Founder keynote: Human+AI collaboration at scale | Team '26 | Atlassian

0:46 Heat. Heat. Heat up here.

1:33 Heat. Heat. Please welcome Atlassian CEO and co-founder Mike Cannon Brooks.

2:15 Good day, Anahome and welcome to team 26. >> Thank you. That's a great start. Uh if you look around in this room and online, there are thousands of you, the builders, the operators, and the problem solvers who serve as the heartbeat of the modern organization. Now, we're lucky to be joined today by some of the world's top performing teams, and you'll hear directly from many of them in all the various breakout sessions this week.

2:47 For 20 years though, our mission has been simple. To unleash the potential of every team, but today, that mission has a new urgency. We're standing at the edge of one of the most significant reimaginings of work in our lifetime. Today isn't just about software. It's about the birth of a new species, the AI native organization. And Atlassian has evolved for just this moment. So you might be asking, what is an AI native organization? Is it just a traditional business with some chat bots that float around? No. It's a fundamental rearchitecture of value where humans move to the critical frontiers while execution is increasingly handled by autonomous agents at scale.

3:36 At the frontiers, humans do what only humans can. They define intent, they navigate trade-offs, and they resolve ambiguities. Tools and tasks will change, but our purpose remains the same. solving the problems that matter for all your organizations. Now, let's be clear. This transition will have many growing pains. While model performance improves exponentially, many businesses are still stuck in a linear gear. They're piloting or waiting to see. Now, I would argue that's not caution. That is surrender in slow motion.

4:13 You cannot wait and see your way through an existential shift in technology. The future will belong to the AI native organizations. However, in 2026, that raw intelligence, it's now a commodity. You can literally buy smarts by the token. Therefore, models cannot be your differentiator. The differentiator is your context. That institutional memory of every project, every goal, and every workflow that's finished that only your team understands.

4:44 Think of this as a simple formula. Acceleration for your business is about context multiplied by intelligence. Intelligence is the engine, but context is the fuel. Context is your collective memory. Every decision from every successful and every failed project. If your AI doesn't know what choice you made in 2024, it can't help you win in 2026. We aren't just building tools. Together, we are designing a new anatomy for teamwork.

5:17 And the best part, if you're an Atlassian customer, you already have this. It's what we call the teamwork graph. It's not a database or a set of files. It is the connective tissue between your work, your people, and your tools. Think of the pulse of your company. You define goals and manage projects in Jura. You design in Figma and you drive growth in HubSpot. You collaborate across dozens of platforms from Zoom to Teams and of course in Confluence pages and whiteboards.

5:54 Now, every action you take isn't just work. It's the continuous creation of your company's collective context. For years, we've been connecting these dots across your entire ecosystem. What does that formula look like in practice? Well, we operationalize those variables through the Atlassian system of work. It is the connective foundation of how your teams align to goals, plan their work, and unleash their knowledge. It's the how of the modern organization.

6:24 But a system is only as smart as the data it runs on. That's where the teamwork graph comes in. It is the data fabric for the entire system, the context. And you can multiply that intelligence through the Atlassian AI gateway. The gateway is your secure portal to the world's leading edge AI models. It's the bridge between your private organizational context and that collective intelligence utilizing best of breed models without ever compromising your data security or sovereignty.

6:58 And then there's Robo, the unlock, the accelerator for you and your teams. If we double click into Robo, we see that it's the interface that turns context and intelligence into actual momentum for your business. It brings context AI to every individual in your organization, the builders, the knowledge workers, the leaders. It brings ambient intelligence to every Atlassian app and carries that context far beyond our walls, injecting your organizational memory into all of the apps you use. context without borders.

7:34 Today, you aren't just choosing software. You're choosing what kind of company you want to become. Now, we don't promise a world without chaos. Work will always be a little bit messy. That's where the human ingenuity actually lives. And our job is not to sanitize chaos. It is to give your organization a nervous system that can handle it. So your company compounds with every ticket, every line of code and every decision to make the system smarter for the next day. We are building the memory of the company that you are becoming.

8:10 Now we didn't come to Anaheim for just slides. We came for conversations. Despite all the tech that we're going to talk about, it's still all about the humans. It's about every single one of us in this room. It's about how we collaborate and work with AI to amplify our capabilities and redefine what's possible for our organizations. That's what we mean when we talk about unleashing the potential of every team. Now, this year is going to be pretty different. We're going to have an awful lot of live demos and we're going to break it down through a series of human conversations.

8:44 First, we're going to talk about how you can grow, build, and deepen your context graph. Then how do you harness and command all of that context across all your Alassian apps? And finally, how do you surface that context, not just across the Atlassian ecosystem, but across every other app and platform that you use? For the first of those conversations to talk to you about how you grow your context graph, please welcome Chief Product and AI officer Tamar.

9:25 All right, welcome. Are you ready to have some fun with a few thousand of my closest friends here and do some live demos where absolutely nothing can go wrong? >> I am game in the age of AI. Things are moving so fast that I'm very pleased we're doing live demos so that we can show off all the amazing progress that we've made. So Mike talked a lot about context and I think it's one of the most substantial things that we're working on. If you don't know he is a super fan of the teamwork graph constantly sending DMs and Loom videos to me and the team like did you see this? This is super cool. So why don't we start by you showing one of your favorite examples.

10:05 >> All right. So we thought we'd start with a little comprehensive demo end toend demo of an actual example that I sent to Tamara a little while ago. uh maybe two, three weeks. So, as I mentioned, we're doing all these demos live. So, a couple ground rules so I don't get any trouble with my legal team later. Firstly, we're going to use all real data. Secondly, we're going to show off our products in some really interesting and new ways. Uh here we have uh the new Robo Chat continually improving, lots of great stuff. And the first thing I'm going to do is I'm literally going to tell it that I am live on stage at Team 26 and demoing Robo Chat. And we're going to ask it to remember to take out anything that would be classified as sensitive about a customer. We're going to swap real names with cartoon characters. So that's for PII. And uh if money comes up for any investors in the audience, we're going to replace all the numbers with like ridiculously hilariously large cartoon numbers. And most importantly, like make us look good. Like try to just give us a little little bit of love. uh and we can see that that that memory has gone in there. So then it quickly so here is uh the prompt that I'm going to put in. So I have a customer meeting tomorrow with Service Rocket. Service Rocket has been a customer of Atlassian for more than 20 years, a partner of ours. Thank you. And a member of the community for more than two decades.

11:25 >> Shout out to all the community members in the audience. Woo. >> Um, a great example of looking at more than a 20-year relationship plus recent interactions. And we can see immediately >> we can see that the teamwork graph is showing up. You can see the icon so you know exactly which apps the data is being pulled from. >> That's right. And you'll see a lot about the new Robo Chats uh planning, reasoning, skills, tool use. You'll see a lot of examples. You can read some of the background. Uh but first let me go back to those memories while that's working away. Um we've improved Robo memory massively in the last six months.

12:03 >> Uh indeed it now has two different types of memory which I can manage. First my implicit memory. So it's using the teamwork graph continually every single day every single thing I've done to learn about me and my job and use that to give me answers that I am looking for. And secondly it's got explicit memory. So there you can see at the top the memory that I've just added that it's going to save me from any embarrassment on my legal team right here on stage with actual live company data that during any robo chat demos it's going to uh put in some cartoon characters and replace those numbers.

