answer. The context lives in your head, the conversation resets every time, and you’re the integration layer between the tool and your actual work. That’s a very useful way to leverage AI, but a connected system flips that. Instead of you bridging AI to your work, a connected system allows AI to have direct access to your personal and team context: your data, your docs, your team’s norms, your history, and that access persists between sessions. There are many ways to build this kind of setup, depending on your team’s tools and workflows. For PI, we landed on three layers that stack on top of each other: Foundation layer: Shared infrastructure. The AI agent (Rovo Dev) is connected to enterprise tools like Databricks, Confluence, Jira, and DataHub via MCP integrations using the DS Agent Starter Kit. You can think of this like the electrical wiring in a home, powered by a single source, but connected to all the tools and outlets you need. Team layer: Shared context about how your team oper...

AI that knows your business

AI that knows your business How Atlassian connects your organizational memory across tools, teams, and decisions AI can write a launch plan in seconds, even if it doesn’t know what you’re launching. It can triage incidents without knowing your architecture, onboard new hires without knowing your team, and prioritize a backlog without knowing your customer. It has intelligence in spades and can produce like crazy, yet still miss what counts. AI on its own is unaware of your business. Context is the difference between plugging into a few APIs and a genuinely deep understanding of how an organization works. It’s the kind of understanding that comes from 20 years of teams planning, deciding, shipping, and iterating inside your products every day. That’s the Teamwork Graph , the collective intelligence of your entire organization and your full ecosystem of SaaS apps. It’s a living, permission-aware map of how your business operates, accessible to your agents and teams in one interaction. It’s simple to get started. MCP works so well because anyone can connect it. Connectors are a switch any admin can flip. What’s underneath is what makes Teamwork Graph different: the deepest organizational memory in enterprise software, compounding every day. That’s why your AI + Atlassian context is so much more than a data connection, it’s knowing exactly how work gets done. Your context wherever you work AI should not live in one product, one model, or one vendor’s ecosystem. It should live wherever your teams work. Which means the context layer underneath can’t be locked to a single provider, either. You need the freedom to use whatever AI works best for your teams, the confidence that your organizational context will follow, and the same permission boundaries your admins already set. That’s why we designed it as a bi-directional system. Your Atlassian context flows out to whatever AI your teams choose, while context from your entire toolchain flows back in. And underneath, everything stays connected through th...

Why individual AI speed isn’t delivering the ROI CIOs expected 89% of executives say AI has increased speed. Only 6% can point to org-wide ROI. IDC’s Wayne Kurtzman and Atlassian’s Liz Fosslien unpack what’s missing – and where CIOs should start. Everyone on your team is faster. So why isn’t your organization moving further? That’s the provocation at the center of a recent fireside chat between guest speaker IDC Research Vice President Wayne Kurtzman and Atlassian’s Liz Fosslien, a best-selling author in high-performing teams. The two unpack what Atlassian’s State of Teams 2026 research calls the “fragmentation tax”: the hidden cost of disconnected tools and siloed knowledge that slows organizations even as individuals speed up. IDC’s work with CIOs reveals where the real AI opportunity lies in addressing it. Spoiler alert: It’s AI orchestration. Watch the full webinar on-demand Why is AI making individuals faster but not organizations? AI has delivered on its promise of velocity. According to the State of Teams 2026 report , 89% of executives say AI has increased the speed of work. But only 6% are sure they can point to clear examples of AI’s ROI across their organizations. As Fosslien frames it: “If everyone’s AI-efficient, in that they personally are able to produce 10x the content, you’d expect 10x momentum across the company. But what’s happening instead is that teams are often getting 10x the chaos.” The numbers tell a story of acceleration without alignment. The State of Teams research found that while 73% of executives measure AI success through productivity and 59% through efficiency, only 48% say AI has actually improved collaboration. The engine is revving, but the wheels aren’t turning together. “We’re running AI together, separately” The disconnect is clear: organizations are adopting AI rapidly, but they’re doing it in silos. As Kurtzman puts it: “Organizations are running on AI, together, separately; not AI together . At least not yet.” What he’s seeing in his work with CIOs across the Global 1000 – the top 1,000...

