Atlassian opens Teamwork Graph and evolves Rovo into autonomous agent at Team '26

Atlassian opens Teamwork Graph and pushes Rovo into agentic execution at Team '26 - SiliconANGLE

Atlassian opens Teamwork Graph and pushes Rovo into agentic execution at Team '26 Atlassian Corp. today unveiled a sweeping set of artificial intelligence updates at its annual Team '26 conference, headlined by the broad opening of its Teamwork Graph and the evolution of its Rovo AI assistant from a helper into an agent that can plan and execute multistep work autonomously. The company said the Teamwork Graph, which it describes as a living shared context layer connecting people, projects, documents and decisions across Atlassian and third-party tools, now contains more than 150 billion connections. Atlassian is also opening the graph to outside agents and tools through two new interfaces in open beta: a Teamwork Graph command-line interface for developers and Teamwork Graph tools delivered through Rovo's Model Context Protocol server. The CLI, with more than 300 commands, lets coding agents such as Anthropic PBC's Claude Code and Cursor query work and relationships across Atlassian products through a single interface rather than stitching together individual product application programming interfaces. The MCP integration allows any compliant assistant, including third-party tools, to read from and write back to the graph. Atlassian said its own benchmarks show that grounding AI responses in Teamwork Graph data delivered 44% more accurate results while using 48% fewer tokens. Teamwork Graph Connectors built on Atlassian's Forge platform have also moved to general availability to allow customers to pipe data from proprietary or legacy systems into the graph with permissions intact. On the Rovo side, Atlassian said customers performed more than 14 million Rovo-assisted actions in the past month and that agentic automations across its platform are up sevenfold over the past six months. The company is adding a new reasoning mode called Max to Rovo Chat, available soon in early access, which breaks complex requests into multistep plans, executes them across connected tools, and loops users back in for review. Rovo Studio, a no-code environment for building agents and automations grounded in the Teamwork Graph, is now generally available with built-in roles, approvals, versioning and audit controls. Atlassian says that more than 90% of its enterprise cloud customers are now using Rovo. The company also announced expansions across its product collections. Agents in Jira are now generally available and can be assigned work items with full audit logging. Jira Product Discovery Enterprise reached general availability with portfolio-level governance and a new Feedback capability for capturing customer signals entered early access. A new Incident Command Center unifies incident detection, investigation and resolution with Rovo-assisted root-cause analysis and Rovo Service offers autonomous or supervised Level 1 support. A new product called Dia Reports that generates proactive browser-native briefings such as interview prep documents and decision memos by combining Teamwork Graph context with everyday tools was also announced today. Atlassian said the feature is designed to surface personalized reports before users ask for them, reducing the need for prompting over time. For engineering teams, Atlassian introduced Agent Experience for measuring how agents interact with codebases, AI Code Insights for tracking AI-generated code at the commit level and AI Pulse for surfacing productivity signals to engineering managers. Atlassian also bolstered its administrative tooling for managing AI at scale. Announced today, new org-wide agent lists give administrators a live inventory of who built which agents, where they are running and how often they are used. Permissions for AI access and agent building can now be separated, allowing broader usage without uncontrolled agent sprawl. New dashboards and audit logs track AI adoption and credit consumption and policies governing what third-party data Rovo can ingest sit alongside controls for data residency and Atlassian-hosted large language model selection. "Rovo and Atlassian's Teamwork Graph are the connective spine, pulling together Jira, Confluence, JSM, Slack, email and more, so agents can reason across all of it," Matthew Hargreaves, head of product delivery and automation at Lendi Group Pty. Ltd., said in a statement. "That's what takes us from AI hovering at the edges to AI embedded in the core of how the organization operates." Andrew Boyagi, customer chief technology officer of Atlassian, spoke with theCUBE, SiliconANGLE Media's livestreaming studio yesterday, discussing why AI agents need organizational context to deliver value and how Atlassian is applying developer experience principles to enterprise productivity.

Atlassian Team '26 - new products, faster change, more automation, as the agentic pace accelerates

