Meta Muse Explained: What It Can Access and What It Can Do

 

Meta Muse is not designed to simply answer questions. It is designed to act.

Meta’s new personal AI agent can browse websites, work across connected apps, manage multi-step tasks, make purchases, send messages, create documents and continue working after the user closes the app. That makes Muse more useful than a conventional chatbot—but it also creates a much bigger trust problem.

The important question is not just what Muse can do. It is what information you are giving it access to when you ask it to do those things, which actions require your approval, and how much control you actually retain.

For now, Muse is rolling out to adults in the United States through the dedicated Muse experience on iOS and Android, on the web at muse.ai, and through WhatsApp. Meta says the service is free within usage limits, with paid subscriptions available for heavier use. Reuters has reported subscription tiers of $20 and $100 per month.

What Is Meta Muse?

Muse is Meta’s personal AI agent built around its newer Muse Spark model and a cloud-based computing environment called Muse Secure VM.

The distinction between an AI assistant and an AI agent is important. A traditional assistant might read your request, generate a response and leave the next action to you. An agent is intended to take the next steps itself.

Tell Muse that you want to plan a trip, for example, and it can research options, browse websites, organize information and move through several steps instead of simply telling you what to do.

Meta also designed Muse to continue working in the background. The company says tasks can keep running even after the user stops actively interacting with the application.

That changes the role of the software. It is no longer just a conversational interface. It becomes a software worker that has to be trusted with access to real accounts and real-world actions.

What Can Meta Muse Actually Do?

Meta describes Muse as a general-purpose personal agent rather than a tool built around one narrow task.

Its documented capabilities include browsing the web, answering questions, creating documents, generating images, monitoring goals, setting reminders, researching topics and completing multi-step tasks.

The bigger difference comes from its connectors.

Users can connect services such as email, calendars, Instagram and other supported apps. Once those connections are enabled, Muse can pull information from those services and use it when completing tasks.

That means a request can move across several systems instead of remaining inside the AI's own chat window.

Muse can also navigate websites using its own browser environment. Meta says it can fill out forms, book appointments and handle customer-service tasks.

Shopping is another important use case. Meta says Muse can make purchases after receiving the required approval, while its product page describes a system designed to keep payment credentials away from the agent itself.

The practical implication is straightforward: Muse is trying to move AI from "tell me how" to "handle it for me."

The App Access Is Where Muse Gets Interesting

The most important thing to understand about Muse is that it does not automatically get unlimited access to every service a user owns.

Meta says users choose which apps they connect and can control what Muse is allowed to do.

For email, for example, the permissions can determine whether Muse can only read messages or can also send messages on the user's behalf.

Users can also revoke access later.

This permission model is critical because an AI agent becomes far more useful as it receives more context. An agent that knows your calendar can plan around appointments. An agent that can access your email can understand what needs a response. An agent connected to shopping services can complete a purchase rather than simply recommend a product.

But the same mechanism creates the core privacy trade-off.

More access can produce a smarter and more useful agent.

More access also means there is more personal information available to the system.

Muse Does Not See Everything the Same Way a Human Does

Meta says credentials such as passwords and payment information are stored securely so Muse can use them without directly seeing the underlying secrets.

Its current product documentation also says shopping can use a one-time card number at checkout rather than exposing the user's real card to the agent or merchant.

That is an important security boundary, but it should not be confused with complete data isolation.

The agent still has to work with information contained in connected services.

A recent hands-on test by The Verge illustrated why this matters. After connecting social and shopping accounts, the publication found that Muse could infer detailed interests from Instagram data that were not presented to the reviewer in the same way through Instagram's ordinary user interface. Meta subsequently said users who do not want that behavior can disconnect Instagram from Muse.

That distinction is easy to miss.

An AI agent may not need direct access to your password to know a surprising amount about you.

The useful question is therefore not simply, "Does Muse know my password?"

It is:

"Once I connect an account, what information can the agent derive from the data that account exposes?"

How Muse Is Supposed to Control Its Own Actions

Meta has built a security layer around Muse rather than allowing the model to operate with unrestricted access to the internet.

Muse runs inside a dedicated virtual machine in Meta's cloud. The company says each user's agent and data are isolated within that environment.

More importantly, Meta created a separate security component called Sentinel.

Sentinel sits outside Muse's main runtime environment and acts as the permission authority for external actions. When Muse wants to use a connected service or send information out over the network, Sentinel evaluates the request against the user's permissions and security policies.

That separation is designed to address one of the biggest problems with agentic AI: the same system that understands instructions is also capable of taking action.

A model can misunderstand a request.

It can encounter malicious instructions on a website.

It can follow a prompt injected into content it is reading.

It can simply make a mistake.

Meta's security architecture attempts to put another control layer between the model and the outside world.

Sensitive Actions Still Require Approval

Muse is not intended to silently spend money or send important messages whenever it wants.

Meta says it asks for user approval before sensitive actions such as sending emails or making purchases. The system also provides an activity history so users can review what the agent has done and what it plans to do.

The permissions can be adjusted over time, and Meta says users can disconnect services whenever they choose.

This is one of the most important differences between a helpful agent and an uncontrolled automation system.

The software can perform work independently, but the user remains part of the authorization loop for higher-risk actions.

That does not eliminate mistakes. It limits the consequences of those mistakes.

Muse Can Keep Working After You Close the App

One of the less obvious capabilities is also one of the most important.

