Collaboration and productivity software company Atlassian is widely known across the application development community for its toolset and platform, which also enjoys a healthy user base outside of tech in business scenarios.
Key product brands associated with Atlassian include Jira (a project management tool for teams to track work and issues); Confluence (a collaborative workspace for teams to create, organize and share knowledge) and Trello (a visual tool for teams to manage projects using boards and cards).
Now bringing AI into its platform through channels and processes not wholly dissimilar to the rest of the enterprise software industry, the company’s USP at this level centralizes around its availability to thread context into the data sources that Atlassian oversees.
This was a central theme for this year’s Atlassian Team 26 conference, held in Anaheim this May.
Contextual AI, Platform-Wide
Speaking at a closed press gathering before the main show itself, Atlassian co-founder Mike Cannon-Brookes said that the big picture to take from this event is all about the way the company is “surfacing contextual AI throughout its platform” as a key thread (although he is aware of the ubiquity of AI and would rather play that down if anything), as well as the organization’s ability to now ship on a continual delivery cycle and help customers work at a new cadence as a result.
“I’d really like people to pay attention to Atlassian Teamwork Graph and how we’re world-leading in this area in terms of enabling companies to do phenomenal things that they never could before,” said Cannon-Brookes. “We have no shortage of customers telling us that Atlassian Rovo is the best AI tool that they work with. Let’s also remember that almost a year ago, Atlassian acquired DX [an engineering intelligence platform that measures developer productivity, satisfaction and AI impact] and that has allowed us to provide qualitative survey analytics to find out how a team or company is doing compared to competitors, which they can then dovetail with quantitative analysis to measure productivity.”
Taking attendees through his big picture vision for AI in the workplace, Cannon-Brookes used his keynote address to explain how AI is now playing out inside modern (and modernizing) enterprises. He agrees that many models will fail and that there will be challenges on the road ahead.
Work Is Still Messy (That’s Okay)
“But models cannot be your differentiator (as a business), the difference must be context… and context is the collective memory that stakeholders have that they bring from every project they have ever worked on,” said Cannon-Brookes. Highlighting the Atlassian Teamwork Graph as a central technology proposition, the Atlassian founder agrees that “work will still always be a little bit messy” in the real world, but the company’s job is not to “sanitize away real life”, but more straightforwardly to underpin the way work is carried out in real-world environments.
Atlassian used a detailed section of Team 26 to explain how it is now introducing its Product Collection offering. This new service has come about largely because, in the world of software application development, the hard part of the lifecycle used to be “shipping” chores i.e. all the work associated with delivery.
Although the arrival of Agile, DevOps and CI/CD removed friction from delivery, AI has changed the equation. Today, prototypes can be built in hours. Workflows that once took weeks now take days. The barrier to building has dropped dramatically.
According to Atlassian, when building becomes easy, something else becomes the constraint: decision-making. The winners now need to know: which ideas are worth pursuing; which signals actually matter; and which bets will drive real outcomes across an organization.
AI-Powered Product Operating System
Product Collection is built to solve these challenges. It is an AI-powered product operating system that helps teams build the right thing (or indeed things) at scale, with the ability to deliver prioritized insights. In simple terms, it connects product strategy directly to delivery in Jira – Atlassian’s widely popularized project management tool designed for teams to plan, track, and release work.
In most organizations today, product workflows are fragmented. Customer feedback lives in support tools, sales calls and Slack channels. Discovery happens in one tool, delivery in another. By the time work reaches engineering, the “why” is often lost. This is where Product Collection comes in i.e. it brings together a collection of tools into one connected system:
Pendo will be the first product analytics integration available in Product Collection. This tool is best described as a “product experience platform” that combines behavioral analytics with in-app guidance. It allows teams to track user interactions, deploy no-code walkthroughs to increase feature adoption, and collect feedback.
With the Pendo integration, teams can connect what customers are saying with what they are actually doing in the product. Usage data, feature adoption, and behavioral trends flow directly into ideas and prioritization, helping teams make better decisions based on both sentiment and action.
Loudest Voice, Pipe Down
Instead of relying on anecdotes or the loudest voice, Atlassian says that product teams can see patterns emerge across segments, regions, and customer types, and use that evidence to understand what matters most. Scattered, unstructured input becomes structured insight you can trust: clear themes, supporting signals, and the context behind them. This reduces manual effort, surfaces trends faster, and helps ensure no critical signal is missed.