Are Jira user properties automatically removed when a user is deactivated? (Jira Cloud) Hi everyone, We’re developing a Jira Cloud app (Connect/Forge) and we’re seeing unexpected behavior with user properties that we’d like to clarify. Our setup: When a user logs into our app for the first time, we set a user property on that user via the User Properties REST API ( POST /rest/api/3/user/properties/{propertyKey} ). We remove this property only when the user is deleted from Jira (we listen to the user_deleted webhook). We also track the user.active field on every user_updated webhook, so we know which users are deactivated. The issue: We’ve discovered that some users with active: false are missing our user property, even though our app never removed it. We’ve audited all of our property-removal logs on our side, and we’re confident our code did not delete these properties. This leads us to believe that Jira may be removing user properties automatically on its side — possibly when a user is deactivated, or during some other lifecycle event. Questions: Does Jira Cloud automatically delete user properties when a user is deactivated ( active: false )? Are there any other scenarios where Jira removes user properties automatically without any action from the app that created them? For example: User deactivation/reactivation User deletion and the 14-day grace period App uninstall/reinstall Site import/migration License changes or the user losing product access If automatic removal does happen, is there a webhook or event we can listen to so we can react to it? Any confirmation from Atlassian or pointers to documentation describing the user property lifecycle would be greatly appreciated. Thanks in advance! 1 post - 1 participant Read full topic

Recent changes to ease transition from Bitbucket Connect to Forge Hi, Since our last post Bitbucket Connect to Forge migration tooling , we’ve received valuable feedback from vendors and have made the following changes to address them: Forge app menu items are grouped per app Forge product events have been enriched to include more fields workspaceId now always available in app.installation.contexts.workspaceId in Forge Invocation Token when using Forge Remote Connect application property api endpoints are accessible from its linked Forge app during migration phase. Note: app property api will be deprecated when Connect reaches EOL Thanks Wendy 1 post - 1 participant Read full topic

New research reveals how AI is making jobs bigger When a tool can do in seconds what used to take hours, it’s natural to wonder if there will be any work left for humans. But new research from Atlassian’s Teamwork Lab suggests the opposite: Rather than shrinking an employee’s remit, AI appears to be expanding both the breadth of what employees take on and the depth at which they operate. Earlier this year, researchers at Berkeley Haas studied AI adoption at a single technology company over eight months and found that workers who used AI took on broader responsibilities and tasks that previously sat with other roles. The Teamwork Lab findings expand on that research at a wider population scale, with new data on where the expanded effort is actually going. Key findings AI appears to be expanding roles, not shrinking them. 92% of knowledge workers report their responsibilities have expanded beyond their original job description over the last year, but AI use is the clearest dividing line. Workers who don’t use AI are 7x more likely to say their role has stayed the same, compared to the heaviest AI users. The more you use AI, the more your territory expands. Workers who’ve made AI a daily habit are nearly twice as likely to take on work from other teams — and twice as likely to handle specialized tasks without looping in an expert. AI shifts your job from doing to leading. The more workers use AI, the more their role shifts from execution to oversight. Heavy AI users spend their gained time on management and direction; non-users spend theirs heads-down. The deeper the AI habit, the faster the shift from doing the work to leading it. AI widens your cross-functional reach, but not your relationships. Heavy AI users are up to 72% more likely to take on cross-functional work. But every AI user group ranks time with colleagues dead last, while non-users rank it third. This suggests AI users are collaborating across more teams without actually getting closer to anyone on them. Role expansion is the rule, not the exception At...

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Anyone else experiencing issues searching/selecting "Team" and "Assignee" in Jira Cloud? Hello community, We are experiencing a sudden and critical issue with field selections across our Jira Cloud instances and wanted to check if others are facing the same behavior today. 3 posts - 3 participants Read full topic

Command palette view port size support Hello! Previously we were using Atlassian Connect dialog to open our plugin throughout the jira in dialog. Currently, when we are migrating to Forge something similar seems to be available when using Command palette Forge module. However 2 major limitation exist: viewportsize or sizing of modal is not available and default size is not always suitable displayConditions for the modal is not supported Is it going to be implemented by Atlassian team in the nearest future? Global dialog from Jira side menu would work great for us too Best Regards, Oleh 1 post - 1 participant Read full topic

