
Written by a model from the transcript; not yet reviewed.
This episode of How I AI features Alex Cornell, Design Director on Meta's Muse, discussing the design philosophy behind building a consumer-facing AI agent. The conversation explores how Meta approached agent interfaces by treating them like personal messaging entities rather than traditional coding tools, utilizing familiar primitives like avatars, progressive disclosure, and status messages to build consumer trust and reduce perceived latency. Cornell shares specific use cases and demos of how he uses his own agent, Marlo, to manage personal hobbies, organize data from Instagram and Pinterest, and transform old archives into fun customized formats.
The discussion provides deep insights into the challenges of designing non-deterministic AI software where every user experience differs. Cornell and the host examine architectural choices such as single main chats versus side threads, the role of generative UI elements, and how to prompt effectively when agents go off track. The episode is especially valuable for product designers and engineers looking to make AI applications more accessible, intuitive, and delightful for everyday users.
The host introduces the episode, the guest Alex Cornell, and the sponsor WorkOS.
Alex explains how Muse’s design prioritizes user intent, progressive disclosure, and a conversational interface.
Discussion of Muse’s unique primitives—avatar, status, ideas, feed—and how they shape a distinct persona compared to other agents.
Exploration of Muse’s generative UI elements, such as dynamic status messages and avatar animations, and how they reduce latency frustration.
Alex showcases how Muse organizes Instagram saves, suggests shopping ideas, and creates themed catalogs like Skymall.
The host wraps up, thanks Alex, and invites listeners to try Muse, give feedback, and follow on social media.
The host asks about Muse’s role in golf, and Alex describes data‑driven insights, practice analytics, and long‑term goal tracking.
Alex explains Muse’s chat architecture, the purpose of side chats, and how it balances organization with a single conversational thread.
Alex demonstrates Muse’s ability to repurpose old blogs and chess data into podcasts, visual summaries, and snarky coaching.
Alex reflects on learning to design for AI’s unpredictable behavior and the shift from deterministic UI design to flexible, system‑centric thinking.
Designing Agents Like Messaging Appsinsight
The guest explains how framing the AI agent interface as a messaging exchange with an embodied entity clarified key design decisions, such as keeping chat bubbles and proactive messaging features.
Mom's First Software Experiencestory
The guest shares a story about his mother texting him to say she apologized to her Muse for working it too hard, proving the agent successfully bridged accessibility gaps for non-technical users.
Layered Progressive Disclosurestrategy
The guest details how the UI reveals status progressively, showing simple indicators to average users while hiding complex chain-of-thought code unless specifically requested by an engineer.
Fast Models Reduce Latencyinsight
The host points out that Muse's fast underlying model makes waiting for agent actions feel natural and non-painful, reducing the need for distracting reasoning UI elements.
Acknowledging Complex Requestsstrategy
The guest explains that when an agent handles difficult tasks, it should immediately acknowledge the request with a reaction or an initial message so it never feels like it has ghosted the user.
Personalized Generative UIinsight
The host highlights how Muse uses generative UI by pairing consistent navigation primitives with dynamically generated ideas, status emojis, and animated avatars that match the user's custom character.
Rendering Instagram Saves as Skymallstory
The guest demonstrates how he had his agent parse his Instagram saves and render shopping suggestions in the nostalgic vernacular of a 90s Skymall Sharper Image magazine.
Reasoning with Visual Datainsight
The host describes using agent connections to visual platforms like Instagram and Pinterest to generate design concepts and unlock creative use cases from hard-to-reason-with personal records.
“Like I've joked before, because we have bubbles around our messages, you know?”
“And so like, just simply having that status, I think, has been one of the most important decisions that we made.”
“And I think a lot of people think, when they're thinking about generative UI, they think, like, every time I log into my dashboard, it's different.”
“So put all of my saves into folders and categories, useful baseline.”
“in my life, which is like, it's achievable, it's hard, it's kind of like the perfect goal because I can talk to Muse about it anytime”
Alex Cornell is a design director at Meta, where he leads design on Muse, Meta’s consumer AI agent. He came to the role with a long background in software product design, including time as a founder, and has spent the past year building one of the most thoughtful agent interfaces in the consumer AI space. Muse ships with a custom avatar, a three-layer status display, a personalized ideas surface, and a generative feed. Alex has been sharing the reasoning behind each of those design decisions publicly on Threads. What you’ll learn: Why Alex built Muse to feel like messaging, not an AI appHow Muse’s three-layer status system accommodates different levels of technical expertiseWhat generative UI actually means in a shipped consumer productHow Alex turned years of forgotten Instagram saves into a daily shopping catalogWhy his mom apologized to her Muse, and what it means for AI designHow he uses Muse to track his golf game, shot by shot, as he works toward a single-digit handicapThe mindset shift designers need when the output is never pixel-perfectWhen to use the main chat versus a side chat, and where the architecture still falls short— Brought to you by: WorkOS https://workos.com/?utm_source=lennys_howiai&utm_medium=podcast&utm_campaign=q22025—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more — In this episode, we cover: (00:00) Welcome and guest intro (02:50) Designing Muse (05:33) Choosing which primitives to surface: characters, ideas, feed (08:50) Speed of model and response times (11:01) Generative UI and personalization (14:25) SkyMall demo: Instagram saves turned into a shopping catalog (17:16) Shopping, monitoring, and price alerts with Muse (19:07) Instagram and Pinterest connectors for visual data (21:40) Golf use case: round recaps, shot data, practice breakdowns (25:28) Main chat versus side chats (27:56) Old blog turned morning podcast: 20 years of posts via the Wayback Machine (29:39) Chess dashboard: game analysis with a snarky coach (30:59) What designers need to learn for AI, and what still works (34:33) Prompting strategy when Muse is being obstinate (36:31) Where to find Alex — Tools referenced: • Meta Muse: https://muse.ai https://muse.ai/ — Other references: • The Wayback Machine: https://web.archive.org https://web.archive.org/ • Instagram: https://www.instagram.com https://www.instagram.com/ • Pinterest: https://www.pinterest.com https://www.pinterest.com/ • Chess.com: https://www.chess.com https://www.chess.com/ • SkyMall/Sharper Image aesthetic: https://www.skymall.com https://www.skymall.com/ — Where to find Alex Cornell: LinkedIn: linkedin.com/in/alex-cornell-5449a99 http://linkedin.com/in/alex-cornell-5449a99 X: https://x.com/alexcornell — Where to find Claire Vo: ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
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