The SDLC You Build Yourself — Andy Wong, Andrei Bocan & Shane Wolf, Atlassian Transcript, AI Summary & Key Points

00:13 Welcome everybody. Welcome to the AI Engineers World Fair. Uh thank you everyone for joining for our workshop today. Quick introduction for myself. My name is Andy Wong. I've been working at Alaskan for four years now and I've been working in the AI space for two and a half. Joining me today are two of my wonderful teammates. We have Shane Wolf uh manager down in Devi space as well as Andre Bohan down here as well.

00:33 So today we're just going to go over a lot of the things how how the agentic systems are kind of changing how we are uh handling the software development life cycle and how orchestration does change everything. Can you repeat? >> Yeah, he's Shane just is gonna walk around give hand out numbers. Uh it's going to be a little bit weird at the beginning, but once we finish up the uh introduction talk, we'll be it'll make a lot of more make a lot more sense after uh after the kind of like the introduction talk kind of

01:12 finalizes up. Um but we just want to quickly get started on the first intro slides. Um I do want to run through these probably quickly because since it is the AI engineers welfare we want to build. So I just want to give a quick rundown of everything we've been kind of building in Atlassian for the last I would say one and a half years or so. Uh just to give you like a highle overview of all the tools we will be offering you within Atlassian itself.

01:38 So I think as as you guys might know AI agents have kind of changed the software development life cycle like drastically. Um, as time kind of goes on, you can kind of see that as we have more AI crewing tools, kind of productivity gains from those tools are kind of tapering um, as we have more and more tools coming out. And thus us at at lasting we believe that what our biggest opportunities are tapping on the on the everything left and right of code.

02:02 So everything involving planning, orchestration, uh, the code review process as well as the operations part and such. We've been developing a lot of the things that um we can say like are not as apparent during the software development life cycle. Uh we have things such as the teamwork graph coming out as well as a bunch of different agents that we've been trying to do left or right of code um such as AI planner, AI autodev, reviewer, s sur um and and central of all that is something we call the AI context machine.

02:32 Just to quickly run through all those tools, what we we what we will predominantly be testing out today is something called the Jura coding agent. Uh that's our that's the Atlassian coding agent natively uh that has deep organizational knowledge within the Atlastian ecosystem. So it's good it is a deep enterprise context agent that has uh a deep understanding of everything within the elastian ecosystem whether or not you have juror tickets confluence documents um it will read through email slack slack uh slack messages

03:01 as well um to be able to gather all the context it needs to kind of plan out and implement a specific jur ticket that you might have uh within the jur coding agent itself uh you are able to uh interact with your pull request within the Jura ecosystem itself. So you everything is everything you'll be able to maintain and edit within the cloud. Addition to that you'll be a be able to assign your work to specific agents directly.

03:32 Uh so if you don't want to use your juring agent that's fine. What we do provide for you as well is you are able to assign to uh agents such as claude cursor codeex whatever agent you personally prefer. uh you are able to assign the jur agent jur ticket to um during during that process as well. And finally we also have SDLC automations as you some of you might know we do have an automation process within Jura itself.

03:58 Uh whenever there is a condition met you are able to generate like you are able to generate code uh using the jur coding agent or whatever agent you you personally decide to use. As we go further right through code, we do have the AI reviewer. Uh something similar to kind of similar to a lot of tools you've seen in the in in the industry today. Uh we are trying to implement uh a review of your agent to kind of go over the pull request to catch bugs, enforce standards and verify requirements every time you do make a

04:36 pull request itself. So review does go beyond the code. Again, we are trying to build something that uh retrieves all the context within Jura Confluence, whatever SCM you kind of use as well as Slack to to develop like kind of like a holistic view of what your code PR needs to implement um and provide a review on that end. On top of that, we do have centralized me uh manage standards.

05:01 Um every team, every organization kind of has their own standards that they want to implement in each uh each repository. Um it get it gets kind of tedious and what we're trying to do with standards is trying to trying to make it a lot easier for managers, software engineers to implement those standards in all in all the poll requests that come within a specific rep repository.

05:26 [snorts] And as and additionally, this will work on all SCM platforms whether or not it's GitHub, GitLab, Bitbucket Cloud. um you will be able to apply standards to your your poll requests. One thing we are trying to do at Alaskan with our AI reviewer is we're trying to reduce the amount of like amount of noise that comes from the code review process as a whole.

05:46 [snorts] Uh what we do find is that some of the code review processes uh do provide do are maybe a little bit overly nitpicky or like you know provide a little bit too many comments that are not of a high value. So we what we've been doing at Devi is we uh train a few models ourselves filter those and what we have done is use those models to filter out uh let's say low value comments or like noise that we have deemed uh and try to and filter those like unneeded comments during the poll request comments itself.

