Chapter 1: What is the main topic discussed in this episode?
Okay, welcome to Coder Radio. It's Mike again, as always. Today we have Zach Lloyd from WarpOn again and Ben Holmes, who is their developer guru, I want to say. Even though it's not his real title, I just like it. It sounds better. It's pretty good. We're talking about their relatively new Oz agent system. which is, if you're familiar with the concept of agentic AI, it's Warp's take on it.
Pretty cool stuff. It's accessible remotely via a web app. You can, of course, use Warp Terminal and the web and mobile. They have some interesting ideas. They're really pushing this forward in a way In terms of user interface in a way that I think is going to make this a lot more accessible, this is definitely one of the areas where there's, of course, a lot of AI hype out there.
We talk about it all the time. But this area of the agentic AIs, particularly those that can manage other AIs, is really interesting to me. And I think of all the stuff that, come on, we know some of the stuff in the AI world is a little bit hypey, right? A little vapor-y. I think this is actually going to stay where you have one agent managing multiple other agents.
Chapter 2: What is Warp's Oz agent system and how does it work?
Think of it, for lack of a better term, if you're in the DevOps world, as like a Kubernetes orchestration type situation. So these are early, early days for this stuff. So, you know... Take everything under grain of salt that I say about my crazy plan to how to orchestrate these AIs. They obviously have their way of doing it, and it seems to be working for them, so that's good.
I'll have to check it out. Again, we are, as always, brought to you by the Mad Botter. It's my company. You all know it. We do manage your data end-to-end, and we do custom software development. I'm still running that promo because I think this airs on the 6th. It's currently the 2nd. So I'm running the promo, 10% off for Coder Radio listeners through the end of April. And that's on anything.
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So check that out. The madbotter.com is the easiest way to do that. And I'm not going to hold you much longer. Just check out that Discord. And okay, here's the guys from Warp. All right, we have a rare trio today. I'm here with Ben Holmes and Zach Lloyd of Warp, of course. You all remember Zach, right?
So we're talking about something really new that I've gotten to play with a little, Oz, as in I'm assuming the Wizard of. How are you guys doing? We're doing great. Thanks for having us on. Good to be back. So let's just jump right in, Ben. Ben, why don't you start us off? Give us the kind of TLDR pitch on what Oz exactly is.
Yeah, totally. And I meant to introduce myself, Ben Holmes, Developer Relations Lead here at Warp. Oz is an agent orchestration platform that helps you use agents in every touchpoint outside of your terminal. I'm sure that you've played with agents locally all the time with Cloud Code, Codex, Warps Agent, whatever you happen to use. Oz is our answer for getting things off of your computer.
So if you needed to tag in an agent to... plan out an approach while you're having a Slack conversation. You could trigger a cloud agent that way. You could trigger agents on a schedule. Maybe you need to run weekly reports or detect fraudulent activity, which is one of our workflows.
And any other touchpoint that you need an agent to be deployed and you don't want to be at your computer, Oz is kind of the platform to help you do that.
Interesting. So when you say not at my computer, is there some sort of like remote, I don't know, mobile tie-in here or...
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Chapter 3: How can Oz agents be triggered remotely?
It's so cool. It's like a piece of software we could have been paying 10 grand a year for or something. And he built it and it's exactly for our workflow. And so these things are like popping up like crazy internally at Warp. And it's often the people who are not engineers and not in engineering functions who feel like most empowered to build this kind of stuff now. It's really neat.
Okay. So we've said a lot of things here about what you can do, but I guess, Ben, what is the minimum expertise someone needs to be effective with Oz?
Yeah, I mean, I will say that the minimum is quite low because I do feel like echoing what Zach said, some of the most powerful automations we built so far have not come out of the engineering team. They've come out of creative, marketing, developer experience, support, all of these other areas that need some way to automate their work.
And we've kind of made the interface to set up these Oz agents very agentic, even down to creating an environment to spin these things up. You can talk to the agent and craft the environment with it. Like, oh, I need an environment. It needs these repositories, probably needs these tools. Or if you're not a developer, you can kind of just say, I just need an environment.
