How Meta designed Muse: an AI agent people actually want to talk to | Alex Cornell (Design Director)

Claire Vo (0:00)

Designers have struggled with how much do we narrate along the way what is happening. So it's quite obvious to the user, so that latency doesn't feel so painful. My experience in the Muse app is, I do like that progressive disclosure, and it's one of the things I called out in our review, but also it's just fast.

It is just fast.

Alex Cornell (0:18)

If you are an engineer, you may want to know exactly what your agent has done, and you can find that in Muse. But if you're a normal person, you maybe just want to know, is it doing something? So at the top, it'll tell you, it's working. I'm very happy with our solution currently, because we've sort of like exposed the benefit of the main chat to people. But I feel like there's a lot more that we could do in terms of helping people organize. I had a blog 20 years ago, and I had Marlo go and find it, and then accumulated them into one PDF first. But the way that I like to read it now, just again from memory, is via a podcast. And Muse is able to take it and just like re-form it into a way that's like really interesting and useful for me.

Claire Vo (1:01)

When your Muse is being obstinate, what is your prompting strategy? What do you do?

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, we have Alex Cornell, Design Director on Meta's Muse, and he's here to talk a little bit about how to design a great agent experience for consumers, and a little bit more about some Meta Muse use cases you might not have thought of, especially ones that involve shopping on Instagram and going really hard into some of your personal hobbies. Let's get to it. This episode is brought to you by Work OS. AI has already changed how we work. Tools are helping teams write better code, analyze customer data, and even handle support tickets automatically. But there's a catch. These tools only work well when they have deep access to company systems. Your co-pilot needs to see your entire codebase. Your chat bot needs to search across internal docs. And for enterprise buyers, that raises serious security concerns. That's why these apps face intense IT scrutiny from day one. To pass, they need secure authentication, access controls, audit logs, the whole suite of enterprise features.

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Alex, welcome to How I AI. Thanks for joining us.

Alex Cornell (2:55)

Thank you for having me. Big fan, happy to be here.

Claire Vo (2:58)

I am, I was just saying, I am a big, big fan of Muse. And I have used probably every AI agent on the planet.

And Muse, I will tell you, fastest time to pure delight I have had with an AI product in a very, very long time. So tell us a little bit about how you approached designing this agent. Because I think some of the primitives you picked are very different than what we see in other agents. It seems like the persona that you're going after is different. Walk me through how you all ended up where you ended up from a design perspective.

Alex Cornell (3:37)

Yeah, well, thank you for saying that.

It was definitely a really interesting challenge. And I felt like a unique one. I think we all could feel that something was different this time around, I think, from a capability perspective. And I think just simple things like the fact that an agent can message you proactively changes the nature of how you think about the product from a design perspective, even in super simple ways. Like I've joked before, because we have bubbles around our messages. And that's typically something that you associate more with messaging than like an AI app. Like long ago, I think most AI apps kind of strip those out. And that is there, among other reasons, to help with the separation that you need when an agent is possibly messaging you at different times of the day. You kind of need to know where and when the different messages begin. And so simple things like that, I think, inform some of the design. But the basic framing that always we went back to, and I think was probably the most helpful, was just imagining that this interface was more of an exchange between you and an entity, and that's why it's embodied at the top of the screen. And so a lot of the patterns that we looked at were much more messaging patterns and not thinking about, how did this work before in different AI apps or using kind of like the traditional industry, I guess, as a reference. It was more like, how would you handle this if you were actually speaking with another person? And that was immensely clarifying, I think, almost every decision that we had to make.