Custom UI and Typescript 5 (vite build) breaks invoke forge/bridge CHANGE-3285 Hi, I am currently upgrading all @forge /* dependencies to their typescript 5 versions. e.g. forge/bridge to 6.0.0. My vite build that worked fine now builds a Custom UI frontend that cannot connect to the forge bridge. Somehow some global variables the bridge relies on get mangled up in the build… Has anyone got any solutions? Some vite things I tried are: reserved __bridge a.s.o but nothing works so far terserOptions: { compress: { drop_console: true, drop_debugger: true, }, // Prevent Terser from breaking Forge's global iframe communication properties mangle: { reserved: ['__bridge', 'ForgeBridge'], }, }, I am always getting the error: ERROR 23:34:30.820 04099926-379a-49cb-ba17-d9291965da5e BridgeAPIError: Unable to establish a connection with the Custom UI bridge. If you are trying to run your app locally, Forge apps only work in the context of Atlassian products. Refer to https://go.atlassian.com/forge-tunneling-with-custom-ui for how to tunnel when using a local development server. But this has nothing to do with tunneling. It appears in the deployed version. And somehow Atlassian changed how ui bridge hooks into the page from window to global variables or something. Any help would be appreciated. Thanks 5 posts - 2 participants Read full topic

Display rovo action results in a tabular format Hello , I am developing a forge rovo agent which search for information in confluence and returns the result to user in a tabular format here is my manifest D. Search Confluence pages related to the user's topics. To do this, follow these steps: 1. Invoke the search-in-confluence action to retrieve the user's stored topics. 2. Use the pages returned by the action and present the result in a Tabular format with columns: Page Title (linked), Space, Last Modified, Summary. 3. Example Table Format: Page Title Space Last Modified Matched Topics https://site/wiki/page1Rovo Adoption Guide ITDOC 2 days ago Rovo 4. DO NOT invent or assume any information. Use only the data returned by the action. 5. DO NOT return bullet points or lists. Present the information in a clear, user-friendly table format. My action returns a JSON with page counts and list of pages filtered using cql. return { count: pages.length, pages: pages }; I am able to get the details in bullet points but i want the result to be in a table. Kindly help. 1 post - 1 participant Read full topic

Can't access Ecosystem service desk — need help transferring a Forge app between my Developer Spaces Hi — I’m a Marketplace Partner and need account help, but I can’t register or log in to the Ecosystem service desk portal (password reset completes, then every page errors — multiple browsers tried). My account has four Developer Spaces, which is the root of the problem: Two are published vendors (one is my real vendor profile; the other is a duplicate I’ll eventually want removed) Two are unpublished (one of these contains my Forge app) I need my Forge app transferred from its unpublished space into my published vendor space. Self-serve transfer is blocked because I can’t grant myself the core Admin role on the published destination. Please don’t action anything from this post alone — with four similarly-named spaces, I’m deliberately not posting IDs publicly. Could an Atlassian team member reach out (DM or ticket) so I can confirm the exact app and space IDs privately first? Thanks 2 posts - 2 participants Read full topic

Fix flaky tests with AI, and track future test work in Jira In January we launched Tests in Bitbucket Pipelines – a single place to track, organize, and understand your test health over time. In April we added automatic flaky test detection so unreliable tests get flagged before they slow your team down. But spotting a problem is only half the battle. Day to day, your team still needs to act on a test – track it as work, clean it up, or route it to the right person. Until now, that meant context-switching to Jira, copy-pasting details, or hoping a teammate remembered to follow up. Today we’re closing that gap with three capabilities that turn test insights into resolved outcomes: What’s new: Fix flaky tests – AI-powered remediation, right from the Tests tab. Create Jira work items – turn failed, flaky, or slow tests into tracked, owned work in seconds. Remove tests – keep your test data clean by clearing out stale or irrelevant entries. Together, these bring your mean time from detecting a flaky test to merging a verified fix from days to minutes, without leaving Bitbucket. Fix flaky tests – AI does the heavy lifting Flaky tests erode CI trust, waste engineering time, and utilise the build resources unnecessarily to run the same tests. Bitbucket already detects and flags them automatically – now it can fix them for you . Click “Fix flaky test” on any flagged flaky test and an AI agent diagnoses the root cause, writes a fix, and opens a draft pull request – all in minutes. You review and merge on your terms. Want full control? Bring your own agent with custom prompts to match your team’s coding standards. Learn more about the Fix Flaky Test AI Agent definitions: agents: fix-flaky-test-agent: prompt: "!.rovodev/fix-flaky-test-prompt.md" triggers: fix-flaky-test: - condition: "true" pipelines: - fix-flaky-test-pipeline pipelines: custom: fix-flaky-test-pipeline: - step: name: Fix flaky test script: - agent: fix-flaky-test-agent Create Jira work items – track it, don’t forget it Not every test issue can be fixed right now. But w...