12:34 >> And with the explicit memories you can delete it. So after he's done you can delete that memory. And Mike, did you know that in my Robo memory I have an explicit memory for you? >> Okay. I told Rovo that if I'm preparing something for Mike, there should always be a TLDDR followed by tons and tons of details. >> That sounds like what what I would be into. Um, we can see a lot across this trace. We can see lots of the planning reasoning. You'll see skill uses going past a huge number of new robo features which you can see at all different sessions. Um and as Timar mentioned you'll see a lot of data from the new connectors in this case uh Salesforce but also Jura Confluence Bloom Teams etc.

13:19 >> And we've made a ton of enhancements to our connectors. We've had last couple months over 50 enhancements. So for example Google Drive we now can index images and the text in images. So it's now searchable along with comments and tables and slides for Microsoft Teams. We not only index the messages but also the transcripts of your meetings. You mentioned Salesforce. We now index the the campaigns, the cases, the context, all with field level controls. Uh GitHub, PRs, deployments, index issues.

13:52 I could go on and all of this is much faster. So all of the data is now synced real time, near real time in minutes. It will be updated and a full sync is way way faster. Our goal is to make sure any change is inside the teamwork graph in under 10 minutes. Uh massive amount of speed improvements. Kudos to all of tomorrow and the engineering team across the place. We're now ingesting more than multiple billions of objects every single week into the teamwork graph across all of your organizations uh to give you the context that you need and lots more admin controls uh for all the admins out there of all of the different connectors.

14:27 >> So, it looks like we've got a result. Uh let's go down and see if I if I zoom in up here. We will see if our first live demo is going to work. So firstly, it says it's prepared by your brilliant CEO. So that's Robo making me look good. You want it to talk to you in really nice ways. It can do that. Uh clearly runs the best organized jury boards on the planet. We get a different result every time. So this is fun for us as well. Um I can see here that Oh, it looks like it's worked. Okay, so first part of the stress, the memory has worked well. Uh I don't think the annual revenue is probably $347 billion. Uh, and I'm pretty sure the account owner is not Fogghorn Legghorn. So, memory has worked. Again, kind of a trivial example of Robo memory to help it sync into your memories. Many, many things that you can do in a real world business case. Um, we can see instantly links like here, I can go to the account, which is the most important thing. And as I mentioned, lots of new rendering options. So, in this case, we've generated a chart or robo has for us. Think of it as a real-time BI dashboard of a 20 plus year customer relationship uh that took data from Salesforce and from Confluence databases and built me a chart in real time of showing me the things that I wanted to see. Multiple charts in fact lots of other opportunities we have open uh again memories coming in to help us there. uh data you'll see from across teams, lots of other places, live intelligence all the way down to details of my meetings tomorrow with Scooby-Doo, which is going to be great. It's going to be really exciting. Full stakeholder map of the company and a series of use cases.

15:59 And then we can see that has come from 61 different sources across the graph in about 3 minutes there, giving me a 20-year customer relationship in one single query. Go back to the slides. >> An amazing example of the breadth of the context of the customers data that you can access and this saved Mike so much time preparing and there obviously a number of infinite number of examples you could use with the teamwork graph and this is all available now for all of you.

16:34 But what I'm really excited to show is how do we build the teamwork graph? Whether you're migrating from data center or you're already in cloud, all of your data is already in the teamwork graph. So let's see how this happens behind the scenes. So let's say you create a Jira work item. It goes into the graph and then you assign it to Bell and now it's linked to Bell in the graph. And then you link the work item into a Confluence page and then all of the people who read, edit, comment on the page are also now linked in the graph.

17:10 You can add a Loom recording and the transcript of that Loom recording is in the graph and searchable. But work doesn't just happen in Atlassian apps. It happens everywhere. You might add a Figma link to the Confluence page or a Google doc to the Jira work item or you bring in your HIS data from our partner workday or any other system and who you talk to and who you meet with comes through the Microsoft and Google connectors.

17:39 We've added assets, one of Mike's favorites, to the graph. And something I'm really excited about is code is being added to the graph. So we can index your code and you can ask questions about it. You'll be hearing more about that later. And all of this is helping you grow your graph. And importantly, agents can access everything in your graph. Customers are already running 5 million agent invocations every month. And this is growing very rapidly.

18:13 >> Indeed, something we're super fired up at. Thank you for all of you. 5 million agent accesses each month is an incredible number already. And that's what an AI native organization is going to look like. Agents are showing up everywhere. Indeed, they're across all the surfaces now of the Elassian platform. They're embedded in your Jurro workflows picking up tasks. You can write alongside them. They can write alongside you in conference pages and whiteboards. Uh they can get assigned work just like any other teammate.

18:40 You've put them into many, many millions of your automation rules to handle those repetitive tasks and speed up your organization. You can chat to them in Robo, but also in Slack, Microsoft Teams, and Google Gemini, wherever it is you hang out. And most importantly, we've made them intuitive to access just like you are familiar with. For example, jumping into a conversation, just at mention an agent anywhere in any platform surface and it will appear. That is what we mean when we say you're choosing the type of company that you want to become in the AI era. With the Alassian platform, agents and teammates are already truly integrated across all the surfaces you work on. It's up to you how you use them.

19:18 >> And many of you are building agents in Robo Studio and you're connecting those agents from your favorite apps to Atlassian via MCP. As you develop more and more agents, it's a living map of how your organization thinks, works, and grows. And that is your enterprises world model. So Mike, I know assets is one of your favorites. It's a rich new source of data in the graph. Uh assets for all of you can range from the physical a spare part or a machine or to components or services. So you told me recently a great story about the Alassian Williams F1 team and how they use assets. So why don't you share that with us?

20:02 >> Sure. Look like 50% of our customers, you out there in the audience have real world physical objects as a core part of your business. logistics, manufacturing, building things, cars, trucks, satellites, and 100% of our customers have some form of physical assets. In our case, that's things like meeting rooms and projectors and laptops. Assets allows you to bring those objects, physical reals matters. If you miss one, you're not going to win a race. Can't do that if you've only got three wheels on the car.

20:44 So that's what Atlassian Williams have done. They have modeled all of the components of a Formula 1 car as first class objects in assets. They're no longer in spreadsheets or in some sort of disconnected system. They now show up in the teamwork graph. >> But that means they are connected to their work, their people, their code, service requests, projects, and all the knowledge that Atlassian Williams has from their 48-year history. Now, why is that so powerful? Well, between each race, if you don't know about Formula 1, the team's constantly making upgrades.

21:16 They're constantly changing the car, like upgrading a suspension bracket, connecting one of those wheels. But when they when they're upgrading that, they want to know, is it worth it to do the upgrade? And can it get done in time for the next race? Now, that's a hardware question, but it's also a knowledge question based on their history. So, one of their race engineers can now ask Robo exactly that. Rovol pull in the information from lots of different sources related to that specific part that's stored in assets across service requests and projects and knowledge bases. Then it makes actual recommendations about prioritizing the upgrade while making certain trade-offs.

21:53 Now the important part is that answer crossed system and team boundaries. Mhm. >> No single person at Alaskan Williams actually had that context, but the teamwork graph did and they accessed it via robo. >> I bet when you were building the teamwork graph, you never thought about a use case like this. >> We certainly did not think about that as a use case. Uh but it's the amazing thing about our job and building platform technologies for all of these amazing customers to build things that we never expected. It's one of the best parts about the job.