06:16 Um as a whole we have seen that there has been a higher quality uh comment resolution as you can see the bottom here. uh using these models we have been able to get around 40% uh human code resolution rate which is rather close to human baseline of 45 and as always we can always measure and you know how see how impactful all these tools are. Um at lasting we have been building a lot of like dashboards for our various tools to see how many reviews or how many code like uh how many agents you've run uh to kind of give

06:47 you an idea of what the value of all of our tools are uh within the devi space. Going on to a few like the final few few products we've been implementing recently. Uh something called AI autodeev. Um it's pretty self-explanatory what it can do. Uh it goes through your jur jura board uh identifies which uh which which tickets are I would say able to be able to be autonomously ran.

07:14 Um applies all the standards initiatives um and then creates a poll request ready for review. Um, all of that is triggerable within the Jira ecosystem itself. Um, you can configure all a bunch of different needs such as maximum number of credits you want to use per month. Uh, maximum number of like tickets you want per per user to run autodev on. Um, and it's able you can also specify which repos you want to have this running on or if you want to run it on all repos within your your organization itself.

07:45 And as always, we have a dashboard to show all the uh all the autodev runs, how it's kind of working, um how how many autodev pull requests are need to review and how many story review requirements. As always, we're trying to give you like as much uh metadata as possible for the engineers, for the teams to really understand like how how autodev is overall performing for you.

08:06 [snorts] One other thing we do have autodev integrated on is slack. So once you do have uh autodev integrated within the repository itself um it will start working on a specific work items based on whether or not it's available it's able to use um or if there's any items that you need to uh any input to finish them more ROI insights. Um as always we'll we will try to measure as much as possible uh to see how what is the overall ROI you are seeing from autodev as a whole.

08:38 And finally AI sorry uh we've all been through incidents we all understand how difficult is to diagnose and kind of go through it especially at like 2 3:00 a.m. at night. Um what AISR aims to do is try to grab all that context again um within previous incidents jura confluence um and try to root cause why an incident is happening and provide suggestions of how to remedi uh remediate the specific uh specific incidents once they do come out again it is integrated with Slack uh so you should be able to get a quick summary

09:12 of what's going on uh understanding whether or not it's impacting multiple external customers or if it's only impacting a small section of customers and it will provide you uh detailed reason detailed uh detailed steps of how to possibly mitigate the issue that you could you are seeing as you can see here uh it will provide like like I said we'll provide root cause analysis um it gives you a bunch of different theories of how to possibly remediate the issue as you as you see as you see it coming I've been saying a lot

09:44 about context and how the context will be helpful for agent itself. What we've been trying to build here is the AI context agent. Um, many of us have been working in large monor repos and all that context is scattered within the whether or not it be different different repositories itself or different pieces of documentation all throughout whatever uh all whatever workspace you kind of work in.

10:06 What the AI context engine is aiming to do is try to grab all of that and create, you know, like a standardized context for you for all of your agents to kind of run in kind of like a more efficient way. Um, I know I've said context a lot. That's the biggest thing we're trying to do in the last year. We're trying to grab all the context as possible and try to create uh a really powerful engine for us to power our agents in the future.

10:28 Um so as you can see here AI context engine all of our tools Jira confluence um all of our previous instance everything uh will be will be run using semantic semantic code search and we will be able to provide all that context to whatever agent you kind of use in the future. What we're trying to do is also make a lot of our tools like uh plug-in accessible.

10:49 uh you so hopefully what we can do is uh allow the allow the engineer to simply just add the tool to their browser to their to their IDE or to their CLI and allow them to use it out of box as simply as possible. As I said, I wanted to keep this really short because again we're at the AI engineers world's fair. We want to build. So what we got to do here, what I do provide for you here is a small workshop for you to play around with Jurro coding agent.

11:14 Um, you might see Shane walking around with a specific number uh specific number uh and handing handing that over to you. Um, that is going to be your account number. So, let me just quickly go over here. How this will work is that that number will be tied to your login and the login is the following. Apologies, some Wi-Fi issues. So, it's not just me.

12:09 All right, got it. going back to this just so just so everyone does have the QR code available to them. Yes. Yeah. Yeah. Yeah. Let's see. Yep. Let's see. I will stop sharing my screen. Yeah. Yeah. Yeah. I'll do this. >> Oh god. Yes. Not quite. Is the internet really slow? Let's see. Oh, the Wi-Fi is quite slow. Did you connect to this? >> Oh, there you go.

14:09 network changed. >> Do you still have cloud fire on? Try try turning it off. >> It's off. It is off. Let's see. How's it going? >> Yeah, we're trying to do right now. >> Yes. Yeah. Yeah, that's the Wi-Fi. Is that not working? >> Yeah, it's not working. >> Okay, gotcha. >> Yeah, I'm going to try mine. >> Got it. Yeah. have any questions that we can [snorts] talk through while we're figuring out Wi-Fi.

