Easy Agile Podcast Ep.4 Em Campbell-Pretty, CEO & Managing Director at Pretty Agile

"I had an inspiring conversation with Renae around the benefits of leaping out of your comfort zone and aligning team behaviour " - Chloe Hall

Chloe Hall- Marketing Coordinator at Easy Agile is joined by Renae Craven - Agile Coach, Agile Trainer, Scrum Master Coach and QLD Chapter Local Leader at Women in Agile.

Join Renae Craven and Chloe Hall as they discuss:

• Renae’s journey to becoming an Agile Coach and Agile Trainer
• Taking a leap out of your comfort zone
• The importance of taking time to gather feedback and reflect
• Building a team environment where everyone feels safe to contribute
• Aligning team behaviour and how prioritising learning impacts team delivery
• Why sitting all day is bad for you and how to bring movement into your work routine
• + more

Transcript

Chloe Hall:

Hello and welcome back to the Easy Agile Podcast. I'm Chloe, Marketing coordinator at Easy Agile, and I'll be your host for today's episode. Before we begin, we'd like to acknowledge the traditional custodians of the land from which we broadcast today, the people of the Dhuwal speaking country. We pay our respects to elders past, present, and emerging, and extend that same respect to all Aboriginal Torres Strait Islanders and First Nations people joining us today. Today we have a very exciting episode for you. We will be speaking to Renae Craven. Renae is an Agile coach, Agile trainer, scrum master coach, BASI Pilates instructor, and runs her own Pilate Studio.

Renee is also a chapter local leader at Women in Agile Brisbane and is the host of the podcast The Leader's Playlist alongside David Clifford. Renae's passion in life is to help people to be a better version of themselves by raising your awareness of areas they wish or need to improve them and to support them in their learning and growth through these areas. According to Renae, coaching is not about telling people what to do. It is about questions to allow them to dig deeper, uncovering realizations and their desire for change. Welcome to the podcast, Renae. Thank you so much for coming today. Really appreciate it and very excited to unpack your story, your journey, and all the success you have achieved, which is amazing. How are you today anyways?

Renae Craven:

I'm all, I'm good. Thank you, Chloe. It's Friday, so I'm always a bit wrecked on a Friday. Looking forward to sleeping in on the weekends and things like that. So yeah, Friday I'm already, always a little bit dreary, but other than that I'm fine.

Chloe Hall:

Well, that's good. Friday afternoon definitely can always do that to you. I'm very pumped for a sleep in as well. I think let's just get straight into it. So some of that I wanted to start was I just want to unpack you as a person, Renae, and kind of your story, who is Renae and the journey you've taken to become so successful today. So if you wanted to provide a little bit of background about yourself.

Renae Craven:

How far back do I go? So I did IT at uni, Information Technology at uni. So I started my career out as a graduate developer, software developer, pretty crap one at that.

Chloe Hall:

Surely not, I don't agree with that. I can't see it.

Renae Craven:

I knew enough to get by, but it was definitely not going to be something that I was going to do for the rest of my life. But back then I was 20 and kind of just was doing things that you were supposed to do when you grow up. You're supposed to go to school and you're supposed to do well in grade 12 and go to uni and get a degree and then get a job.

Chloe Hall:

Definitely.

Renae Craven:

So yeah, I ticked all those boxes and found myself with a degree in a job in a good organization. And I was in that development job for a couple of years and then I kind of moved more into team leadership and I was a team leader for a while and then I became a scrum master back in 2010. So that was when I discovered Agile.

Chloe Hall:

Okay. Yup.

Renae Craven:

And I think the rest is kind of history. So when I discovered Agile, things started to make more sense to me. Talking to people, having teams, working together, collaborating together, solving problems together, getting multiple brains onto a problem. That kind of thing was one thing that I never made sense to me when I was a grad straight out of uni. And I'm like, "What do you mean?" Because even during my university, I was a little bit different and I was remote. I did university remotely years ago and with a group of four others, there were four others, it was a group of five. We did everything together, we did all our group assignments, we studied together, we ate lunch together, we just kind of did.

Chloe Hall:

So with the exact same group?

Renae Craven:

Yeah. All the way through uni. I went from that kind of group setting to working and more of an individual on my own like if I've sat in a cubicle with walls that were higher than me, I didn't have to speak to anyone else if I didn't want to. And that never really sat well with me. It was never kind of who I was. So when Agile was, Scrum specifically was here's all these people we're going to throw together in a team and here's all of the problems and you work out together how you're going to solve it.

