Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
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Meta spent years talking about a future of every individual having a powerful AI agent working on their behalf. On September 8, 2026 that future has a name, an app icon, and a required payment card. The product is called Muse and it is Meta’s first real attempt to get beyond chatbots and build something that acts.
This tells you what Muse really is, what makes it work under the hood, what its present price is, what it can and can’t do so far, and what security problems have occurred since it launched. If you’re trying to decide whether to give Meta access to your email, calendar, and bank card, this is what you should read.
Muse is a personal AI agent developed by Meta Superintelligence Labs under chief AI officer Alexandr Wang. Meta describes it as the world’s first personal AI agent built for everyone. This move is to differentiate Muse from the wave of chatbots before it – A chatbot gives answers, An agent takes action.
Ask a generic AI chatbot to schedule your week and it gives you a list. Ask Muse and it opens a browser, checks your schedule, cross references your inbox, makes reservations or booking, and continues working after you close the app. Meta’s own framing puts the bar high and stated that Muse is designed to be a personal assistant that proactively helps people with their goals, gives people ideas, and takes action on their behalf, without needing a new prompt or instruction from them each time.
The name Muse was not the internal codename. So before launch, the project was called Hatch within Meta and the presence of this could still be seen in the CDN files for Apple’s app store where the icon asset was called HatchAppIconPublic as noted in a Medium breakdown of the launch shortly after launch.
Muse was not born out of the blue. Meta has been working for over a year making it clear that they want to compete, not with another social feed, not with another chatbot, but with personal agents. The company is investing heavily in doing just this.
The wider industry ecosystem is also relevant. OpenAI, Google, and Anthropic have all shipped their own agentic browsing, clicking, and task-completion tools in the last year, and Amazon has been building its own agentic shopping tools. None of those companies has Meta’s distinct upper hand though: unmediated access to WhatsApp, Instagram, and Facebook, and as a result, billions of people spending hours a day inside Meta’s apps. Muse is Meta’s effort to turn that existing distribution into a new product category pre-emptively, before an enemy’s solution gains traction.
Wall Street noticed in days. JPMorgan analyst Doug Anmuth upgraded Meta from Neutral to Overweight on September 10, just two days after launch, lifting his price target from $640 to $820, a nearly 30 percent premium above where the stock closed the previous day. In his note, Anmuth credited Meta for its quick action on frontier models and AI products a step beyond advertising, citing the Muse agent and access to the Meta Model API in particular as the grounds for the upgrade.
Meta launched Muse on a Tuesday and the choice of day was no coincidence. It came less than two weeks after Meta had entered into a $18 billion multistate settlement in connection with a lawsuit over the consumer harms of social media, a fact that most of the outlets reporting on the launch mentioned in the very same sentence as the privacy pledges surrounding the product.
CEO Mark Zuckerberg announced the product as integral to Meta’s vision in a lengthy internal manifesto, and Axios called it the next logical step for Zuckerberg’s construction. Launch was limited to the US, accessible by anyone 18+ via the web at muse.ai, a dedicated iOS and Android app, and within WhatsApp, which Meta already has billions of conversations to improve upon. Meta has announced AI glasses support is coming.
Muse runs on three separate pieces that Meta built to work together: The Muse Spark model that is reasoning and thinking, the private computer where the agent is executing is known as the Muse Secure VM and the gatekeeper system that supervises the entire process and verifies everything before it exits that computer is called Sentinel.
Still, every user receives their own VM in Meta’s cloud. That VM keeps the agent, the sandboxed browser the agent can view, click, and operate, connected accounts, and your task history. Based on Meta’s marketing materials, this sandboxing is why they are willing to send Muse the sensitive things it asks for. As Meta describes it, Muse resides on a dedicated secure computer with its own browser, so it can perform tasks on users’ behalf across the apps they use every day, expanding its knowledge from conversations and becoming more effective over time.