15:49 Oh, fair, but also like um >> or stand right in front. >> Stand right here. Okay. So, while we're figuring out Wi-Fi, as I was saying, anybody have any questions that we can talk through in terms of I don't know where we're thinking of going with like the the product line, what stuff is available, what stuff people are keen on, workflows, so on. Uh, happy to go through any of that.

16:16 >> Sorry, go for it. >> Tell us a bit more about the context engine. Okay, so essentially with a context engine, so um we have a number of I guess graph databases inside of Atlassian that connect to all of the data that you end up plugging into an ATL, uh into your Atlassian products as an Atlassian customer. So like Confluence, um Jira, Bitbucket, all that stuff is sort of connected together.

16:43 We ma in make those connections either through inferences as you use the product or you can like have the connections be explicit. So like you're working on a Jurro ticket. The Jurro ticket is based off of like some spec document that is in Confluence. We also link those to like third party um um what you call a third party product. So like we will integrate with things like notion.

17:08 We'll integrate with things like Slack. All of that stuff kind of wraps into this whole tapestry of context. And at the end of the day, the thing that we're uh we're building now is how do I word this? Uh so it's it's a gentic search on top of all of all of that context. And as you're this happens a lot like internally in Atlassian like we end up building on one side of the uh on one side of the ecosystem in our platform and that needs to pull in context from either some other team like some platform documentation.

17:42 You have uh some other initiative that's happening on the other side of the organization that somehow happens to impact this bit of what you're building on. and through the context engine all of that stuff gets pulled into um your current agentic coding session like that is the stuff that Shane who's currently working on Wi-Fi so connectivity basically is his through line um that that's the stuff that's that he's his team's working on the things that I think we've had a lot of success with um has been uh so we have a

18:17 new code search product that we're um we're launching um that's made it so that I think historically we've uh we've seen a lot of teams try to do like opt for monor repos in order to like have all their their agents like have the context available at all times that has some shortcomings and like means building out like a bunch of tooling uh stuff gets slow I don't know internally it's been the proc opting for a monor repo ends up meaning that you're opting for the debt of having a monor repo building the tooling around

18:54 it. You spend a lot of time building tooling to build the thing. Um, and I think with a with a code context engine, we've been able to like just have product repos like monor repos because you can pull in that engine at hawk. It's super fast like you get code level um code level access and that means that any logic that you have that would otherwise be potentially stale, right?

19:18 So like a conflence document is just a snapshot of a thing that happens once but like the truth is in the code. Being able to like access that at high throughput and like having uh good search semantics around it is been really useful. Sorry that was like very soap boxy. Does that did that answer your question? Okay cool. Anything else? Please. >> For example, if you have a GR >> uh sorry, so I'll I'll restate the question.

20:10 So the first question was uh if we generate the context on the fly. Sorry, what was the second one? >> The second one is we get conflict information. >> Oh, how do we resolve conflicts, right? Because like Yes. Okay, that's great. Uh and I think there's going to be probably a bunch of talks at the conference about about memory. And I think uh that's kind of one of the the the hot spots of like agentic memory like you have agents that derive knowledge from like a bunch of different sources, but like at the end of the

20:36 day, you need a human to tell like yes, this is actually true. this thing is like incidentally true or like it's true for like this specific case. Okay. Um in terms of deriving the information um it's uh usually going to be uh you don't snapshot it to some extent because of that like I think the the value of this ends up being that you have the information on the fly.

20:53 It's available like it's the latest uh latest and greatest. um if you have it if you cach it then like it risks being stale and like there's all complexity around it in terms of um what the the way that we we've historically dealt with um authoritiveness I guess for lack of a better word um has been to provide that as uh as a feature for like the the human in the loop to like be able to say like yes cool this is verified this has higher authority than this other piece of content um that's harder to do with things like

21:27 I don't know juror work items. I think juror work items in general. You can do it but it's just more manual work and like the risk by automating that stuff is like oh you end up like having things automated automatically end up being authoritative which is weird because you can end up in that in that same situation. >> All right. Are we interneted? >> I think so.

21:52 I think we should be interneted now. Yeah. So, the p I think the page is like hopefully working for everyone now. Again, I'm going to put the I'm going to put the go link plus the uh QR code back up right now. Um, but just to clarify really quickly, everyone should have a number. Um, and that will be your login. Uh, so we will be using something called like Alaskan University.

22:12 So, whatever number you have, it should be student and include and and then the number of whatever number that like Shane provided you earlier. Um, [snorts] that will be your login. Uh, that will be your login to test the jury coding agent. um and kind of just play around with it within the juror ecosystem itself. Uh so please go to uh so from that confluence page you should have the link to our uh to our kind of like test jur workspace um as well and from that you can kind of just like build whatever you kind of like

22:37 um we do have a starter repository for you guys um uh it's called like Atlassian swag within bitbucket itself um I can show that in a little bit but please feel free to like once everyone logs in uh start playing around with it and start playing around with the agents directly. Oh, the the network is still not working. >> Oh, you are you on the same network?