Someone's not going to tell you what to do or how to solve it, you've got to figure it out as a team, it was a much more, cool this is what makes sense, this works better. Why wasn't it always like this? So yeah, that's kind of where my Agile journey started and it kind of progressed as I did scrum mastering for quite a few years in different organizations, different scenarios, different contexts. And then I guess I was able to comfortably call myself an Agile coach I would say maybe 5, 6 years ago. I mean, there's nothing really that you can do that you go tick, Oh, I'm an Agile coach now.

Chloe Hall:

There's no kind of straightforward degree or certification.

Renae Craven:

No, it's really just experience. And I had experience around and people were telling me, "You can call yourself a coach, an Agile coach now, you've got plenty of experience". I'm like, "Yeah, but I feel like there's so much more that I need to know or that I could learn". So I don't really feel comfortable. But I was working for a consultancy, so that was just how I was being marketed anyway. So that was kind of 5, 6, 7 years ago that that started to happen. And then I do other things as well, like Agile training. I love training people, I run training courses, do the coaching as well. And then I've got my Pilates as well.

Chloe Hall:

Just an all rounder, a lot going on, that's for sure. I think as well, I just want to unpack, you had that transition when you were a graduate developer and you found it quite isolating. And then you came into this concept of Agile when you are working in teams. Was it when you started doing that Agile, did that kind of spike like a passion, a purpose of yours and that's what led you down that Agile training, Agile coaching road?

Renae Craven:

I think, I mean purpose, I still don't know if I know what my purpose is in life. Passion. I think what it helped me understand about myself is where some of my strengths were. And my strengths aligned with what was needed to be a scrum master and a coach later on. So the ability to facilitate, that's a big part of being a scrum master, a big part of being one of the key things about being a coach. And that was just something that I was kind of naturally able to do, but I didn't know until I started doing it, if that kind of makes sense.

Chloe Hall:

Yeah. I feel like, isn't that always the way, It's like you don't know something or you don't really know your strengths until you just step into it. You've really got to get out of your comfort zone and just try new things, experience new things. Otherwise, you're never going to know.

Renae Craven:

Yeah, exactly. So yeah, can't trying to create that equal participation in a room or in a workshop from a facilitation and facilitating a group of people from different walks of life to an outcome and just letting it kind of flow and let the conversations flow. But still, you've got to get to this outcome by the end of the day or end of the workshop. That was something that I was naturally able to do. And I mean, my first workshop, how I facilitated that, I don't even remember what it was, but I'm sure how I facilitate now is very, very different. But it was still something that I loved doing, that I enjoyed doing. And the training part of it, it's funny because at school I used to hate public speaking. I used to hate.

Chloe Hall:

You sound like me.

Renae Craven:

Yeah. All of that, how I used to get up in English and do an oral exam and things like that. I hated all of that stuff. I was very happy to just hide in the background and never answer a question or never cause any trouble or be disruptive or whatever. Except in maths class I was a little bit disruptive in math class.

Chloe Hall:

I am resonating so much with you right now because I was literally the exact same. And I've always had a bit of a passion for math. So in maths I was super outgoing, would ask so many questions. But in English my biggest fear was public speaking. I just could not stand up for the life of me. It was the worst. I was always so nervous, everything about it. And I think that's really interesting to see how far you've come today from what you thought back then. Was there any type of practices, lots of work that you had to do on yourself to get to this point today?

Renae Craven:

I think similar to what you said before, you got to get out of your comfort zone. And I think, especially early on in my career, that being pushed out of my comfort zone. There's a few leaders that I was working for at the time that, well a handful of people that over the years have pushed me out of my comfort zone. And in the earlier days where I wouldn't have done that for myself. So doing that for me or I didn't really have a choice because I was a good girl and I followed orders back then. It was just something that I went, "Oh okay, well that's cool". I'm glad in hindsight, I'm glad he did that because I wouldn't be where I am right now if I wasn't thrown into the pilot team, the pilot agile team. So yeah, there's things like that where I've been pushed into my comfort zone and just had a go and found out that, oh, it wasn't so bad after all.

Maybe I could do that again. And then you start to build your own kind of resilience, you go, well I've did this before so that's not much harder. I reckon I could do that. Or it's kind of thinking about it like that, but it's also changing. It was shifting my mindset to be you've got to get out of your comfort zone, you've got to screw up to learn. The way that it was at school where you got rewarded for being correct, you got rewarded for doing the right thing. And that's not how I learn. That's not how a lot of people learn. You have to screw up to then go.