They intentionally separate the messaging agent from the system that substantively authorizes its behavior. In a technical blog post, Meta VP of Superintelligence Labs Tarek Sheasha explained that the agent’s thinking lives in isolation out of sight from your actual login, never makes a call directly to the outside world, and all responses to outside interaction run through Sentinel, a system that the agent cannot override. When Muse attempts an action outside a policy you’ve already approved, Sentinel will block the action and query you directly rather than letting the model, which could be manipulated by something like a scam email or malicious website, make that call itself.
This divide is important because of the actual failure of large language models. A model that is manipulated by slightly complex phrasing encounter between an AI and an internet browser, text that appears as someone appearing to instruct the AI while in reality originating from a third party website. If the person, or model, with the power to act is fooled as well, then that scam has been realized in the real world. By putting a separate screen in the way of anything leaving the machine, Meta is making a wager that even the fooled model cannot do harm itself given the fact the fooled model must be able to pass through a filter that was never exposed to the spammy text to begin with.
Muse is powered by Muse Spark, which Meta calls its most capable model to date, built specifically for agentic work rather than open-ended chat. Muse Spark 1.3 is the version in operation behind the consumer app and a short while earlier Muse Spark 1.2 was used to power Meta’s developer facing code agent, Muse Code.
Muse Spark 1.2 has a context window of a little over a million tokens, roughly about 786,000 words in a single prompt, enough to contain entire email conversations, lengthy calendars, or complete research papers simultaneously without losing track of the previous information. That context window is important for an agent who must remember your preferences, your current objectives, and the progress of a task over dozens of iterations.
Meta also offers access to Muse Spark via their own Model API and via 3rd party platforms like OpenRouter for $1.25 per million input tokens and $4.25 per million output tokens for the standard tier. A more affordable contributor tier is available at a fraction of the cost for programmers who accept to let Meta use their prompts and completions for training new models. This is only offered to the developer back-end API – not the consumer Muse app.
Perhaps the most intimate design decision is also the least expected from a productivity tool: each Muse user builds and names his or her avatar, the agent to whom they are speaking. Meta’s designers revealed that this aspect was born from an observation the user research team made while the team was designing the product: giving users deep, long-lasting conversations with an agent without a face or personality started to feel very odd, a little like talking to a corporate logo.
So the team took it personally. Everyone who worked on Muse ended up building their own, giving it a name and a personality that felt theirs. One of the product’s own designers, Mona Sarantakos, named hers Veda, a Sanskrit word that conjures notions of wisdom, and has seen people “respond best” to creating and naming their own avatar once they start using the product.
Most chatbots reset with every new chat session, or at best can recall a summary of prior dialogue. Muse was designed from the ground up to be a one-on-one persistent conversational application, more like a single thread of text contact with one other person than separate chat sessions. Muse has a persistent memory, and the capacity to retain a large amount of context about your life, your wants, and what you’re working on at any given moment.
Muse also has the advantage of not needing to wait for a turn when it is engaged in a task, like most chatbots. It can be interrupted while working on a task, or can actually receive multiple separate messages in a row without one having to wait for a reply, almost how one would text a person with more than one things going on at once.
That design created a separate, less visible problem for Meta to address. When an agent can speak to the user and the user can speak back multiple times in succession, a single, unbounded transcript isn’t something a user can actually keep track of. For that Meta went to chat bubbles, which visually spell out the end of one message and the beginning of the next in the same way as normal text messaging.
Initial testing revealed a different demand. As users embarked on more lengthy, involved projects with Muse, some wanted a separate space to work through a specific topic without cluttering the main thread. Meta responded by building side chats, letting a user branch off into a focused conversation for one project while keeping their main ongoing thread with Muse intact.
Muse is not tied to a singular application. Meta built it to be with you wherever you already talk to your phone.
That spread is a planned distribution play. Meta already has the messaging behaviors of a massive chunk of the world in the bag thanks to WhatsApp, Instagram, and Facebook, and Muse is meant to fit right into that pattern.
