Easy Agile Podcast Ep.28 Team23! + the world of work

On this episode of The Easy Agile Podcast, tune in to hear developers Angad, Jared, Jess and Jordan, as they share their thoughts on observability.  

Wollongong has a thriving and supportive tech community and in this episode we have brought together some of our locally based Developers from Siligong Valley for a round table chat on all things observability.

💥 What is observability?
💥 How can you improve observability?
💥 What's the end goal?

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"This was a great episode to be a part of! Jess and Jordan shared some really interesting points on the newest tech buzzword - observability""

Be sure to subscribe, enjoy the episode 🎧

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Transcript

Jared Kells:

Welcome everybody to the Easy Agile podcast. My name's Jared Kells, and I'm a developer here at Easy Agile. Before we begin, Easy Agile would like to acknowledge the traditional custodians of the land from which we broadcast today, the Wodiwodi people of the Dharawal nation, and pay our respects to elders past, present and emerging, and extend that same respect to any aboriginal people listening with us today.

Jared Kells:

So today's podcast is a bit of a technical one. It says on my run sheet here that we're here to talk about some hot topics for engineers in the IT sector. How exciting that we've got a couple of primarily front end engineers and Angad and I are going to share some front end technical stuff and Jess and Jordan are going to be talking a bit about observability. So we'll start by introductions. So I'll pass it over to Jess.

Jess Belliveau:

Cool. Thanks Jared. Thanks for having me one as well. So yeah, my name's Jess Belliveau. I work for Apptio as an infrastructure engineer. Yeah, Jordan?

Jordan Simonovski:

I'm Jordan Simonovski. I work as a systems engineer in the observability team at Atlassian. I'm a bit of a jack of all trades, tech wise. But yeah, working on building out some pretty beefy systems to handle all of our data at Atlassian at the moment. So, that's fun.

Angad Sethi:

Hello everyone. I'm Angad. I'm working for Easy Agile as a software dev. Nothing fancy like you guys.

Jared Kells:

Nothing fancy!

Jess Belliveau:

Don't sell yourself short.

Jared Kells:

Yeah, I'll say. Yeah, so my name's Jared, and yeah, senior developer at Easy Agile, working on our apps. So mainly, I work on programs and road maps. And yeah, they're front end JavaScript heavy apps. So that's where our experience is. I've heard about this thing called observability, which I think is just logs and stuff, right?

Jess Belliveau:

Yeah, yeah. That's it, we'll wrap up!

Jared Kells:

Podcast over! Tell us about observability.

Jess Belliveau:

Yeah okay, I'll, yeah. Well, I thought first I'd do a little thing of why observability, why we talk about this and sort of for people listening, how we got here. We had a little chat before we started recording to try and feel out something that might interest a broader audience that maybe people don't know a lot about. And there's a lot of movements in the broad IT scope, I guess, that you could talk about. There's so many different things now that are just blowing up. Observability is something that's been a hot topic for a couple of years now. And it's something that's a core part of my job and Jordan's job as well. So it's something easy for us to talk about and it's something that you can give an introduction to without getting too technical. So we don't want to get down. This is something that you can go really deep into the weeds, so we picked it as something that hopefully we can explain to you both at a level that might interest the people at home listening as well.

Jess Belliveau:

Jordan and I figured out these four bullet points that we wanted to cover, and maybe I can do the little overview of that, and then I can make Jordan cover the first bullet point, just throw him straight under the bus.

Jordan Simonovski:

Okay!

Jess Belliveau:

So we thought we'd try and describe to you, first of all, what is observability. Because that's a pretty, the term doesn't give you much of what it is. It gives you a little hint, but it'll be good to base line set what are we talking about when we say what is observability. And then why would a development team want observability? Why would a company want observability? Sort of high level, what sort of benefits you get out of it and who may need it, which is a big thing. You can get caught up in these industry hot buzz words and commit to stuff that you might not need, or that sort of stuff.

Jared Kells:

Yep.

Jordan Simonovski:

Yep.

Jess Belliveau:

We thought we'd talk about some easy wins that you get with observability. So some of the real basic stuff you can try and get, and what advantages you get from it. And then we just thought because we're no going to try and get too deep, we could just give a few pointers to some websites and some YouTube talks for further reading that people want to do, and go from there. So yeah, Jordan you want to-

Jared Kells:

Sounds good.

Jess Belliveau:

Yeah. I hopefully, hopefully. We'll see how this goes! And I guess if you guys have questions as well, that's something we should, if there's stuff that you think we don't cover or that you want to know more, ask away.

Jordan Simonovski:

I guess to start with observability, it's a topic I get really excited about, because as someone that's been involved in the dev ops and SRE space for so long, observability's come along and promises to close the loop or close a feedback loop on software delivery. And it feels like it's something we don't really have at the moment. And I get that observability maybe sounds new and shiny, but I think the term itself exists to maybe differentiate itself from what's currently out there. A lot of us working in tech know about monitoring and the loading and things like that. And I think they serve their own purpose and they're not in any way obsolete either. Things like traditional monitoring tools. But observability's come along as a way to understand, I think, the overwhelmingly complex systems that we're building at the moment. A lot of companies are probably moving towards some kind of complicated distributed systems architecture, microservices, other buzz words.

Jordan Simonovski:

But even for things like a traditional kind of monolith. Observability really serves to help us ask new questions from our systems. So the way it tends to get explained is monitoring exits for our known unknowns. With seniority comes the ability to predict, almost, in what way your systems will fail. So you'll know. The longer you're in the industry, you know this, like a Java server fails in x, y, z amount of ways, so we should probably monitor our JVM heap, or whatever it is.

Jared Kells:

I was going to say that!

Jordan Simonovski:

I'll try not to get too much into-

Jared Kells:

Runs out of memory!

Jordan Simonovski:

Yeah. So that's something that you're expecting to fail at some point. And that's something that you can consider a known unknown. But then, the promise of observability is that we should be shipping enough data to be able to ask new questions. So the way it tends to get talked about, you see, it's an unknown unknown of our system, that we want to find out about and ask new questions from. And that's where I think observability gets introduced, to answer these questions. Is that a good enough answer? You want me to go any further into detail about this stuff? I can talk all day about this.

Jared Kells:

Is it like a [crosstalk 00:08:05]. So just to repeat it back to you, see if I've understood. Is it kind of like if I've got a, traditionally with a Java app, I might log memories. It's because I know JVM's run out of memory and that's a thing that I monitor, but observability is more broad, like going almost over the top with what you monitor and log so that you can-

Jordan Simonovski:

Yeah. And I wouldn't necessarily say it's going over the top. I think it's maybe adding a bit more context to your data. So if any of you have worked with traces before, observability is very similar to the way traces work and just builds on top of the premise of traces, I guess. So you're creating these events, and these events are different transactions that could be happening in your applications, usually submitting some kind of request. And with that request, you can add a whole bunch of context to it. You can add which server this might be running on, which time zone. All of these additional and all the exciters. You can throw in user agency into there if you want to. The idea of observability is that you're not necessarily constrained by high cardinality data. High cardinality data being data sets that can change quite largely, in terms of the kinds of data they represent, or the combinations of data sets that you could have.

Jordan Simonovski:

So if you want shipping metrics on something, on a per user basis and you want to look at how different users are affected by things, that would be considered a high cardinality metric. And a lot of the time it's not something that traditional monitoring companies or metric providers can really give you as a service. That's where you'll start paying insanely huge bills on things like Datadog or whatever it is, because they're now being considered new metrics. Whereas observability, we try and store our data and query it in a way that we can store pretty vast data sets and say, "Cool. We have errors coming from these kinds of users." And you can start to build up correlations on certain things there. You can find out that users from a particular time zone or a particular device would only be experiencing that error. And from there, you can start building up, I think, better ways of understanding how a particular change might have broken things. Or some particular edge cases that you otherwise couldn't pick up on with something like CPU or memory monitoring.

Angad Sethi:

Would it be fair to say-

Jared Kells:

Yeah. It's [crosstalk 00:11:02].

Angad Sethi:

Oh, sorry Jared.

Jared Kells:

No you can-

Angad Sethi:

Would it be fair to say that, so, observability is basically a set of principles or a way to find the unknown unknowns?

Jordan Simonovski:

Yeah.

Angad Sethi:

Oh.

Jess Belliveau:

And better equip you to find, one of the things I find is a lot of people think, you get caught up in thinking observability is a thing that you can deploy and have and tick a box, but I like your choice of word of it being a set of principles or best practices. It's sort of giving you some guidance around these, having good logging coming out of your application. So structured logs. So you're always getting the same log format that you can look at. Tracing, which Jordan talked a little bit about. So giving you that ability to follow how a user is interacting with all the different microservices and possibly seeing where things are going wrong, and metrics as well. So the good thing with metrics is we're turning things a bit around and trying to make an application, instead of doing, and I don't want to get too technical, black box monitoring, where we're on the outside, trying to peer in with probes and checks like that. But the idea with metrics is the application is actually emitting these metrics to inform us what state it is in, thereby making it more observable.

Jess Belliveau:

Yeah, I like your choice of words there, Angad, that it's like these practices, this sort of guide of where to go, which probably leads into this next point of why would a team want to implement it. If you want to start again, Jordan?

Jordan Simonovski:

Yeah, I can start. And I'll give you a bit more time to speak as well, Jess in this one. I won't rant as much.

Jess Belliveau:

Oh, I didn't sign up for that!

Jordan Simonovski:

I think why teams would want it is because, it really depends on your organization and, I guess, the size of the teams you're working in. Most of the time, I would probably say you don't want to build observability yourself in house. It is something that you can, observability capabilities themselves, you won't achieve it just by buying a thing, like you can't buy dev ops, you can't buy Agile, you can't buy observability either.

Jared Kells:

Hang on, hang on. It says on my run sheet to promote Easy Agile, so that sounds like a good segue-

Jess Belliveau:

Unless you want to buy it. If you do want to buy Agile, the [crosstalk 00:13:55] in the marketplace.

Jared Kells:

Yeah, sorry, sorry, yeah! Go on.

