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Muse is Meta's personal AI agent app, with over 2.5 million downloads within two weeks of launch, topping the U.S. free charts. It can handle multi-step everyday tasks such as generating a moving packing list, sending emails on your behalf, organizing restaurant recommendations, and comparing prices for purchases. You can customize the AI agent's name, and it operates web pages through a cloud-based virtual computer, with real-time visibility and the ability to intervene. Payments are integrated with Stripe virtual cards to protect credit card numbers.

Things to note: It cannot directly contact sellers through Facebook Marketplace—it can only draft negotiation messages. Some platforms, such as Amazon, prohibit scraping product data, and Target blocks automated checkout. Tasks involving phone communication cannot be handled. After first linking a payment method, the confirmation threshold is low, so it's advisable to carefully check orders. It is currently suitable for planning and purchasing tasks, but information accuracy has flaws—for example, it may recommend restaurants that have closed. On privacy, Meta says it runs in an isolated cloud environment and cannot read passwords or payment information, but such products require access to substantial personal data, and public trust remains divided. If you want to try it, you can use it as an assistant for daily itinerary planning, price comparison shopping, and information organization, but for important matters, human verification is best.

AI agents are reshaping users' purchase decision journeys, and brands need to pay attention to the impact of tools like Muse on brand marketing and channel strategy. Muse can automatically compare prices across channels and surface cost-effective options, which means brand premium space may be compressed and pricing strategies need to account for an AI-driven comparison environment. Brands should prioritize being searchable and recommendable by AI agents when building channels, since users may complete product selection through AI.

User behavior observations show early adoption outpaced ChatGPT's initial growth, indicating strong demand for AI agents. However, 64% of consumers believe AI agents benefit businesses more than consumers, suggesting brands need to proactively design shopping flows that fit AI agents. Privacy and trust are critical to brand endorsement. Meta has been affected by the Cambridge Analytica incident, and users remain wary of how it handles payment information; brands should assess platform risks when partnering. From a product development perspective, Muse's personalized interest feed and prompt reference section create new scenarios for brand content reach. After users authorize agents to access personal accounts, brands need to improve customer service and page structuring for AI interactions to reduce the risk of being misunderstood by AI.

Muse surpassed 2.5 million downloads within two weeks and topped Apple's free app chart, creating a new traffic gateway and consumer scenario. AI agents can automatically complete product purchasing, price comparison, and inquiry submission, which means sellers need to consider whether their product information can be effectively crawled by AI. However, note that Amazon bans scraping and Target blocks automated checkout; platforms have different stances on AI agents, so sellers should monitor policy differences across platforms.

Risk alert: Muse automatically compares prices and selects cost-effective options, which could intensify price competition. Sellers need to optimize costs or differentiate. Opportunity alert: In local life service scenarios, Muse filters nearby service providers, collects information, and submits inquiries. Home-service businesses can optimize their online information to make it easier to be discovered and recommended. Key takeaway: When making purchases, Muse automatically identifies products that require professional licenses, reminding sellers to display credentials clearly. On collaboration, Meta says it can complete negotiations within preset parameters; sellers should explore customer service and quotation mechanisms that interface with AI agents. User trust remains a hurdle—early feedback is positive, but privacy concerns could slow mass adoption. Sellers should invest cautiously.

Muse's AI agent capabilities reveal new demands for product production and design: users expect personalized packing, streamlined processes, and automated purchasing. This means product manuals, specification parameters, and SKU information should be structured and easy for AI to crawl. For example, purchasing needs such as coffee machine accessories and camping gear require complete and clear product data so agents can accurately identify and recommend them.

Business opportunity: Muse can identify merchant qualifications—for instance, beauty supply stores that require professional licenses. If factories are involved in licensed products, they should highlight certification information on their pages. In product design, Muse generates daily packing lists and storage reminders, showing that consumer-grade AI favors actionable life scenarios. Factories can develop bundles or consumable combinations that align with AI recommendation logic.