Getting on board with Muse has two easy-to-overlook conditions until you click on them. First, any device offered is only for those who are 18+, and only in the US, with an exact international launch date yet to be announced.
Second, Meta asks for payment information up front, even if you’re just planning to use the free tier. This is a much more stringent ask than most AI chat products make at sign-up, and the TechCrunch article pointed it out, noting that Muse wants a payment card for signup because the free plan is metered and the card is there for when you go into paid territory, or approved purchase needs funding. Meta states the app has a usage meter that displays your exact remaining free allotment.
Muse employs a pricing model that differs from most consumer software you have bought. Rather than on a per seat or per month basis for a fixed set of features, it is measured in usage tokens, the measure AI companies use for its compute billing.
The clearest public indication of price comes from a launch day post by Meta CEO Mark Zuckerberg. He said that Muse is free to use for up to 100 million tokens a week, with paid plans available for people with greater compute needs. The Meta newsroom post repeats that idea, emphasizing that the product will be free for most users, with paid plans for those wishing to go further:
Two paid tiers were confirmed at launch:
The launch day coverage on TechCrunch reports both values directly, saying Power is priced at $20/month, Max is at $100/month, and that Meta claims that most users will remain on the free tiers instead of upgrading. Since then independent price comparison sites have given the value offering a mixed review, with one comparison giving the paid tiers a value score of 55 out of 100, citing that Meta has not published exact usage quotas for either paid tier other than to say they are the higher and highest usage levels.
It seems reasonable to charge by token rather than by seat for a product where usage can vary so dramatically according to what a user asks an agent to do. A user request that prompts a long multi-step browsing session and a bunch of tool calls can require orders of magnitude more compute than a straightforward question, and per-seat pricing doesn’t really reflect that.
To put that 100 million tokens per week guarantee in perspective, a single straightforward interaction with an agent like Muse might burn off a few hundred/thousand tokens based on extent of browsing, tools used, and back-and-forth; whereas more elaborate multi-step tasks (for example, planning a trip consisting of booking several suppliers) might burn through tens of thousands of tokens in a single sitting. For most people doing simple, everyday stuff, that free weekly allotment should be sufficient for most normal use. Those running Muse constantly on scores of background objectives at once, or heavily using it for work rather than personal use, are more likely to hit the ceiling and need Power or Maximum.
Since Muse calls for a more intimate understanding of an individual than many traditional Meta products, the company created a specific, transparent way for people to opt-in and out of the use of their conversations rather than throwing that option in with a broad privacy policy that few read. A reporting on the app’s settings describes two distinct toggles “individual conversations and task history are used to train and improve Meta products,” and one representing “individual conversations and task history are not used to train and improve Meta products” allowing people to opt-in or out of their conversations and their history feeding into future work at the company.
That level of explicit, binary toggle is a more straightforward control than most AI products provide out of the box, where use in training is opt-out by default and embedded several menus deep rather than exposed as a straightforward option at initialization. Coupled with Meta’s claim that Muse information won’t be passed to its advertising services, the company appears to be attempting to draw an explicit boundary around the personal, high-access nature of Muse and the ad-heavy model that makes up the rest of its enterprise. Whether users believe that boundary to be in place is, as with the security design itself, a separate matter from whether it is in place in practice.
The headline pitch is that Muse handles real work and real money, not just conversations. As Meta pitches it, Muse is about how we manage our lives, carry out tasks, and convert longer-term objectives into action plans.
In practice, that spans different everyday categories:
Meta’s own launch materials provide a concrete real-world scenario to demonstrate the range: Muse arriving at a family’s back-to-school task list by reading school emails and the district Web site, copying important deadlines onto a common calendar, filling a shopping cart with classroom essentials, finding a sale on a desired toy, and reserving a dinner to celebrate the first day of school. In that same scenario, Muse detects a sports team tryout included in an email that was about to expire and alerts the family so it doesn’t go unnoticed, point by which Meta claims a proactive agent captures something a busy person legitimately would not.