And then we'll figure it out from there. And the agent can self-update depending on what is needed. Either way, you can kind of go back and forth with an agent to get it deployed. And a lot of early success can just come from writing some skill files, making sure the agent has access to those, giving it some API keys if it needs to access external services like customer analytics.
That's a common one. If you just need to embed access to BigQuery so that it can access all of your customer data and answer questions about it, you can have an agent set that up, guide you through the keys that you'll need in that environment, and then get an early prototype. From there, you do need some amount of...
perhaps engineering work to make it sustainable, something that a whole team can use with authentication gateways and things like that. But those kind of come as a step two after you have your V0 in place. So I feel like the bar to a V0 is kind of on the floor.
As long as you know your domain and you know the requirements of whatever you're trying to build, you can definitely get these things spun up without the technical experience.
I agree. I think as far as where we could make the product even easier is that setup piece. Because as of today, you still need to know a little bit of Git where your skills live. You need to know enough to set up a Docker thing, even though our agent will basically do it for you. I think if you're non-technical, you kind of might be like, I'm doing what now with Docker?
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Chapter 4: What are the advantages of using Oz for managing agents?
or if you just use our normal like slash plan planning facility, we will now on the preview bill, try to decompose complicated tasks that can be done by multiple agents. And it's very early for us in doing this, but what you actually see is you sort of have a pattern of one agent that is like the orchestrator, and then you get, you know, and other agents that are doing tasks.
And you can actually, because we can control the whole UI of Warp, You don't have to use like Tmux or anything like that to do this. It will just automatically create like one pane per agent and you can watch the agents work. You don't have to watch them work. You can sort of like guide them, steer them, and they will pass messages back to the main agent. It's pretty cool.
And then the, so that's working today on our preview build, but it's all local. And then the natural evolution of this, which we're working hard on right now is to make this work in the cloud. And I think this is like the coolest thing. So you can sort of start a task might be a hard task. Like, hey, I want to migrate this big library.
Or a good example, I just built mermaid diagram rendering in Rust.
Chapter 5: How can Oz be integrated into CI workflows?
So I had agents port the entire mermaid.js into a Rust library. And I had like 20 agents doing this. And I had to kind of like manually coordinate them. But the future here is like, you're going to be able to ask sort of one agent to set up a plan that coordinates all these. It'll fire them all off in the cloud. They will do their individual pieces of work.
That main agent will then sort of be responsible for merging in the component pieces, testing them, making sure they all work together. And I was able to already do this kind of manually, but the brains of doing it are going to be done by an agent pretty soon. It's really cool. It's sci-fi.
That is really cool. Yeah. So I'll tell you offline over email, I have a crazy idea for an agent, but can you persist the agent? So like, or how about have like an agent blueprint where it's, I don't know, I need a merge master agent or a, you know, in your case, a mermaid agent, right?
Yeah. So the way that we do this today, and it's going to change a little bit, the way that we do this today is if you go into Oz in the web app, you'll see there's a section called agents and those agents are basically defined by like a skill. So you, exactly what you said.
You could have like your merging agent, you could have your data analysis agent, and then you can invoke that agent just by like name or by path to the skill. You can set that agent on a timer and it basically gives you a named way of referring to the agent.
We're sort of improving this concept so that it's not just a skill because I think the other important thing it needs to have actually is a bunch of permissions around what services it can access and like
Thank you.
It needs like a little bit more of like a first class agent concept. But we were I think that's like the right concept is like you have a named agent that is like this repeatable bundle of work that has permissions that it can for things that it can access.
Is that kind of what you have in mind? Yeah, I think that makes sense. Right. Kind of a kind of a prefab deal. Yeah. So so there's a lot here. I know there's a lot more to learn on the website. Ben, I know you've been all over the place talking about this. There's tons of people just need to go to YouTube and find more tutorials. Right. It's pretty, pretty straightforward.
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