Atlassian's annual user conference has come round again with the usual avalanche of product launches today -- but with a distinct upbeat this year both in the tempo and scale of change and the very nature of those products themselves. Agentic AI is transforming both Atlassian's offer to its customers and what its customers are aiming to achieve with its products. As its CEO Mike Cannoh-Brookes told diginomica's Alyx MacQueen yesterday, whereas the vendor used to ship products twice a year, new features now arrive almost every week. And whereas those products used to help customers organize their work, now they're reshaping how customers' organizations actually operate. Setting the scene in an advance briefing with industry analysts last week, Amita Abraham, Head of Product Marketing, explains Atlassian's view of the AI-native organization: It isn't just a traditional business with a chatbot layer. It's a fundamental re-architecture of value. You're probably all hearing this, feeling this, living through it, much like we are at Atlassian. The AI-native company, in our minds, is one where execution will increase its value. We'll significantly shift to AI agents, while humans move to the critical frontiers of work. And at those frontiers, we do what only we can do -- define intent, navigate hard trade-offs, resolve those deep ambiguities. The tools and workflows might change. But Atlassian's mission to unleash the potential of teams will remain the same. Many of today's announcements are in support of that shift of execution to AI agents, with advances in the complexity of processes that can be handed off to agents and in the use of AI to build automations, along with more extensive monitoring and governance of agent activity. Human users also get new applications to help them share ideas and enhance products. Enterprise context and agentic 'superpowers' Underpinning all of this is Atlassian's Teamwork Graph, which has long been a core feature of the company's platform, but which takes on a new significance this year as recognition grows of the crucial importance of enterprise context when instructing agentic AI. This core theme is explored further in MacQueen's interview with Cannon-Brookes. In last week's briefing, Abraham spoke about context as "the fuel ... for accelerating businesses," and gave a simple but telling example of the role of context: It's that institutional memory of every decision, every failed project, every trade-off buried in an old Slack thread. And if a customer's AI doesn't know why they made that choice in 2024, chances are it can't help them in 2026. Bear in mind, too, that this context is constantly changing and therefore has to be continuously updated -- across Atlassian's customer base, 12 billion changes happen in the Teamwork Graph every day, while the number of agentic automations performed on the Atlassian stack every day has grown 7x over the past six months. Another example of the pace of change is that Remix with Rovo, an enhancement to the Confluence app that was only announced a month ago, today enters general availability. This lets a user highlight text, tables or lists on a Confluence page and Rovo, Atlassian's AI agent, instantly transforms it into a chart, diagram or infographic without the user having to leave the page. The capability is now taken even further with today's announcement of a new Confluence Slides app. Enhancements to Rovo itself include general availability of Rovo Studio, the no-code app builder for creating AI agents and automations. Rovo also gets a boost with the introduction of Max mode in Rovo Chat, a set of additional capabilities, or "superpowers" as Jamil Valliani, Head of AI Product at Atlassian, calls them. These include the ability to inspect a new API or MCP service and figure out how to use it, self-correction when Rovo encounters issues executing code, and an advanced planning engine for figuring out how to fulfil a request. He explains: From a vague question like, 'Help me understand our sprint health,' it actually decomposes that into the many subtasks that are required, including things like pulling velocity data, checking blockers, comparisons to prior sprints, and assembles a coherent plan and narrative around how to actually get that done. From feedback to new feature Atlassian today completes the regrouping of its products into distinct collections that each serve specific teams with the launch of the Product Collection, based around the existing Jira Product Discovery (JPD) product but now also adding a new Feedback product, which is in early access with selected customers. This is an AI-driven product that collects customer feedback from across Atlassian and third-party sources, ranging from customer service apps to review sites or demand intelligence apps such as Pendo, and can then tie the resulting insights back to goals and plans in JPD. John Kinmonth, Head of PMM for Rovo Dev, DevEx and Product Management Solutions, provides an example of how an idea surfaced in the Feedback app could then be taken forward into development using the AI-driven planning engine: You could pull an idea from the feedback app and then say, 'Hey, we want to implement this into our current service or feature stack.' What it does is, it goes through, assesses all the risks -- it doesn't just go and make a ton of architectural decisions for you. It looks at it and says, 'Hey, there's a couple different data API calls, here's the recommended one. This one's used across eight different services. This one's used against three, via your standards that are in your Confluence pages. This is probably the recommended one. And you can ask questions in Rovo and be like, 'Why did you make that decision?' And then it generates the full plan with architectural diagram. And you can say, 'Hey, you know what, I think it's safe for me to implement this plan.' Other additions to Atlassian's development tools expand the DX developer intelligence capabilities acquired last year. One particularly striking addition is a new agent experience, analogous to the existing developer experience function, but which takes snapshots of how an agent experiences a process, allowing engineers to optimize their flow in exactly the same way as they would for a human developer. Other new capabilities include AI Code Insights, which provides real-time visibility into AI-generated code across an organization, and how developers are using AI tools, and AI Pulse, which proactively surfaces productivity data to frontline engineering managers, to help them prioritize their efforts. We'll provide more coverage of these and other announcements from Team '26 through the week. My take The race is on between enterprise application vendors to become the preferred home for their customers' context -- to become the platform of understanding and knowledge on which their AI executes. It's no surprise therefore to hear Atlassian make its pitch at this year's Team event to win that race, opening up access to its Teamwork Graph to a far greater extent than we've seen from other vendors. At first glance, this seems to have much to recommend it. As Valliani told me: It's actually fairly easy for an agent to learn and understand how to go and access the relevant bits of data in a structured way in another graph, essentially, and then quickly go and bring that data in to meet their needs. That's one of the reasons why we've provided our new Graph CLI, is to actually help folks get that sort of easy access to our Graph from their favorite AI builder tools. We see that when they do use those tools, they actually both save tokens, because it's a lot less reasoning to go do, and actually get dramatically higher quality results. But for now one of the questions I have is, how does an enterprise reconcile the understanding of that context landscape across multiple vendor domains? Every vendor, including Atlassian, is offering its own proprietary context platform, and is asking its customers to make sense of data and processes from their other application stacks by effectively translating them into its own domain. Here's what Maggie Roney, Head of Product Marketing - Platform at Atlassian told me when I asked about this: Our graph is not just Atlassian data, it has been pulling in data from other third parties... A document could be a Confluence page or a Google Doc or whatever it is across all of these different vendors, but we're trying to bring them to some of the rawest forms in how we're defining the classification of some of those objects and then surfacing that back first within our portfolio... which we've done to date and then now, off-Atlassian as well. Despite what Roney says, there's still no sense that I can see in which the classification of entities and relationships in that graph is an open standard rather than proprietary to Atlassian. Therefore, providing CLI access to the Teamwork Graph, for all its initial advantages, simply binds enterprises even more firmly to Atlassian. Maybe that's fine, but what if there's data or knowledge in another vendor's context domain that isn't accurately rendered when it's transferred across into Atlassian's, or vice-versa? I'm not hearing the answers to this question at the moment, and at some point I think it's going to need answering, well before the race has come to an end. Enterprises certainly need to find ways to move fast, but they must still do so with caution.