Fix Bugs Faster with Atlassian Rovo MCP

Introducing new MCP capabilities that turn context into action

What 5M+ daily MCP tool calls taught us about the future of AI at work

AI made your people faster. But it’s your office that’s slowing them down. AI at work has a physical dimension that almost no one is talking about. Across industries, companies have spent the last two years accelerating individual productivity with AI. Drafts get written in seconds. Code gets reviewed in minutes. Research now takes days, instead of weeks. And yet teams don’t feel faster. Decisions still stall. Projects still drag on. The bottleneck hasn’t disappeared, it’s just moved elsewhere. Our research on 12,000 knowledge workers and 172 Fortune 1000 executives reveals why: as AI compresses execution, the constraint shifts to judgment, alignment, and decision-making — the work that happens between people, not inside a single person’s workflow. This constraint adds up, and costs the F500 an annual $1.61B in what we call the fragmentation tax. Coordination happens online. But even though Atlassian is fully-distributed, we still invest in our offices, because we believe in the value of coordinating in-person. The offices of today, however, were designed under the assumption that the hardest part of knowledge work is execution. Now that AI handles much of that execution at remarkable speeds, the office needs to shift focus to make coordination similarly frictionless. “What we learned from going distributed-first still holds in the AI era: the office serves as an incredibly important release valve. AI doesn’t need an office, but the humans using it do — for the energy, creativity, and momentum that can only come from being face-to-face,” says Dr. Alisa Yu, Senior Research Manager, at Atlassian’s Teamwork Lab. our research at a glance To understand the role of offices in the human-AI era — and what to do about it — the Atlassian Workplace team partnered with Teamwork Lab in an intensive multi-day sprint. We brought together behavioral research on how AI is changing the nature of knowledge work with workplace design expertise on how physical environments shape team behavior. We drew on three bodies of evidence: The 2026 State of Teams report...

Secure AI adoption with data loss prevention (DLP) Your AI strategy is only as good as your data security foundation AI makes your organization’s knowledge easier to find and use. That’s the whole point. But it also means sensitive data moves faster, surfaces in more places, and becomes harder to track. The pressure to act is real, but the playbook isn’t new. You still need to know where sensitive data lives, govern how it’s being used, and prevent it from unauthorized exposure. AI is now giving you a reason to revisit your data security posture and make sure it’s strong enough to keep up. The teams getting this right are closing visibility gaps, strengthening existing controls, and adding protections designed for AI. The goal is to build a layered security strategy that understands your data where work is created, not after the fact, so protection is built in from the start. That’s where data loss prevention (DLP) comes in, and why this foundation matters even more for AI. Build an AI-ready data security foundation DLP is a security discipline focused on preventing sensitive data from being exposed or misused, and it’s most effective when it’s embedded where work actually happens: in pages, work items, and AI-powered workflows. A strong DLP framework helps answer three questions: Discover: where does our sensitive data live, and do we have blind spots? Classify: what kind of data is it, and how critical is it? Control: how do we enforce the right actions based on what we know? These steps give teams the visibility and control they need to strengthen their foundation for secure AI adoption. Here’s how each one works in practice. Discover Teams are shipping fast and creating a lot of new content across spaces, pages, and connected tools. In fact, teams on Atlassian create more than a million Jira work items every day and over two million pieces of content in Confluence every week. That’s why visibility matters, whether you’re scaling AI, onboarding new teams, or managing years of accumulated content. You need to know...