Meta says Muse can continue working in the background after the user closes the application.

That matters because many tasks are not naturally completed in one conversation.

A user might ask Muse to monitor something, gather information over time, track a goal or wait for a condition before taking the next step.

This is closer to task automation than ordinary chat.

It is also another reason security matters so much.

A chatbot that gives you a wrong answer is inconvenient.

An agent that remains active in the background and takes a wrong action can create a much larger problem.

Meta Says Your Muse Data Is Not Used for Advertising

Meta says the data and conversations inside a user's Muse environment are not shared with its advertising systems.

The company also says users can opt out of having their Muse interactions used to train Meta's AI models.

Users can instruct Muse to forget specific information it has learned, while connected services can be disconnected at any time.

Those controls are meaningful, but they are still company-designed controls rather than proof that the system can never expose sensitive information.

The strongest way to interpret Meta's privacy architecture is therefore not "Muse cannot access your data."

It can.

The stronger and more accurate claim is that Meta has designed boundaries around what Muse can access, how it can act on connected services, and when it has to ask the user for approval.

That is a much more useful way to evaluate the product.

The Future Confidential VM Could Matter Even More

Meta says it plans to introduce a feature called Muse Confidential VM later in 2026.

The company says that version will encrypt the complete virtual machine—including the user's data and conversations—with a key controlled by the user, so that even Meta would not be able to access the protected environment.

That would represent a more substantial privacy boundary than the standard Muse architecture.

But it is not the same thing as a feature users can already rely on today.

For now, the appropriate distinction is simple: the standard Secure VM architecture is available, while Confidential VM is a future upgrade announced by Meta.

Muse Is Currently Limited by Geography

Muse is not globally available today.

Meta's current rollout is focused on the United States and is intended for users aged 18 and older. The company says the experience is available through its dedicated applications, the web and WhatsApp, while integration with its AI glasses is planned.

That matters for anyone outside the U.S.

For users in markets such as Pakistan, Muse should not currently be treated as a generally available Meta service simply because Meta has announced it publicly.

Availability can change as the rollout expands, so this is an area worth checking before assuming access.

What Does Muse Cost?

Meta says Muse is available for free with a usage limit. Once users reach that limit, they can either wait for the allowance to refresh or move to a paid subscription.

Reuters has reported two paid tiers priced at $20 per month and $100 per month for heavier usage.

That pricing places Muse in a very different category from ordinary free chatbot access.

The business question is whether people will pay for an AI that does work on their behalf rather than simply generating text or images.

For Meta, that could be strategically significant.

The company is investing enormous amounts of money in AI infrastructure, and an agent that can become part of a user's daily workflow gives Meta a potential way to turn that investment into a direct consumer service rather than relying entirely on advertising.

The Bigger Battle Is Not Really About Chatbots

Muse is arriving as the AI industry shifts toward agents.

The competitive question is becoming less about which model gives the best answer and more about which system can safely complete the most useful work.

That puts Meta in competition with other companies building agents that interact with websites, applications and digital services.

Meta's potential advantage is distribution.

Muse is closely tied to an ecosystem that already includes WhatsApp, Instagram, Facebook and other widely used services. That gives Meta an opportunity to build an agent around an enormous amount of user context and an existing communication habit.

But distribution does not automatically solve the trust problem.

In fact, it may make that problem larger.

The more accounts and services users connect, the more useful Muse becomes—and the more sensitive the information becomes that users are asking Meta's software to process.

The Most Important Limitation May Be Trust

The technical architecture behind Muse is more sophisticated than simply giving a language model access to a browser.

Meta has built isolated virtual machines, a separate permission authority, credential protections, audit trails and approval controls around the agent.

Those are meaningful engineering decisions.

But technology alone cannot decide whether people will trust an agent with their inbox, shopping accounts, calendars or personal preferences.

The Verge's hands-on experience is revealing for exactly that reason. The software successfully completed real tasks, but the amount of information it could infer from connected services created a different kind of concern: not whether the tool works, but whether users are comfortable with how much it can understand.

That may become the defining test for personal AI agents.

The technical challenge is getting an agent to act correctly.

The consumer challenge is convincing people that giving it permission to act is worth the risk.

What Users Should Realistically Expect

For users who have access to Muse today, the best way to think about it is as an early personal automation layer rather than an all-purpose digital replacement for every app.

It can handle useful tasks.

It can maintain context.

It can work across connected services.

It can keep working in the background.

It can ask for approval when the action becomes sensitive.

But Meta itself warns in its product materials that Muse is still learning and can be inaccurate or take unexpected actions. That makes human oversight important even when the system is designed to operate autonomously.

The safest approach is to start with low-risk tasks, understand the permission settings and only connect services when the practical benefit is worth the additional access.

What Happens Next

Meta is already signaling where Muse could go next.

The company plans to expand availability, add more integrations, bring Muse to its AI glasses and introduce the more privacy-focused Confidential VM architecture.

If those pieces come together, Muse could move from being an interesting AI product into something closer to a personal operating layer that sits between users and many of the digital services they rely on.

That is the real significance of the launch.

Meta is not simply asking people to talk to another chatbot.

It is asking them to let an AI system understand parts of their digital lives—and then act on their behalf.

The success of Muse will ultimately depend on whether the usefulness of that arrangement grows faster than the user's discomfort with the access it requires. The agentic AI race may be won not by the company that gives its AI the most power, but by the company that gives users the most convincing reason to trust that power.