Editor's note: On September 8, Meta released its personal AI agent Muse. After launch, its downloads climbed steadily: in less than a week it surpassed ChatGPT and topped the U.S. App Store free chart; by September 21, iOS and Android downloads had reached about 1.5 million and 1.1 million respectively, and a Mac version was subsequently released.
But trouble followed. On the 12th day after launch, Amazon banned Muse from shopping on its marketplace, citing that Meta had not given prior notice, that Muse did not identify itself as an AI while browsing the web, and that it might store users' login credentials. The idea of "letting AI place orders for people" collided head-on with a major platform for the first time.
At the same time, rivals are accelerating. Musk's Grok Bot launched in August and also gives each agent a cloud computer. According to a September 21 report by The Information, OpenAI is preparing a product tentatively named "Codex Bot," built on the open-source agent OpenClaw and positioned closer to Grok Bot. A product directly targeting Muse is still in internal discussions, led by OpenClaw founder Peter Steinberger. The battle for personal AI agents has moved from concept to direct confrontation. Below is the translated original article:
On September 8, U.S. local time, Meta launched Muse, billed as "the world's first personal AI agent," open to U.S. users aged 18 and above, accessible via iOS, Android, muse.ai, and WhatsApp, and later coming to Meta's AI glasses.
The biggest difference between Muse and ordinary chatbots is that it can directly handle tasks for users. A user tells it a goal, and it can open a browser, search the web, fill out forms, send emails, book trips, buy products, and continue processing tasks after the user closes the app.
Meta gives each Muse an independent cloud virtual computer with its own browser, file system, and terminal. It can call services such as the user's authorized email, calendar, and fitness apps, and can also remember what the user cares about based on long-term conversations.
Taking it a step further, Muse proactively reminds users, breaks down long-term goals, continues working on background tasks, and can also generate web pages, PDFs, documents, and interactive tools.
In other words, Meta wants to put AI agents directly in the hands of everyday people, and make using them as simple as chatting.
01 AI Transforms from Assistant to Executor
The most noteworthy aspect of Muse is that it has already begun handling entire tasks from start to finish.
Meta product lead Mona Sarantakos mentioned that during product testing, she had her Muse handle preparations for her child's back-to-school season. Muse continuously checked school emails and district websites, added important dates to the family calendar, organized school supplies that needed to be purchased, and even helped her find a hoodie her child wanted and discovered a discount, while also booking dinner for the first day of school.
One detail in particular made her trust the Agent's capabilities.
A school email contained information about her child's upcoming sports tryouts, with only 12 hours left until the deadline. Muse proactively caught this and sent her a message before she boarded her flight. She then contacted her husband and completed the registration 4 hours before the deadline.
This kind of thing is hard to accomplish in a single chat session. Muse needs to continuously monitor information, know what's worth paying attention to, and then decide when to remind the user. That's why Meta also designed background working capabilities for Muse. After a user assigns a task, it can continue processing even if the App is closed. It only comes back to notify the user when it finds important new results or needs user approval for the next step.
Users can also assign multiple tasks at the same time, without needing to wait for Muse to finish the previous one before continuing.
To make these background tasks visible, Meta added an activity log. Users can click on the Muse avatar to see what it's currently doing, what it has done, and which permissions have been previously approved.
For longer-running matters, Muse also has a separate Goals page. It can break down a larger goal into multiple steps and then continuously track progress.
These design choices indicate that Meta is trying to solve "how to let AI keep doing things for users continuously."
Roberto Nickson, an early adopter in tech and creativity, after gaining early access to Muse, believes that personal AI agents have long lacked true product-market fit, and Muse's advantage lies in Meta's own vast trove of social connections and app data.
He is particularly optimistic about Muse's performance in online shopping and local recommendations. However, he also said it remains uncertain whether Muse will immediately become the go-to agent he uses every day.
02 Giving AI a Computer
Muse can connect to email, calendars, fitness apps, and also Meta's own services such as Instagram and Facebook.
On the web, it can search for information, browse websites, fill out forms, and complete bookings and purchases.
For example, in Meta's demo video, Muse noticed a flight ticket price dropped by $40, then reminded the user whether to rebook. It also proactively reminds users of upcoming golf tee times and tells them which hole on the course is the hardest.
In shopping scenarios, Muse can find products on its own, compare options, and ask the user before final payment.
If a user has saved a recipe from Instagram, Muse can also turn it into a shopping list, plan a menu for a gathering, remember friends' dietary restrictions, and help send invitations.
More complex tasks rely on Muse's own file system and terminal programs.
Based on these, it can write its own code and create the tools needed to complete tasks. It can also generate PDFs, web pages, and other "Artifacts." For example, if a user wants to track spending, they can have Muse build a continuously updated expense tracking tool; when preparing to study, they can have it create an interactive study guide.
When designing Muse, Meta's product team also added an Ideas feature. Early testing found that Muse could do too many things, and some users didn't know where to start.
So the system proactively suggests things to do based on users' goals, long-term conversations, and discovered habits.