Jordan Simonovski:

You can buy tools that make your life a lot easier, and there are a lot of things out there already which do stuff for people and do surface really interesting data that people might want to look at. I think there are a couple of start ups like LightStep and Honeycomb, which give you a really intuitive way of understanding your data in production. But why you would need this kind of stuff is that you want to know the state of your systems at any given point in time, and to build, I guess, good operational hygiene and good production excellence, I guess as Liz Fong-Jones would put it, is you need to be able to close that feedback loop. We have a whole bunch of tools already. So we have CICD systems in place. We have feature flags now, which help us, I guess, decouple deployments from releases. You can deploy code without actually releasing code, and you can actually give that power to your PM's now if you want to, with feature flags, which is great.

Jordan Simonovski:

But what you can also do now is completely close this loop, and as you're deploying an application, you can say, "I want to canary this deployment. I want to deploy this to 10% of my users, maybe users who are opted in for Beta releases or something of our application, and you can actually look at how that's performing before you release it to a wider audience. So it does make deployments a lot safer. It does give you a better understanding of how you're affecting users as well. And there are a whole bunch of tools that you can use to determine this stuff as well. So if you're looking at how a lot of companies are doing SRE at the moment, or understanding what reliable looks like for their applications, you have things like SLO's in place as well. And SLO's-

Jared Kells:

What's an SLO?

Jordan Simonovski:

They're all tied to user experiences. So you're saying, "Can my user perform this particular interaction?" And if you can effectively measure that and know how users are being affected by the changes you're making, you can easily make decisions around whether or not you continue shipping features or if you drop everything and work on reliability to make sure your users aren't affected. So it's this very user centric approach to doing things. I think in terms of closing the loop, observability gives us that data to say, "Yes, this is how users are being affected. This is how, I guess the 99th percentile of our users are fine, but we have 1% who are having adverse issues with our application." And you can really pinpoint stuff from there and say, "Cool. Users with this particular browser or this particular, or where we've deployed this app to," let's say if you have a global deployment of some kind, you've deployed to an island first, because you don't really care what happens to them. You can say, "Oh, we've actually broken stuff for them." And you can roll it back before you impact 100% of your users.

Jared Kells:

Yeah. I liked what you said about the test. I forgot the acronym, but actually testing the end user behavior. That's kind of exciting to me, because we have all these metrics that are a bit useless. They're cool, "Oh, it's using 1% CPU like it always is, now I don't really care," but can a user open up the app and drag an issue around? It's like-

Jess Belliveau:

Yeah, that's a really great example, right?

Jared Kells:

That's what I really care about.

Jess Belliveau:

The 1% CPU thing, you could look at a CPU usage graph and see a deployment, and the CPU usage doesn't change. Is everything healthy or not? You don't know, whereas if you're getting that deeper level info of the user interactions, you could be using 1% CPU to serve HTTP500 errors to the 80% of the customer base, sort of thing.

Angad Sethi:

How do you do that? The SLO's bit, how do you know a user can log in and drag an issue?

Jordan Simonovski:

Yeah. I think that would come with good instrumenting-

Angad Sethi:

Good question?

Jordan Simonovski:

Yeah, it comes down to actually keeping observability in mind when you are developing new features, the same way you would think about logging a particular thing in your code as you're writing, or writing test for your code, as you're writing code as well. You want to think about how you can instrument something and how you can understand how this particular feature is working in production. Because I think as a lot of Agile and dev ops principles are telling us now is that we do want our applications in production. And as developers, our responsibilities don't end when we deploy something. Our responsibility as a developer ends when we've provided value to the business. And you need a way of understanding that you're actually doing that. And that's where, I guess, you do nee do think about observability with a lot of this stuff, and actually measuring your success metrics. So if you do know that your application is successful if your user can log in and drag stuff around, then that's exactly what you want to measure.

Jared Kells:

I think that we have to build-

Jordan Simonovski:

Yeah?

Jared Kells:

Oh, sorry Jordan.

Jordan Simonovski:

No, you go.

Jared Kells:

I was just going to say we have to build our apps with integration testing in mind already. So doing browser based tests around new features. So it would be about building features with that and the same thing in mind but for testing and production.

Jess Belliveau:

Yeah and the actual how, the actual writing code part, there's this really great project, the open telemetry project, which provides all these sort of API's and SDK's that developers can consume, and it's vendor agnostic. So when you talk about the how, like, "How do I do this? How do I instrument things?" Or, "How do I emit metrics?" They provide all these helpful libraries and includes that you can have, because the last thing you want to do is have to roll this custom solution, because you're then just adding to your technical debt. You're trying to make things easier, but you're then relying on, "Well I need to keep Jared Kells employed, because he wrote our log in engine and no one else knows how it works.

Jess Belliveau:

And then the other thing that comes to mind with something like open telemetry as well, and we talked a bit about Datadog. So Datadog is a SaaS vendor that specializes in observability. And you would push your metrics and your logs and your traces to them and they give you a UI to display. If you choose something that's vendor agnostic, let's just use the example of Easy Agile. Let's say they start Datadog and then in six months time, we don't want to use Datadog anymore, we want to use SignalFx or whatever the Splunk one is now.

Jordan Simonovski:

I think NorthX.

Jess Belliveau:

Yeah. You can change your end point, push your same metrics and all that sort of stuff, maybe with a few little tweaks, but the idea is you don't want to tie in to a single thing.

Jordan Simonovski:

Your data structures remain the same.

Jess Belliveau:

Yeah. So that you could almost do it seamlessly without the developers knowing. There's even companies in the past that I think have pushed to multiple vendors. So you could be consuming vendor A and then you want to do a proof of concept with vendor B to see what the experience is like and you just push your data there as well.

Jared Kells:

Yeah. I think our coupling to Datadog will be I all the dashboards and stuff that we've made. It's not so much the data.

Jess Belliveau:

Yeah. That's sort of the big up sell, right. It's how you interact. That's where they want to get their hooks in, is making it easier for you to interpret that data and manipulate it to meet your needs and that sort of stuff.

Jordan Simonovski:

Observability suggests dashboards, right?

Jess Belliveau:

Yeah, perhaps. You used this term as well, Jordan, "production excellence." And when we talk about who needs observability, I was thinking a bit about that while you were talking. And for me, production excellence, or in Apptio we call it production readiness, operational readiness and that sort of stuff is like we want to deploy something to production like what sort of best practices do we want to have in place before we do that? And I think observability is a real great idea, because it's helping you in the future. You don't know what problems you're going to have down the line, but you're equipping your teams to be able to respond to those problems easily. Whereas, we've all probably been there, we've deployed code of production and we have no observability, we have a huge outage. What went wrong? Well, no one knows, but we know this is the fix, and it's hard to learn from that, or you have to learn from that I guess, and protect the user against future stuff, yeah.

Jess Belliveau:

When I think easy wins for observability, the first thing that really comes to mind is this whole idea of structured logging, which is really this idea that your application is you're logging, first of all. Quite important as a baseline starting point, but then you have a structured log format which lets you programmatically pass the logs as well. If you go back in time, maybe logging just looked like plain text with a line, with a timestamp, an error message. Whatever the developer decided to write to the standard out, or to the error file or something like that. Now I think there's a general move to having JSON, an actual formatted blob with that known structure so you can look into it. Tracing's probably not an easy win. That's a little bit harder. You can implement it with open telemetry and libraries and stuff. Requires a bit more understanding of your code base, I guess, and where you want tracing to fire, and that sort of stuff, parsing context through, things like that.

Jordan Simonovski:

I think Atlassian, when you probably just want to know that everything is okay. At a fairly superficial level. Maybe you just want to do some kind of up time on a trend. And then as, I guess, your code might get more complex or your product gets a bit more complex, you can start adding things in there. But I think actually knowing or surfacing the things you know might break. Those would probably be your quickest wins.

Jess Belliveau:

Well, let's mention some things for further reading. If you want to go get the whole picture of the whole, real observability started to get a lot of movement out of the Google SRE book from a few years ago. The Google SRE stuff covers the whole gamut of their soak reliability engineering practice, and observability is a portion of that, there's some great chapters on that. O'Reilly has an observability book, I think, just dedicated to observability now.

Jordan Simonovski:

I think that's still in early release, if people want to google chapters.

Jess Belliveau:

The open telemetry stuff, we'll drop a link to that I think that's really handy to know.

Angad Sethi:

From [inaudible 00:26:12], which is my perspective, as a developer, say I wanted to introduce cornflake use Datadog at Easy Agile. Not very familiar, I'm not very comfortable with it. I know how to navigate, but what's a quick way for me to get started on introducing observability? Sorry to lock my direct job or at my workplace.

Jordan Simonovski:

I would lean, I could be biased here. Jess correct me or give your opinion on this, I would lean heavily towards SLO's for this. And you can have a quick read in the SRE-

Jess Belliveau:

What does SLO stand for, Jordan?

Jordan Simonovski:

Okay, sorry. Buzz words! SLO is a service level objective, not to be confused with service level agreement. An agreement itself is contractual and you can pay people money if you do breach those. An SLO is something you set in your team and you have a target of reliability, because we are getting to the point where we understand that all systems at any point in time are in some kind of degraded state. And yeah, reliability isn't necessarily binary, it's not unreliable or reliable. Most of the time, it's mostly reliable and this gives us a better shared language, I guess. And you can have a read in the SRE handbook by Google, which is free online, which gives you a pretty good understanding of Datadog.

Jordan Simonovski:

I think the last time I used it had a SLO offering. But I think like I was mentioning earlier, you set an SLO on particular functionalities or features of your application. You're saying, "My user can do this 99% of the time," or whatever other reliability target you might want to set. I wouldn't recommend five nines of reliability. You'll probably burn yourself out trying to get there. And you have this target set for yourself. And you know exactly what you're measuring, you're measuring particular types of functionality. And you know when you do breach these, users are being affected. And that's where you can actually start thinking about observability. You can think about, "What other features are we implementing that we can start to measure?" Or, "What user facing things are we implementing that we can start to measure?"

Jordan Simonovski:

Other things you could probably look at are, I think they're all covered in the book anyway, data freshness in a way. You want to make sure the data users are being displayed is relatively fresh. You don't want them looking at stale data, so you can look at measuring things like that as well. But you can pretty much break it down into most functionalities of a website. It's no longer like a ping check, that you're just saying, "Yes, HTTP, okay. My application is fine." You're saying, "My users are actually being affected by things not working." And you can start measuring things from there. And that should give you a better understanding, or a better idea, at least, of where to start with what you want to measure and ow you want to measure it. That would be my opinion on where to get started with this if you do want to introduce it.