23:09 >> I'm on the same network. Yeah, it should be on the It take a little bit of time. It take a little bit of time to load. Yeah, we we Yeah. Yeah. >> Yeah. Yeah. Yeah. I think we've been we we talked with the uh the IT folks here and to see if we can uh see if there is any any resolution on that space. I do apologize for that. >> Yes, I can put up the QR code again.

23:40 >> Yeah, no worries. >> Uh numbers, numbers, numbers. Yeah, sorry. Let me >> Did Shane go away with the numbers? >> Pardon? >> Uh where's Shane's numbers cup? >> Shane's number cup. Good question. One second. Need more numbers for the numbers. Okay. The best of numbers. I think that's a prime. Maybe >> I don't think anybody's able to connect to the Wi-Fi.

24:30 >> Yeah. Yeah. Yeah. >> I think that was like the the the great unsolved problem of uh 2026. And I think if if anything's going to save us from uh AGI, it's going to be the Wi-Fi. >> Or if they fix it, then we don't need savings, so we're good. >> Uh >> [laughter] >> just you gota you got to tear the traffic, you know, you got to optimize it. Uh, >> okay.

24:57 Until we figure out the Wi-Fi. Any other questions that I Oh, please go for it. >> Verification requirements. Okay. Sorry. So, the question was like, can we talk a little bit about the verification of requirements aspect? Um essentially that that boils down to uh when we have like a specific work item that uh the juro coding agents work agent works on that goes through and um like the juro coding the we expect the work item to have a checklist of hey these are the these are the acceptance criteria and as the juro

25:36 coding agent uh generates the code for that particular task it'll go in and like verify those specific things once it thinks it's um given that it has like a the way that the jury coding agent runs is like it has a full sandbox. It can install things, it can run things, it can do whatever whatever it is with that specific repository. So as long as you have the instructions in the repository in terms of oh this is how you run this, it can pop open a browser and then like go and like run that those verification steps.

26:04 >> What is the sandbox? >> Uh what is the sandbox? >> Where is it? Is it like hosted? >> Yeah. Yeah. Yep. uh >> or is it something that you can bring in? >> We don't have that ability yet. So it's like it's it's a it's a proprietary Atlassian sandbox. Uh it's again it's a container so you can mostly run like whatever it is that you want like it has Docker enabled and like the rest of stuff.

26:26 Yeah. Go for it. >> Just related to billing and that type of thing your own key. It sounds like some if you wanted to all of these agents can use your API keys with with anthropic and open or they currently hosted models. >> Uh right now you can use uh so the billing is through Atlassian credits currently Shane am I wrong? >> Uses an API key so it uses your own API keys.

26:55 >> So there's >> every one of these agents can be pointed officed wherever okay but they don't need to be >> yeah the juror coding agent supports like multiple uh I think right now we have uh all the anthropic models all the open AAI models and uh I think that's it. Do we have Gemini as well? I don't remember. >> I think we can guarantee both the cloud and the open AI models.

27:20 >> Yeah. Yeah. Yeah. It's credits. It's a slightly different but like Yeah. Same same thing. It's like pay as you go. >> On top of that, >> there's usage. Yeah. Yep. >> Is this all available now? >> Sorry. >> Uh jeroding. Yes, it's out now. It's been out for for a hot minute. Yeah, go forward sir. >> So mention credit usage credit like for the developer like at the end of the month in terms of from a company perspective like you need to report like what kind of work that you have done or it's just some >> uh the if I

28:04 recall correctly and I may be wrong I need I might need to follow up. Sorry, the question was uh how do we do uh credit reporting at the end of the month given that there's like multiple experiences that where people could you be using AI credits for right um if I recall correctly the way that that gets that gets split split out by product so like specific user has used a JCA with like this specific in this specific experience sorry JCA being juro coding agent right in this specific experience and like it uh that's

28:31 kind of the the level of um uh detail you get right now not like a test based. >> Yeah. No, no, not yet. um that is something that we're working on because at the end of the day I think the thing that like will uh it's kind of becoming apparent is as uh we have like more and more people using uh uh agentic coding uh you want to make sure that like the ROI is there that like people when somebody is working on something is going to going to spend like I don't know uh 1 billion tokens on a thing then you want to make sure

29:03 that like that is something that you actually want to have done as opposed to like somebody's open claw running somewhere off in the uh in the periphery. Uh, sorry. So, like what's the latest and greatest on the Wi-Fi? >> Uh, any other questions while AGI is coming? Yeah, sorry. Go ahead. Whoa. Sorry. >> Uh, login, Andy. There we go. >> Right. Right here.