22:22 >> I agree. I love it. Uh so the teamwork graph can help your organization keep track of assets and work but also people. This is especially useful for me. I don't spend my days building cars. I spend my days uh building teams. So many of you may identify with this challenge that I often face. If you're on a working on a project which is blocked and you need to find the right people with the right skills to put the project back on track. So, a combination of the teamwork graph, the talent app, and rovo can help you do this. In our talent app today, you can connect your HIS system to get your orchard data, but we've taken it further.

23:04 We've added talent profiles. We now infer skills based on what people actually do. Code commits, confluence contributions, Jira assignments. you get a full 360 view of what somebody actually works on. So if I see an atrisisk project, I can ask Robo to explore available talent. This invokes a robo query which uses a teamwork graph to determine the kind of expertise required to unblock the data. After some reasoning, it gives a list of potential engineers who can help. From there, I can deep dive into their profiles in the talent app. I can see their focus areas, levels, and skills. Again, this is inferred from actual work.

23:56 I want to call out that this is both insanely useful for someone like me in a leadership position and something incredibly unique to Atlassian. It's something you can only do on the Atlassian platform because of the connectivity between the work, the people, and the code. So, we've shown you how you can grow the teamwork graph. You can take those connectors. You grow it with every single workflow and process and action that you take across our and other systems. We've added new whole categories of content from people to assets to code. And we're now inferring data on top of the teamwork graph to continue to make you smarter, compounding that value across every tool and every workflow. Thank you to Tamara.

24:37 That was awesome. Great start for us today. Thank you. All went well. So now we've shown you how you can continue to grow and expand your context graph. Let's show you how we can harness all of that context that you've built and put it to work in our apps. To do that, let me welcome Sharief, our head of AI and product craft.

25:11 How you doing, man? >> Doing good, good. I was listening to you and Tamar backstage. >> Ready for a live demo? >> Always. We're small group. Small group of group of friends here, so it's all intimate and nice. Yeah. >> Um, you know, Mike, I was thinking backstage when you were talking about the connectors with Tamar. Um, when I think about the context that comes from those connectors, uh, we often think about things like the business context, uh, spreadsheets, strategy documents, presentations, and so forth. But if you're an organization that builds and maintains any sort of technology as part of your core business, then actually code is a huge part of your context. And Mike, you always remind me that Elassian is all about connecting technology teams and business teams together to achieve amazing things.

26:12 So this provides semantic code intelligence to solve some of the toughest challenges that your teams will face. It means you can now ask questions across thousands of repositories and get answers in an instant. And it's not just simple patent matching. It actually understands your code uh every line of your source code and every files intent. So Mike, we've been using code search internally at lassin for the last few months and I thought this would be a good candidate for our first demo.

26:40 Should we do it? Absolutely. It's one of the most amazing and hard technical things that our engineering team's ever built and it's it's pretty amazing. Let's show it off. >> All right, let's jump here. So, I'm going to ask it a question here about uh our codebase and this is going to use our new code intelligence skill that we have. Now, for bit of context folks, many years ago, I used to work in Confluence and uh what often happens every few years still today is that we'll do a design system upgrade and we'll need to revisit all the places in our code that use a particular style and upgrade it. a fairly significant change.

27:10 So over here I've asked it, hey, tell me all the buttons in our Confluence codebase that don't use the latest style. Who are the people working on it and and what are the style guides? So you'll notice here I haven't told it where the Confluence codebase is and I haven't told it what the latest style is either, right? So and just connect me with people so I can understand where where work is at today. Now Mike, this is running in our internal codebase which is pretty large, right? It is indeed that uh in real time uh we're looking across Bitbucket data center, Bitbucket cloud and GitHub, three places that we store our code. We have more than 11 million code files being searched and 1.5 billion lines of code in the time it takes Sharief to come back with this uh answer made over 25 years.

27:56 >> Yeah. And um what's incredible here, one of the things I want to call out is this is a really good example of me with a business ask or requirement. Hey, we're about to do a design system change. So that tells me, hey, where do we want to go? And I'm asking the code, what do we have today? So, where do we want to go? What do we have today? And this is helping me bridge those two worlds together right here within moments. All right, Mike, we got some results. Let's check it out. I already like it's used an emoji button. Get it button. AI's got some humor sometimes. Uh, it's identified a the legacy library and the new library. So, I haven't told it where the new library is. It's uh it knows where to make the changes. It's got some internal posts on where the new updates are. Oh, and this is quite nice, Mike.

28:41 It's given me an audit of all the button styles across the codebase and the percentage breakdown of how much they're adopted today. So, a huge piece of information that can help me make a decision on how hard this change is and how broad this change is. Uh it's also got specific areas that might I need to focus on on migration. Oh, and check this out. people, teams that I need to work with and what their roles are on each of that and even the Slack channel so I can contact that team for uh some more detail. Pretty incredible power there. It's absolutely unbelievable.

29:15 Like the amount of work that it has just done to look across one and a half billion lines of code, find the confidence codebase, find the context, find the design system and write that full document all with clickable links to code if you want to deep dive into them in that 2 minutes is is insane. >> Yeah, it's pretty cool. Can you give us one more example what else we can do? >> So one more. You got one more >> example. Uh, this folks I have to publicly announce might be a career limiting move on my side. Um, >> been a good run, man.

29:50 >> So, Mike, why don't you tell our small group of close friends here when was the last time you committed code in Atlassian? >> I'm guessing that you know that it has been a while. Uh, I'm going to guess late 2000s, like 20 odd years. >> Okay, >> close to 20 years. >> To date us in front of a lot of people. Um, so I thought it'd be fun earlier backstage to ask what to-dos from Mike Kenns are still in our codebase and has anyone done anything about them recently and just to make Mike feel young again.

30:19 I've also asked it to include which which year this stuff happened for. Um, and so here's some recent results. Now I actually quite like what it does here because uh Mike is also sometimes known as MCB Mike Kenan Brooks. Sometimes people like to call him Uncle Mike. Um, it's found all the different aliases for Mike. Actually, a really good example of it using the context that is across our knowledge base and our conversations in in Slack and all that to pull that in.

30:47 >> People planning and reasoning. Smart smart way of doing it. >> Let's see what it's come up with here. I found two fossil tier to-dos. Jeez, it's it's gone a bit emoji wild here today. Just to It's uh we needed the dinosaur there. Thank you. That's >> when my hair was a different color walking around. >> All right. Uh icon on Java to do make this private. All right, Mike, you got a bit of work to do. Uh I like just to make it easier for us to scan the AI here. It's bolded. This is over 21 years old now. Um and it's uh no discussion found about implementing it. Well, let's let's let's see how we go with the next one.

31:25 >> Public variable survive 21 years. I think it's all right. >> Uh the next one here is the new code plug macro plugin. So, uh, for those of you who use, uh, the Elassian editor and Confluence injur, it has a way to format code in there. This is actually one of our first ever macros in there. And so, Mike was one of the original authors of there. It looks like he had to support Confluence 2.8 as a to-do and, uh, still hasn't done it. Uh, which is pretty funny. Um, now the good news is it's picked up here that that's an old version of Confluence. So, it's actually worked that out. Uh, >> worked out that that's deprecated. So, for those who don't know, Confluence 2.8, We're talking pre-cloud. In fact, we're talking pre- data center. So, there's before data center even existed as an example of how quickly we can search a 25 plus year codebase.