Jared Kells:

We're going to talk a little bit about state and how with some of these, like our very front end heavy applications that we're building, so the applications we build just basically run inside the browser and the traditional state as you would think about it, is just pulling a very simple API that writes some things into the database with some authentication, and that sort of stuff. So in terms of reliability of the services, it's really reliable. Those tiny API's just never have problems, because it's just so simple. And well, they've got plenty of monitoring around it. But all our state is actually, when you say, "Observe the state of the system," for the most part, that's state in a browser. And how do we get observability into that?

Jess Belliveau:

A big thing is really, there's not one thing fits all as well. When we talk about the SLO stuff as well, it's understanding what is important to not so much maybe your company but your team as well. If you're delivering this product, what's important to you specifically? So one SLO that might work for me at Apptio probably isn't going to work for Easy Agile. This is really pushing my knowledge, as well, of front end stuff, but when we say we want to observe the state as well, we don't necessarily mean specifically just the state. You could want to understand with each one of those API's when it's firing, what the request response time is for that API firing. So that might be an important metric. So you can start to see if one of those APIs is introducing latency, and so your user experience is degraded. Like, "Hey when we were on release three, when users were interacting with our service here, it would respond in this percentile latency. We've done a release and since then, now we're seeing it's now in this percentile. Have we degraded performance performance?" Users might not be complaining, but that could be something that the team then can look into, add to a sprint. Hey, I'm using Agile terms now. Watch out!

Jared Kells:

That's a really good example, Jess. Performance issues for us are typically not an API that's performing poorly. It's something in this very complicated front end application is not running in the same order as it used to, or there's some complex interaction we didn't think of, so it's requesting more data than expected. The APIs are returning. They're never slow, for the most part, but we have performance regressions that we may not know about without seeing them or investigating them. The observability is really at the individual user's browser level. That makes sense? I want to know how long did it take for this particular interaction to happen.

Jess Belliveau:

Yeah. I've never done that sort of side of things. As well, the other thing I guess, you could potentially be impacted in as well as then, you're dealing with end user manifestations as well. You could perceive-

Jared Kells:

Yeah sure.

Jess Belliveau:

... Greater performance on their laptop or something, or their ISP or that sort of stuff. It'd be really hard to make sure you're not getting noise from that sort of thing as well.

Jordan Simonovski:

Yeah. There are tools like Sentry, I guess, which do exist to give you a bit more of an understanding what's happening on your front end. The way Sentry tends to work with JavaScript, is you'll upload a minified map of your JS to Sentry, deploy your code and then if something does break or work in a fairly unexpected way, that tends to get surfaced with Sentry will tell you exactly which line this kind of stuff is happening on, and it's a really cool tool for that company stuff. I don't know if it'd give you the right type of insights, I think, in terms of performance or-

Jared Kells:

Yeah, we use a similar tool and it does work for crashes and that sort of thing. And on the observability front, we log actions like state mutations in side the front end, not the actual state change, but just labels that represent that you updated an issue summary or you clicked this button, that sort of thing, and we send those with our crash reports. And it's super helpful having that sort of observability. So I think I know what you guys are talking about. But I'm just [crosstalk 00:35:25], yeah.

Jess Belliveau:

Yeah, that's almost like, I guess, a form of tracing. For me and Jordan, when we talk about tracing, we might be thinking about 12 different microservices sitting in AWS that are all interacting, whereas you're more shifting that. That's sort of all stuff in the browser interacting and just having that history of this is what the user did and how they've ended up-

Jared Kells:

In that state.

Jess Belliveau:

In that state, yeah.

Jordan Simonovski:

I guess even if you don't have a lot of microservices, if you're talking about particular, like you're saying for the most part your API requests are fine but sometimes you have particularly large payloads-

Jared Kells:

We actually have to monitor, I don't know, maybe you can help with this, we actually should be monitoring maybe who we're integrating with. It's actually much more likely that we'll have a performance issue on a Xero API rather than... We don't see it, the browser sees it as well, which is-

Jordan Simonovski:

Yeah, and tracing does solve all of those regressions for you. Most tracing libraries, like if you're running Node apps or whatever on your backend. I can just tell you about Node, because I probably have the most experience writing Node stuff. You pretty much just drop in Didi trace, which is a Datadog library for tracing into your backend and your hook itself into all of, I think, the common libraries that you'll tend to work with, I think. Like if you're working for express or for a lot of just HADP libraries, as well as a few AWS services, it will kind of hook itself into that. And you can actually pinpoint. It will kind of show you on this pretty cool service map exactly which services you're interacting with and where you might be experiencing a regression. And I think traces do serve to surface that information, which is cool. So that could be something worth investigating.

Jess Belliveau:

It's funny. This is a little bit unrelated to observability, but you've just made me think a bit more about how you're saying you're reliant on third party providers as well. And something I think that's really important that sometimes gets missed is so many of us today are relying on third party providers, like AWS is a huge thing. A lot of people writing apps that require AWS services. And I think a lot of the time, people just assume AWS or Jira or whatever, is 100% up time, always available. And they don't write their code in such a way that deals with failures. And I think it's super important. So many times now I've seen people using the AWS API and they don't implement exponential back off. And so they're basically trying to hit the AWS API, it fails or they might get throttled, for example, and then they just go into a fail state and throw an error to the user. But you could potentially improve that user experience, have a retry mechanism automatically built in and that sort of stuff. It doesn't really tie into the observability thing, but it's something.

Jared Kells:

And the users don't care, right? No one cares if it's an AWS problem. It's your problem, right, your app is too slow.

Jess Belliveau:

Well, they're using your app. Exactly right. It reflects on you sort of thing, so it's in your interest to guard against an upstream failure, or at least inform the user when it's that case. Yeah.

Jared Kells:

Well, I think we're going to have to call it, this podcast, because it was an hour ago. We had instructed max 45 minutes.

Jess Belliveau:

We could just keep going. We might need a part two! Maybe we can request [cross talk 00:39:21].

Jared Kells:

Maybe! Yeah.

Jess Belliveau:

Or we'll just start our own podcast! Yeah.

Angad Sethi:

So what were your biggest learnings today, given it's been Angad and I are just learning about observability, Angad what was your biggest learning today about observability? My biggest learning was that observability does not equal Datadog. No, sorry! It was just very fascinating to learn about quantifying the known unknowns. I don't know if that's a good takeaway, but...

Jess Belliveau:

Any takeaway is a good takeaway! What about you, Jared?

Jared Kells:

I think, because I we were going to talk about state management, and part of it was how we have this ability, at the moment to, the way our front ends are architected, we can capture the state of the app and get a customer to send us their state, basically. And we can load it into our app and just see exactly how it was, just the way our state's designed. But what might be even cooler is to build maybe some observability into that front end for support. I'm thinking instead of just having, we have this button to send us out your support information that sends us a bunch of the state, but instead of console logging to the browser log, we could be console logging, logging in our front end somewhere that when they click, "send support information," our customers should be sending us the actions that they performed.

Jared Kells:

Like, "Hey there's a bug, send us your support information." It doesn't have to be a third party service collecting this observability stuff. We could just build into our... So that's what I'm thinking about.

Jess Belliveau:

Yeah, for sure. It'll probably be a lot less intrusive, as well, as some of the third party stuff that I've seen around.

Jared Kells:

Yeah. It's pretty hard with some of these integrations, especially if you're developing apps that get run behind a firewall.

Jess Belliveau:

Yeah

Jared Kells:

You can't just talk to some of these third parties. So yeah, it's cool though. It's really interesting.

Jess Belliveau:

Well, I hope someone out there listening has learned something, and Jordan and I will send some links through, and we can add them, hopefully, to the show notes or something so people can do some more reading and...

Jared Kells:

All thanks!

Jess Belliveau:

Thanks for having us, yeah.

Jared Kells:

Thanks all for your time, and thanks everybody for listening.

Jordan Simonovski:

Thanks everyone.

Angad Sethi:

That was [inaudible 00:41:55].

Jess Belliveau:

Tune in next week!

TL;DR

Henrik Kniberg, the agile coach behind Spotify's model, discusses how AI is fundamentally transforming software development. Key takeaways: AI tools like Cursor and Claude are enabling 10x productivity gains; teams should give developers access to paid AI tools and encourage experimentation; coding will largely disappear as a manual task within 3–4 years; teams will shrink to 2 people plus AI; sprints will become obsolete in favour of continuous delivery; product owners can now write code via AI, creating pull requests instead of user stories; the key is treating AI like a brilliant intern – when it fails, the problem is usually your prompt or code structure, not the AI. Bottom line: Learn to use AI now, or risk being left behind in a rapidly changing landscape.

Introduction

Artificial intelligence is fundamentally reshaping how software teams work, collaborate, and deliver value. But with this transformation comes questions: How do we maintain team morale when people fear being replaced? What happens to code quality when AI writes most of the code? Do traditional agile practices like sprints still make sense?

In this episode, I sit down with Henrik Kniberg to tackle these questions head-on. Henrik is uniquely positioned to guide us through this transition – he's the agile coach and entrepreneur who pioneered the famous Spotify model and helped transform how Lego approached agile development. Now, as co-founder of Abundly AI, he's at the forefront of helping teams integrate AI into their product development workflows.

This conversation goes deep into the practical realities of AI-powered development: from maintaining code review processes when productivity increases 10x, to ethical considerations around AI usage, to what cross-functional teams will look like in just a few years. Henrik doesn't just theorise – he shares real examples from his own team, where their CEO (a non-coder) regularly submits pull requests, and where features that once took a sprint can now be built during a 7-minute subway ride.

Whether you're a developer wondering if AI will replace you, a product owner looking to leverage these tools, or a leader trying to navigate this transformation, this episode offers concrete, actionable insights for thriving in the AI era.

About Our Guest

Henrik Kniberg is an agile coach, author, and entrepreneur whose work has shaped how thousands of organisations approach software development. He's best known for creating the Spotify model – the squad-based organisational structure that revolutionised how large tech companies scale agile practices. His work at Spotify and later at Lego helped demonstrate how agile methodologies could work at enterprise scale whilst maintaining team autonomy and innovation.