Chloe Hall:

Definitely.

Renae Craven:

Okay, well next time I do that I'll do this instead.

Chloe Hall:

Yeah, definitely.

Renae Craven:

Or getting that feedback of how you did this, well next time maybe you could do this or whatever it is. Just getting that feedback. Whereas, I never got any of that at school. It was always Renae's perfect angel child, whatever it was.

Chloe Hall:

Still, nice though, but yeah.

Renae Craven:

Nice for the parents. Can we have more of Renae's in our class, nice for mom and dad. But in hindsight, it didn't really do much for setting me up for how.

Chloe Hall:

For reality.

Renae Craven:

Yeah

Chloe Hall:

Really.

Renae Craven:

Exactly.

Chloe Hall:

Especially because I've recently gone through that transition from graduating uni into a full time job and working for Easy Agile, I'm always being pushed out of my comfort zone in a good way. Everyone's so supportive, they're always like, "Oh Chloe, try this, try that". And I'm just like, "okay, yep, I can do it". And if it doesn't go amazingly well that's okay. I've learned something and I can do it better next time.

Renae Craven:

Yeah.

Chloe Hall:

You can't just sit in your comfort zone forever, you don't get that feeling of when you do something outside of your comfort zone, you just feel so good after and you're like, oh, prove to myself I can do this.

Renae Craven:

Yep. And I think the big part of that is acknowledging the learning is sitting down. So one of the things we do, I do as a coach is one of the key times for a team or an individual to learn is to actually sit down and reflect back and then what was good, what was bad, and what am I going to do differently the next time. And I coach teams to do that, but I have to do that myself as well. So kind of realizing that as a practice, that's something that I have to do is sit down and when I do these things I would need to gather feedback and then I have to sit down and reflect on how it went. What I think I can do better or do differently the next time around I do something like this so that I am also myself improving in the things that I do. So it's really having that time and that practice to learn to sit down and what did I learn?

Chloe Hall:

Yeah, I do. And I agree with that. You need to take the time to understand, reflect, realize what you have learnt. Otherwise, life is so busy and you just keep going and going and going and you can just completely forget and it's good to take that moment. I really like how that's something that you do in your Agile coaching as well. What else do you do when you're coaching teams? What other elements are there?

Renae Craven:

Some of the stuff I've already spoken about, having that equal, trying to get that equal participation, equal voice. Trying to, the buzzword is psychological safety, but trying to make, trying to build an environment for a team where everyone feels safe to ask a question or to voice their opinion or whatever it is. And when we've come from, as a coach, what we're doing is usually coaching teams, people, organizations, through a shift from a certain way of working to an Agile way of working. And that means that the whole telling people what to do and when to do it and how to do it is gone. That's gone. And now you want to build that capability within the team itself. So creating that safe space so that the

team can ask questions and understand what they have to do so that they can collectively deliver something as opposed to someone just telling them what to do.

So it's using your brain, using the collective group brain as well, instead of just having, not using your brain really, just waiting to be told what to do and then you'll know what to do, you just do it. But collectively solving a problem together as a team and then figuring out as a team how we're going to solve that or how are we going to deliver that is something that is quite, that's the bit I love as a coach, working with teams, building that kind of environment where they do feel safe to ask the dumb questions and things like that.

Chloe Hall:

And not have to be like, I think this is a silly question, but you definitely want to remove that.

Renae Craven:

And I think the other part is the learning still, it's exactly the same. It's taking the focus, trying to get the focus off, we must deliver and then we'll do some learning stuff if we get time trying to flip that around so that your, "No, no, no, you need to learn in order to get better at delivery". So take that focus, because a lot of teams will just say, we've got all these deadlines, all of this delivery pressure, we have to get this stuff done. We don't have time to sit down and think about what we've learned or how we can get better as a team. They're never going to get better as a team if they just keep in this endless delivery cycle. Making the same kind of time wasting things over and over and over again. So it's kind of flipping the mindsets of the teams as well to go, "No, hang on, we need to do this otherwise we're not going to get better as a team".