Some of these categories are better established by launch than others. Email triage, calendar management and simple shopping tasks are the most common categories that reviewers agree is working well at this stage since they largely entail reading, sorting and filling in forms that Muse has observed thousands of forms done along similar lines previously. ‘Bill negotiation’ and health data pattern identification are newer, less mature categories, and initial coverage indicates that results there are more variable as, which service Muse connects to, and how well that service’s interface works through an automated browser.
The one link between all of these tasks is that Muse is designed to help you tackle many steps and many services, without needing you to sits down and follow each individual click, only reminding you when something needs your approval.
None of that helps if Muse can’t sign in to your other accounts. At launch, the integrations reporting identified connectors including Gmail, Google Calendar, Outlook, Plaid, OpenTable, Google Docs, Spotify, Function Health, Withings, Tailscale, and Peloton.
Meta’s own selection of apps partner without any effort. Facebook, Instagram, and Threads are able to link via the shared Accounts Center immediately, rather than needing to link manually, because Meta already owns the login layer across these three.
Reuters’ coverage of the launch categorized Muse’s scope into six broad buckets that encompass most of a person’s digital life: email, calendar, payments, health, shopping, and smart home. That kind of scope is precisely why the security architecture is so important. An agent that can just talk is hardly dangerous if it goes a little off-the-rails. An agent connected to your bank, your health information, and your smart locks is a different matter.
Payments route through Link by Stripe, with Shopify’s Shop Pay and 1Password support both marked as coming soon instead of available on launch.
Not every connector also has the same aim, and it’s interesting to think about the two broad categories they fall into. Utility connectors like Gmail, Google Calendar and Outlook are there so Muse can just look up the information it needs to plan (dates deadlines messages we care about). Action connectors like Plaid and OpenTable are there so Muse can really do something on your behalf, moving money or seating a reservation, rather than just know about a website’s existence. Connectors of health & fitness like Function Health, Withings, and Peloton fall somewhere in between, mostly just delivering information to Muse for now instead of fully autonomous action on your behalf in that domain, which makes sense given health is one of the least mature activities Muse will be able to do at launch.
More connectors will be rolling in as time goes on, per the industry standard for AI agents: emphasis on a small set of tested, reliable integrations at launch, expanding slowly as each subsequent connector undergoes its own security check.
A chat window by itself was insufficient for what Meta was attempting to create; the Muse app has several tabs that are somewhat detached from the conversation.
Meta’s design team invented a feature called Artifacts precisely because written response is often the wrong form in which to respond to an agent’s actions. If you asked a person to plan a trip, Meta’s own product write-up argues, you would not want a two-thousand-word reply back — you would want an itinerary..
Muse is capable of outputting the information as a document, PDF, web page, spending tracker, interactive study guide, or a dashboard designed to surface trends in something like your sleep data delivering each through chat, while simultaneously allowing it to exist and be accessed elsewhere. Meta is claiming that people will want more and more robust and context-specific interfaces, rather than everything arriving in them as reams of prose; Artifacts is Meta’s response to that demand.
Giving an AI agent control of your money is perhaps the one detail most likely to cause new users to stop and think; rather than leave the transaction up to mutual trust, Meta developed a uniquely appropriate response.
Muse initiates payments using one-time virtual cards. With each payment, you don’t forward your actual card, but instead a single use token, which offers another layer of security in case a transaction went awry. Link also means standard protections, including guaranteed no-fee returns on qualifying transactions.