Henrik's educational videos have become legendary in the agile community. His "Agile Product Ownership in a Nutshell" video, created over a decade ago, remains one of the most-watched and shared resources for understanding product ownership, with millions of views. His ability to distil complex concepts into simple, visual explanations has made him one of the most accessible voices in agile education.

More recently, Henrik has turned his attention to the intersection of AI and product development. As co-founder of Abundly AI, he's moved from teaching about agile transformation to leading AI transformation – helping companies and teams understand how to effectively integrate generative AI tools into their development workflows. His approach combines his deep understanding of team dynamics and agile principles with hands-on experience using cutting-edge AI tools like Claude, Cursor, and GitHub Copilot.

Henrik codes daily using AI and has been doing so for over two and a half years, giving him practical, lived experience with these tools that goes beyond theoretical understanding. He creates educational content about AI, trains teams on effective AI usage, and consults with organisations navigating their own AI transformations. His perspective is particularly valuable because he views AI through the lens of organisational change management – recognising that successful AI adoption isn't just about the technology, it's about people, culture, and process.

Based in Stockholm, Sweden, Henrik continues to push the boundaries of what's possible when human creativity and AI capabilities combine, whilst maintaining a pragmatic, human-centred approach to technological change.

Transcript

Note: This transcript has been lightly edited for clarity and readability.

Maintaining Team Morale and Motivation in the AI Era

Tenille Hoppo: Hi there, team, and welcome to this new episode of the Easy Agile Podcast. My name is Tenille Hoppo, and I'm feeling really quite lucky to have an opportunity to chat today with our guest, Henrik Kniberg.

Henrik is an agile coach, author, and entrepreneur known for pioneering agile practices at companies like Spotify and Lego, and more recently for his thought leadership in applying AI to product development. Henrik co-founded Abundly AI, and when he isn't making excellent videos to help us all understand AI, he is focused on the practical application of generative AI in product development and training teams to use these technologies effectively.

Drawing on his extensive experience in agile methodologies and team coaching, Henrik seems the perfect person to learn from when thinking about the intersection of AI, product development, and effective team dynamics. So a very warm welcome to you, Henrik.

Henrik Kniberg: Thank you very much. It's good to be here.

Tenille: I think most people would agree that motivated people do better work. So I'd like to start today by touching on the very human element of this discussion and helping people maintain momentum and motivation when they may be feeling some concern or uncertainty about the upheaval that AI might represent for them in their role.

What would you suggest that leaders do to encourage the use of AI in ways that increase team morale and creativity rather than risking people feeling quite concerned or even potentially replaced?

Henrik: There are kind of two sides to the coin. There's one side that says, "Oh, AI is gonna take my job, and I'm gonna get fired." And the other side says, "Oh, AI is going to give me superpowers and give us all superpowers, and thereby give us better job security than we had before."

I think it's important to press on the second point from a leader's perspective. Pitch it as this is a tool, and we are entering a world where this tool is a crucial tool to understand how to use – in a similar way that everyone uses the Internet. We consider it obvious that you need to know how to use the Internet. If you don't know how to use the Internet, it's going to be hard.

"I encourage people to experiment, give them access to the tools to do so, and encourage sharing. And don't start firing people because they get productive."

I also find that people tend to get a little bit less scared once they learn to use it. It becomes less scary. It's like if you're worried there's a monster under your bed, maybe look under your bed and turn on the lights. Maybe there wasn't a monster there, or maybe it was there but it was kind of cute and just wanted a hug.

Creating a Culture of Safe Experimentation

Tenille: I've read that you encourage experimentation with AI through learning – I agree it's the best way to learn. What would you encourage leaders and team leaders to do to create a strong culture where teams feel safe to experiment?

Henrik: There are some things. One is pretty basic: just give people access to good AI tools. And that's quite hard in some large organisations because there are all kinds of resistance – compliance issues, data security issues. Are we allowed to use ChatGPT or Claude? Where is our data going? There are all these scary things that make companies either hesitate or outright try to stop people.

Start at that hygiene level. Address those impediments and solve them. When the Internet came, it was really scary to connect your computer to the Internet. But now we all do it, and you kind of have to, or you don't get any work done. We're at this similar moment now.

"Ironically, when companies are too strict about restricting people, then what people tend to do is just use shadow AI – they use it on their own in private or in secret, and then you have no control at all."

Start there. Once people have access to really good AI tools, then it's just a matter of encouraging and creating forums. Encourage people to experiment, create knowledge-sharing forums, share your own experiments. Try to role-model this yourself. Say, "I tried using AI for these different things, and here's what I learned." Also provide paths for support, like training courses.

The Right Mindset for Working with AI

Tenille: What would you encourage in team members as far as their mindset or skills go? Certainly a nature of curiosity and a willingness to learn and experiment. Is there anything beyond that that you think would be really key?

Henrik: It is a bit of a weird technology that's never really existed before. We're used to humans and code. Humans are intelligent and kind of unpredictable. We hallucinate sometimes, but we can do amazing things. Code is dumb – it executes exactly what you told it to do, and it does so every time exactly the same way. But it can't reason, it can't think.

Now we have AI and AI agents which are somewhere in the middle. They're not quite as predictable as code, but they're a lot more predictable than humans typically. They're a lot smarter than code, but maybe not quite as smart as humans – except for some tasks when they're a million times smarter than humans. So it's weird.

You need a kind of humble attitude where you come at it with a mindset of curiosity. Part of it is also to realise that a lot of the limitation is in you as a user. If you try to use AI for coding and it wrote something that didn't work, it's probably not the model itself. It's probably your skills or lack of skills because you have to learn how to use these tools. You need to have this attitude of "Oh, it failed. What can I do differently next time?" until you really learn how to use it.

"There can be some aspect of pride with developers. Like, 'I've been coding for 30 years. Of course this machine can't code better than me.' But if you think of it like 'I want this thing to be good, I want to bring out the best in this tool' – not because it's going to replace me, but because it's going to save me a tonne of time by doing all the boring parts of the coding so I can do the more interesting parts – that kind of mindset really helps."

Maintaining Code Quality and Shared Understanding

Tenille: Our team at Easy Agile is taking our steps and trying to figure out how AI is gonna work best for us. I put the question out to some of our teams, and there were various questions around people taking their first steps in using AI as a co-pilot and producing code. There are question marks around consistency of code, maintaining code quality and clean architecture, and even things like maintaining that shared understanding of the code base. What advice do you have for people in that situation?

Henrik: My first piece of advice when it comes to coding – and this is something I do every day with AI, I've been doing for about two and a half years now – is that the models now, especially Claude, have gotten to the level where it's basically never the AI's fault anymore. If it does anything wrong, it's on you.

You need to think about: okay, am I using the wrong tool maybe? Or am I not using the tool correctly?

For example, the current market leader in terms of productivity tools with AI is Cursor. There are other tools that are getting close like GitHub Copilot, but Cursor is way ahead of anything else I've seen. With Cursor, it basically digs through your code base and looks for what it needs.

But if it fails to find what it needs, you need to think about why. It probably failed for the same reason a human might have failed. Maybe your code structure was very unstructured. Maybe you need to explain to the AI what the high-level structure of your code is.

"Think of it kind of like a really smart intern who just joined your team. They're brilliant at coding, but now they got confused about something, and it's probably your code – something in it that made it confused. And now you need to clarify that."

There are ways to do that. In Cursor, for example, you can create something called cursor rules, which are like standing documents that describe certain aspects of your system. In my team, we're always tweaking those rules. Whenever we find that the AI model did something wrong, we're always analysing why. Usually it's our prompt – I just phrased it badly – or I just need to add a cursor rule, or I need to break the problem down a little bit.

It's exactly the same thing as if you go to a team and give them this massive user story that includes all these assumptions – they'll probably get some things wrong. But if you take that big problem and sit down together and analyse it and split it into smaller steps where each step is verifiable and testable, now your team can do really good work. It's exactly the same thing with AI.

Addressing the Code Review Bottleneck

Tenille: One of our senior developers found that he was outputting code at a much greater volume and faster speed, but the handbrake he found was actually their code review processes. They were keeping the same processes they had previously, and that was a bit of a handbrake for them. What kind of advice would you have there?

Henrik: This reminds me of the general issue with any kind of productivity improvement. If you have a value stream, a process where you do different parts – you do some development, some testing, you have some design – whenever you take one part of the process and make it super optimised, the bottleneck moves to somewhere else.

If testing is no longer the bottleneck, maybe coding is. And when coding is instant, then maybe customer feedback – or lack of customer feedback – is the bottleneck. The bottleneck just keeps moving. In that particular case, the bottleneck became code review. So I would just start optimising that. That's not an AI problem. It's a process problem.

Look at it: what exactly are we trying to do when we review? Maybe we could think about changing the way we review things. For example, does all code need to be reviewed? Would it be enough that the human who wrote it and the AI, together with the human, agree that this is fine? Or maybe depending on the criticality of that change, in some cases you might just let it pass or use AI to help in the reviewing process also.

"I think there's value in code review in terms of knowledge sharing in a large organisation. But maybe the review doesn't necessarily need to be a blocking process either. It could be something you go back and look at – don't let it stop you from shipping, but maybe go back once per week and say, 'Let's look at some highlights of some changes we've made.'"

We produce 10 times more code than in the past, so reviewing every line is not feasible. But maybe we can at least identify which code is most interesting to look at.

Ethical Considerations: Balancing Innovation with Responsibility

Tenille: Agile emphasises people over process and delivering value to customers. Now with AI in the mix, there's potential for raising some ethical considerations. I'm interested in your thoughts on how teams should approach these ethical considerations that come along with AI – things like balancing rapid experimentation against concerns around bias, potential data privacy concerns.

Henrik: I would treat each ethical question on its own merits. Let me give you an example. When you use AI – let's say facial recognition technology that can process and recognise faces a lot better than any human – I kind of put that in the bucket of: any tool that is really useful can also be used for bad things. A hammer, fire, electricity.

