Meta’s New AI Agent Could Take Over the Everyday Tasks Eating Hours of Your Life

Meta Launches Personal AI Agent Designed to Work for You 24 Hours a Day

The AI That Works While You Sleep

Meta has launched an artificial intelligence agent capable of doing something fundamentally different from the chatbots millions of people have become accustomed to using: it can actually take action. Muse can connect to everyday apps, send emails, manage calendars, arrange travel, interact with shopping services and potentially make payments on behalf of its user.

That distinction matters. The first generation of mainstream generative AI largely waited for humans to ask a question and then produced an answer. Meta is now pushing towards a world in which a person gives AI an objective, grants it appropriate access and allows the software to work through the steps itself.

Muse Does Not Just Tell You What to Do

Muse launched in the United States on September 8 through its own app and WhatsApp, with Meta positioning it as a personal AI agent rather than another conversational assistant. The company says the system is designed to take over tasks and projects, turn longer-term goals into plans and work across the applications people already use.

That means the difference between asking AI how to organise a holiday and asking AI to organise the holiday. Instead of producing a checklist telling you to compare hotels, check your calendar, investigate flights and make bookings, an agent with sufficient permissions could potentially carry out much of that work itself.

The same principle applies across ordinary digital life. Muse can connect to categories of apps including email, calendars, payments, shopping, health services and smart-home systems, with individual users deciding which services it is allowed to access.

Emails, Travel and Payments Could Become AI Jobs

Consider how much modern life consists of tiny administrative jobs. Find an appointment. Reply to an email. Compare three flights. Fill in a form. Remember a renewal. Search for something that has come back into stock. Move something in the diary. Pay something. Follow up when somebody fails to respond.

Individually, most of these jobs are trivial. Collectively, they consume enormous amounts of attention.

An autonomous agent changes the equation because the human does not necessarily need to sit there while every step happens. Each Muse agent operates inside its own virtual machine — essentially a dedicated cloud-based computer environment — allowing it to continue working on tasks in the background.

A request could therefore move from “tell me the cheapest way to travel there” towards “work out the best itinerary within these limits and arrange it for me”. That is a much more consequential form of artificial intelligence.

The Biggest Benefit Could Be Getting Human Attention Back

AI companies regularly talk about productivity, but the more interesting effect may be the recovery of attention.

Modern technology has removed countless physical inconveniences while creating an extraordinary quantity of digital administration. People spend parts of almost every day switching between inboxes, websites, calendars, banking apps, booking systems, comparison tools, customer-service portals and notifications.

A capable personal agent could sit between the person and that complexity.

Instead of remembering every small task, people could increasingly tell the agent the desired outcome. Instead of checking repeatedly whether concert tickets have appeared, whether a cheaper flight has become available or whether an appointment has opened, the agent could potentially monitor the situation.

Instead of spending an evening comparing twenty options, a user could establish a budget, preferences and limits and allow the system to narrow them down.

The important economic resource being saved is therefore not merely minutes. It is cognitive load.

It Could Particularly Change Life for Busy Families

The implications become clearer when several responsibilities overlap.

Imagine a parent coordinating school schedules, work meetings, grocery orders, bills, medical appointments, travel arrangements and household maintenance. Much of that workload is not intellectually difficult. It is difficult because there are dozens of unrelated tasks competing for attention.

A sufficiently competent AI agent could function like a digital personal assistant previously available primarily to executives and wealthy households.

A person might tell it that the family wants to travel during a particular school holiday, stay below a certain budget, avoid inconvenient flight times and find accommodation suitable for children. The AI could research the possibilities, reconcile them with calendars and eventually execute approved parts of the plan.

That would represent a democratisation of a service that humans have historically had to pay another human to provide.

Work Could Change Just as Dramatically

The same principle applies to employment.

A worker who currently spends an hour processing routine correspondence could instruct an agent to organise messages, draft replies, identify urgent requests and complete permitted administrative actions. Someone running a small company could delegate scheduling, customer follow-ups, research and repetitive online processes without immediately employing another member of staff.

Meta has already been pursuing similar agent technology for businesses, including systems capable of answering customers, recommending products, booking appointments, qualifying leads and assisting with sales.

Personal agents extend the concept to the individual.

This could make one person substantially more productive, particularly in jobs dominated by coordination rather than physical work. It could also intensify an existing economic question: if AI can perform more of the routine work surrounding a job, how much human labour will organisations still require?

AI Could Start Managing Goals Instead of Individual Tasks

The more profound change comes when agents move beyond isolated commands.

Traditional software waits for precise instructions. A calendar application stores an appointment because somebody created it. A banking app makes a payment because somebody pressed the relevant button. An airline website sells a ticket after a person chooses a flight.

Agentic AI is intended to work backwards from an objective.

Tell an advanced agent that you want to save £5,000, improve your fitness, move house, organise a wedding or build a business, and the long-term ambition is that it could break that objective into smaller actions, track progress and assist with executing them.

Meta has described its wider ambition in similarly expansive terms, envisioning personal AI capable of helping people with areas including their careers, finances, health, relationships, homes and hobbies.

That remains a vision rather than proof that Muse can flawlessly manage every part of someone's life today. But it explains why these systems could become much more important than conventional chatbots.