Muse's interaction model was also adjusted. It uses a single long-running main conversation where users can interrupt at any time or assign multiple tasks in sequence. For projects that need independent context, Meta added side chats.
AI analyst @kimmonismus believes Muse is positioned closer to providing ordinary users with a low-barrier personal AI agent entry point. It has its own computer and browser, and can also be accessed through WhatsApp.
For users already active in the Meta ecosystem, this kind of access may be easier to get started with than AI agents that require learning complex operations.
03 Handing Permissions to AI?
Letting AI directly operate email, shopping, and payments brings the biggest problem to the surface. If an agent can open web pages, fill out forms, send emails, and even shop on your behalf, the impact of its mistakes is greater than that of an ordinary chatbot.
To address this, Meta designed a separate security architecture for Muse. Each user has their own Muse Secure VM, an independent cloud virtual machine. Muse and user data are stored inside, and other users' agents cannot enter.
Inside this virtual machine there is also an independent Sentinel agent. When Muse wants to access the internet, it must be approved by Sentinel. When situations arise that require user decisions, the system will further ask.
Passwords and payment information are also not directly exposed to Muse.
After users connect services, credentials are placed in secure storage. Muse can invoke them but cannot see the actual passwords. Meta also states that passwords users enter themselves in the browser will not be visible to Muse. For sensitive operations such as sending emails and shopping, Muse will require user confirmation.
Meta also gives users permission controls. For example, email can be set to allow only Muse to read it, or to allow it to draft and reply to emails on the user's behalf.
Muse also provides a complete activity log, letting users see what it has done and what it plans to do.
Meta says users can choose not to let Muse's interaction data be used to train Meta's AI models. Conversations and virtual machine data in Muse will also not be provided to Meta's advertising systems.
Later this year, Meta also plans to launch Muse Confidential VM. By then, the entire virtual machine, including user data and conversations with Muse, will be encrypted with keys held only by the user, and even Meta will not be able to access them.
Beyond security, Meta has already launched a public bug bounty program. The company had previously looked for issues internally through real-world scenario testing, agent red teaming, and a private bug bounty program.
04 Betting on a billion-user entry point
Muse is not about building a stronger AI assistant, but about bringing a personal AI agent, the "Jarvis" from the Iron Man movies, to users of Meta's services.
Meta Chief AI Officer Wang Tao said that making the product simple and easy to use is one of Muse's design principles. It does not require users to have technical experience, but instead hopes to let ordinary people use AI agents directly through a chat-like approach.
Pricing also serves this positioning.
Most of Muse's features are offered for free, and users with higher computing needs can choose subscription tiers of $20 per month or $100 per month. Meta expects most users will stay on the free version.
This is also where Meta differs from many current AI agent products. The latter's use cases are still more concentrated in programming and business tasks, and a mature product model has not yet formed in the consumer market.
Meta hopes Muse will be aimed at daily life from the start, packaging a personal AI agent as a consumer product that ordinary users can understand and use.
Another advantage Meta has is its massive application entry points. Muse is already able to connect to Instagram, WhatsApp, as well as services such as Gmail, Google Calendar, Google Drive, Ticketmaster, and OpenTable.
For other services with APIs, users can also have Muse create custom connectors. For Meta, this means Muse doesn't need to cultivate an entirely new user scenario on its own—it can plug directly into the apps and services users are already using.
This aligns closely with Mark Zuckerberg's previously stated vision for personal AI. He has said that in the future, everyone will have a highly capable personal agent that understands their goals and interests. Muse is Meta's first attempt to bring that vision to market as a consumer product.
Tech analyst Kyle Reidhead believes consumer AI agents are becoming the next major trend in the AI industry. After Grok, Meta launched Muse, and Apple and Google are also expected to continue embedding similar capabilities into their phones and systems. He concludes that the large-scale adoption of agents will continue to drive up computing demand, and once digital agents become widespread, physical AI such as robots will demand even more compute.
However, Meta itself acknowledges that it is still early days.
Muse needs to obtain enough app permissions to truly be effective; and the more permissions it has, the higher users' demands for privacy and security become. Meta has chosen to make the entry point as simple as possible, while using virtual machines, permission controls, Sentinel, and human confirmation mechanisms to contain the risks.
If users are willing to hand more of their daily affairs over to Muse, personal AI agents will have a chance to evolve from a new feature into a tool used every day.
Early tester @alanchen on X believes that Muse's most standout quality is its ability to genuinely work continuously around user goals, while placing security and privacy at the core of the product.
This is also the most noteworthy aspect of Muse's next phase. Technically, it is already capable of handling many tasks that previously required users to do themselves; on the product side, Meta is also working to lower the barrier to use. But whether personal AI agents can ultimately become a high-frequency tool depends on whether users are willing to use them long-term and gradually entrust more real-life matters to them.
For Meta, Muse's value therefore lies not just in adding another AI product.
It is more like an entry-point test aimed at billions of users: if personal AI agents can make their way into everyday apps, Meta will have the opportunity to bring AI further from the chat window into users' daily lives.
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