That doesn't have so much to do with the tool itself. It has much more to do with the rules and regulations and processes around the tool. I can't really separate AI in that sense. Treat it like any other system. Whenever you install a camera somewhere, with or without AI, that camera is going to see stuff. What are you allowed to do with that information? That's an important question. But I don't think it's different for AI really, in that sense, other than that AI is extremely powerful. So you need to really take that seriously, especially when it comes to things like autonomous weapons and the risk of fraud and fake news.

"An important part of it is just to make it part of the agenda. Let's say you're a recruitment company and you're now going to add some AI help in screening. At least raise the question: we could do this. Do we want to do this? What is the responsible way to do it?"

It's not that hard to come up with reasonable guidelines. Obviously, we shouldn't let the AI decide who we're going to hire or not. That's a bad idea. But maybe it can look at the pile of candidates that we plan to reject and identify some that we should take a second look at. There's nothing to lose from that because that AI did some extra research and found that this person who had a pretty weak CV actually has done amazing things before.

We're actually working with a company now where we're helping them build some AI agents. Our AI agents help them classify CVs – not by "should we hire them or not," but more like which region in Sweden is this, which type of job are we talking about here. Just classifying to make it more likely that this job application reaches the right person. That's work that humans did before with pretty bad accuracy.

The conclusion was that AI, despite having biases like we humans do, seemed to have less biases than the human. Mainly things like it's never going to be in a bad mood because it hasn't had its coffee today. It'll process everybody on the same merits.

I think of it like a peer-to-peer thing. Imagine going to a doctor – ideally, I want to have both a human doctor and an AI doctor side by side, just because they both have biases, but now they can complement each other. It's like having a second opinion. If the AI says we should do this and the doctor says, "No, wait a second," or vice versa, having those two different opinions is super useful.

Parallels Between Agile and AI Transformations

Tenille: You're recognised as one of the leading voices in agile software development. I can see, and I'm interested if you do see, some parallels between the agile transformations that you led at Spotify and Lego with the AI transformations that many businesses are looking at now.

Henrik: I agree. I find that when we help companies transition towards becoming AI native, a lot of the thinking is similar to agile. But I think we can generalise that agile transformations are not really very special either – it's organisational change.

There are some patterns involved regardless of whether you're transitioning towards an agile way of working or towards AI. Some general patterns such as: you've got to get buy-in, it's useful to do the change in an incremental way, balance bottom-up with top-down. There are all these techniques that are useful regardless. But as an agilist, if you have some skills and competence in leading and supporting a change process, then that's going to be really useful also when helping companies understand how to use AI.

Tenille: Are you seeing more top-down or bottom-up when it comes to AI transformations?

Henrik: So far it's quite new still. The jury's not in yet. But so far it looks very familiar to me. I'm seeing both. I'm seeing situations where it's pure top-down where managers are like "we got to go full-out AI," and they push it out with mixed results. And sometimes just completely bottom-up, also with mixed results.

Sometimes something can start completely organically and then totally take hold, or it starts organically and then gets squashed because there was no buy-in higher up. I saw all of that with agile as well. My guess is in most cases the most successful will be when you have a bit of both – support and guidance from the top, but maybe driven from the bottom.

"I think the bottom-up is maybe more important than ever because this technology is so weird and so fast-moving. As a leader, you don't really have a chance if you try to control it – you're going to slow things down to an unacceptable level. People will be learning things that you can't keep up with yourself. So it's better to just enable people to experiment a lot, but then of course provide guidance."

AI for Product Owners: From Ideation to Pull Requests

Tenille: You're very well known for your guidance and for your ability to explain quite complex concepts very simply and clearly. I was looking at your video on YouTube today, the Agile Product Ownership in a Nutshell video, which was uploaded about 12 years ago now. Thinking about product owners, there's a big opportunity now with AI for generating ideas, analysing data, and even suggesting new features. What's your advice for product owners and product managers in using AI most effectively?

Henrik: Use it for everything. Overuse it so you can find the limits. The second thing is: make sure you have access to a good AI model. Don't use the free ones. The difference is really large – like 10x, 100x difference – just in paying like $20 per month or something. At the moment, I can particularly strongly recommend Claude. It's in its own category of awesomeness right now. But that of course changes as they leapfrog each other. But mainly: pay up, use a paid model, and then experiment.

For product owners, typical things are what you already mentioned – ideation, creating good backlog items, splitting a story – but also writing code. I would say as a PO, there is this traditional view, for example in Scrum, that POs should not be coding. There's a reason for that: because coding takes time, and then as PO you get stuck in details and you lose the big picture.

Well, that's not true anymore. There are very many things that used to be time-consuming coding that is basically a five-minute job with a good prompt.

"Instead of wasting the team's time by trying to phrase that as a story, just phrase it as a pull request instead and go to the team and demonstrate your running feature."

That happened actually today. Just now, our CEO, who's not a coder, came to me with a pull request. In fact, quite often he just pushes directly to a branch because it's small changes. He wants to add some new visualisation for a graph or something in our platform – typically admin stuff that users won't see, so it's quite harmless if he gets it wrong.

He's vibe coding, just making little changes to the admin, which means he never goes to my team and says, "Hey, can you guys generate this report or this graph for how users use our product?" No, he just puts it in himself if it's simple.

Today we wanted to make a change with how we handle payments for enterprise customers. Getting that wrong is a little more serious, and the change wasn't that hard, but he just didn't feel completely comfortable pushing it himself. So he just made a PR instead, and then we spent 15 minutes reviewing it. I said it was fine, so we pushed it.

It's so refreshing that now anybody can code. You just need to learn the basic prompting and these tools. And then that saves time for the developers to do the more heavyweight coding.

Tenille: It's an interesting world where we can have things set up where anyone could just jump in and with the right guardrails create something. It makes Friday demos quite probably a lot more interesting than maybe they used to be in the past.

Henrik: I would like to challenge any development team to let their stakeholders push code, and then find out whatever's stopping you from doing that and fix that. Then you get to a very interesting space.

Closing the Gap Between Makers and Users

Tenille: A key insight from your work with agile teams in the past has been to really focus on minimising that gap between maker and user. Do you think that AI helps to close that gap, or do you think it potentially risks widening it if teams are focusing too much on AI predictions and stop talking to their customers effectively?

Henrik: I think that of course depends a lot on the team. But from what I've seen so far, it massively reduces the gap. Because if I don't have to spend a week getting a feature to work, I can spend an hour instead. Then I have so much more time to talk to my users and my customers.

If the time to make a clickable prototype or something is a few seconds, then I can do it live in real time with my customers, and we can co-create. There are all these opportunities.

I find that – myself, my teams, and the people I work with – we work a lot more closely with our users and customers because of this fast turnaround time.

"Just yesterday I was teaching a course, and I was going home sitting on the subway. It was a 15-minute subway ride. I finally got a seat, so I had only 7 minutes left. There's this feature that I wanted to build that involved both front-end and back-end and a database schema change. Well, 5 minutes later it was done and I got off the subway and just pushed it. That's crazy."

Of course, our system is set up optimised to enable it to be that fast. And of course not everything will work that well. But every time it does, I've been coding for 30 years, and I feel like I wake up in some weird fantasy every day, wondering, "Can I really be this productive?" I never would have thought that was possible.

Looking Ahead: The Future of Agile Teams

Tenille: I'd like you to put your futurist hat on for a moment. How do you see the future of agile teamwork in, say, 10 to 15 years time? If we would have this conversation again in 2035, given the exponential growth of AI and improvements over the last two to three years, what do you think would be the biggest change for software development teams in how they operate?

Henrik: I can't even imagine 10 years. Even 5 years is just beyond imagination. That's like asking someone in the 1920s to imagine smartphones and the Internet. I think that's the level of change we're looking at.

I would shorten the time a little bit and say maybe 3 or 4 years. My guess there – and I'm already seeing this transfer happen – is that coding will just go away. It just won't be stuff that we humans do because we're too slow and we hallucinate way too much.

But I think engineering and the developer role will still be there, just that we don't type lines of code – in the same way that we no longer make punch cards or we no longer write machine code and poke values into registers using assembly language. That used to be a big part of it, but no longer.

"In the future, as developers, a lot of the work will still be the same. You're still designing stuff, you're thinking about architecture, you're interacting with customers, and you're doing all the other stuff. But typing lines of code is something that we're gonna be telling our kids about, and they're not gonna believe that we used to do that."

The other thing is smaller teams, which I'm already seeing now. I think the idea of a cross-functional team of 5 to 7 people – traditionally that was considered quite necessary in order to have all the different skills needed to deliver a feature in a product. But that's not the case anymore. If you skip ahead 2 or 3 years when this knowledge has spread, I think most teams will be 2 people and an AI, because then you have all the domain knowledge you need, probably.

As a consequence of that, we'll just have more teams. More and smaller teams. Of course, then you need to collaborate between the teams, so cross-team synchronisation is still going to be an issue.

Also, I'm already seeing this now, but this concept of sprints – the whole point is to give a team some peace of mind to build something complex, because typically you would need a week or two to build something complex. But now, when it takes a day and some good prompting to do the same thing that would have taken a whole sprint, then the sprint is a day instead. If the sprint is a day, is there any difference between a sprint planning meeting and a daily standup? Not really.

I think sprints will just kind of shrink into oblivion. What's going to be left instead is something a little bit similar – some kind of synchronisation point or follow-up point. Instead of a sprint where every 2 weeks we sit down and try to make a plan, I think it'll be very much continuous delivery on a day-to-day basis. But then maybe every week or two we take a step back and just reflect a little bit and say, "Okay, what have we been delivering the past couple of weeks? What have we been learning? What's our high-level focus for the next couple of weeks?" A very, very lightweight equivalent of a sprint.

I feel pretty confident about that guess because personally, we are already there with my team, and I think it'll become a bit of a norm.

Final Thoughts: Preparing for the Future

Henrik: No one knows what's gonna happen in the future, and those who say they do are kidding themselves. But there's one fairly safe bet though: no matter what happens in the future with AI, if you understand how to use it, you'll be in a better position to deal with whatever that is. That's why I encourage people to get comfortable with it, get used to using it.

Tenille: I have a teenage daughter who I'm actually trying to encourage to learn how to use AI, because I feel like when I was her age, the Internet was the thing that was sort of coming mainstream. It completely changed the way we live. Everything is online now. And I feel like AI is that piece for her.