Before anything with real financial or practical consequences happens, Meta displays what it calls an approval card: a time-structured, clearly categorized accept/reject screen, which is outside the normal chat process. That placement is deliberate, since an approval presented as simple chat text could be mimicked or skewed by a carefully maneuvered scam page or snuck-in prompt. An approval card presented by the app, outside the model’s own response space, is much more difficult to fake convincingly:
Meta paid attention to a second, even more subtle type of failure: cluttering the page with approval clicks to make a popup go away (known as ‘banner blindness’). Meta’s formal treatment was to have Muse roam freely and take generally reversible actions, while stopping automatically to ask a person for anything hard to undo, like sending a message or placing an order. The user can tune that default in either direction to make Muse either more hesitant or more autonomous.
Alexandr Wang has also said Meta is thinking about taking a commission on transactions made through agents like Muse for a user, but the company hasn’t decided exactly how. With Meta forecast to spend upwards of $130 billion on AI infrastructure this year, a transaction fee on agent-sourced transactions would give the company another revenue stream beyond both subscription fees and advertising, which Muse’s privacy pitch explicitly isolates from each other.
Meta built Muse for more than just consumers, though. Muse Code is a standalone developer oriented coding agent that also operates on the same family of Muse Spark models and is invoked by calling a terminal instead of a dedicated app after a single command install. It pitches itself by touting a feature that Meta calls multi-agent by default where the runtime fans a task out in parallel across sets of child processes at the same time with an upper limit scaling naturally with a system’s number of cores, but staying between too and sixteen.
And that capability comes with a token cost attached to every step. One early user reported that simply saying hello as a first prompt consumed over 20,000 tokens before any real task had started, a reminder that agentic workflows add up quickly and can eat up a lot of tokens before you’ve even asked for anything. Meta charges the same $1. 25 for a million input tokens and $4. 25 per million output tokens used in the Muse Spark API for standard access, but offers a steeply discounted contributor tier to developers who allow Meta to use their prompts and completions for training an option that is exclusive to the developer level and isn’t available within the consumer-facing Muse app.
A few weeks after the consumer launch, Meta sent a separate version targeted for desktop use. Muse for Mac is Meta’s first Muse version that runs on your own machine rather than solely within the cloud based Secure VM, allowing it to access Files Mail Messages, Calendar and Notes natively.
Access is opt in everywhere. Full Disk Access is always optional, never required, can be changed at any time, and deleting a file or sending a message always requires approval first, just like the approval card system used elsewhere in the product. The free quota for Muse for Mac is up to 100 million tokens/week, the same free tier as the consumer app, and US only, like the rest of the Muse family.
The exception can be stated just as Meta does it. Secure VM protects the individual user’s data from other users of the service but it does not prevent Meta from accessing that data when they consider it necessary for operation, security and support of the product. A ‘more locked down’ alternative, Muse Confidential VM, is slated for late 2026, and would encrypt the VM with a key exclusively held by the user, plugging the hole for those that want it.
Meta understood that the trust issue would shape how Muse was adopted (and was very vocal about that), and its security argument fell pretty much completely on architecture vs just words.
The basic idea is to isolate three components that are often all included inside a single AI system: the reasoning of the model, your own real credentials, and the outside internet. This is Sheasha’s technical description… and it is the clearest, because it is one paragraph, rather than a listing of multiple modules. He explained that the harness runs in its own isolated cell, it doesn’t see real credentials, and everything it does to interact with the outside world is routed through a Sentinel that the agent cannot override.
That final clause actually counts for more than it might seem. A common historical vulnerability of AI agents overall is prompt injection, where concealed text on a web page or in an email can lure the agent into executing a command its human user isn’t requesting. By deflecting approval powers to a distinct system that it can’t talk its way around, Meta is attempting to make that type of attack ineffective even should the AI code itself be duped.
On credentials, the model interacts with “placeholder tokens” not your actual login info or credit card according to reports from German publication Stock3 on the architecture of Muse, saying “Muse’s model never saw the real passwords or credit card numbers, relying on substitute tokens to interact with these accounts instead. ” And, per Meta, “conversations and data from the user’s VM are not shared with the company’s ad targeting system, ” a marked distinction considering how integral ad targeting is to the rest of Meta’s products.