Fix Bugs Faster with Rovo MCP

Tabs coming to Confluence Hello everyone, Today we’re announcing an upcoming feature that is coming to Confluence Cloud. We’re building a native tabs macro that will be available to users in the Confluence editor. We know this impacts some of you who have built tabs apps and have served customers’ content formatting needs for a long time. We want you to have a clear picture of what’s coming so you can plan your roadmap and keep delivering value beyond the experience we’re building. What we’re building A native tabs macro that can be inserted in Confluence pages and live docs Users can add/remove/reorder tabs within the macro Tabs titles will support emojis and background colors Tab operations will be supported by Rovo / agents Early release will support horizontal tab layout only, with vertical layout planned for a future release Why we’re building this Tabbed layouts are how many teams organize content like specs, runbooks and release notes, and this pattern shows up consistently in customer feedback as something Confluence needs to support natively in the editor. We are building tabs to meet our customers’ expectation for core creation and editing in a modern knowledge tool, including support for AI-assisted flows through Rovo and agents. Timeline : We’re aiming to release tabs at the end of July / early August. We know this may be difficult news for partners who have invested deeply in tab experiences for Confluence. Our goal is to communicate transparently and keep an open dialogue with you as this work progresses. If you’d like to test the tabs feature once it’s available in early release, please join the Developer Canary Program . If you have questions or feedback, please share them in the comments. Thank you! 12 posts - 7 participants Read full topic

Forge Realtime is now Generally Available (GA) We are thrilled to announce that Forge Realtime has officially moved from Preview to General Availability (GA)! Forge Realtime allows you to build interactive, collaborative experiences by enabling realtime communication for your app. With this GA release, Realtime is ready to use in production apps with full platform support and Atlassian’s operational readiness. Why Use Realtime? Atlassian customers are increasingly demanding apps that keep pace with the way modern teams work: fast, collaborative, and always in sync. Whether it’s live editing, instant notifications, or seamless multi-user collaboration, the information should flow in real time. Forge Realtime is our answer to this need. It lets you build apps where data updates instantly across all users, without manual refreshes or polling. Imagine collaborative pages, live dashboards, or instant feedback tools, all natively integrated. Here are just a few of the experiences you can build: Real-time status indicators: Show when team members are viewing, editing, or working across the same Forge app context. Collaborative activity feeds: Broadcast project updates and team actions across all users viewing the same workspace context, keeping everyone in the loop as things happen. Live collaborative editing: Enable teams to co-edit content, tickets, or diagrams with changes reflected instantly for everyone. Instant notifications and alerts: Push updates to users the moment something changes. Real-time dashboards: Keep project metrics, sprint boards, or incident status up-to-date for all stakeholders, all the time. Interactive widgets: Build voting, Q&A, or brainstorming tools that update as users interact. What’s New in GA? Based on your feedback during the Preview phase, we’ve introduced key features to give you more control and security: Granular Permissions (Subscriber vs. Publisher Roles): You can now issue tokens that grant specific access levels. Using the new permissions argument in signRealtimeToken , you can...

Fix Bugs Faster with Rovo MCP Most new tools get introduced through a feature list. I’d rather start with a workflow. That’s how I approached the new Rovo MCP server. Instead of talking about it abstractly, I wanted to test it in a workflow developers hit all the time: fixing a bug. The question I wanted to answer was simple: can I take a bug from issue creation to deployed fix without leaving the same working session? That’s the real friction in this kind of work; not the fix itself, but all the tool-switching around it. In this post, I’ll walk through that end-to-end workflow using Rovo MCP: creating the Jira issue, fixing the bug in code, redeploying the app, validating the change, and updating Jira once the work is done. For this walkthrough, I’m using Cursor, but the workflow itself isn’t tied to Cursor. The same pattern applies in other AI coding tools that support MCP. The Workflow Developers Already Know Let’s start with the workflow most of us already recognize. You’re testing an app and notice a bug. Maybe it’s a status badge rendering incorrectly, a button that doesn’t update a state variable, or a UI field isn’t saving what it should. The fix itself might be straightforward, but that’s rarely the only task. In a typical bug-fix workflow, you’re doing something closer to this: Find the bug Open Jira and create a ticket Write the summary, description, and reproduction steps Attach screenshots or other context Switch back to your IDE and find the relevant code Make the fix Deploy, install, or run tests to validate Go back to Jira and update issue status That process works. It’s also full of context switching. You move from observing the problem, to documenting it, to fixing it, to validating it, and then back again to manually update the system of record. None of those steps are individually hard. The friction comes from the constant jumping between surfaces and the little bits of ceremony around your work. And more importantly, every switch breaks your flow. You lose momentum, you lose...