29:46 So, there should be a link. You can just like type this out yourself. Uh, 1-ish-devo.atlassian.net. Um, with your with your number, uh, your login will be student your number at university. And the password is right here, uh, ZXC ZXC on the academy. We're going to get it wiped at the end of the um at the end of the session. So like if it's open. >> Yes.

30:12 Some questions matter. >> Doesn't matter. Okay. >> Thank you. >> No worries sir. >> I got you. >> So let me see really quickly. Let me come down. >> Yes. >> Oh god. Can you number you go to made with you? Made for you. >> Sorry. Made for you. Here. >> Made for you. Yes. There. >> Oh, there's >> this is the closest. Oh god. >> God. One second. Please.

30:54 What is which? What is your number? 95. Give it a second. Yes. >> Yep. >> That's a great question actually. So I think right now um Andre you actually I think you have a little bit more here. >> Uh Sorry, the the question was uh whether we do anything smart about model routing. So right off the bat with uh the geocoding agent currently I think we let you select which model like you want to route to.

31:46 >> Yes. But I think that's the uh you can also with uh um sub agents and skills you can decide like oh this particular uh skill needs to use like so and so agent. I don't think we do any sorry so and so model. Uh we currently don't have anything out uh that would do be doing like doing smart routing. That is something that we're working on with uh in in autodev to help you essentially figure out like oh this specific task this is a high cool level.

32:13 We've seen success with it and like give you the metrics in order to be able to like have that decision and make sure that that's a policy that you want to enforce or not. >> Please stacking that in terms of like you've got your agents then you've got your skills. Do we define the agent or agents preset by you and then we define the skills that they might automot?

32:44 Uh the skills are basically up to you like we have like a bunch of bunch of skills but specifically for the juro coding agent there's a um bifurcation to some extent between like rode dev >> uh as as they were called previously um and with the juro coding agent that's essentially we uh give you a harness and there's model selection at the top level of the harness and then with the skills you can figure out like right all right like this is for this specific task, for this specific skill, I want this model, this sub

33:17 agent needs to be using blah blah blah. >> Um, >> yeah. Yeah. And that is up to up to the model. >> Yeah. Oh, we're still doing Wi-Fi. >> Oh. >> Uh, okay. Well, we're still doing Wi-Fi, please. >> Yeah. Yeah, that's probably what we're going to do. Uh, sorry. The question was like, can we keep the demo site open for a hot minute so that like when people actually get to Wi-Fi, they can actually go through it?

34:04 Yes, I think that's a that's a reason to ask. Thank you. >> Um, >> how long will this be? >> We can keep it open for as long as we really want. I think maybe for maybe if like for the duration of AI AIS fair, >> uh, we'll keep the we'll keep the workshop open and so when we do have Wi-Fi, I do apologize for the Wi-Fi issues, you can kind of like play around with for yourself and kind of like play around with the different tools we we will we do have.

34:29 Uh, one thing to sort of keep in mind as you're like inputting stuff onto these sites, as you can tell, the login are like very secure. So, like don't put in anything that you wouldn't be comfortable putting on a GitHub gist somewhere or like on a a bit bucket snippet. So, yeah. Uh, keep that in mind as you're kind of adding stuff to and and messing around with all these things.

34:55 Um, all right. Any other questions that we can go into? Please, sir. >> What's the importance of cleaning up stale context? >> Interesting. Can you say more? Like what do you what do you mean? >> Atlas for 15 20 years. There's tons of product >> out of all the things. >> It's a good Okay. Sorry. So like the good question. Um the the the question is essentially so like you have a large organization that's been using Atlassian for like 10 15 years.

35:33 Um how important is it to like prune some of that stuff so like it doesn't end up showing in search results or like the agent doesn't end up picking it up while it's looking for random stuff to give you an answer to the question. uh [sighs] uh our experience has mostly been okay with this by in in large part because we tend to favor like more recent results.

35:56 Uh I think that's where authoritiveness sort of comes in where like if you have something that's been around for 10 years and it's still like the whatever tip of the spear is like that document for 10 years ago. um those I think we currently shipped a thing in uh well shipped a thing a while back in in um conference that's the verified badge like that's basically the thing that we use internally to say like yes cool this is like still the latest and greatest and that this context should be favored uh across others

36:42 Hello. Okay, awesome. I think we've had a few questions about standards. Uh, unfortunately, the standards aren't built into the demo itself. Uh, but I do want to take a little bit of time just to like show you how standards kind of work within, uh, the Robo Dev product itself. uh and kind of kind of like how we would be assigning standards overall uh to your specific repositories itself.

37:02 Um so just a quick demo here. Uh so this is the standards page. Uh we have a few built in already, but what I want to show you right now is like you know how to create your own standards. Um there's also a bunch of different templates you are able to select. Uh but what I'm going to do is just create a new one. Uh new standard. Uh give a quick description of how what the standards really is.