Chloe Hall:

Yeah, definitely. And I think that's where the Agile retrospective fits in perfectly. And I know I actually just came out of my retrospective with my team and we do that weekly and it's so good to come out of that with action items too. And it's like, okay, next week this is how we're going to get better. This is how we're going to advance, this is our focus and there's also no hidden problems because it comes up every Friday, we talk about it. So you're not going into Monday the next week with a grudge or you're annoyed about something with the workflow of the team. You've addressed it, you've left it in the last week, you've brought the action with you obviously, and hopefully it's going to get better from there.

Renae Craven:

Yeah, absolutely. And that's the key. It's the whatever we've decided in our retrospective of what we're going to do differently, we're doing that differently the next day or Monday in your case. It's not something we talk about and then we just kind of ignore it and we just talk about it again in two weeks time or whatever it is. It's the putting into practice the decisions you make as a team and those retrospectives all of the time. They're not massive actions either. They're just little tweaks here and there.

Chloe Hall:

Yeah, there's small things.

Renae Craven:

They just kind of build up over time.

Chloe Hall:

And that's the thing, it's like if you do it on a regular occurrence, they are small things, but if you are not doing it regularly, then that's when they build up and they become big things, big problems and massive blockers within the team as well.

Renae Craven:

Yeah, absolutely.

Chloe Hall:

Yeah. So I'm wondering too, Renae, when you do your Agile coaching and your Agile training, so you do that on an individual basis as well as teams. Do you think there's an aspect of the mindset, the agile mindset there, and does each individual need to come to work with that agile mindset for the team to be able to flow better?

Renae Craven:

Mindsets. If everyone had the same mindset then it would be robots or.

Chloe Hall:

True.

Renae Craven:

The world would be very boring.

Chloe Hall:

Very good point.

Renae Craven:

I think that's a bit, for me when I think about a team, an agile team, as long as there's some alignment on how the team behaves, why they exist, what their purpose is and how they treat each other and how they solve problems together, then the mindsets of the individuals within that team, they can be different. And that's fine as long as there's that agreement amongst everyone of this is how we are going to behave. I come up against people all the time who have been forced to work in this agile way. So their mindset's definitely not in the mindset that you need for an agile team, but if they're in an agile team and there's people in that team that have got the mindset or the behaviors that you need to have in order to deliver in an agile way, over time it kind of balances out.

And over time those the mindsets will start to shift as well as they see how other people in their team are behaving, how their leaders are behaving, things like that. So I kind of always think of it as more of a behavioral thing than a mindset thing. How do we make decisions, like I said, how do we treat each other, how do we approach problems, who are our customers, all of that sort of stuff. It's more that behavior that I like to, instead of me thinking, oh, they don't have the mindset, they don't have the mindset, I just kind of look at how they behave. Because at the end of the day, you can't force that

mindset. But as a team, when they start humming to working together as a team, they're going to be delivering what they need to deliver. And they all just, that's the whole cross-functional part of it. You're bringing together different minds, different backgrounds, different experiences, different skills, all of that stuff.

Chloe Hall:

Definitely.

Renae Craven:

You're putting them in a team together so that they can use their skills. They're all those different pieces to solve these problems.

Chloe Hall:

Yeah, no, definitely. I think the way people behave, it has a lot to do with it as well. And I think on that too, you can be in the right type of mindset, you can behave in the right way. And that has a lot to do with the way you're showing up at work as well. It's the way you come to work. If you're had a bad morning, then that's going to impact how you are that day. Or if you've waking up that morning and you have kind of a set morning routine that gets you into that good routine for the day, that good mindset and behavior, then it can help a lot. And I think as well, this is something I'd love to chat to you about too, because you've got the background of Pilates, you're in your own studio and you've been a instructor for how many years now?

Renae Craven:

It'll be a year and a half since I qualified.

Chloe Hall:

Yeah. Nice. Yeah, so I'm also an instructor. I've been teaching I think for about six months now. But I'm just wondering too, so you've got your two passions, Pilates studio owner and then also an Agile coach. Is there that element of setting yourself up for the day in the morning, do you think if someone, they meditate have the type of morning routine they exercise, can they behave better at work essentially? What are your thoughts on that?

Renae Craven:

Yeah, I think definitely the better you feel in yourself or the way feel within yourself, definitely has a direct correlation to how you come across how you behave at work. So yeah, if you've had a rushed morning or a traffic was crap on the way to work or whatever it is, then definitely you're going to be quite wound up by the time you get to work.

Chloe Hall:

Yeah, definitely.

