How Microsoft's AI VP automates everything with Warp | Marco Casalaina

Warp is pretty magical, but you can add to the magic and make it work more smoothly. You're talking about setting up these little micro-agents that do little tasks for you, either one-off ones, like we saw in Warp, or recurring and triggered ones. And then this is making your life just easier. As soon as I started using it for certain things, like managing Azure, giving Azure subscriptions and stuff like that, then I was hooked. I was like, man alive, this is a really capable tool. Until you start working with these agents, you don't really discover all the things that you can do with command lines. But I think once you start to test those, then it kind of opens up your mind to what is really possible. Welcome back to How I AI. I'm Claire Vo, product leader and AI obsessive, here on a mission to help you build better with these new tools. Today I have Marco Casalaina, VP of Core AI Products and AI Futurist at Microsoft. Marco is going to speed run through five AI use cases where micro-agents can reduce the friction of getting little tasks done, whether they're technical or not-so-technical. Let's get to it. Meet Rovo, your AI teammate connecting knowledge, people, and workflows so teams can work smarter and move faster. It helps people find answers, make decisions, and automate work securely and with context through search, chat, agents, and studio. Rovo runs on the teamwork graph at Atlassian's intelligent layer that unifies data across your first and third-party apps so no knowledge gets left behind and you always get personalized AI insights from day one. And the best news? It's already built into Jira, Confluence, and Jira Service Management paid subscriptions, so the power of Rovo is already at your fingertips. Know the feeling when AI turns from tool to teammate? If you Rovo, you know. Discover Rovo, AI that knows your business, powered by Atlassian. Get started at rovo.com. That's R-O-V as in victory, O.com. Marco, thanks for joining How I AI. I am excited because we're going to see a tool, Warp, that we haven't yet seen on the podcast. And we're going to see you use it for maybe not its primary pitched use case, which is kind of agentic coding, but for some sort of more ancillary support use cases that you've found to be really useful. So before we get into them, why have you hooked so deeply into Warp in particular? I started using Warp, ironically, because our own, one of our own teams here at Microsoft tuned me into it. It was our PowerShell team. And they were like, you should try this Warp thing. It automates PowerShell really well. And so I tried it. And as soon as I started using it for certain things like managing Azure and, you know, giving Azure subscriptions and stuff like that, then I was hooked. I was like, man alive, this is a really capable tool. And if you're looking for the sexiest episode of How I AI, it is this because we are going to show you how to manage Azure resources with AI, which actually I'm making a joke because I think it's so funny. But these are the kinds of things that if you are a software engineer or an engineering leader or just building something, you are spending so much time on DevOps, admin, configuration, IAM. All that kind of stuff takes all your time and you don't actually get to the fun part of coding with AI. So, you know, show us maybe that specific use case and why you think Warp was such a good fit for that and what the pain was before you had a tool like this. Yeah, let's do this. So I was working with my colleague Govind the other day, and I needed to assign him access to a number of Azure resources. And, you know, you give them granular roles. So here I needed to give him Azure AI user and Azure AI project manager. And this was part of a big project that Govind and I are working on. Now, to do this, it's actually not that easy, to be honest, to do this in Azure, you know, and especially if I do it with the web interface. There is a web portal where I can go in there and for each individual role, I can go find the role and assign it to Govind and then the next role and assign it to Govind. It's not very efficient. If I were, for all the roles I needed to give Govind, I mean, this would have taken me an hour. So instead I do stuff like this. This is my prompt. I say, you know, I found Govind's email address in here to begin with, and then I'm like, okay, now give him Azure AI user and Azure AI project manager on this subscription that I'm looking at. And here it does it, right? So it will call AZ. AZ is this command line interface. And this is Warp's superpower. Aside from being a coding agent, which as I know, you know, a lot of people use it for and I mostly don't actually, I actually use it more like this. Whenever there's a command line interface, a CLI that can do something, Warp is freaking great at that. And so it will call AZ repeatedly until it runs to the ground. Now here, I think it made a mistake somehow, whatever it was doing in AZ role, this one, it kind of made a mistake here and then it got right back to it and it did it. And it's like, okay, I'm done. And then I say, okay, actually I needed to give him contributor role on the whole subscription. And it does that too. No problem. And so I use this for all kinds of stuff here. But, you know, for Azure administration and close your ears, Microsoft people, I have also used this to administer GCP. It works just as well with G Cloud, the G Cloud CLI. So it's great at this stuff. I was going to say, if you have been victimized by AWS, Azure, or GCP admin interfaces for assigning roles, this is exactly the kind of workflow you want to see. And a meta thing I want to call out for people, because I've worked in dev tools for quite some time. And one of the challenges as a product person and an engineer working on dev tools is exposing a GUI on these very complex, very interactive sets of permissions, capabilities, configurations. It's actually a really hard design problem. It's like a very hard, you know, front-end design problem. And what I love about AI having access to CLI tools, APIs, MCPs, all these ways to more programmatically access these capabilities is you can actually abstract away all of that front-end stuff and just let a user kind of query the system and get what needs to get done. And so if you're on the