37:21 Uh so so this one would be design token enforcement. So if we go to standard standard name go to call it design token enforcement uh add the description itself and add the rules. So after you add the standard [snorts] and the rules itself you can specify which repositories you want these standards to be applied to. uh you can select manually all the repositories or repositories matching a pattern.

37:52 Um so as these repositories load you can decide okay let's apply to these following repositories and then after every all that is set up you can just create your standard um like I said within software we have our own standards um instead of trying to enforce it uh manually trying to like enforce that within the PR process itself what we can do is you can set up the standard set up set up which which repositories you want the standard to uh be enforced on and the robo dev jur code like the coding agent itself will be

38:22 running through the pull request process and adding those uh adding comments to see if those p if your pull request isn't adhering to all the standards that um are kind of the specified within the standard itself. Um a few other things I did want to demo uh something called AI planner as well as like autodev. Um we can't give a full-fledged demo but we do have what we do want to iterate that like you know we're trying to go from the left or right of code and I would say like AI planner and autodev are more on the left

38:50 of code. So, um I don't know if we have a full demo, but I can kind of talk about it with the uh slideshow itself. So, we're going to get start uh autodev. Actually, Andre, uh you've been working on autodev pretty heavily. Can you kind of like give us a brief uh brief overview of what the main goals you want to accomplish with AutoDev? Um, and kind of kind of works as he sets up his mic.

39:23 >> Trying to figure out how mics work. >> Supposed to touch it. >> Not supposed to touch it. Oh, there we go. Oh my god. Thank you. >> Um, all right. Sorry. Let me I'll just be here then. Um, okay. So, for for autodev, the this is essentially our for into like the AISDLC orchestration space. Um the thought process is to basically get you back some capacity from all the in order to let your engineering team focus on feature work like the uh AI the for autodev we the initial um the initial release is going to focus on

40:01 grabbing work items from your backlog and grabbing the ones that we feel confident that uh the agents can handle autonomously and then just putting up PRs for your team to review in order to make sure that like we're working on the right things that we're abiding by the right standards and then sort of iterating on your harness as well so that we can get code bases agent ready in a fairly light touch way.

40:23 Um do you want to oh sorry do you want okay and this this essentially kind of goes where uh uh establishes where where we're trying to go. So um you we right now again the definition is going to be like we will we will grab your backlog and then we will allow you to also help us identify which specific things you think auto you would like autodev to to grab.

40:50 Um we have some some thoughts around like where we would like to this to go next. So there's standards enforcement um and I think Andyy's going to talk probably talk about that in the context of the review. So the way that we think about that is I don't know standards are to some extent pretty similar to linting to some degree but they're a lot more uh loose uh when you have the uh agents enforce them.

41:15 We have one part of the I guess the the the linking workflow in forward-looking things in the reviewer right like you add standards to code reviewer and that takes care of any code that will end up landing in your codebase going forward. uh we have a workflow for autodev where we would be able to scan existing part like your existing codebase and then figure out how we land uh PRs progressively in order to get all of your existing stuff into compliance.

41:43 There's some complexity there in terms of like so nobody's going to approve a PR that's like change half your codebase because now you have a new standards uh or nobody's going to approve anything that like causes like a high amount of churn. So with autodev we want to be mindful of how much work we're actually hoisting on your team. uh nobody wants to spend time reviewing uh 50 PRs a day.

42:02 So like we have configuration for letting you say cool like my team wants to get assigned I don't know each individual gets assigned three five PRs a week and like those are the things so that makes helps you get like incremental progress and also frees up your team to actually do the feature work that they uh they actually want to do and like may move forward your u other initiatives.

42:23 Um and then yeah that kind of goes into like both initiatives as well. I think the the space there is I kind of think about it as organizational steering. So this happens a lot in uh bigger organizations where you have I don't know everybody's trying to move from whatever some version of Rails to a different version of Rails and it's a big thing and we have like 10,000 services and like those are pretty similar uh and some of the learnings some of the memory some of the workflows that you go through are fairly uh sy.

42:52 So you would want something that is concentrated on one team and they're just like spinning up PRs and like the agents essentially do the do the work and then you'd only have a human do the coordination and then we also tie the loop around that with uh some sort of memory. Sorry. So again that's that's sort of the vision that that's where we see this going and that's where like we see this being beneficial to our workflows because large organization bunch of services that's where we spend a bunch of time that like we

43:16 would like to not have engineers be involved in that deeply and we see agents as like a really big opportunity to to move that ball forward. I think you can grab on to okay um this is the other thing uh if uh autodev is doing work uh on your team's behalf autonomously you kind of make want to make sure that like you are aware of like what it's doing and want to be are able to interject when you want to when it's either picking up the wrong thing or like it's not assigning the wrong the right person and we will learn

43:47 from that experience and uh the reason why you want that to be in your jura board is because that's the thing that most people stare at like it or not when they're trying to like figure out what's the next thing to to work on. Um, and we were talking about this before uh just the um the the work in progress limit is the thing that we were that I was mentioning where you want to make sure that like your team isn't getting inundated with PRs or like other work that might not be necessarily the work that you want to drive

44:13 forward. Um the other thing that we we're also thinking through is um limiting cost which I think is very much uh in the site guys now. Like you don't really want agents running roughshot. Like if you want to spin up a a stream of work for autodev, you want to be able to set limits for how much we want to spend per ticket, how much we want to spend per week.

