Meta has announced that developers can now build their own hardware projects using its Muse AI agent through newly released open-source code and software development kits. The company has presented the move as a way to connect Muse to low-cost hardware such as ESP32 boards, Raspberry Pi devices, displays, buttons, sensors and other components.

While this is not, on its own, a major enterprise product launch, it is a notable sign of how AI agents are moving beyond chat interfaces and into physical devices. For European businesses, that matters most in areas such as prototyping, workplace automation, retail experiences and internal operational tools.

What happened

According to Meta, developers can use open-source tools to create their own “Muse gadgets” built around the company’s AI agent. Examples referenced by the company include E Ink reminder displays, HDMI-connected screens and small touchscreen devices.

Based on the source material, Meta is positioning Muse gadgets as do-it-yourself projects built with off-the-shelf hardware rather than as finished enterprise devices. The announcement appears to be focused on enabling experimentation by developers and makers, not on launching a complete business platform with verified enterprise support or compliance features.

Why it matters for European businesses

The practical importance is less about the specific hardware examples and more about the broader direction of the market. AI agents are increasingly being embedded into business environments through kiosks, displays, smart office tools, warehouse interfaces and customer-facing devices.

For SMEs and digital teams, open-source hardware integrations can reduce the cost of testing new AI-assisted workflows. A business may be able to prototype internal tools for reception areas, meeting rooms, stock handling, workshop environments or retail counters without waiting for a fully packaged commercial product.

That said, businesses should distinguish between experimental developer tooling and enterprise-ready infrastructure. Open-source access can accelerate innovation, but it does not automatically address security, support, device management, privacy, reliability or regulatory requirements that European organisations may need to consider before deployment.

Who may be affected

• SMEs and founders exploring low-cost AI prototypes for operations, customer service or internal productivity.
• IT and digital teams evaluating whether AI agents can be embedded into physical interfaces or connected devices.
• E-commerce and retail businesses interested in in-store displays, service points or AI-assisted customer interactions.
• Digital agencies and developers building proof-of-concept devices or custom client experiences.
• Regulated businesses that may see innovation potential but need stricter review before using AI-enabled hardware with business or personal data.

What companies should consider

• Treat this as a prototyping opportunity first. For most businesses, the immediate value is in testing ideas rather than deploying production systems at scale.
• Review data handling carefully. If a device captures user inputs, images, audio or operational data, GDPR and internal privacy controls may become relevant depending on the use case.
• Check security and device management. Connected AI hardware can introduce new attack surfaces, especially if it relies on consumer-grade components or ad hoc network access.
• Assess integration needs. A useful business device typically depends on integration with calendars, CRMs, inventory systems, service platforms or internal knowledge bases.
• Clarify support expectations. Open-source code may be useful for developers, but businesses should confirm who will maintain devices, apply updates and handle failures over time.
• Separate marketing interest from operational fit. Not every AI gadget idea will deliver measurable business value. Pilot projects should be tied to a clear workflow, cost-saving target or customer experience goal.

At this stage, Meta’s release is best understood as an enabling development for experimentation around AI agents in hardware. Its direct business impact will likely be strongest for technical teams, agencies and innovation-led companies that want to explore custom AI interfaces at relatively low cost.