Henrik: Isn't it weird that the generation of small children growing up now are going to consider this to be normal and obvious? They'll be the AI natives. They'll be like, "Of course I have my AI agent buddy. There's nothing weird about that at all."

Tenille: I'll still keep being nice to my coffee machine.

Henrik: Yeah, that's good. Just in case, you know.

---

Thank you to Henrik Kniberg for joining us on this episode of the Easy Agile Podcast. To learn more about Henrik's work, visit Abundly AI or check out his educational videos on AI and agile practices.

Subscribe to the Easy Agile Podcast on your favourite platform, and join us for more conversations about agile, product development, and the future of work.

Dave Elkan, Co-Founder and Co-CEO of Easy Agile is joined by Jean-Philippe Comeau Principal Customer Success Advocate at Adaptavist.

"Hearing from JP is a sure-fire way to get excited about Atlassian Team '23. We spoke about where we are hoping to see conversations focus + more."

JP is passionate about teamwork, meeting new people, presentations of all kinds - loves a microphone and a captive audience, new technologies and most of all problem-solving.

In this episode, JP and Dave are talking about one of the most anticipated events in the tech calendar - Atlassian’s Team23! They’re talking about what to expect, tips for first timers and what they’re hoping to take away from the event.

They also dive into the future of work and the significance of coming together as a team.

We hope you enjoy the episode!

Transcript:

Dave Elkan:

Hi, all, and welcome to the Easy Agile Podcast. My name is Dave Elkan and I'm co-founder and co-CEO here at Easy Agile. Before we begin, Easy Agile would like to acknowledge the traditional custodians of the land from which we broadcast today, the people of the Dharawal speaking country. We pay our respects to elders past, present, and emerging, and extend that same respect to all Aboriginal, Torres State Islander and First Nations people joining us today. Today I am joined by Jean-Philippe Comeau or JP. JP is the principal customer success advocate at Adaptavist and is passionate about teamwork, meeting new people, presentations of all kinds, loves a microphone and a captive audience, this podcast definitely fits that mold, new technologies, and most of all, problem-solving. JP, thanks so much for being with us today.

Jean-Philippe Comeau:

Thanks for inviting me.

Dave Elkan:

Hey, no worries. It's great to have you on. We want to take some time today just to talk through Atlassian Team '23. The ecosystem is gearing up for one of the biggest events of the calendar and the ultimate event for modern teamwork. You've been to a few Atlassian Team events before and last year being the first one back in a while. Quebec to Las Vegas is quiet a gear change. What are your tips for people attending Team for the first time?

Jean-Philippe Comeau:

Ooh, yeah, that's a good question. I mean, yeah, Teams to me is a massive event. It's a beautiful moment to actually take in everything that has happened in last year for Atlassian. What I mean by that is actually more and more what's happening with Atlassian is actually what's happening in the world of work. So I think it's just a great time to reassess where you're at. So for me, it's about planning out the main things you want to hit and don't overcrowd your schedule. That's a mistake I made the first time was just I wanted to see the most of everything and I was like, "Yeah, I can absolutely do back to back to back. It's going to be fine. I'll be walking from one thing to another." Truth is after talk, you'll have some questions. Some things will popped-up. "Oh, that's interesting. I could maybe explore that."

You're going to want to do maybe some floor hunting, which is like, hey, looking through the partners. Maybe you've heard about something like an app that you really want to go look at or something like that. So, that's always going to happen and then you're going to miss that next talk. So make sure that what you highlight is really things you want to see and plan according to that. That to me is the number one thing. Don't try to do it all. Do what you feel is really, really important than the rest. Try to make it work because it's going to be a lot of walking, a lot of listening, a lot of talking. The second thing which I remind everybody is to hydrate, get a bottle of water. There's going to be plenty over there, but everybody's going to have their own branded bottle of water, so don't worry about having one or not, but get one and just hydrate. I mean, we all get very busy during the day and we all know how the nights can go, so keep drinking some water. Yeah, those are my two tips.

Dave Elkan:

That's great advice. I think hydration is certainly something to consider. I remember particularly a wall of donuts at one point distracting me from good habits like that. So yeah, it's really important to make sure you've got the basics in line. What are you most looking forward to from the lineup at Team '23?

Jean-Philippe Comeau:

Yeah. I mean, every year the keynotes are what's going to hit the most. Obviously, getting a chance to hear James Cameron talk is going to be very, very interesting. I think especially in the year of Avatar 2 is just great timing, obviously probably planned. He's probably on a tour, but it's going to be really great to hear some stories of how that movie came about. It's been a long time in the making, probably the closest thing we got to really long development on a film. It feels like a long software development cycle thing. That's a very long time. And then hearing Van talk about some of the things that he's seeing in today's world. Van Joseph, I believe, is the name of the second talker, and remember seeing him a lot on the CNN broadcast and stuff during the elections and the impact that he brought to the whole broadcast was quite something. It'd be very interesting to hear them talk.

And then as far as maybe not the big ticket items, really interested to... I think this is the year where the practices on the different tracks that Atlassian usually promotes, I think this is the year what they really start to hit. What I mean by that is I think before this year, so when you look at last year's Team and then before that, tracks were kind of like wishy-washy. Now, they actually have the products to back them. I think JSM's in a very, very good spot. I think their agile tooling is in a very good spot. I think their DevOps, which is what I expect, is going to be pushed the most, or DevOps tooling with the Jira product discovery and all their Point A stuff is got to be where it's at. So I think you're going to get really good talks on those practices. I think that's going to be the year where the tracks actually make a ton of sense and are very valuable to people.

Dave Elkan:

Absolutely. Thanks for sharing. It's really interesting. Yourself, you're a Canadian and James Cameron is a Canadian and he's talking about creating the impossible, and I think that's a theme that's coming through and what Atlassian is promoting and bringing that through. It's really interesting to see or hear you talk about the both building movies and media and CNN, the reference there, and how that can apply with a strongly software development-based audience. It's really interesting to see that building a movie is a very much a waterfall process in that you have this huge deliverable at the end, but I know that there are Pixar, for example, use this concept of Demo Trusts, we call them, or the Pixar Demo Trust. Yeah. So essentially you can test along the way as you go before you deliver this huge thing. It's really intriguing to think what we're going to hear from James in regards to how he builds these amazing projects.

Jean-Philippe Comeau:

Yeah, I think you're spot on. So I'm actually a huge Marvel fan. I don't have my book with me, but the Creativity, Inc. is a book that I love by Ed Catmull and how they built Pixar as a business, as a delivery team, not just about the movie side of it, the creative side of it, but how do you bring creativity into a more structured world that is the corporate world kind of thing, which they're now a part of? So, very interesting that you bring that up because I'm very fascinated by their process as well. I think they were the pioneers in the movie-making business or industry into bringing the agile methodologies or thinking to movie-making.

Now, what would happen historically in movies? Okay. So you don't know this, but my background is actually enacting. So when I started, when I studied, when I was a young lad, young adult, let's put it that way, I wanted to be an actor and then things changed. Obviously, I am not a prolific actor. So I'm very, very passionate about the movie-making industry. Movies historically has always been about you shoot, you shoot, you shoot, to develop, develop, develop, and then at the end, you cut it. So you make mistakes. So like we said, very, very waterfally. I think now that technology is almost like 50 to 60% of a movie now more days... If you look at Marvel movies and all that, you could argue it's 50 to 60% is going to be computer-generated, which can be a bad or good thing. Now, that I'm not going to get into that debate.

The nature of previz and all the animation work that goes behind it makes the process more agile, meaning that what they're going to do is they're going to build for a week and then they're going to review the film that's been made and then they're going to correct and do it again, right? So already you got your feedback loop going. You got your process. You got your sprints going. I can map all that out to some agile processes and I wouldn't be surprised that you're looking at something that are looking to scale up. You could even argue what are you guys going to do for your scaling methodologies? There's a lot of things that are very interesting.

I think going back to our first point, sorry, I really went on a tangent here, but going back to Avatar, when you have such a long cycle and you have a movie that's built, that one is heavily computer-generated. I mean, every actor has stuff on their face and they're acting in a blank studio. Now you're talking about agile processes because if you're building hours and hours and hours of work and you're just building and building and building and never review, I can't... Maybe James will say that's how they did it, and I'll be like, "Well, you guys were... It's very difficult. You made your life very, very difficult." But it'd be very interesting to hear because I cannot imagine them not going into some type of an agile way of building this movie.

Dave Elkan:

Oh, of course. I think that if you imagine the cutting room floor, it's an old adage and literally they used to cut the film and they'd leave it on the floor as that's something we're not doing anymore. And so, I dare say that there's a vast amount of film which is thrown away and redone. I feel that if we could see past that to this beautiful thing that they're doing behind the scenes, which is testing and iterating on their shots, it's actually quite a simple concept to apply these agile processes to filmmaking. It's just at the end you have got this big bang, same in game production. When you produce a game, you cut back. People do early access, which is fantastic. You can't early access a movie.

Jean-Philippe Comeau:

No, exactly. Yeah.

Dave Elkan:

Yeah. Going back to Pixar, that reference, I actually made the mistake. It's not actually the Demo Trust. So this is the Playbook by Atlassian. There's a play called Demo Trust, but it's the Brains Trust and it's bringing together the team to talk through does this fulfill the vision of Pixar? Does this make Pixar Pixar? And helping the team understand, so directors get that ingrained Pixarness through that process. So yeah, there's a whole team behind the scenes here. There's not one person who's just driving this at the director level. There's actually a whole team of people collaborating on this movie. So I'm really intrigued to hear that from James to hear how the teamwork comes out.

Jean-Philippe Comeau:

Yeah. I think when you look at a movie like Avatar, again, another thing that we don't think about is the connecting remote teams, which is a big, big part of what we do in 2023 is connecting remote teams so that they feel they're working on one project. When you have a movie like Avatar, your VFX is going to be somewhere. Your actors are going to be another place. And then you're going to have music and sound's going to be somewhere else. Your editors are probably going to be somewhere else. And so, there's a lot of remote work that you do. How do you bring all that together?