32:08 >> Yeah, actually Mike, that's a really good point because uh for those of you out there who are running data center and doing your cloud migrations right now, this is an excellent example of all that knowledge you have in the data center and instances you have that goes to your team of graph when you migrate to the cloud. So all that knowledge, the learnings, all of it comes with you when you migrate to the cloud. And just to finish us off here again, it really loves uh these emojis. Um do you want us to raise a ticket mic? So we you want to finish that off after this? Maybe vibe code it or something.

32:38 >> Let's do that. We'll vive the answer to that now. >> All right. Uh hopefully that's a entertaining example, but you can see the power there. >> That's right. I I hope you can see in these slightly humorous examples, right? Uh firstly, I'm off the hook. I think that code is going to be okay. Uh, but on a serious business note, searching one and a half billion lines of code with all of your logic over the last 25 years and combining it with people, with context, the insanity of writing those reports in real time. That would have taken some of our senior engineers a few days to go and pull all the information and write it with that amount of clarity. Just amazing what we can do nowadays. Um, and had the business context of knowing what we're trying to do.

33:17 >> So, we've now got Thank you. So we had your pull requests, we've got your repositories in the teamwork graph. Now we have all the source code that can be semantically indexed and understand in the teamwork graph broken down, cross referenced with a worldleading code search engine and understanding the intent of all of those files. What does that mean? It means any AI coding agent that you run will get better quality results. It will get faster results and your CFO will like this far cheaper results because it will use far less tokens to get the same answers. Any coding agent will benefit from connecting to the teamraph.

34:01 Now coding is only a small part of the software development life cycle and we've been building for software teams for over 20 years. As you can see from some of those examples, uh AI is rewriting that playbook pretty fast as I'm sure we're all aware. Sharief, you talk to customers every single day. Tell us about how those workflows are shifting, what we're doing for them. >> Yeah, Mike, everyone is feeling the change. We've all designed our business processes around one fundamental assumption, and that is code takes time to produce. But if reality is if code is no longer the blocker, then planning and judgment and making sure we are all building the right thing the right way is key. And so at Lassian, we've been building some experiences to help your teams use the context that you already have across your organization to help your software teams make better plans with AI. Want to jump into an example for this one?

34:55 >> Absolutely. >> All right, let's do it. So over here I've got my Juror and my team's working and the demo I'm about to give you folks is some of the stuff's already shipped. Some of it we're working in progress. So I'll call that out as we go. And I'm sure my boss here, Uncle Mike, will want to geek out as we go along. All right. So, imagine we work for a financial services company and we're adding a new uh personal investment dashboard. You know, the kind of dashboard that shows you how my finances are going and all that kind of stuff. We want to add that to our app.

35:25 Now, unless you're working at a brand new startup, all of us are dealing with code complexity. Uh tech debt, legacy code, deprecated libraries, maybe some f founder code in there as well. And so making you don't want to vibe code your change into production here or oneshot it. Uh this is a fairly big change with a bit of risk. So this is a really good example of putting humans in the loop and why that's so critical and this is how the team of graph is going to help them. So on my board here I'm going to use uh the capability we're working on to be able to create an AI plan right from my project in Jira. And I'm going to give it a high level overview of what we're trying to do here. uh working on the financial overview feature here in our app uh to show on the dashboard how my personal finances are going >> straight to the tumor graph.

36:13 >> Correct. Yeah. Pulling in all those extra sources of information. Now I haven't given it much business context here. I've said we're just working on this highle dashboard and it's pulling in the additional context from the conversations and sources here confluence pages Google drive documents uh GitHub pull requests and so forth. Uh, and as it keeps going here, it is going to invoke the code intelligence skill that we just saw earlier. >> Again, all those skills can be invoked either with the slash command if you want to call them out explicitly or you can include them in agents you build in studio, all reusable automation rules or here it's inferred automatically that it needed to use the code intelligence skill.

36:48 >> Yeah. And it's found this is a fairly significant change. It looks like six repositories it'll go through. Oh, and it's asked me for a question I need to answer here. demoing another new feature of Robo. So, your AI is pretty dang smart, but sometimes it reaches a point where it thinks, am I going to go down this path or that path? And if that's close to like a 50/50 call, for example, now Robo will ask you rather than going a long way down the wrong path. So, you can keep guiding it and steering it and helping it go in the right direction. In this case, it's asked a really important question when it's understood the business context and the code. Yeah, it's there looks like there's two ways to store data in this app and it's referred to the code to understand that, but it's also referred to the business context on what the preferred way is.

37:33 And when you're doing a demo in front of thousands of people, always go with AI suggestion. So, we'll just go ahead and do that. So, it's given me a plan here. So, it's created a proposed technical architecture plan of how to implement this change. I can see the f the files it's repositories it's proposing to change and it's giving me sources and references so I can make sure it's doing the right thing. I'll go in and modify them if I need to and it's got all the requirements for the change and this part here Mike uh my favorite part. So get this it is got it found a confluence whiteboard that someone in my team has previously created for the architecture of this app. It is then copied it used the context of the change in the team of graph to propose the new architecture.

38:18 That is insane. It is very very cool. And of course it's about human and AI collaboration. It's given you a start. Sometimes it'll get it totally right. Other times it'll give you the start. Maybe you've just a hint that you need to update the architecture diagram that otherwise would have been a legacy diagram and been old. In this case, it's actually added a sticky note to tell you what it has actually done. Yeah, nice little change log feature there. Now, this is a good example, folks, of a feature that you actually all have today. You can go to your existing Confluence whiteboards and give it some additional context from anywhere in your graph. Ask it to update it based on that or copy someone else's whiteboard with your new project proposal and see what it comes up with there. So, that's a good example there of stuff you already have today. Now, it keeps going here and recommending all the files that it needs to change and additional sources for that.

39:03 >> And gives you a little hint if you look at the top of how much it estimates that this will cost in tokens. >> Yeah. and and it's using that based on the code intelligence capability that it has and it knows what models we use. So it's giving me an estimate of how big this change is. >> All right, let's accept this plan. And now it's suggest it's asking do we want to break down the work into smaller chunks for our team. So we'll go ahead and do that. Now this particular part of the demo uses the intelligent work breakdown skill that you all have today except it's using that with a new code intelligence skill that we demoed earlier. So what it's doing here, it's recommended a bunch of tasks for me to do. It's probably knows that I often do the easier ones.

39:42 Some tasks for claude code and a task for a custom agent. So it's using all the horistics that it has to recommend work items for specific people, teams, and agents to pick up next. So we'll go ahead and create them on my backlog. And now we're off to the races. So we've broken down a pretty high level chunk of work into smaller, more deliverable tasks. And because our team has already got agents orchestrated in Jira with automation, Claude code automatically picks up the tasks here and gets to work on them straight away and I'll pass it back to my team members in review when we're when it's done.

40:16 It's a great example of using agents, automations, workflow steps all together in a way that works for your teams to get you moving as quickly as possible. Uh, and let you orchestrate your agent workflows right there from Jurro. So to give you a quick recap of what we've just seen because that was an awful lot in about four or five minutes. We showed an example of how Rovo used the knowledge context, the documents, the architecture diagrams, the text that you have written alongside the people context and the code context and use that understanding to build a plan. We can help and edit it, steer the plan, make sure it's correct. turn that plan everywhere into uh a set of work items to update our architecture diagrams all the way through starting the execution with automations and agents and workflows in a couple minutes and that is what makes Jira amazing in the agentic era. It is your AI control plane across both agents and human workflows.