44:35 All that stuff seems like it's uh just table stakes at this point. Um yeah this is essent like the whenever we whenever our dev starts working on stuff we will um give you like this this broad reviewer like all right how much how many things needs uh oh man I I forgot to mention this okay need requirements ah uh for whenever we troll through backlogs there's um a very high likelihood that we have work items that are in your backlog that don't have that are underspecified like there's a thing that somebody put in like

45:08 that has just one summary line that says do the thing. Um those can be valuable but we don't know and we don't have the confidence that we can work on them because we don't know exactly what the goals of them are. And that's where like we will pull a pull in a human in the loop and say hey we have this work item. What is what actually is this thing?

45:26 Do we want it to work on it? Then can you help me specify it? And then once we get to a large good level of specification that's when like we can get to actually working on it. And that's where again needs review is when we pull in a human to to actually do the review. Um the other bit that we've also noticed with some of these autonomous workflows is the the the reviewing step is where things go to die.

45:48 Um because people again are uh busy with other things. Um, and that's one of the systems that we're putting together in order to reach out to people at the opportune time in order to pull them in and go like, "Hey, can you like take an eye to take a look at this real quick and like figure out whether um whether it's good to merge or like it's it needs to go away."

46:07 And the [clears throat] the chief vehicle for that is going to be Slack for for the the initial release of AutoDev. Um, and then again, we were talking about this, the ROI insights, you making sure that like you're getting um, you're not overpaying for the amount of work that we're doing and then it's work that you actually want to get done and being able to like sort of push the limits of like how much stuff you actually want us to look at.

46:30 >> Awesome. So, I think the one I think the one part that we want to touch upon again uh, really quickly is this part, the the planning part. Uh, so we do have one more product out coming out called AI Planner. Uh I would say this is more geared for I would say like um say uh if you have a spike or project managers or say you're a manager that wants to see if something is feasible for your current repository.

46:53 That's where AI planner comes in. you can kind of provide your idea of what you want to build and what the a planner will kind of do again goes through our context engine kind of figures out okay what is in your repository what is your plan and is it feasible and if it is feasible it will tell you and the second part is it will kind of give you a breakdown of what you need to implement um if you do want to build out the spec specific uh specific feature or the specific you know idea that you do kind of have it will

47:20 give you the plan uh and it kind of give you a few ways to execute it um talked a lot about the context engine. Um Shane is actually here. He's working a lot on code chat. Um Shane, do you want to talk a little bit more about the code chat capabilities and like all everything within like a context engine itself? >> Are there slides? [snorts] >> Are there slides?

47:42 >> Uh there's no slides. >> All right. Um yeah, so he was mentioning uh context engine code chat. The way we've been thinking about it and at at lastian we have many repositories. is I think we have 15,000 repositories across the company and asking a simple question like where do I implement this change or where is X Y and Z implemented or what services are using this other service are really really complicated and so at the core of the context engine it is narrowing down the retrieval and the search space to scope it

48:12 to like where this answer is most likely uh lives within the code and then reaching out from there and like digging into the code to answer the questions and then on top of that len and all of the um information from the teamwork graph. So documentation historically out ofdate and inaccurate. If you're searching only across documentation, you're going to get something that was true at some point in time.

48:37 Um but it's also a lot of really valuable information. So this project built this thing at this point in time. It was true. It's no longer true. But the reason why those things are decided at the time are valuable for making changes in the future. And so we can leverage the code as it exists today, the decisions that went into the code as it existed at some point in the past and use that to inform um implementation paths or or or planning for you know why we should change code in a certain way.

49:09 Does that make sense? Any questions? But yeah, like historical information about why decisions were made are something that's often left out of um agent generated code, but it's really important um historical context for like not re remaking mistakes of the past. And so that's how we think about the context engine. >> Awesome. Thanks. >> We have a question over here.

49:39 Yeah, it's um a lot of it is used like AI planner is leveraging it for a lot of things that I don't know if either of you can speak more about but a AI planner is using it a lot right so if you have something that you're trying to figure out um how to build it can look at documents for projects related to it context around it and so it's it's kind of at the core of a lot of these new capabilities we're building out um autodev uses it um and you could probably speak more to it.

50:05 Um AI reviewer doesn't use it a lot today, but we're starting to experiment with how to pull more um into there. And then the other place it's exposed today is within Robo Chat. So if you're talking to Robo and um we're in early access with a few customers now. just announced it a couple months ago, but if you ask it code related questions today, it answers those through um mostly Confluent Search, some other uh connected tooling.