Meta also operates an open bug bounty scheme for Muse that allows outside security researchers to look for precisely the flaws this sort of access would be most damaging for. This is a common approach for high-value software and it works best when the company takes seriously those more serious reports and responds quickly once something is found, something that will become apparent over the next few months once the bounty scheme begins to reveal its first publicly available findings.
And, it is valuable to simply understand what each layer is actually guarding against, since all three are not the same thing; the VM’s isolation will keep one user’s data and agent activities from impacting upon another, the kind of cross-account breach that would be catastrophic at Meta’s scale. Sentinel prevents the agent itself from being tricked into taking unwarranted action, whether through a poorly worded request or a malicious webpage. Token credentials guard against compromised agent environment leaking out bank account details or passwords, even if an attacker gets that far. Each layer closes a very different door, and Meta’s sales pitch builds the system on them all working together.
Numbers moved swiftly in the early days after launch, which seemed to tell two distinctly different stories according to where you looked.
On iOS, the ascent of Muse was sudden. Per CNBC, the Muse app had bumped off ChatGPT as the number-one free app on the US App Store within a matter of days, garnering 730,000 downloads in around five days following its launch on September 8, based on analytics company Sensor Tower. That’s an unusually rapid ascent for a completely new product category, even one with Meta’s entrenched distribution footings.
Digging into day-to-day rankings reveals a very bumpier experience than what the lone number on the headline implies one independent charting exercise tracked Muse fluctuating throughout a handful of hours from number four to number five on Apple’s Top Free chart in mid-September while Apple’s own chart for its products category page at separate times ranked the program at number two, leading to the (possibly mistaken) early categorizing of it as a flatter ‘number two app ‘
And everything was very different on Android. While the reporting that was so precise on iOS download volumes recorded Muse as only being at number 338 in the Google Play Productivity list at the same time (a gap large enough to suggest the iOS numbers flatter Muse’s actual reach).
Reading between the lines of CNBC’s reporting too was a reminder that other AI products have gone through the same rises and falls. For example, OpenAI’s Sora and Meta’s earlier Vibes AI video tool, which drove a surge of downloads but failed to become part of people’s long-term lives.
The true test for Muse is not the download curve in week one that’s something any well-marketed app that has access to Meta’s global web of influence can generate. It’s the retention curve in the months afterward, whether the agent’s actual task completion holds up well enough across enough real errands that people trust it with more of their life over time. That number will take longer to emerge than a Sensor Tower download count, but it is the number to watch far beyond the launch coverage cycle.
Not all companies were so happy to have an AI agent arriving at the site for the user. Amazon has already excluded Muse from navigating its marketplace altogether, citing privacy and security reasons a reflection of a larger pressure in the industry for the platform owners to manage how users interact with their stores, versus the agent developers’ desire to do that on the user’s behalf. It is a significant obstacle for Muse’s shopping intentions given Amazon’s power, and a reminder that the agent’s effectiveness relies in part on the willingness of sites it does not control.
There is also a more down market nagging question of trust at the launch that has nothing to do with the technical architecture. An unnamed analyst interviewed in CNBC’s reporting summed up the inchoate anxiety at the launch in this blunt statement: “Meta cares about security, but it’s less clear how much the company cares about privacy. ” These are related but separate promises: it is possible to build a legitimately laser-beam invulnerable system and yet still operate a company that still collects and uses more of your personal data than you’d prefer. It’s worth teasing out those distinctions before making the call about how much access to share with Muse.
Muse joins a category that OpenAI, Google, Anthropic, and Amazon are all competing to define for themselves, and Reuters conveniently summarized where they saw Meta going with its launch: Meta is betting its distribution advantage of more than three billion app users on being first to make the agent category concrete rather than winning on raw model capability alone.
It is not necessarily any one of these factors that differentiates Muse from most competitors but a combination of all three at once: reaching into WhatsApp’s billions of existing conversations, launching actual payments on a user’s behalf (unlike just proposing up-front ideas), and a security architecture that is designed and presented as being an integral part of the product, rather than an afterthought.