41:15 It puts them together in ways that make sense for your specific team. It understands what's happening and helps orchestrate those complex multiplayer multi- aent environments that we're all heading into. and Mike, we're really pleased to announce that agents injur is also now generally available today. So today you can connect any agent to Jura using MCP. We also have the GitHub copilot agent available for you out of the box. And in the coming weeks we're working with our partners Claude Code, Cursa and OpenAI's Codeex to get them implemented as well. So, let's just watch a recap of everything we've just seen so far.

42:15 Heat. Heat. N.

42:51 1 2 3. Yeah. Thank you. And like everything we're showing you today, the teamwork graph has been working behind the scenes on your context.

43:30 Every workflow, every decision compounds the value of your Atlassian platform. The plan, the code, the specs, the work items, the architecture diagrams, all got written back into the teamwork graph. What that means is the teamwork graph is now smarter than it was at the start of that demo and will continue to compound over time. So, we talked a lot about how it's powering software teams. We're seeing more and more knowledge workers and business teams accelerating on the Alassium platform with that context.

43:59 Marketing teams, HR teams, finance teams. So, probably time we hop across to those. >> Yeah, Mike. Uh, and it's true for Atlassian. We've seen all sorts of teams run many use cases with Robo that are quite inspiring. So, today I want to walk you through an example of a sales enablement team that do some of these workflows. today. Let's do it. >> All right, let's do it. Now, this one, folks, takes some time to run. So, we pre-recorded it yesterday uh for you.

44:24 So, let me run through it. So, picture this. I run a sales enablement team and we just had our offsite and we went through a whole lot of documents during the offsite. Lots of decisions, spreadsheets. Here are some of them. And we need to collect all this information, a week's worth of work into something that's digestible that the rest of our teams can understand. So, I'm going to ask Rover to do this for me. I'm going to ask it to pull together a Confluence whiteboard. And in this prompt, I'm going to ask it to summarize the Q3 planning offsite we just had with my team and I will give it some of my pro preferences. And pro tip, by the way, folks, if you always want to look good in front of any manager, just say you also want it in a 2x two, they love that stuff. So, notice I haven't told it where to look for the Q uh 3 offsite, it'll just work that out.

45:12 So, it's getting to work here and you can see Robo in action processing and searching the team to understand the prompt I gave it and work out the context. And it starts streaming live on the dashboard. It's grouping our strategic priorities into that awesome 2x two grid there. And then next up, it'll look at all the decisions we've made, give me a summary of them, and one of my favorite things, if there was a meeting recording during that time, it'll embedded right in line in addition with the Google documents that we referenced at that time.

45:46 So, it's incredible amount of power here, summarizing all that work for us. And now it's actually streaming some of the charts we made at the offsite with the new Confluence remix feature. So it's got all like quarterly quarterly look back with the numbers there. So all of this happened within seconds and the artifact is immediately useful to the whole team. Now Sh asked for a conference whiteboard there but you could have asked this for a conference page. If you wanted a more textual format it would have done the same thing with all the remixes, all the links etc. Robo will tap into the teamwork graph wherever it is that your teams like to work and whatever type of content they want to consume. Yeah, you can even ask it to create a Google document with this or my recent favorite, ask it to populate a confluence database for you. It does an incredible job. All right, let's switch gears here a bit. Uh, this this sales enablement team tracks all their work in Jura and they're they do their work with their human teammates as well as multiple agents. So, some of these agents they work with are the Canva agent for managing creative assets, the Gamma agent for creating beautiful decks and presentations, as well as a custom brand guidelines agent that they've created themselves.

46:57 Now, they've designed their workflow to enable them to orchestrate their work with agents. So, to do this, they simply drag and drop the cards into the steps of the workflow and the agents automatically get to work. So Mike, you know all those demos that a lot of people are seeing of engineers with like lots of consoles open. I'm going to fire off an agent here, fire off an agent here. Has that one finished and come back to them?

47:20 >> Many big monitors. >> Yeah. All of you have this today in your Jira boards. It's an incredible power that available to to all of your teams. So let's dive into a deeper example of how this works. So this team is getting ready for their revenue kickoff. They call it RKO. So RKO26 is coming up and they need to draft a deck that has all the opening remarks. So they simply drag the work item into the status for the gamma agent and the agent gets going.

47:49 Now on this work item, we've added a bit of context here, the description of what it needs to use as its context, the design guidelines we'd like to use for the presentation. We've even attached an image we wanted to use so it's consistent with the visual look and feel. And the gamma agent has started to work. Just the agent takes all that context and then within moments it'll come back with a plan and then I can accept it. I can modify it. I can go back and forth with it if you like and then you can confirm it when you're ready.

48:21 Now at any time I can get out of this work time this work item and go check on the progress of other agents. Now if I ever want to come back to it it's as simple as tapping on the agent like I would just chat to another teammate. You see, agent assignment injur creates another robo chat session which you can resume anytime. So if I'm using robo mobile on my phone, I can continue that agent session from wherever I am. Could be on the bus, could be in a taxi, could be on a plane, I can keep it going and I can keep it going. Same with Robo on the desktop or the Jurro Confluence mobile apps that also have Robo embedded in them.

48:56 >> Yep. Switch apps, go to Confluence, keep chatting to another agent, do something else, come back. The sessions are always there with you. So it looks like Gamma has now finished uh with this new deck. Let's take a look at it and we'll open it up and it's got that image that we asked it to use from the juror item. It's followed the design guidelines and used the information we've attached on that juror item as context there. So as you can see agents in Jura an incredibly powerful tool for supercharging all types of teamwork, not just technical teams. Again, amazing examples of agent and jura live for all of you today. Uh, and that was recorded last night on actual live production data.

49:46 So, we've showed you what we can do for software teams. We've covered more broadly what we can do knowledge workers in all sorts of business teams, but obviously Alassian covers many, many types of teams. So, there's a lot at the show. Sharief, can you give us a quick fly through of some of those more specialized teams and what people can expect over the next few days? >> Yeah. Uh, in brief folks, there's a lot of stuff you'll see. Uh, so for example, for IT ops and S sur teams, we're introducing a brand new incident command center powered by this teamwork graph.

50:14 For example, when an incident occurs, Rover Ops will automatically kick in. It'll investigate, pull all that context from the graph. It'll find the recent dev changes from GitHub. It'll look at log files and latency metrics from data dog and tools like New Relic. It'll identify service dependencies and visualize that for you so you can understand the blast radius of the incident. Rover ops can then automatically look at validating the root cause for you using all the observability data that you've pulled in from all your connected tools and it will proactively surface the most likely causes give you confidence levels right in the incident command center.

50:51 Another great place where the code intelligence skill comes in very handy not for generating new code but looking at existing code when it maybe it goes wrong. >> Yeah. The whole system of work coming together here. Um and once it's confirmed migration steps are automatically generated for existing playbooks. >> Mitigation steps. >> Yeah. Yeah. Maybe a migration. >> That's what we need there. >> Um and status updates could be also published automatically. So once the incident is resolved, a post incident review is created in Confluence along up with follow-up tasks for both your humans and your agents like adding a dev fix to the backlog in Jira. So from alerts all the way to fix, the team graph has your teams covered at every single step of the incident. In fact, Forester Consulting recently did some research and found that on average teams adopting the service collection save over 55 minutes per incident. So that was just one example. The incident command center of Rovo and the team of graph use used together to achieve amazing things >> and credit to the service collection team. There's a lot more in the service collection that's coming in terms of things you can build in the team of graph. You'll see it elsewhere. Oh, thank you. There's a quick applause there. Uh just a quick shout out. Do go to all of the solution keynotes cuz there's far more this year in every single one of those solution keynotes. Many many other demos using AI the teamwork graph and all the things for those specialized teams. That's just one of the many things in service collection. Uh we got another type of team >> another type of team that's close to my heart Mike uh product teams. So with all that throughput you're seeing from engineering they're producing code faster than ever before. That puts a lot of pressure on product managers designers uh leaders to figure out what are we building next. So, for all you product teams out there, we're announcing a brand new app today called the Feedback App.