I remember watching the old documentaries around the Lord of the Rings movies, and they were literally flying people in and out with the actual roll of films because they were so afraid that people would steal them and so that they wouldn't put it on the internet and they would actually carry them around. So they had to fly from London to New Zealand to... It's kind of nuts when you think about it in 2023. Really, you had to take a 10-hour flight just to get your film across? It's probably easier also with the data, just the bandwidth and everything. So I think that's also going to be an interesting part is how did you connect teams?

You brought up a great point around the Pixar way or that's how they call it, the Pixar way. When you think about that, there's some really, really cool ideas behind bringing a team together and rallying them around one project. I think as teams get more remote and distanced from products and things that they're working on, and I do it myself at work. Things become generic. At some point, you're just doing the same thing over and over again. You lose touch a little bit with the work that you do. I think it's a beautiful thing to be able to rally a team around a project and say like, "Do you believe in this project? I believe in this project. Do you believe in this project?" And making sure the team does and if they don't, why don't you? What's preventing you from that? I think there's a lot of good conversations, sorry, that can come from that. Yeah.

Dave Elkan:

Absolutely. So yeah, you talk about going more remote. Is that a trend you're seeing, that we're continuing to see more and more teams go remote, or are we seeing a reversal of that to some extent?

Jean-Philippe Comeau:

It depends on what sphere you're working with, or in my position, I get to touch everything. I tend to gravitate towards the more creative teams of gaming and software development and stuff. I do work with banks. I do work with, well, corporate America, the classic suit and tie kind of places, everything. I see everything. There is absolutely out right now a battle of old versus new, old ways of working, new ways of working. There's a huge clash happening. I to this day do not know who's going to win, because even the big Silicon Valleys, I mean, we are all seeing what's happening with Apple and them putting mandatory office dates and stuff like that. You see that from an executive that is leading maybe one of the more bleeding edge companies in the world, but he's still an old school vibe of creativity.

I hate bringing it back to Pixar. I'm going to bring it back to Pixar. They have such a great office. So like I said, I'm very fascinated about what they do. They call it unplanned creativity. They truly believe that unplanned creativity happens in the office, and when you have unplanned meetings, unplanned interactions. So one of the things that they did, it's now very common, but when I was 14 years old and I was reading about them, I was like, "Oh my God, these are such cool things to do," they were doing those ping-pong places and activities and games to get people to play together and start talking about what they were doing.

And then all of a sudden you got an engineer talking to a VFX artist that's talking to a 3D or conceptual artist, that's like they would never meet in a meeting or anything like that. But because they're playing ping-pong and throwing ideas around and all of a sudden they're like, "Hey, maybe we could build this thing. That'd be amazing." Because the artist saying, "Well, now I could do clouds this way. Yeah. Nuts, I could create clouds that look like this." Then the engineer goes, "Well, you can just tweak a little bit of things."

Anyways, so I think there's this old school mentality at this. It's a question I've asked myself in our Slacks and where we talk about work. I don't know what the future is for unplanned creativity. I don't know how you recreate that in a virtual world. I think it's a big problem that some software companies have tackled with some tools. I don't know how you force someone to sit behind a computer and do something that's unplanned. How do I stumble across some... I don't know. But yeah, I think there's a bit of that in the old school mentality. I need people in an office so that they can meet and they can interact together. I still struggle to find where they're wrong, let's put it that way. I don't know where they're wrong about that theory of when you're with someone, when you're with people things happen in a different way.

Dave Elkan:

I can't agree more. I think that if I have any perspective on this, it's that there is not... Often, it's not a black and white or a zero sum kind of game. It's a combination of things that will occur and that will move forward for better or for worse. You can look back in history to Bell Labs and the creation of the semiconductor and the way that the building was designed essentially to allow people to walk past and have cross-collaboration and cross-functional conversations. Have you ever considered that the unplanned creativity that Pixar was talking about was actually planned-unplanned creativity, so they made these spaces on purpose? How can we make things on purpose to have things unknown to us happen?

Jean-Philippe Comeau:

Yeah. Yeah. Actually, you're absolutely right. I mean, yeah, they built the Pixar offices this way because of that. To me, that is the secret. If someone finds it, it's like the caramel milk or whatever, just bottle it up and sell it to people, I guess. I don't know. I have no idea what the answer is. I've looked and it's... There's an app out there. I can't remember the name of the app, but you're like a 2D sprite and it looks like an NES game and you're moving around from places to places. You can decorate your office. It's got this vibe of Animal Crossing, which is a game by Nintendo where you can just create stuff and people can visit your island and all that.

You can do that with your office space and then you can create a common area where people walking. When you look at it in a video, it's brilliant. Great, I can actually be in the office without being in the office. It has this whole technology of proximity. So if you're having a conversation with someone in an open area, people could walk by and hear what you're saying and join in. Beautiful technology, doesn't work with the humans when you really think about it. Why would I go online to walk around an office to go talk? I'll ping you on Slack, it'll be easier. All right. I don't need to walk through your office. So it's like I don't know what the secret is.

Yeah, you're right, it is planned in a way. I think we do that. I don't know for you guys at Easy Agile, how you do it. In Adaptavist, we do like to travel with teams. So whenever we do things, even if it's customer work or if we're going for an event or something, we try to make it a point to make it about also us and what we do. So we rarely traveled alone. If I'm going to a customer, we're trying to get two consultants in there, or what I'm trying to say is bring more people. It's a point, I think, Adaptavist is trying to make and I think that's what Simon, our CEO, is trying to make is use these opportunities to be with people. I think it's a beautiful thing, but it's one of the myriad of solutions. I don't know. I really don't know. What do you think? What are your thoughts on this?

Dave Elkan:

Oh, I can share how we work at Easy Agile. So here I am today in the office. This is a great place for me to do this recording. We have a room for about 50 people here in the office in Wollongong, south of Sydney. We have about 10 to 15 who usually arrive on a daily basis, and that's great. We don't mind. We love people working from home and working away, which is more convenient and relaxing for them. At the same time, we do have quarterly plan, like planning sessions that we go to. We have Advanced Easy Agile every quarter. We come together in person. We've strategically ensured that we hire in a way so that's possible, so people aren't flying across vast sways of ocean to get to this conversation. In a way, it's planned-unplanned. So we do our planning ahead of time.

When we come for Advanced Easy Agile, we'll have something that we want to either upskill the team with or whatever, and then we'll have some team bonding where people can choose from a range of different activities they want to do together. And so, for us, it's more about getting together in person because we know that's really valuable to both build an understanding of each other as a team and to build that rapport. It can't be done over Zoom to an extent. So, absolutely, our business runs entirely in a remote-friendly way and we don't rely on people to be in person, in sync in person to move forward. However, we do see there's a great value there. So we try to live in both worlds and we get the benefit from both of them. Yeah. And so, that's one thing that can work. It's not for everybody. If you have a truly distributed global business, it's not exactly easy or affordable to bring everybody together on a quarterly basis.

Jean-Philippe Comeau:

Yeah. I think it's beautiful though. So I've been in Adaptavist for close to six... I'm on my sixth year now and we used to be able to do... We didn't do quarterly. We did a yearly thing at the end of the year where everybody would get together. We called it Winter Con for the last two years, which I actually loved the idea, which was very much we could pitch ideas of what we wanted to talk about. It could be about work, could be about customers, could be about last year, whatever you wanted to talk about, could be about yourself, could be about a cool thing you did this year, whatever. We had a voting system, but really pretty much anyone that said any, you could get in.

You could just walk around and it was literally a conference center. We'd set up some rooms and you could walk in, look at a presentation, literally like Teams or whatever. It was the best experience every time that we did that. I love these because there's value. There's an ROI in having everybody learning and upskilling and breaking down these silos of, "Hey, I never worked with marketing, but here's an hour talk around something we did in marketing. I really want to join," and all these things. That's great. There was also the unplanned ROI, where you were coming out of there with multiple ideas of like, "Oh, I could explore this. We could explore that. I got this meeting set in Jan now that whenever I come back in January, we're going to be talking about this thing that we talked about for cloud migrations." All that was happening at Winter Con.

Now, we grew exponentially post-COVID, well, during and post. So while COVID was happening and all of a sudden everybody wanted work. And then as companies that were remote, I think a lot of the companies that were remote grew during COVID versus because companies that were local or anything, they slowly diluted down a little bit, let's put it that way. As we grew, we can't support that anymore as a one-time thing where you'd have... We're close to a thousand now. There's a lot of people to move and a lot of conferences, a lot of conference rooms and presentations and stuff that we just can't accommodate. So, I miss it a lot. We've been doing it remotely, but like you said, it's not the same to go on a Zoom call.

I remember sitting down in these presentations and you're sitting down next to people that someone from Arkansas, someone from Cambridge, and you start talking. Yeah, you're listening to conference, but we all know what happens when you're listening to a presentation. You start talking like, "Yeah, that's an interesting idea. What did you do last weekend?" You start talking. Those are things you can't do on Zoom. You can't really reproduce that on Zoom. It's not going to happen really and I miss that dearly. I don't know what the solution is when you have these kind of global distribution. I mean, I guess you do in a smaller way, maybe all of North America meet up or things like that, but it's just not the same, not the same at all. I think it's beautiful that you guys can still do these because everybody's close by. I think it's really nice.

Dave Elkan:

Oh, thanks. Yeah, it's something we're hoping to hold onto as long as we can. We understand that these things don't scale. At one point, we'll have to break it into different events so that people can have, I think, a higher level of involvement in that. If you have too many people at the same time, it can be just a bit read only, the way I see it. It's as if to seek participant.

Jean-Philippe Comeau:

That's nice. Yeah. Yeah, I like that. Yeah. Yeah, you're right.

Dave Elkan:

So I'd love to just quickly touch back on Atlassian Team '23.

Jean-Philippe Comeau:

I'm sorry.

Dave Elkan:

You did mention at the beginning... That's all right. We'll get there. There's these new apps, especially in the DevOps tooling space that Atlassian's working on, so Discovery. Can you just talk to me a little bit more about what you see there and why that's coming to fruition now?