Some open source and independent alternatives pitch themselves directly against Muse on the data ownership debate. One rival goes so far as to advertise its offerings: open source, running locally, or in its own cloud, supporting any frontier or local model, and full data ownership for the user, which a closed ecosystem limited to one entity’s inner workings could not necessarily promise. This is a valid comparison to consider: Muse chooses (at least theoretically) between shedding a degree of openness in return for a product that needs not even a modicum of configuration but will work right away after you download an app.
Almost all consumer AI products today still function as advisors who formulates a suggestion for another human to execute. The entire idea of Muse is closing that final gap and that is both the main reason for its appeal and the reason it’s being scrutinized more closely than a typical launch.
The distinction is most stark at the point where the agent is about to take an irreversible action. Competitors have tended toward in-chat prompts, requiring someone to retype “yes” to confirm a booking or purchase. Meta approval cards are presented outside of chat, by the app instead of generated by a model, which is a meaningfully different security posture even though the eventual human click to approve looks the same.
Muse is genuinely new, and a handful of practical limits are worth knowing up front rather than discovering later.
None of these are grounds to stay away from Muse altogether, rather they are reasons for the first few months of ownership to be considered a trial period in which you are discovering the product’s actual limits rather than taking the marketing copy as the final word.
If you do try it, there are a few habits that make the experience noticeably better and safer:
Muse is Meta’s clearest bet so far that the next chapter of consumer AI will be judged on what an agent accomplishes, not how it converses. The architecture behind it, a dedicated virtual computer per user paired with an independent approval system the model cannot talk its way around, is a genuinely thoughtful answer to the risks that come with giving software this much reach into a person’s life.
Whether that architecture will earn the trust Meta is seeking after two weeks of a launch that has already yielded both a record-breaking week of downloads and stories of internal guardrail failures is still to be seen. The most productive way to view Muse at this point is as a product experiencing its first meaningful public test, not a finalized system. The Secure VM and Sentinel design is sound on paper, the once-planned Confidential VM would plug a real hole, and Meta’s history with regulators is exactly why that gap should be watched, not dismissed.
It is worth testing Muse cautiously at the moment: offer it small requests initially, read the approval cards instead of clicking through them, take more and more frequent peeks into the activity log than necessary at the outset, and choose whether and when to surrender that much of your digital life to an agent that persists even after you’ve exited the application. That choice is yours, and a conscious one should be made, instead of a default that occurs as soon as the prompt for entering a credit card number surfaces.
Q 1: What is Meta Muse?
Muse is an AI agent that debuted on September 8, 2026. It performs tasks like checking emails, managing calendar appointments, reservations, and making payments for a user, not just responding to questions in a chat window.
Q 2: Is Muse available outside the United States?
Not yet. Muse was only available in the US and limited to those over 18. There has been no announcement of an international release date.
Q 3: How much does Muse cost?
The free tier covers up to 100 million tokens of usage per week. Beyond that, Meta has two paid plans, Power for $20/month and Maximum for $100/month.
Q 4: Does Muse require a payment card to sign up?
Yes – even for the free tier Meta needs a card on file since any usage over the free limits and any purchases made by Muse require a funding source.
Q 5: Can Muse actually spend my money without asking?
No. Muse offers approval cards for anything difficult to reverse, like purchases. These are displayed outside of the conversation with the chat window so a manipulated conversation cannot pretend you have approved.
Q 6: What is the Muse Secure VM?
It is a dedicated virtual computer Meta assigns to each user, holding the agent, its browser, and connected account data in an environment isolated from other users.
Q 7: What does Sentinel do?
Sentinel is a different application running alongside the Muse agent and monitors each action meant to go outside, it can block the action or ask for your permission, but the agent cannot override this.
Q 8: Can Meta see what is happening inside my Muse VM?