52:43 So, the feedback app joins Duropart Discovery in our newest collection, the product collection. Let me briefly show you how this works. So, with this um with this uh feedback app, it's powered by the Timber Graph. So feedback flows in automatically from all the connected sources that you already have in the graph. Example the customer service management app as well as other Atlassian sources such as Salesforce, Zenesk or Microsoft Teams as well. And you can also bring in additional data from other tools like Pendo or any MCP of your choice. But here's where it gets really powerful.

53:21 Robo can automatically generate new ideas straight into your Juro product discovery backlog based on those insights and then associate them with goals in your goals app automatically. So you'll hear a lot more about this in the solution keynote for software teams. So be sure to check that one out. So, we've shown you a little bit here what's possible, a small taste of all the things that are possible inside your Atlassian apps with the teamwork graph. And you can harness that context across all the types of teams that we help technical teams through to service teams, product teams, leadership teams, and all your business teams.

53:59 But all that context that you've built shouldn't be locked inside one platform. Your teams rely on apps everywhere to get work done. and Atlassian has always been super integrated with all the things that you do. So in a big step today, we're surfacing that context through the teamwork graph to all those other applications with two big announcements. First, we're bringing the teamwork graph to the Atlassian MCP server. So any app Oh, thank you some MCP server fans. It is one of the most used MCP servers on the planet. um any app, any AI app or tool that your team's already used can tap into all the context that you've already built from your whole history.

54:40 And second, we're introducing the teamwork graph CLI for two types of people. One, those who know what CLI stands for. I see we have some of them in the audience. It's command line interface. And for all your agent harnesses, it's a different door to the same context. Mike, I think this tells you a lot about uh the organizations that will succeed in the future with enterprise AI. The ones that will succeed won't be the ones who lock back their context and lock it away. It'll be the ones who make sure that context is available for all their teams. Making sure that context is most reachable, most connected, most useful in all the apps they use. So, Mike, those two announcements, which one do you want to start with? Look, let's start with the MCP because Sherie, you got your black t-shirt on.

55:30 >> So do you, by the way. >> So do I. You've got the cool glasses, though. And I know you have strong opinions about whites space. And you're pretty much like a product designer. So maybe embrace that power, fire up our MCP server, and show me what we can do for designers uh outside of the Alassian platform. Yes, folks. The running joke at Alassian is that I'm a bit of a wannabe designer. So I'll totally happily play this role right now. Um, so let's pretend I'm a designer and uh over here folks, I am in Jura and uh I've been assigned this Jura work item to design a new analytics dashboard for a sales retail company. I'm new to the team. I don't have a lot of context. I can sit here and scroll through documents or find them. Um maybe read through a lot of related tickets. Um but let's get the teamaph to do the hard work for me. I'm going to pop out to Figma. Shout out to our partners at Figma there. And I'm going to show off their team uh the Rover MCP using the team graph here. I'm going to simply ask Rovo here to please design a custom analytics dashboard uh for my for stack retail and just give it the Jura work item.

56:33 >> Now Mike, don't forget to say please. Always >> I noticed you did say please there. It's very it's very important. You never know one day like it's good to be polite. Always good to be polite. Good manners. >> Always tell tell my kids to stop yelling at the Google Home. Google play. I'm like be polite kids. >> Good way for us to just spin through a tiny bit of the thinking time here. Figma. It is a live demo. So thank you.

56:51 And you can see there it started to call the teamwork graph context and is looking through all the details. So let's jump into one we ran earlier this morning. And so you can see here it's called um the team graph context tools >> multiple times right >> multiple times traversing the whole tree but it's given us a boatload of context uh everything from multiple related work items confluence specifications Google documents and it's used that context to make my first version of my prototype a lot better. Um, incredible power right there available uh in MCP in any of the apps you want to connect it to.

57:24 >> Yeah, it's amazing. What we've seen is uh taking Figma make coded up the design using the relevant tokens from all of the team graph, right? One prompt, one work item, and the agent did what would have taken maybe half a day of different Slack messages, contacting colleagues, and tab switching, especially if you're new to an organization. And that's an example of the teamwork graph working for your knowledge workers, designers, marketers, product managers using the Atlassian context inside our partner apps. What about those technical teams though? Take the designer glasses off.

57:56 What What do we got for the back to the CLI folk? >> Let's jump to the CLI demo. Um, all right. Over here folks, I've got a terminal open. And um, the command line interface can just be invoked simply by just typing TWWG T-word graph. And >> you should say that the the CLI is built for agents primarily. >> Yeah, that's right. Right. So, we just type TWWG and we get all of these uh commands immediately.

58:18 >> There's a lot. >> Pretty hard to read all that. >> There's a lot there. Yeah. A a really good pro tip is in claw code or your agent of choice, go up there and just ask it, hey, tell me everything you can do with the CLI tool. Give me some ideas and it'll run through uh the all the tools in a lot of detail, what you can call with examples. Um as well as, you know, give you a really good insights on what you the Lego pieces that you have to play with. So, what's an example here? Over here, Mike, we've got um the team graph documents. So, not only your Confluence documents, but all your connected sources, your Google documents, your SharePoint and so forth.

58:49 One you and Tamar were talking about, Mike, the or tree. That's right. So, the teamwork CLI currently comes with more than 380 tools and commands that all of your agent harnesses can run to access all the parts of the teamwork graph. And they're incredibly smart about knowing what it is that they need to call. In this case, we talk about people a lot. We think they're incredibly important. Yes, we can call individual users and teams. Uh you can look at your collaborators across all of those different surfaces. We have a great new algorithm. It's continually trying to work out who your closest collaborators are. Or in this case now, the org tree command, which is incredibly new. So it will come and tell you exactly your org structure, whether you've connected to active directory or workday or wherever your org structure is stored. Why is that important? Well, your agents might want to ask something about someone in finance connected to some piece of knowledge. They might want to ask about anyone that reports to Sharief and what have they done in terms of pull requests recently or whatever it is. Your agent will be able to switch those tools around. Um, and it's an incredible new tool to look at skill, sorry, to look at your entire or structure.

59:48 >> Yeah. And so one of the tools that we also have in the team at Graph CLI is a visualization tool which comes in really handy to investigate the hard things or if you're trying to work out how did your agent get to that conclusion. So let me go through an example of using all of these things together. So here I'm in uh claude code and I'm going to open up a juror work item here that someone has assigned to me. I'm the architect that just just joined the team and we've got some session management issues uh with a particular app. Now people are sometimes lazy um on this particular work item. I have no context.

60:22 Someone's like, "Hey, please go fix it. Go work out. Go work out what wrong. Please fix it." This happens all the time, but all the knowledge to help me fix it and understand what happened probably already exists across the whole organization. So on the left here, I'm going to ask claw code a simple question. Hey, why don't you uh research uh Jurro mobile uh the jur mobile 63, find all the comprehensive work items for this context and visualize it for me in a clickable graph. So Claude goes off to work here using the teamwork graph CLI to help me answer these questions.