Renae Craven:

It's going to impact the way that you respond to questions or respond to people or respond to your team or whatever it is. Yeah, absolutely. But myself, I don't really have a set routine in the morning. I go to gym but I don't go to gym every day. But the mornings that I do go to gym, I never feel like going because no, I just want to sleep.

Chloe Hall:

It's early. Yeah.

Renae Craven:

Yeah. But I have to go in the morning or I won't go to gym. Gym's something that, it's a bit of a love hate relationship. I know I have to do it, but I don't like doing it.

Chloe Hall:

Not even after? That feeling after?

Renae Craven:

Afterwards is good. It was like, but from, oh thank God that's done.

Chloe Hall:

Yeah.

Renae Craven:

Tick I'm done for the day.

Chloe Hall:

Out of the way.

Renae Craven:

If it was in the afternoon, if I went to gym in the afternoon I wouldn't go. It would just be, "Nah, it's too hard or I can't be bothered, I'm too tired". So getting up first thing in the morning, I set my alarm 15 minutes before my gym class starts.

Chloe Hall:

Wow. That is effort.

Renae Craven:

I know.

Chloe Hall:

That is good.

Renae Craven:

I race to get there but I have all my clothes set out the night before so I don't even have to think. I just get out of bed, I put my clothes on and I get in the car and I drive to the gym and.

Chloe Hall:

I do the same thing.

Renae Craven:

I do my class, I haven't had time to talk myself out of it just yet. But afterwards it's like, oh yes, excellent. That's done for the day. And yeah, it is nice to know that you have done that for the day as you start your work day as well. So on my gym days, that's probably my routine to get myself ready for work. But other days they're a little bit more relaxed I guess. I think if anything having a coffee is my, I cannot deal with the world without coffee. So whether I'm at home or I'm in the office, the first thing I'll do is if I get to the office I'll get a coffee on the way in. So I'm drinking coffee as I walk into the office. So yeah, I guess that you could call that my routine.

Chloe Hall:

No, I think a lot of people, a lot of listeners as well will be able to resonate with that. And I used to be like that and then it just, coffee wasn't sitting well with me. I found it was just really triggering my nerves for the day and everything. So it was so hard. I went from drinking two to three coffees a day to getting off it and now I'll drink like a matcha instead. But that was such a big part of my morning routine as well and getting off it was one of the hardest things I've had to do.

Renae Craven:

Yeah, I did that once. I detoxed for one of those health retreat things years and years ago and I had to detox off coffee and everything actually.

Chloe Hall:

Oh really?

Renae Craven:

Before two weeks leading up to it and yeah, coffee was hard.

Chloe Hall:

Yes.

Renae Craven:

Very, very hard. Because I love the taste of my coffee. I just have it straight, I don't have any milk so I love the taste of my coffee.

Chloe Hall:

Yeah, wow. Okay.

Renae Craven:

But maybe it's also the other benefits of not wanting to kill people that coffee does to me as well. I can deal with the world now. I've had my coffee.

Chloe Hall:

You're like okay, all right. Who needs coaching now? Who needs training? And I'm ready to rock and roll.

Renae Craven:

Yeah, I'm good now.

Chloe Hall:

Yeah. Nice. Yeah. Well the reason as well why I wanted to talk about the whole exercise correlation with work was because I did read your article on LinkedIn about what sitting all day is doing to your body and you're saying how Pilates can help with that. The section that I think resonated really well with me was when you said, when COVID-19 shut down the world and confined everyone working from home, those people who were working in the office environments, you found yourself sitting bent over a PC at home all day and it's back to back virtual meetings, you don't really have that chance to get up, have a break, go for a walk around and everything. And I think, I'm sure a lot of our listeners will be in that reality and even after COVID it is still the case. So I think just for the sake of everyone listening, is there any tips or anything to get you up, get you moving so you're not experiencing that on the daily.

Renae Craven:

I think the other difference is before COVID, sure you were sitting at your desk all day at work but you are also walking to the office and walking to meetings and walking to the kitchen and walking to go and buy your lunch and things like that. And you weren't kind of back to back meetings either. So you had that chance and if you were walking from room to room so you were getting up. Whereas at home it's just back to back meetings and I don't know about you but I run to go to the bathroom in between meetings.

Chloe Hall:

Yeah. I do. I actually do. Yesterday actually bit triggered by that.