Anyone else just...not want Rovo in the Confluence GUI? Hey, if you like using this thing, that’s cool. I do not like using Rovo and I do not like it cluttering up my user experience in Confluence Cloud which is otherwise pretty solid. Every time I have clicked the Rovo options it was a mistake, often because the UX designers have chosen, or been ordered, to put those options in the best first-order-retrievable location. That implies that you expect users to access an LLM as the first and most useful feature, every time. Was there any user research to back this decision up? I know the option to turn off the Rovo options in the user settings entirely has been considered, because I found an FAQ several months ago that stated “No! It is not possible to turn off Rovo, thanks!”, so I know you did enough research to find out that people would like to be able to turn it off. Can I speak to the manager? What’s going on? How hard do people have to scream for any administrator or executive to hear? ALOT of people do no want this stuff . 1 post - 1 participant Read full topic

/MOHAMED.COM https://developer.atlassian.com/platform/forge/getting-started/#prepare-to-build-your-first-forge-app /MOHAMED.COM/MOHAMED.COM https://developer.atlassian.com/platform/forge/set-up-cicd/#set-up-continuous-delivery-for-forge-apps 1 post - 1 participant Read full topic

How We Brought Enterprise-Managed Authorization to Rovo MCP with XAA and ID-JAG

Entity property conditions on user entity not working for globalPage/backgroundScript (Confluence Forge) Hi all, I’m trying to use Entity Property Conditions in a Confluence Forge app across two modules - Global Page and Background Script . Per the Confluence display conditions docs , both modules support entity property conditions on the user entity. My goal is to show the page in sidebar and run the background script only if the specified property exists on current user. Minimal reproduction: GitHub - aljazomwf/forge-confluence-user-entity-property-issue: Repository that reproduces the issue where user entity property is not respected by display conditions. · GitHub Here’s the relevant part of my manifest: modules: confluence:globalPage: - key: settings-page-module resource: main render: native title: Settings Module route: settings-page resolver: function: backend-resolver - key: hello-world-page-module resource: main render: native title: Hello World Module route: hello-world-page resolver: function: backend-resolver displayConditions: and: isLoggedIn: true entityPropertyExists: entity: user propertyKey: testprop confluence:backgroundScript: - key: background-script-module resource: main render: native displayConditions: and: isLoggedIn: true entityPropertyExists: entity: user propertyKey: testprop What I’m doing: The Settings page has a button that sets/deletes the testprop property for the currently logged-in user. After setting it and refreshing the page, I still don’t see the Hello World page in the sidebar, and the background script’s console.log never runs. What I’ve already verified: Both modules appear/run correctly when I remove the displayConditions block, so the modules themselves are fine - using only entityPropertyExists without the isLoggedIn also does not work The property is actually set I’ve tried both running the API calls with asApp and asUser but it makes not difference Trying the functionality with different user accounts Logging in/out of the Confluence Try it in private browser mode Questions: Are user entity property conditions actually supp...