60:56 Now, this takes a few minutes to run. So, here's one uh I had run earlier backstage >> and a good example, I should say, of the messy work world that we talk about. We're giving you that nervous system to handle the messy work world. There were no links in that issue. It's not connected to anything. >> Yeah. >> At all. >> So, over here, it's looked at the work item uh and it's understood what it is, but then it's found a lot of related people and teams that I can talk about, talk to for more information. uh uh everyone here also that's working in that team. It's also got ooh a lot of you very useful artifacts that I didn't even know existed were not linked on that juror work item uh previous PRs that may have caused the issue branches in GitHub designs and Figma confluence and so forth. There's a lot of context here to understand. So how do I make sense of this all? Well, let's check out that new tool I was talking to you about that's also available in the teamraph CLI. It's created the visualization for me like I asked it to. So over here we've got mobile 63, the work item we've got, and it's found multiple related jerk items that I did not even know existed.

62:01 So on mobile 59 here, previous uh work items someone worked on. We're getting a lot more context. We're getting previous pull requests, the GitHub branch where this change happened before. We also oh the design so we can understand how is this intended to work. We can dive deep for details. Take a look at the design. We also have people in teams that I can talk to to get more uh information. So I can open up this team and go which team can help me with this particular task.

62:34 Ah it looks like uh this is the team members on the mobile infrastructure team and uh agents uh that are available there as well which is pretty cool. And check out Mike. This particular work item was also worked on by claude code. So even your agents contribute to the graph view. >> Oh man, like that that is just an awesome example, right? What we've seen there is an awful lot in a very short space of time. Firstly, the CLI 380 tools available to all your agent harnesses to access the context that you're growing and compounding every single day across all of those tools.

63:05 Secondly, it's used inference to figure out related items in that case which weren't actually connected because your organizations are probably a little bit messy on the inside as are ours. Um, and lastly, the visualization tool. So, Sharief has visualized a small fragment of your giant organizational graph which helps him do that particular job. Can help you visualize, move around, there's a lot more that it can do. So, that's a pretty incredible set of information. take in scattered information, put it all together, put it into a CLI that's available for your terminal, your pipelines, or any of your agent harnesses. So, pretty nerdy and I do love a good terminal demo. I appreciate it's hard to do a demo in in a terminal shar uh but when you sum it up, people are going to be asking what's the business value there, right? What is the ROI of the Teamwork graph? Well, it's pretty simple. When you connect the Teamwork graph CLI in this example to any agent or AI or or agent harness for any non-trivial query, you get instantly better quality AI results from that app.

64:14 You'll get faster results and most importantly, you'll get cheaper results. How do we know this? Quite simply, we have benchmarked it. So in our internal testing, we're seeing up to a 44% improvement in answer quality in that case in clawed code when you attach it to the teamwork CLI because of the knowledge and the context that you already have and up to a 48% reduction in the number of tokens used. That's straight money.

64:48 pretty amazing results that'll make your CFO very happy and you can be sure that we're going to keep improving your context every day which will continue to improve those results. So, all right, I think it's time we grief a break. That was a marathon of live demos. Thank you, champ. >> Thanks, folks. So, we've seen how our MCP server can help surface context in your apps like Figma and how the CLI can surface that same graph in your terminal or in your agent harnesses like claude code. But there's one more way that we're bringing the teamwork graph to everywhere that you work. We're surfacing it in your browser.

65:30 Now, a browser is where I'm guessing everyone in this room spends the majority of their time. It is the broadest expression of your context. No single app can see what a browser sees about your particular day. It's your tabs, it's your history, it's your bookmarks, it's on your machine, and it's the apps that you keep coming back to every single day. So just imagine what we can unlock when we pair the power of your organizational teamwork graph with that browser. And that's exactly what we've done in our amazing browser called DIA.

66:07 Oh, just just wait till you see this. So to show you it and to introduce DIA, I need to bring up Josh, the CEO of the browser company of New York. Mike, I'm so excited to be here. >> I can tell, man. Uh, I've got to say, I've been using deer for a while now, personally, and I'm having way too much fun with it, as probably everyone at Alassin will tell you. Uh, but I think you should be the one to give everyone a tour here. Uh, a couple minutes. We want them to see what I have been seeing in DIA.

66:42 >> Let's do it. DIA is a browser for people who work in their tabs. There's a vertical sidebar. If you're like me and you have a bajillion tabs, really small at the top, you can now read all of the tab titles. We have special app integrations with apps like Google Calendar, Spotify, can change a song. And of course, tab groups specially designed for projects and people who have projects that span all of their tabs. The point is Oh, >> ding.

67:15 >> Live demos. Is that the morning brief? >> That is the morning brief. A little bit early, but let's do it. >> My favorite feature. >> This is the morning brief. The point of it is simple. You should not need to prompt AI. DIA should just proactively help you. So, every morning I get a cup of coffee. I open my laptop and Dia presents this beautiful memo for me personalized to me and my day ahead. So, there's a piece of artwork, a little oneliner about what I got coming up. And then Dia says, "Hey, if you got if you do one thing today, please do not miss this." And it even proactively offers to get started on that task for me. Down below, it has all my to-dos, which it's pulled from my apps, my tabs, the teamwork graph, Slack. And I got to say, it is very satisfying to check these off. And then my favorite bit overnight while I'm sleeping, Dia goes and make sure it's checking all of my apps, all of my files, so that when I wake up, I know anything that changed overnight. I don't have to check anything. It's waiting for me. But I got to say, the best part of all of this is are not any of these features. It's the feeling it gives me. I start my day calm, focused, dialed in, and ready to go.

68:28 >> Now remember, Josh didn't ask for this. Dia is the first browser that goes and does work for you. It's working for you. It did that autonomously in the background. It pulled from his uh his calendar, his open tabs, what he had browsed yesterday, uh his Slack or Teams history, and the teamwork graph, his organizational context from the whole of his uh company, and built a briefing specifically tailored for him for this particular day. And the best part about this, there's no setup required because it's my browser. It already has my tabs.

69:03 I'm already logged into my apps. But Mike said he's having a lot of fun with this. I want to show you something kind of wild. So DIA is a browser, so of course it can open tabs. But DIA is the first browser that can make tabs for you, too. It can generate personalized web pages on the fly for whatever it is you're working on. Let me give you one example. So this is my first team, and this is incredibly intimidating. How much is going on? all the events and on the plane number I said, "Hey, Dia, I I want to show up for Mike and Alassian, but I also kind of want to go to Disneyland, so can you please check out everything going on and make sure I don't miss anything important, but then find a moment that I can sneak away to Space Mountain." So, I haven't been there since I was 10. So, what I did is I came over to that tab group I mentioned. I rightclicked and I chatted and said, "Hey, can you make me an interactive web page?" So, it's been a long keynote. I'm going to skip the loading and just show you what it made.

69:57 How nuts is this? DM made me a personalized web page on the fly just for that task. It's interactive. I can click between the different days. You'll see it called out conflicts that I have and need to resolve. If I come over to today, it knows that I have to be on stage for this keynote. It actually knows that I have coffee with my mom after this who's in the audience. Shout out mom. Uh thank you for clapping. That made my day. Um, and then if I scroll all the way to the bottom, you'll see it even found time for me to go to Disneyland and said Space Mountain is better at night.