Atlassian openly stated that “much of the focus” showcased at the Team 26 conference held this month was designed to gravitate around the Atlassian Teamwork Graph. A “graph” in the sense that it is a widely dispersed yet intelligently network-connected software services platform with a central remit designed to connect people, tasks, and data across tools to provide a unified view of project progress.
As we know, AI agents are only as good as what they know. Right now, most don’t know enough. Not because the AI is broken, but because the data is. Information is scattered across tools, siloed by department, stripped of the human context that makes it useful. Agents guess. They hallucinate. Teams splinter around different versions of the truth. Context isn’t a file or a ticket. It’s the space in between: why a decision was made, who owns it now, what broke last time. That’s where Atlassian tools come in.
“The Teamwork Graph connects those dots, stitching together people, goals, code, and content across Atlassian and other connected SaaS apps. It becomes your enterprise’s living map of how work actually happens,” states Atlassian. “That graph now holds over 150 billion objects and relationships, and every Jira update, every pasted link, and every connected tool compounds the context your AI can reach. By mapping these connections before AI starts reasoning, the Teamwork Graph gives your agents the precision of a teammate who’s been there since day one.”
What’s New: Context, Everywhere
Today, Teamwork Graph will become accessible across your favorite agents—whether you’re in a browser, a mobile app, or a terminal. Every AI tool your team uses can now run on the collective intelligence you’ve already built.
Context is the difference between AI that guesses and AI that knows. Per our benchmarks, grounding responses in Teamwork Graph data delivered 44% more accurate results while using 48% fewer tokens. Translation: faster, cheaper, and more trustworthy answers across the board.
Teamwork Collection
Atlassian’s Sanchan Saxena paints a picture to give us the final product update here. She says that we’ve all been there – toggling between six tabs, copying content from one tool into another, and wondering if anyone actually read the brief. The promise of AI was supposed to fix this. Instead, most teams got a chatbot bolted onto the side of their screen.
“We think AI should work the way a great teammate does: show up where the work happens, understand what’s going on, and actually move things forward. Not from a separate window. Not after a five-paragraph prompt. Right there, in the project, on the page, inside the ticket,” she said.
That’s the backdrop for why Atlassian used Team ’26 in Anaheim to ship “a wave of updates” across Jira, Confluence, Loom, and Rovo that put AI agents and teams on the same page.
Agents in Jira (now generally available) take ownership of tasks, such as assigning issues or updating the code in a Jira bug. Agents pull context directly from work items, whether they’re assigned, @mentioned in comments, or automatically tapped in when work moves into a designated status. Atlassian’s first-party Studio agents work alongside third-party tools such as: Amplitude, Canva, Cursor, Figma, Gamma, GitHub Copilot etc.
Every agent action is logged in Jira with a full audit trail. Admins control which agents run and where. The team picks the right agent for the job. Agents and teammates work from the same Jira space, moving work forward together. Together, that’s the foundation of an AI-native organization: agents that take real ownership of work, with the same accountability, visibility, and guardrails as the rest of your team.
“Let’s be honest, nobody wants to read a wall of text. Teams pour hours into writing up decisions, plans, and research, and then half the audience skims past it. The information is there. It’s just trapped in the wrong format. Remix with Rovo (now available in beta) changes what you can do with text-based content that you’ve already written,” stated the company.
Developer Engagement
Here’s how it works: users select any content on a Confluence page and transform it into a chart, timeline, infographic, geo map, org chart, quadrant, or flip card, without touching your original source of content. Remix with Rovo then turns written content into a visual format, all based on a Confluence page. Confluence pages that include visual elements are nearly 2x as likely to be read by a wider audience compared to pages without. Remix with Rovo makes that accessible without the help of a designer or an extra tool.
With Confluence slides (available in beta this month), teams can now create AI‑generated presentations in seconds. This feature sits on top of the Teamwork Graph – Atlassian’s map of how your teams actually work – enabling Rovo to pull context from across Atlassian apps ensuring your final product has all of the relevant context included.
Atlassian staged a packed event and CEO Cannon-Brookes carried the entire keynote himself, staying on stage for his board-level intro, the developer demos that went on for over an hour… and then finally his bullish (but politely so) wrap-up to evidence a bit of bravado and swagger. This conference is growing and it would not be a surprise to see it move to Las Vegas or the Moscone… as agents enter teams, it really feels like teamwork itself is growing.