Employees were not supposed to read inside a user’s VM, although Meta says that access is technically possible with the current design. A more isolated Confidential VM, encrypted with the user’s key, is scheduled for release later in 2026.
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One security mistake Mac users can make is assuming that attackers aren’t interested in Apple devices. Cybercriminals generally follow opportunity. Macs have also become attractive targets as they’ve become more common in workplaces. Malware designed for macOS exists while phishing campaigns can target Mac users just as easily as anyone else.
Modern attacks don’t depend on exploiting the operating system. A convincing phishing email can trick someone into entering their password on a fake login page. There can also be a malicious browser extension that can collect sensitive information. A compromised account can then provide access to business applications. The lesson is that a Mac’s security depends partly on the person using it.
Imagine what happens when you take your Mac to a public location. Connecting to WiFi is convenient but you don’t always know who operates the network. Some networks may be poorly configured while others could be impersonated by someone attempting to trick users into connecting. This becomes crucial when you’re working with sensitive information.
Logging into a company dashboard can create more security considerations. Modern websites typically use encryption but that doesn’t mean every aspect of the connection is without risks. A VPN can provide another layer of protection. Mac users who regularly work away from trusted networks can get a reliable VPN app for your Mac here as part of a broader approach to protecting their online activity.
Apple regularly releases macOS updates that can include security fixes. Ignoring those updates for months can leave known vulnerabilities unpatched. It’s easy to postpone an update because you don’t want to interrupt your workflow. There is always another meeting or project that seems more important. But delaying updates indefinitely can create unnecessary exposure.
The same principle applies to applications. Your applications can contain security vulnerabilities too. Keeping them updated reduces the chance that attackers can exploit weaknesses developers have already addressed. A simple habit can help. When your Mac notifies you about an important security or software update, don’t treat it as something you can ignore forever.
A highly secure computer doesn’t help much if an attacker can easily take over your accounts. Think about how many services you access from your Mac. There may be email, cloud storage, project management platforms, along with business applications. One compromised account could potentially put others at risk if you reuse the same password across several of them. Using unique passwords for important accounts is therefore essential.
A password manager can make this easier by storing complex passwords so you don’t have to remember dozens of different combinations yourself. Additional authentication adds another layer. Someone obtaining your password may still be unable to access the account without the additional authentication factor. This can make a significant difference for business users in particular. Your email account isn’t just a place where you read messages. It may also be connected to customer information or internal documents.
One advantage of Apple’s ecosystem is that macOS has mechanisms designed to help users identify potentially unsafe software. But those protections can’t make every installation decision for you. Be cautious when downloading applications from unfamiliar websites. It also helps to avoid clicking on software recommendations in unsolicited messages.
Attackers can disguise malicious software as productivity tools. Some may even use credible branding to appear trustworthy. Consider whether you actually need it before installing an application. A website telling you that your Mac is infected so you must install a tool immediately is a reason to stop rather than click. Security comes down to recognising when something doesn’t look right.
Much of what people do on a Mac happens inside a browser. That makes browser security an important part of device security. Keep your browser updated while also reviewing the extensions you have installed.
It’s also worth being cautious about permissions. An extension or application asking for access to browsing data or other information should have a clear reason. Don’t automatically approve every permission request to get through an installation quickly. Small decisions like these can reduce unnecessary exposure.
The biggest misconception about Mac security is that there is one switch you can turn on and forget about. There isn’t. Apple provides a strong security foundation but how you use your Mac still matters. Keeping software updated, using unique passwords, enabling additional authentication, along with precautions on public networks can all strengthen that foundation.
A VPN can also be useful when you’re working from unfamiliar networks if you frequently travel or work from shared spaces. None of these steps needs to make using a Mac complicated. The goal is to stop treating the device as if its brand alone makes it immune to threats. A Mac can be a secure tool for work but that security works best when the person sitting behind the screen plays an active role in maintaining it.
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