Jean-Philippe Comeau:

Yeah, I think it's all about cloud. I'll be the first to say that big fan of data center, big fan of on-prem. That's how I learned the Atlassian tool set. So, a little skeptical when cloud came about. As it grew and it got better, it got better, that was great. I think it's now at a mature spot where the Point A program, which is where all of these tools are coming out of, so the product Discovery, Atlas and all that, those are the fruits of cloud. That's because now that we have cloud, they can churn out products and try things and see if they stick or not. I think that's why I think this year is the year where I think the program is mature enough. Migration's ready. I mean, we're one year out of server end-of-life. I think we're finally in a place where we can actually talk about all these opportunities. Most of the people at the conference will be able to get value from it.

I remember last year where talks were heavily around JSM and all the cool things it would do, but you still had a lot of people on server, still had a lot of people on data center. So it fell a little bit on deaf ears. A lot of people in the crowd were just like, "Yeah, it's not for me." Both keynotes were about that. So anyways, I think this year it's going to be better because of that, because everybody's bought in. I think it's right now because yeah, it's cloud. You can ship easier, faster. You can ship better. You can iterate better. You can get a product ready much, much quicker than if you're on-prem, and I think that's why you're seeing this blow up. I also think they're great ideas. Big fan of Atlas specifically. Big, big fan of Atlas.

Dave Elkan:

Yeah. Fantastic. So, how are your customers seeing the migration to cloud? On the larger end, is that something that they're open to? Is that something that they support?

Jean-Philippe Comeau:

Everybody is intrigued, I'll start there. Everybody's intrigued. Now, the level of interest depends on the industry and the size. When you have a massive... I'll use banks because to me, banks are kind of like countries. So if you look at a massive bank where you have 30, 40,000 users, usually they have solid infrastructures. They have solid administrators. They have teams that are kind of living off this. It's built its own economy, basically. It runs itself. When you go in there and you try to teach them about cloud and all the great things it'll do, they start asking questions that are very technical and they're very good. There's not really an answer in cloud for yet, and so it gets skittish. Whereas if I go to a 500,000 people organization and they start asking questions about cloud, and usually we have more answers for that. It's just easy, an easier conversation. They don't have the same worries or the same thing troubles on their mind than the admin of 40,000 people. It's just not the same reality that they're seeing.

So I think for now, and I know Atlassian's making a big push into that enterprise space, I think for now you're going to see that growth. But as long as we don't have full autonomy of where our data is and how accessible that data is, it's going to be a problem, as long as FedRAMP isn't available to all, as long as all these different SOCs and compliances aren't available to all. These are very difficult because you've built an ecosystem around a lot of integrations and Easy Agile being to me, one of those integrations because their third-party app, however you want to look at it. Adaptavist has their own third-party app. So you have script runner and all that. We all have third-party apps. So Atlassian can't be like, "Oh, yeah, I'll make a blanket statement. We can do all these things." It's not really true. I'm like, "Hold up, you got to take into account all these different app partners out there that are doing their things and you can't put us all into one roof."I think they're victims of their success. What still making Atlassian great is the partner ecosystem, apps, solutions, sorry, everything, but it's also what's causing the adoption and the speed to which adoption of cloud is happening. It's making it slower than they would want to. I think that was maybe the misstep a little bit when everything got announced was like, "Oh, you guys do rely on these apps a lot." Yeah. A lot of our customers actually would say that the apps are even more important to them than the core. It's just a thing that you're seeing. So to go back to your question, depends on the complexity of the instance. The bigger the instance, usually the more complex it is. So if I go to over 10,000 users, it's going to be a very long conversation. Very, very long conversation.

Dave Elkan:

Yes, it is. It's funny that Atlassian did ship this and say, "Hey." Well, actually, there was a presumption that the apps were covered by SOC 2 or the like as well, and that was a missing... But it was this misunderstanding. But I say as a business owner going through SOC 2, it's a very rewarding and good process to go through. It's hard. We are doing it far earlier than Atlassian did in their own journey, but the sooner you do it, the easier it is. Ideally that as a smaller company, you have less things to worry about and the processes you put in place will be easier to maintain and monitor. So we're excited to really go down the SOC 2 path and to provide that peace of mind to our enterprise customers. So yeah, very good process to go through.

Jean-Philippe Comeau:

Yeah, you guys are going through it right now. Have you acquired it yet? Did you get your compliance yet or you're on your way to getting that?

Dave Elkan:

No, we're on the way to SOC 2 type 1 at the moment.

Jean-Philippe Comeau:

Wow. Nice.

Dave Elkan:

Yeah.

Jean-Philippe Comeau:

Yeah. Yeah. We got security group now in and they're handling all that. I'm not good with the compliances. I'll say it right now, right off the bat, I don't know them very well. I know they're like letters I would like to see next to every apps. That's what I know. I don't know how in depth the processes, but I know it's very involved to the point where you need to have a team dedicated to making that happen. So what have you guys seen so far? It's coming along great. What are some of the challenges that you've seen maybe? I'm just intrigued.

Dave Elkan:

Yeah. Oh, look, so our cloud apps are all architected in the same way, so they all use the same code base to an extent, like the deployment methodology. We haven't done any acquisitions which have bolted on to make that more complicated, so we're making the most of that situation. We've done a fair bit of work over the last quarter or so to put in all the checks and the controls around that deployment. The next thing is to really put in place the processes to ensure that our team understands how to deal with different situations and the like. So, that's something we're going to tackle in the next quarter. I'm excited to go through that and do a bit of a sprint with Nick, my co-founder and co-CEO, to really see how much we can get done in a period of time and really focus on that. I think that the benefit will be that we have a much more understood and clear way of running our business, which is obvious to our customers as well, which is a very good thing. I'm all in favor of it. Yeah.

Jean-Philippe Comeau:

Yeah, that's awesome. Yeah, I think we're seeing some of the similar things, but we did acquire a bunch of stuff and so that is making everything a bit more difficult, for sure.

Dave Elkan:

I can understand. That would be very tricky to try and bridge those gaps and to homogenize enough to be able to have a really clear statement going forward. Yeah. Okay. So we touched quickly on the Atlassian apps that they're bringing. Are there any apps in the marketplace that you have got an eye on that you'd love to go and talk to, of course, Easy Agile aside?

Jean-Philippe Comeau:

I mean, of course. Yeah. A big need that I'm noticing now in the market... I don't know if it's a secret or something, I should wait because I know Team '23, they're going to be doing some stuff and I'm really excited for them. So one of the things that we're noticing is... So backups, so enterprise support, basically. Right now, when you're on the cloud, most companies, again, in the 40,000 and plus have strong backup needs and they actually have requirements, laws, things that they need to abide by as far as how long they maintain data, how long they have backups of data and all that. Right now, the way that it's done in cloud isn't nice at all. You actually have to go into the UI. You get a backup. If your backup is large, it's going to take multiple days to process and you got to remember to... It's all manual. There's nothing that really automated.

So, there is a growing market for these kinds of apps. I've been talking all that to these people at Revyz, R-E-V-Y-Z. What they do is they basically automate that process for you and they host your data. Right now, they only do it for a year, but it's still much better than what we're seeing out there. There's a lot of need for services like that, where they... Because I mean, part of the appeal of cloud is obviously hands off, don't have to worry about things anymore and Atlassian only guarantees backups for 21 days. So if you're an enterprise and you're looking for six months at least of data recovery, at least you're not going to get that. So by having a partner like Revyz or all these, there are other apps out there, I'm talking about Revyz specifically because I talk to them a bunch, but a lot of interesting things are happening.

Also, what's amazing about these apps, what these developers have found, and once they've have that process, they now get access to the structure of the data and they've started building tools around that structure. So for instance, that app can actually restore projects and issues and custom fields and configurations. So you don't need to do a full restore. You can actually pick what you want to restore, which is brilliant. It's something that even in data center wasn't easy to do. You couldn't just say like, "Hey, give me that issue." You'd have to restore the snapshot, go into the system, find your stuff. Now being able to go into my UI and Jira, go into my backup app, go and look the issued I deleted by mistake, find it, restore it the same day, it has comments saying, "This was restored by revisits, so make sure blah, blah, blah, yada, yada, yada." It's just brilliant and I'm really excited to see that grow this year.

Dave Elkan:

That's amazing. Yeah, it's a really intriguing part of this piece that I've never really thought through that that's actually a really important part of running an enterprise, that you have those continuous backups. Yeah. Cool. Yeah, that's a great insight.

Jean-Philippe Comeau:

Yeah, it's going to be an interesting market to dive into because we've been asked, even as a service partner, "Can you deliver on this?" The truth is without an app, you can't. There's no real way for me to get a backup. I'd have to go into your instance every day. I don't think you want a consultant going into every day your instance, downloading a backup and throwing it. I'd rather spend my money elsewhere. So these apps are going to be very... I think they're going to be big and I'm really interested to see what happens with all these different ventures.

Dave Elkan:

Well, certainly, a booth I'll be popping by to see if we can include the Easy Agile data in that backup as well.

Jean-Philippe Comeau:

Yes, exactly. So they are looking at other app partners and seeing what they can do. So I think, yeah, absolutely, if you want to have a chat, they're great people.

Dave Elkan:

Beautiful. Thank you so much for your time today, JP. That's a wrap. Hey, is there anything else you wanted to touch on before we wrap up? Is there anything you are hoping to get away from the event, to take away from the event? Anything on the sidelines you're going to see when you're there?

Jean-Philippe Comeau:

I mean, obviously, App Day is going to be a big thing. Really excited to meet y'all in person, see everybody. So App Day is the time where I get really technical, get my hands dirty. I don't do that a lot these days. I miss it sometimes just sitting down and doing some good old admin work. So anyway, the App Days are usually when I really get back to the nitty-gritty of let's talk about script runner, where we're at now, and let's meet with Easy Agile, with Temple, with all these different app vendors and talk about what's coming up and what they're seeing. So really looking forward to that. But other than that, no, just looking to have a good time. I'll hopefully get some good social time as well at the evening. Like I said, we won't get ourselves half the fun is also after the events every day, so really looking forward to that, for sure, and meeting all my fellow ecosystem partners and talking to everybody and seeing what they've seen in the past year.

Dave Elkan:

Likewise. I'm at least 1,000% more excited now having talked to you about it. So thank you so much for taking the time today, JP, to talk through that and I can't wait to see you there.

Jean-Philippe Comeau:

Yeah, I can't wait to see you. Thanks for having me.

Dave Elkan:

No probs. Thanks, mate.