other side of this, you're not the user, but you're the builder of something like Azure. This makes it so much simpler to expose a quote-unquote like user interface to to someone like you who needs to get a job done. And then I have to call out a second thing, which is you're also doing what I would, I used to, sorry, RIP Stack Overflow a little bit, but you know, I used to like Google, how do I kill all processes for Adobe and then like find the command line, you know, the command and then paste it in. And then, you know, you get the error and you paste it back into search and you try to find it. And what I love about these more agentic processes that have access to the terminal and the command line is you can just do that all in one, all in one interface. Totally. Yeah, exactly. Now I will tell you though that there is a trick to making this stuff work. I mean, Warp is pretty magical, to be honest, but you can add to the magic and make it work more smoothly. And there's a couple of ways you could do that. I mean, if you think about what I did with AZ really, if you look at my MCP servers, well, this one's off right now, but I do connect this to the Microsoft Docs MCP server when I'm doing like Azure administration, because sometimes, you know, in this case, I knew exactly what roles I wanted to give Govind, but there are times when I have no idea what role somebody needs to do something. Like I'll be like, give this person whatever role they need to use Azure Document Intelligence. And like, you figure it out, right? But rather than leaving it to its own devices, I can do, as I'm doing here, I can connect it to the Microsoft documentation MCP server, which is a pretty good MCP server. And then it'll go look it up and that makes it work much better. Another piece of this, and we'll see it again in a moment, is the rules. So now originally, like out of the box, when I tell it, so I give it these rules. And so like, if I'm giving rules on a resource group, roles on a resource group, I should say, I do need to activate my owner access first. So this is one of the common problems that I have is that I have not activated my owner access, which is like a hurdle I have to go through. And so I make Warp remind me and Warp will be like, so, hey, did you activate your owner access before I start doing this? Because otherwise it's going to fail. So you can give it these rules and MCP servers that kind of help it along and help it use this stuff. Of course, that's useful for coding as well. But, you know, I find it super useful for these kinds of things that I use it for. Well, and what I will call out, and this is no shade, but this is not the most sophisticated prompting I've ever seen This episode is brought to you by Lovable. If you've ever had an idea for an app but didn't know where to start, Lovable is for you. Lovable lets you build working apps and websites by simply chatting with AI. Then you can customize it, add automations, and deploy it to a live domain. It's perfect for marketers spinning up tools, product managers prototyping new ideas, or founders launching their next business. Unlike no-code tools, Lovable isn't about static pages. It builds full apps with real functionality. And it's fast. What used to take weeks, months, or even years, you can now do over the weekend. So if you've been sitting on an idea, now's the time to bring it to life. Get started for free at lovable.dev. That's lovable.dev. I'm just laughing because again, I think my pitch at the beginning of this podcast, which is, this is the most glamorous episode of How AI, which is, we're going to do Azure role assignment. We're going to do drivers for your scanner that can be run via the CLI. And then you have one more use case, which is... I do. As somebody who does a podcast and works with a lot of videos, is really useful that I thought maybe you could walk us through. This one is for you. So I record a lot of videos myself, actually. And I have my little YouTube channel, although I can't say I have a podcast. It's not as regular as yours. But yesterday, I used the game bar thing, the Xbox game bar thing, to record a video off my screen. I said, maybe I'll try this, see how it goes. Well, for 10 minutes of video, this thing recorded a 1.7 gigabyte file. I don't know what it was doing. But I mean, it was insane. And I was recording this new tool that we're working on called Opal. Anyway, so I was like, what is up with this to warp? As you can see in my prompt here, I say, why is this file so big? Use FFmpeg to re-encode it, still keeping it at 1080p because I didn't want it to like nastify the resolution and make it more normal size. FFmpeg is a CLI that you can use to edit videos. And I use this all the time. I use it to strip audio off of videos. One day, my coworker sent me a video where, like, from seven seconds to 17 seconds, it suddenly went really quiet and then it went back to normal. So I said to warp, I'm like, use FFmpeg to, like, make the sound 300% from seven seconds to 17 seconds. And it totally does. But here, it looks at the file and it's like, okay, the video is 1.7 gigs because it has a huge bit rate, and it's at a huge resolution for some reason. And then it followed my instructions. It ran FFmpeg with whatever the switches were to re-encode this thing. And it did re-encode this down to 13 megabytes, which is what you would expect for like a 10-minute video of a screen share. Thank you. Yeah. And so, again, I think this is one of those things that in an alternative world, somebody would have, like, gone to search and say, like, video compression software, try to open something and, like, export and compress and figure this out. And instead, in just a couple seconds, you can use this more technical tool and get a lot of stuff done and also sort of understand the root cause. You know, another thing that I think people don't really appreciate about AI enough, and we had an episode with a producer from Ken Burns documentary production agency, is files are very rich with information. And giving an agent access to a file, you can tell a lot about that file. And then if you layer on an LLM, you can tell a whole lot about that file. And so I do think file manipulation is a real underappreciated use case. Like, we do so much file generation, but I actually think file manipulation is a really underappreciated use case