Digitalization implications: Muse operates web pages through a cloud-based virtual computer, which means e-commerce pages need to be compatible with automated operations. When expanding online business, factories should pay attention to standardized page loading, form filling, and payment interfaces. Data indicates AI shopping agents are seen as delivering high benefits to businesses; factories should adopt AI-friendly product data standards early and build clear product databases to gain visibility in the agent economy.

Industry trend: Meta launched the AI agent Muse, with over 2.5 million downloads in two weeks, signaling that consumer AI agents are moving from chat to task execution. Service providers should watch this niche. Muse can handle itinerary booking, purchasing, and local service coordination, backed by a combination of new technologies such as cloud virtual computers, automation, and payment integration.

Customer pain points: User privacy and trust are the core obstacles to large-scale adoption of AI agents. Clients worry about payment information and personal accounts being compromised. Solutions can include technically providing isolated cloud environments and security architectures that cannot read passwords or payment information, similar to Muse's approach. Channel pain points: Some platforms prohibit scraping and block automated operations. Service providers can develop data acquisition solutions compatible with platform rules or introduce human intervention workflows.

Gaps in Muse's features: Phone communication cannot be automated, which leaves opportunities for call center integration and voice agent service providers. The initial version's emails are too long and stiff, which can be improved with smarter prompt engineering. During purchase execution, it identifies merchant license requirements, indicating service providers need to add qualification verification logic. Overall, customers need a one-stop multi-platform task agent rather than a single-point tool. Service providers should offer integrated services around data access, payment security, platform compliance, and human-machine collaboration.

Business demands and issues for platforms: AI agents like Muse automatically compare prices, filter products, and submit orders during purchases. Platforms must decide whether to allow agents to access on-site data. Amazon has already banned scraping, and Target blocks automated checkout, showing that platforms are conservative toward AI agents because they could disrupt on-site traffic distribution and payment flows.

Latest platform practices: Meta integrates Muse through a standalone app and WhatsApp, and operates it on a cloud-based virtual computer with multiple built-in feature sections. Platforms can learn from its product design, such as interest-based feeds and prompt reference areas, to improve user stickiness. In terms of merchant recruitment, Muse's purchasing scenarios introduce new matching mechanisms. Platforms could consider opening APIs to compliant AI agents in exchange for transaction data—but they must establish admission standards to avoid malicious scripts.

Risks to watch: AI agents rely on users' private data, and the Center for Democracy and Technology notes that security requirements are higher than for ordinary chatbots. When supporting agent features, platforms must strengthen user authorization, data isolation, and payment protection mechanisms to prevent trust crises caused by privacy vulnerabilities. Early data shows fast user growth but low trust; platforms should balance feature openness with risk control.

Industry trend: Meta launched the AI agent Muse, an important milestone in the evolution of consumer AI from conversational to action-oriented. With over 2.5 million downloads in two weeks and user growth surpassing ChatGPT's early phase, autonomous agents show enormous market potential. However, current limitations—such as recommending closed restaurants and being unable to handle phone calls—indicate the technology is still immature.

Emerging issues: AI agents that automatically operate web pages, process payments, and access personal data pose new challenges for privacy protection and security. Meta has been scarred by the Cambridge Analytica incident, compounded by smart glasses privacy controversies, leaving user trust low. Pew Research found that two-thirds of U.S. adults think AI is moving too fast, and Gallup shows 41% have almost no trust in big tech companies. AI agents need massive data to act directly, carrying far higher risk than chatbots, which creates urgency for policy and regulation.

Recommendations: There is a need for dedicated rules governing AI agent data collection and usage, clarifying responsibility boundaries among platforms, developers, and users. On business models, Muse isolates payment information via Stripe virtual cards and uses an isolated cloud environment to prevent data cross-access—these practices can serve as reference models. In the competitive landscape, OpenAI is also considering similar products. Investors and researchers should watch topics such as data permissions, platform access, and liability determination at the agent layer.