Renae Craven:

I did that too yesterday actually. And even at the height of COVID, the back to back meetings were so bad. I didn't even have a lunch break. I was working, I was making my lunch in meetings and daylight saving as well. It always throws things because Queensland stays where they are and it throws everything out so. So in my article actually, it was more of a paper that I had to submit as part of my instructor course.

Chloe Hall:

Oh cool. Yeah.

Renae Craven:

And as well as my 600 hours of practice and.

Chloe Hall:

Yeah. I can relate, I didn't have to do the article though.

Renae Craven:

So I kind of just pulled bits out of that and because I thought this is still relevant and maybe it will resonate with people and especially the people that I'm linked, LinkedIn is the audience, right? So that just things that happen from sitting, sitting down's bad for you, full stop. Where you're working or sitting on a couch all day, whatever it is, sitting down's bad for you. And the longer you sit, the more kind of slouched you get. The more your spine is always kind of in the rounded state, the less you are using your back muscles, your back extensors, the more you're sitting down your pelvis, your hip flexes are shortening because you're always sitting down and that kind of tightens your lower back. And then you've got your, even just using your mouse, you've got that shoulder that's doing extra stuff or backwards and forward stuff constantly. And then your neck as well and your traps, everything gets kind of tight.

So things that you can do. I wrote a, my article's got an example class plan to undo the effects of sitting down all day in an office job. But that class plan uses all of the apparatus. So there's things you can do on the mat or the reformer or the Cadillac or under chair. But I run a few online classes after work and they started during COVID and they're still going. And I designed those specifically to undo, I know those people have been sitting down all day. So my classes are very much unraveling everything that they've done the all day.

Chloe Hall:

The body.

Renae Craven:

I mean my classes, my math classes anyway, they're usually focused around, I mean tips for people not actually coming to a class but undoing, you're doing the opposite of what you've been doing all day. So if you sit all day, stand up, walk around, at least listen to your smart watch when it tells you take a break. Stand up and take a break. And walk out to the letter box and get some sunshine at the same time, if you're lucky there's not much suns around these days.

Chloe Hall:

If it's out, make a run for it.

Renae Craven:

Doing kind of shoulder rolls and neck stretches and hip flexors stretches so that you, like I said, just undoing, doing the opposite of what you do when you're sitting. So think about the muscles or the tendons or whatever they're, even if you're not familiar with what they are, you know there's some at the front of your hip. And when you're sitting you can imagine that they're not being used, they're just being stuck there. So straighten them. Stretch them. If you're rounded all the time in your spine, then press roll your shoulders back, press your chest for and use your back muscles. And I don't even know if people are that familiar with back extensors. I don't know if people understand that. Because you've got your spine and then you've got these muscles that they're twisted that run either side of your spine. I can't remember the scientific name for them right now.

Chloe Hall:

No. Me neither.

Renae Craven:

We just call them back extensors. And when you straighten in your spine, they're working and you're switching them on. It's just working your bicep, strengthening that muscle when you straighten your spine and you can even go past straight and go kind of backwards. You are using those back muscles and you're strengthening those back muscles and it'll stop you being like a rounded.

Chloe Hall:

Yeah, just bent over in the computer all day.

Renae Craven:

Hunched over.

Chloe Hall:

Yeah. That's it. You don't want that.

Renae Craven:

So it's really just doing the opposite or yeah. Joining online classes. I can put you through some exercises.

Chloe Hall:

Yeah, well we'll definitely share that article as well with this podcast so people can see that program or might be something that helps. For me at work we're very fortunate that we have a standing desk and I think that that is just so amazing. Because if I work from home, I don't have a standing desk and I can feel the difference. My body just feels, you just don't feel right and I feel more fatigued and yeah, I just need to get up and move more often.

Renae Craven:

Yeah. If you stand all day, it's the same thing. You've got to sit as well. You've still got to do the opposite. Standing is like, because you can get slouch when you stand as well, so you can still over time get tired and kind of slouch over or you're still kind of tense in your shoulders and things like that. So you can kind of need to still be aware of your posture when you're standing and just self-correct or still go for walks, still give everything a chance to move the way it's supposed to move not stand still all day.

Chloe Hall:

Yeah, definitely. On that, Renae. Yeah. Thank you so much for coming on the podcast today. Really enjoyed this chat with you. I think there's a lot that our listers will get out of it and I definitely want to continue more of this Pilates conversation too.

Renae Craven:

Thank you Chloe. Thanks for having me.

Chloe Hall:

No worries, thank you.

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""

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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.

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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.

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