70:31 >> It knew everything that's going on at Disney. How did it know that? >> Oh, it searched the web. It searched a lot of things. Let me show you. But it it's nuts. Your browser can now make web pages for you. It's never been able to do that before. >> So, super cool. Show us a little bit about the trace of what happened in the background there. >> Yeah. So, this is what I did on the plane. Build an interactive itinerary.

70:48 And of course, it read the tab group and read all the tabs in there. It also had the foresight to know, okay, I should probably check Josh's calendar. He has internal obligations in addition to the team uh team conference. It searched Slack. It searched the teamwork graph. It even asked me a couple questions like, do I want to go alone? Do I want to bring my mom? Mom, sorry, I'm going to go alone. Um, so it just made sure I had everything that it need had everything it needed to do the perfect job for me. And it works out of the box.

71:15 No setup required. So, what's important there is it had the context of all of Josh's apps, right? It's what I loved the best about deer. It had it understands that tabs aren't just web pages that you're browsing. They're places that you do work. They're projects, videos, conversations. They're apps like Google Calendar or Gmail or whatever it is. And you have a lot of personal AI context. Josh is browsing what was on at Disney. He's browsing the Team 26 website. And it can combine all that with the teamwork graph to surface the context and make very personalized experiences exactly as it did just there. Took his calendar, took what's on at Disney, took the team 26 schedule and made him a personalized app that worked between the three of them, helped him find some problems and spend some time with his mom. Uh, now the deer team has invested a huge amount of time in crafting a browser. everything from the graphic design to the joyful animations to the beautiful sounds that come out.

72:14 But I know a lot of you are thinking in this room, okay, is this just for kind of the individuals? Is it ready for their teams? What about the stuff that their their chief security officers care about? So, I actually had Dia generate a report for me on this. This is why I'm so excited to be here today. DIA is now ready for teams of shape of all shapes and sizes. We have SSO, SOCK 2, type 2, MDM support, even uh industryleading prompt injection technology and coming soon to enterprises with guard integration and so much more. So for everyone in the audience, you can go to dab browser.com today and start using it right away and we're going to be rolling out to all enterprises very very soon.

73:01 Now, as an enterprise, when Dia uh joined the Atlassian family six months ago, four people internally were allowed by our security team to access it. >> Awkward. >> Yeah, it was a little awkward. Uh today, that's kind of the point. We have more than 10,000 Atlassians using DIA. Indeed, every single demo that you've seen today has been done in DIA. And we want to bring DIA to all knowledge workers. It was important to us that DIA inside Atlassian doesn't get any special treatment. They have to satisfy every normal IT security requirement of a large scale public company with a lot of governance requirements. DS security and data controls a top tier and now we can do that and roll it out to thousands of people.

73:43 >> It really was our first day at Elassie and our first week at Alassie and the security team tapping me on the shoulder and going hey welcome four employees allowed to use it. It was we put a lot of time in getting it ready for all of you. Uh and so excited to roll it out to all enterprises soon. I know I'm going to get kicked off stage very very soon. I just want to say there is so much more DIA can do. It can create slides for you based on your data. You can query dashboards without even pulling them up.

74:06 Mike last Saturday sent me a loom on Saturday. He made these like weird interactive games using teamwork graph data. You should do something else on a Saturday. But the point is there is so much more to show you. Jem on our team is giving a whole talk about this if you're interested. I'll be out at the booth. I'm just so happy to be here. Thank you for listening and hope you give D a spin. >> Uh you might even see me down there.

74:33 I'll build you a mind sweeper clone that'll help uh show you a teamwork graph and all sorts of new examples. Something >> it's amazing. Play with it. You'll have a ton of fun. Uh the point is though that your browser is with you every day. every page you write, every document, every meeting, every rabbit hole you go down, right? DIA is the first browser that actually understands all of that. Not just what's on your screen, but what it means, how it connects together, and proactively does work for you to try to make sure that we're moving your organization forward. It's absolutely truly worldleading. You'll absolutely love it. Huge thanks to Josh and the team.

75:08 >> Thank you, mate. Of course, it's not just deal. We know that trust and security, as we've just talked about, have to run through everything. So, we've made a whole lot of deep investments across the entire platform. Let me just tell you three of those areas. First, data protection. If someone is not allowed to see it, AI, the teamwork graph robo, won't share it.

75:38 And Atlassian Guard goes even further. It'll now block sensitive content before it ever reaches a model across prompts, conversations, connected apps, and you just heard its connection with Dio. Second, agent governance. Super important. You control who builds agents, what they can access, and what they can do. For agents that run autonomously, you can now give them agent accounts. each one with a dedicated identity with explicitly scoped permissions to do what it is that you want them to do and have access to what you want them to have. So every action and every skill is accounted for and stays within the exact guard rails that you've set. And third, visibility audit logs now on every AI action, every skill invocation and usage trends to prove the ROI inside your organizations.

76:26 We're about building AI on your terms. As our Robo capabilities continue to grow, so do the controls that grow alongside them to help keep you protected now and into the future. Now, one small thing before I send you on your way. Teamworkgraph.com, it's now the front door for all things teamwork graph. So, you can go there to download the CLI in a second. Single command, paste it into your terminal. You can plug in the MCP server for Figma, for cloud code, chatgpt, whatever you want to AI apps you want to connect to MCP. You want to find those connectors to grow your team of graph.

77:05 That's how you get all of that context into your tools that you live in today in one simple location. But it's a little bit more than just a download page. It's now a place where you can experience your own organization's graph. So when you sign in, you'll see how rich your graph already is in your existing Atlassian platform. It will show you the work that you've already done, the apps, the comments, the relationships that make your graph so powerful. It's the first time you can actually look at the thing that's powering every single demo that we've given up on stage here.

77:41 Below that, you get to see and explore your personal graph. Spot work maybe you didn't know was related or the contributors that you didn't know were involved in that work. You can see how your whole graph builds and grows upon each connection in real time, giving you and your AI agents, insights into your collaborators, focus areas, all the things that are important to you. Pretty simple. Visit teamworkgraph.com. We've made the invisible visible because we want you to be able to experience the graph for yourselves.

78:13 Now, as you do, I want to come back to very quickly something I said at the start of today. You aren't just choosing software. You are choosing what kind of company you want to become. Every one of us in this room is facing that question right now. And I can tell you the answer is not in a strategy deck. It never was. The answer is in a system of work. how your organization works. Here at Alaska, we've been betting the company on it for more than 20 years. And today, you saw that come even further to life. You saw your context growing, compounding across every action that you took, every new tool you used, every connector you you you put in, and every piece of day-to-day workflow that you uh underwent. You saw it harnessed in our apps. Agents that didn't just assist, they acted. resolving incidents, unblocking project, taking ideas through to production, giving you those decision paths, etc. And you saw it surfaced everywhere, not locked in one tool, not trapped behind one login from robo chat through to our apps, from your terminal all the way through to the browser, wherever your teams are. That is how we unleash the potential of your teams. And finally, simply a big thank you from me.

79:27 I know we have thousands of customers here this week and without you truly Atlassian wouldn't be who we are. We wouldn't get to make all of these wonderful things. So genuinely from me, from everyone in the Alassian team, thank you for being here and thank you for your continued trust. And with that, thanks to everyone on the Alassian team. Thank you all. Enjoy team 26.