How Atlassian and Dropbox are driving effective AI transformation Adopting AI technology without an effective strategy is costing the Fortune 500 an estimated $161 billion a year.* Enterprises are making big investments in this space but are struggling to realise the returns. We know the technology is designed to make businesses more efficient, but we’re still seeing the opposite because most businesses are treating AI as purely a technology transformation. That’s where they get stuck. At Atlassian, we’re approaching AI as a people transformation, not just a tech one. We’ve focused on innovation before efficiency and culture before tools, and we’re seeing the difference. The good news is that we’ve tested these beliefs and strategies ourselves. We’ve always had a customer zero strategy and aimed to build the future of work for our customers by living it ourselves. Every new product or system of work we ship for our customers, we first adopt ourselves across the organization. I had the privilege of joining Allison Vendt, VP of People Operations at Dropbox, on stage at Team ’26. Dropbox has also been on an incredible journey of AI transformation and we were able to compare our learnings. In fact, we have a core shared belief: companies that have been built with an asynchronous and document-rich culture have a natural advantage for AI adoption that makes an impact. In adapting our organizations for the AI era, we have taken on board some important learnings from driving this transformation. Here are some key takeaways from our discussion: AI is not just a tech transformation, it’s a cultural one When Atlassian was building our enterprise AI strategy, one of the first questions was who would own the company-level AI OKR (our success metric). It exposed a gap: AI transformation couldn’t just belong to just one function. We were facing what many organizations are still missing: AI is not just a technology shift. The most important transformation is cultural. AI tools won’t make an impact unless teams are fluent, confident to experiment,...

Introducing global:ui EAP: Build global experiences on the Atlassian platform Hi everyone, We are excited to announce the Early Access Program (EAP) for the new global:ui module! Historically, Forge app UI has been anchored to specific product contexts like a Jira issue or a Confluence page macro. With global:ui , we’re breaking those boundaries. This new module allows you to build end-to-end experiences that live directly on the Atlassian platform, enabling your app to have an independent UI from any Atlassian app. What’s changing? The global:ui module enables your app to: Deliver a full-screen app experience: Create a dedicated workspace with side navigation that you control, while maintaining the familiar Atlassian top navigation for a seamless user experience. Access via the app switcher: Your app can now appear in the Atlassian app switcher, making it easily accessible to users from anywhere within the Atlassian ecosystem. With multi-app compatibility, TWG, agents and other new capabilities recently introduced, you’ll be able to create an immersive and rich app experience. Why join the EAP? This is a significant shift in how Forge apps can be shown on Atlassian. By joining the EAP, you’ll be among the first to explore these capabilities and help shape the future of the module with your feedback. How to get started Sign up: Access to the global:ui module is currently restricted to EAP, sign up here . Read the docs: Once you’re in, check out the documentation to understand the manifest schema and technical requirements. Build & Feedback: Start experimenting! We’re eager to see what kind of “global” experiences you build. We can’t wait to see how you use this to create more integrated and powerful tools, Vicky | Forge UI Product Manager 1 post - 1 participant Read full topic

Bamboo to Bitbucket Pipelines migration tool is now GA Earlier this year, we introduced the beta version of the Bamboo to Bitbucket Pipelines migration tool. Since then, we’ve listened closely to your feedback and expanded its capabilities to make your transition to the cloud smoother. We are thrilled to announce the general availability (GA) version of the Bamboo to Pipelines migration tool. The tool now supports your full Bamboo footprint, including build plans, deployment projects, and custom third-party plugins to automate the heavy lifting and reduce manual effort. What’s new in GA Deployment plan migration: Bamboo Deployment Projects are automatically converted into Bitbucket Pipelines custom pipelines alongside your build plans. Support for custom transformers: Easily extend the tool by writing a simple Python script to automatically map third-party or custom plugins into Bitbucket Pipes or shell commands. Instance-level migration: Migrate all build plans and deployment projects across your entire Bamboo instance with a single command. Improved audit reporting: Audits now surface the exact plugin key for every unsupported task, so you know precisely what needs attention. Why migrate to Bitbucket Pipelines? Migrating to Bitbucket Pipelines shifts your focus from managing infrastructure to doing what you do best: deliver customer value. Bitbucket Pipelines offers a fully managed, cloud-native CI/CD platform purpose-built to accelerate your team’s productivity: Zero wait times for developers: Instant, elastic scaling eliminates the bottlenecks of fixed, on-premise build agents. Builds start as soon as the pipeline is triggered. Massive reduction in operational overhead: Say goodbye to maintaining hardware, patching vulnerabilities, and managing tedious CI server upgrades. Atlassian manages all upgrades and patches for you. Significantly lower costs: By eliminating on-premise operational overhead, teams can reduce their total cost of ownership by up to 70% at scale. Effortless compliance: Enterprise-grade orchestrati...