50:32 And then if you have GitHub or Bitbucket, it will use their search APIs. Um but if you are you know have this new capability called code context enabled it will have like a more cohesive understanding of the code grounded in the truth today the historical decisions that went into it and answer those types of code related questions more effectively >> sorry that access >> yeah so the teamwork graph CLI um if you've seen that that's where we're starting to expose a lot of these capabilities Um the context engine itself

51:09 is more agentic search is the way to think about it. We have a lot of the core primitives that we use uh that we'll expose to your agents through the CLI but I think over time you know we might consider moving more of those into there as well. Yeah, team graph CLI has access to all the um code context skills. >> Question in the back. >> Yeah, I had two questions.

51:33 The first was on standards. Um it looked like standards were like pros and marked down. Is that correct? >> That's correct. Yes. >> Do you have any way to map those to something deterministic like a CI check? >> So kind of like kind of like what we say like a check like a checkbox kind of like uh scenario, right? Um I think just off the top of my head what we would probably have to do is kind of like implement that within a the markdown itself.

52:01 But that's actually really interesting use case where you can just provide it like specific like can like binary yes does this satisfy no does this not satisfy. Um so I currently I don't think so I don't think we can prov we provides that out of box but I will relay that over to the standards team to see if we can get that in the future. Yeah, that could be really neat to make it a little bit more.

52:21 >> That would be awesome. >> Right. >> Epic that you can break it down and decompose it into multiple ages like waves of ages. Usually there's foundational work that will work down the line. foundational unblock. >> Right. Just to reiterate uh I think your use case is kind of similar like where you have an epic um and you maybe want that epic to be like broken down into like smaller issues and then within those smaller issues you can have like you know like a wave of agents kind of like execute each smaller piece.

53:01 Is that correct? Would you want like maybe like one step further once you implement like that first wave like is it if like if we could get a second wave going? >> Yes. >> Okay. Awesome. Yeah. Yeah. Good great question. So I actually played around with this yesterday. Um with JCA what you can do you can take an epic uh you can get JCA can you break this down into like smaller issues for me and then uh it will be able it it is able to break it into like smaller juror task if you provided a jur epic.

53:28 After that what you can what you are able to do um there's a few like automation rules that you can kind of do let's say let's say like uh with an automation let's say like you have the J epic kind of like be broken down once it's broken down you can have automations kind of like re start a JCA uh instance has start a JCI kind of session and it is able to like autonomously kind of like develop each >> and I don't think there's anything that would prevent you from kind of extrapolating that further right like here's all

53:53 the stories I want break these down into multiple epics right the foundational work the like V 0, V1, V2, and then just have it, you know, further refine the tickets into there. And so I think nothing would prevent you from doing >> Oh, you can keep going. >> Yeah, you can do flexible. >> Keep going. >> Sounds more like a JCA automation kind of like I'm not sure if Robo directly could do it, but I definitely think you can do with automations.

54:23 >> Yeah. And I think autodev might be able to do some of that. >> Auto dev. Yes. >> Okay. I do that with gas. [laughter] You guys are doing great. Thank you. >> Yeah, no worries. question. I just want to understand that actually my code is I change the code. >> So just to reiterate uh let's say you make a code change within like your IDE. Oh yeah. Um so I think you have to think about with JCA that like it it is isolated very much the agent sessions.

55:11 So if you say like make a change within the agent session itself um then it will be real time. Uh if you make it make a change within the VS code instance within the cloud then yes all those changes will happen real time and you will be able to be able to reflect that within the cloud session itself. Um whatever changes you make on the ID it's a separate kind of like a separate instance right.

55:28 Uh so yeah with with agents think about it like all within the cloud itself any change you make in Jira session jur agent session itself within jura then then then all of that will be real time >> question over here >> you I guess you >> so just to iterate like so it sounds like your instance is more like on prem and you want to run a bunch of like the agents like on on prem.

56:10 Is that correct? Not even like I guess like how is the develop development environment like is there like a docker file that we would have to use to >> that's >> that's docker. >> It is docker. Yeah. Yeah. >> Okay. >> Cool. I think we have time for one more question. That's a great question. Uh, we actually just started uh supporting skills I think just not too long ago.

57:02 >> Right. Yes. So yeah, I think for just AI reviewer itself, we do support skills like uh like I think less than a month ago, we released leads. Sounds good. >> Cool. All right, thanks everyone. We got to go. We'll be around if there's any other questions. But >> yeah, thanks a lot. Thanks a lot everyone for joining uh joining our workshop. Thanks a bunch for the uh the tech staff for getting all of our Wi-Fi issues sorted out.

57:24 Big shout out to them. Um hope you guys have a great rest of your week during the uh during the conference. Thank you so much, guys. >> [music]