of AI. Right. Now, if you think about what I'm really doing with Warp, the way that I'm using Warp, I characterize it in a certain way. I call this an ad hoc agent because effectively, each one of these things that I'm doing, you know, when I'm assigning the Azure roles or when I'm scanning the stuff or when I'm doing stuff with the videos, I'm kind of creating a little mini agent, an unnamed agent on the fly to do something for me. And that's becoming a trend. Like, this is a thing that's starting to happen, not just in Warp, but in lots of different types of general-purpose agents. Yeah. And what I would say is also, what I love about AI and what I would recommend to people with AI is, like, just get used to ephemeral stuff. Like, just toss it. Like, if you ever need to compress a video again, don't save this script. Don't, like, just come back and do it again, probably with a better model at some point. And it's going to be just as cheap and just as easy. And so I think a lot of people get stuck in their head about, like, oh, how do I make this a product or how do I get this production? It's like, don't get it to production. Just next time, do it over again. Do it over again. Maybe save a rule so you're not rediscovering the steps. Right. But, like, you don't need to build a whole thing here. And that's exactly the right idea, right? So when it, you know, for example, like, it happened once that it tried to call NAPS 2, the scanner thingy, and failed because it couldn't figure out what the path to NAPS 2 was. And so that's why I made that rule that's like, when I tell you to scan, here's where NAPS 2 is. When I tell you to scan from the feeder, this is the switch to scan from the feeder instead of the flatbed scanner thing. And now that it has that rule, it has never done it wrong since then, right? It does it right every single time, even though I'm scanning to a different directory, a different file, maybe a different format. It does it right every single time. You know, I'm not saying I love AI more than humans, but sometimes it would be really, really nice to be able to get that consistency out of the people around me, you know, perhaps my children who are not loaded with rules and context all the time and consistent output. Well, let's switch over to maybe some less technical use cases, but ones I think are really interesting. Again, thinking about ad hoc agents and workflows, how you're using sort of more administrative task-based workflow-based things to kind of be prepped for the work you need to do in a day. Yeah, well, I mean, here I am in M365 Copilot. So this is Microsoft's general purpose agent for business. And a lot of people think of it like this. Like, I can ask it a question, when am I doing how I AI? And it shows it here. Here you go. It knows my calendar. And that's cool. But what's happening now is that this and many general purpose agents like it are becoming agent builders. The line between consuming an agent and building an agent is blurring. So this is the new workflows agent. And this is an agent that builds an agent. So I'm going to kick this thing off. And what I said here is, when I get an email from Clairevo requesting a meeting at a certain time, check my calendar. If that time is free, send her a 30-minute meeting invite for that time. And it will start to build this agent. Now, for the sake of time, I actually ran this in advance here, so I can show you what it will build in a second here. And what it has built is an agent. It's a triggered agent. It's an email triggered agent. And so an email comes from you, and it will extract the time from it. It knows enough to extract it in ISO 8601 format, which is the format that the API takes with the Outlook API. It will check my calendar, and it'll create the meeting invite. And if I save this thing, this becomes a triggered agent that is now associated with my Outlook. So if you send me an email and you're requesting a meeting, you're going to get an invite from me if I'm free. Oh, I'm not going to abuse it, but I do love it. What I would say is really interesting here is the ability to set up synchronous response to asynchronous requests. Meaning, you know, probably when I email you, you are busy. You are in a meeting. You do not have, I mean, I'm projecting now, but like you don't have the time to look at your calendar and say, does this time work for me or not? But you know, when you have five minutes, you know, like, oh, I'm supposed to meet with Claire, and she needs to be at the top of my queue because we want to get this podcast done. And so I'm going to set up the system so as soon as she's ready, I'm ready. And I think that's a really nice flow. Again, I call this like burning down your anti-to-do list, which is if you can get yourself out of the critical path of doing a task and get AI into that path instead, you can be highly responsive and not drop stuff, which I think is really useful. And I will say we got this thing scheduled quite well. So if you ran this on me, it was really good. Well, you know, you're a priority, Claire. So, More effective, that you can hotkey into your AI tools that you know you're going to get exactly what you want. Precisely. I love it, Marco, this has been so great. I just I love the idea of, again, just solving these minor, minor points of friction with our, you know, genius large language models and supporting tools. Where can we find you and how can we be helpful? Well, find me on LinkedIn. That's probably the easiest place. So you'll see me on Marco Castellina on LinkedIn, and I really do look like my picture. Perfect. And any anything exciting coming up or YouTube channel, any anything that we can do to be helpful to you? Just follow my LinkedIn channel. You see that I make blog posts and videos every few weeks or month. And so you can see my new blog posts and video there on my LinkedIn channel. Amazing. Well, thanks for joining How I AI. Thank you for having me. Thanks so much for watching. If you enjoyed the show, please like and subscribe here on YouTube, or even better, leave us a comment with your thoughts. You can also find this podcast on Apple Podcasts, Spotify, or your favorite podcast app. Please consider leaving us a rating and review, which will help others find the show. You can see all our episodes and learn more about the show at howIAIpod.com. See you next time.