Forge {"code":401,"message":"Unauthorized; scope does not match"} I have tested to create a Rovo agent using forge, the agent role is to fetch ccounts in JSM, and return the users haven’t loged in in last 180days, to help admin for users review, but it fails to fetch the API: I get this error: ERROR 2026-06-28T23:46:29.594Z ec49ff6a-940d-49f5-9de5-b8a248e152ba Error: {“code”:401,“message”:“Unauthorized; scope does not match”} at Object.getGroupMembersFull (webpack://users-review/src/index.js:21:13) I have tried both const res = await api.asUser() and const res = await api.asApp() but always same error, could you help me to investigate the issue, for information I’m an ORG administrator. Manifest.yml: modules: action: - key: get-group-members-full name: Get Full Group Membership description: Retrieves all users in a group with their details function: getGroupMembersFull actionVerb: GET inputs: groupId: title: Group ID type: string description: Atlassian Admin Directory Group ID rovo:agent: - key: users-review-agent name: Users Review description: Reviews group membership and inactive users prompt: > You help administrators review Atlassian group membership. Ask for a Group ID if not provided. Use get-group-members-full action and show all returned users. conversationStarters: - Show group members - Find inactive users actions: - get-group-members-full function: - key: getGroupMembersFull handler: index.getGroupMembersFull app: id: ari:cloud:ecosystem::app/1dfefea2-e33d-48c7-af0d-0d73816e08fa runtime: name: nodejs24.x memoryMB: 256 architecture: arm64 permissions: scopes: - read:jira-user - read:group:jira external: fetch: backend: - api.atlassian.com Index.js import api, { route } from '@forge/api'; const GROUP_ID = the_id_of_the_group; export async function getGroupMembersFull() { let users = []; // Always start with a SAFE route object let nextUrl = route`/rest/api/2/group/member?groupId=${GROUP_ID}`; while (nextUrl) { // ALWAYS pass route object directly here const res = await api.asApp().requestJira(nextUrl, { headers: { Accept...

Accelerating government modernization with AWS

Forge `devops:securityInfoProvider`: 401 "scope does not match" when linking a workspace (`linkedWorkspaces/bulk`) Hi, I’m building a Forge app with the devops:securityInfoProvider module to show security data in Jira’s Security tab. It has the read:security:jira and write:security:jira scopes. My understanding is that this works in two steps. First the app links its workspace by calling POST /rest/security/1.0/linkedWorkspaces/bulk , so that Jira knows the workspace exists. After that, Jira calls the app back ( fetchWorkspaces , searchContainers ) so an admin can connect a container to a project. The vulnerabilities themselves are pushed separately with POST /rest/security/1.0/bulk . The problem is that linking always fails. Using the app’s own token ( api.asApp().requestJira ), I get: POST /rest/security/1.0/bulk (push vulnerabilities) → 400 “At least one vulnerability is required”. So the API is reachable and the scope is accepted (I just sent an empty list). GET /rest/security/1.0/linkedWorkspaces → 401 “Unauthorized; scope does not match” POST /rest/security/1.0/linkedWorkspaces/bulk → 401 “Unauthorized; scope does not match” So the same token is fine on /bulk but rejected on every linkedWorkspaces endpoint. I checked the two things that usually cause “scope does not match”, and both are fine: The scope is there. I decoded the token the app actually sends, and it contains write:security:jira and read:security:jira . The URL is correct. It’s exactly /rest/security/1.0/linkedWorkspaces/bulk , no trailing slash and no encoding. The API documentation also explicitly states that “these APIs are available to Forge apps with the devops:securityInfoProvider module”, and lists no Forge-specific restriction on the linkedWorkspaces endpoints, so as far as I can tell this should work from Forge. Because linking fails, Jira never learns about my workspace, so it never calls my callbacks ( fetchWorkspaces / searchContainers ). The provider just stays on “Set up” in the Security tab and never moves forward. I put a minimal app that reproduces this here: EnduIf/forge-securityinfoprovider-4...

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