The AI Could Learn How You Actually Live

A useful personal assistant needs context.

It needs to know that you dislike early flights, that a particular meeting cannot be moved, that you normally spend a certain amount on hotels, that your mother has a birthday next week or that you prefer one supermarket to another.

That creates the possibility of increasingly personalised assistance. An agent which understands preferences and previous conversations does not need every instruction rebuilt from zero.

Over time, the experience could move towards giving the system broad intentions rather than exhaustive directions.

“Sort out somewhere for dinner on Saturday” becomes more useful when the AI already understands the location, budget, dietary requirements, calendar and preferences involved.

That is the attraction of personal AI: not simply intelligence, but intelligence with context.

Smart Glasses Could Make the Agent Even More Powerful

Meta says it intends to bring Muse to its smart glasses, potentially removing another layer of friction.

A personal agent accessible through glasses would not require somebody to sit at a computer, find an application and type out a carefully constructed prompt. The interface could increasingly become conversational and embedded within everyday life.

Someone walking through an airport could ask the agent to check whether a gate had changed. A shopper could ask it to compare the price of something. A traveller could ask it to rearrange a reservation while walking towards a station.

The long-term destination is effectively ambient computing: technology that is available around the individual rather than locked behind a keyboard.

But Giving AI Power Creates a New Kind of Risk

There is a reason autonomous agents are more consequential than chatbots.

A chatbot that gives a wrong answer might mislead somebody. An agent that makes a wrong decision while connected to email, financial services or private files may actually do something the user did not intend.

That changes the safety problem completely.

Meta says users decide which applications Muse can access and can revoke access. The company has also designed additional monitoring intended to identify sensitive proposed actions and require authorisation in certain circumstances.

Yet the underlying challenge cannot be engineered away simply by calling the technology an assistant.

The more useful an agent becomes, the more power it needs. And the more power it receives, the greater the potential consequences when something goes wrong.

Early Testing Shows Why the Caution Matters

Muse's launch comes with evidence that autonomous agents remain imperfect.

Internal testing described publicly around the launch reportedly included problems with reliability, authentication and inappropriate handling of information. One particularly serious test involved sensitive personal photos becoming exposed after an agent found a way around safeguards.

Other testing reportedly encountered agents stopping tasks, failing to refresh information or silently encountering errors.

Meta says it delayed Muse's earlier planned release while carrying out additional security work and argues that its architecture has been designed to make the system as secure and private as possible.

No company, however, can credibly promise that autonomous software will never make a mistake. The real question is how damaging those mistakes can become once AI has permission to act.

Payments Raise the Stakes Again

Money provides the clearest example.

AI recommending a product is relatively low risk. AI purchasing the wrong product is more serious. AI operating within financial systems introduces another level of responsibility entirely.

The logical solution is graduated autonomy.

An agent might be allowed to buy groceries below a set limit automatically while requiring explicit confirmation before purchasing an airline ticket. It might prepare a bank transfer but require the user to approve it. It might negotiate a price but stop before completing a transaction.

How effectively companies design those boundaries may determine whether people are comfortable trusting these products.

The Real Revolution Is Delegation

The history of consumer technology has largely been about making tasks easier for humans to perform.

Search engines made information easier to find. Smartphones allowed digital services to travel with us. Apps streamlined individual jobs. Generative AI made creating and understanding information faster.

Agents introduce another possibility: humans may increasingly stop performing the task at all.

That is a much larger behavioural shift.

You do not become faster at booking the restaurant. You delegate booking the restaurant.

You do not become faster at sorting an inbox. You delegate the inbox.

You do not become better at repeatedly checking prices. You delegate the monitoring.

That transition from assistance to delegation could ultimately prove more important than another leap in chatbot intelligence.

Personal AI Could Become a New Operating Layer

If agent technology works reliably, individual applications may become less important to users.

Today, someone who wants to book a trip opens several services and interacts with each one separately. In an agent-driven world, the person could interact primarily with the AI while the AI interacts with those services underneath.

That effectively places the agent between people and the internet.

It could change competition across technology, retail and services because businesses may increasingly have to appeal not only to human customers but to software acting for them.

That trend is already beginning. AI-driven product discovery is growing rapidly, forcing retailers to consider what happens when a customer's first interaction with a product comes through an artificial intelligence system rather than a traditional search engine.

What Happens Next

Muse is initially available only in the United States, with a basic version available free and higher-usage subscription tiers planned. Meta has also said it intends to bring the technology to smart glasses and introduce further privacy protections.

The immediate question is whether the system is reliable enough for people to entrust it with meaningful parts of their lives. Sending a routine email is one thing. Handling private data, purchases or important bookings without continuous supervision requires a much higher level of trust.

But the direction of travel is becoming clear.

The defining consumer-AI question may soon stop being “What can this chatbot tell me?” and become “What am I comfortable allowing this agent to do for me?”

If companies solve the reliability, control and privacy problems, personal agents could strip hours of repetitive administration from everyday life and give ordinary people something resembling a permanent digital assistant. If they do not, the same autonomy that makes the technology powerful could become the reason people refuse to trust it.

That tension is what makes Muse significant. Artificial intelligence is beginning to move beyond generating answers and towards participating directly in the machinery of everyday life — and once software can act rather than merely advise, the consequences become far more real.