Device-level context is information about a device’s capabilities and current state—for example, battery level, processor load, display size and available connectivity. A personal AI may combine those signals with user preferences and activity, nearby conditions, and network status to decide what to do and when. Device context is therefore one layer of a larger context model, not a synonym for all personal data and not a guarantee that information stays private.
The key privacy question is not simply whether processing happens on the device. You also need to know which signals are read, what summary or raw data is shared, which agents receive it, where those agents run, and whether you can inspect, correct or delete the resulting profile.
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What device-level context includes
In the terminology used by the ITU-R’s Report ITU-R M.2320-0: Future technology trends of terrestrial IMT systems (2014, section 5.3, p. 18), device-level context describes characteristics and conditions supplied by the device itself. The report gives examples including battery state, CPU load, display size and multi-communication capability.
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| Device signal | What it tells a system | Possible personal-AI decision |
|---|---|---|
| Battery state | How much energy remains and whether the device is charging | Delay a computationally heavy task, reduce background work or ask before starting a long process |
| CPU or accelerator load | Whether local processing capacity is available | Run a model locally now, queue work, or use a different execution path |
| Display size and device characteristics | What interface and capabilities the hardware supports | Present a compact control on a small screen or a richer layout on a larger one |
| Multi-communication capability | Which communication interfaces the device can use | Choose among available connections or coordinate with another device |
| Connectivity state | Whether the device can currently reach a service | Use cached or local capabilities when a remote service is unavailable |
“Device-level” does not mean that every piece of information stored on a device belongs to this category. A calendar entry, a message, a location history and a stated preference may be stored locally, but they describe the user or the user’s activity rather than the device’s physical state.
How device context differs from other context layers
The ITU-R taxonomy separates four useful layers. A personal AI can combine them, but treating them as interchangeable causes both product and privacy confusion.
Device context
Device context covers hardware identity and current operating conditions: remaining battery, processor load, display characteristics, available sensors and communication capabilities. It answers questions such as, “Can this device perform the task now?” and “What form should the result take?”
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User context concerns the person’s preferences, activity, location and possible distraction. It answers, “What is relevant to this person at this moment?” A preference for quiet notifications or a pattern of commuting at a particular time belongs here. User context is often more sensitive than a battery reading because it can reveal habits, relationships and whereabouts.
Environmental context
Environmental context describes conditions around the device, including motion, lighting and nearby devices. It can help an AI distinguish a phone being carried during a run from one resting on a desk, or adapt an interface when the surroundings are dark or noisy.
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Network context
Network context includes congestion and available throughput. It answers, “What kind of connection is possible right now?” That can influence whether an AI sends a request to a remote service, waits for better bandwidth or keeps the task local.
| Layer | Typical inputs | Question it helps answer |
|---|---|---|
| Device | Battery, CPU load, screen size, communication capability | What can the hardware do now? |
| User | Preferences, activity, location, distraction | What matters to this person now? |
| Environmental | Motion, lighting, nearby devices | What is happening around the device? |
| Network | Congestion, throughput and availability | What remote connection is practical? |
The report also notes that context-aware adaptations may collaborate across devices and cloud services. A watch, phone and car could therefore contribute different signals, while a remote service performs a task that the local hardware cannot. Such collaboration increases usefulness, but it also creates more places where information must be governed.
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A context-aware system normally turns separate signals into a decision rather than exposing every raw reading to every feature. A simplified flow looks like this:
- Collect permitted signals. The system checks only the device, user, environmental or network inputs that the product and its permissions allow.
- Interpret the current state. It may infer that the battery is low, the user is moving, the screen is small and the network is congested. These are different facts with different sensitivity.
- Select an execution path. The AI chooses a local model, a remote agent, a hybrid sequence, or no action if confidence or permission is insufficient.
- Adapt the interaction. It may shorten a response, defer a background task, change the interface or request confirmation before using a remote service.
- Share only what the selected agent needs. A useful design sends a narrowly scoped context result rather than an unrestricted dump of device and personal data, although the actual boundary depends on implementation.
For example, “battery at 8% and network unavailable” is device and network context. “The user is driving to a scheduled appointment” combines user activity, environmental information and calendar data. The second case may be more helpful, but it also requires a more consequential permission decision.
What Liquid Context announced in September 2026
On September 23, 2026, Liquid AI announced that its Liquid Context layer had been optimized for Snapdragon processors using the Qualcomm Hexagon neural processing unit. Liquid AI describes the layer as learning from device signals with user permission, building an understanding of routines, preferences and needs, and maintaining that context locally.
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The architecture separates where context is built from where it may be consumed. According to the announcement, relevant context can be made available to a user’s selected agents under permissions. Those agents may run on the device, in the cloud or across both. Local maintenance of a context layer therefore can coexist with cloud-agent use; “on-device context” does not mean that no information ever leaves the device.
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The company illustrates possible experiences such as rescheduling a calendar item, drafting a social post and carrying context from a run into a car. These are examples of the proposed direction, not evidence that each integration is shipping, that performance has been independently measured or that the features are available on a particular consumer product.
Liquid AI CEO and co-founder Ramin Hasani framed the approach this way: “Personal AI starts with understanding how you live and what you need, when you need it.” That is the company’s product positioning, not an independent finding about accuracy or user benefit.
What the announcement does not establish
- It does not provide a supported-device list or show that every Snapdragon-powered product supports Liquid Context.
- It does not announce a consumer launch date for a particular phone, computer, vehicle or accessory.
- It does not publish independent figures for accuracy, latency, battery impact, privacy outcomes or adoption.
- It does not give a complete inventory of signals, a retention schedule, a deletion procedure or a full security audit.
The announcement describes manufacturers as a route to built-in experiences and says they can evaluate Liquid Agent. That is an OEM and platform path, not proof of broad consumer availability.
Does on-device AI mean your data never leaves your phone?
No. “On-device” can describe where one part of a system runs, while another part sends selected information to a remote agent. A local component might transform sensor readings into a context summary; a cloud agent might then receive that summary to complete a task. The summary could be less revealing than raw sensor data, but it is still information about the user and device.
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Evaluate a context feature across these concrete boundaries:
| Privacy axis | Questions to ask |
|---|---|
| Signals | Which device, user, environmental and network inputs are read? Are optional sensors enabled by default? |
| Processing location | Which operations stay local, and which are sent to a cloud service? |
| Sharing and permissions | Which named agents receive context? Can access be limited by task, data type or time? |
| Lifecycle controls | Can you inspect, correct, export or delete stored context? How long is it retained? |
| Evidence and availability | Is the feature a proposal, developer offering or shipping capability? Are results vendor-reported or independently measured? |
These questions matter because a system can minimize raw-data transmission yet still create a detailed profile locally and share meaningful inferences with several agents. Conversely, a remote request may be narrowly scoped and temporary. The phrase “on-device” alone cannot distinguish those cases.
ITU-R emphasizes that privacy for sensitive context information—especially user context—is necessary for user acceptability. In practical terms, a trustworthy product should make permissions understandable, identify the recipient of shared context and provide usable controls rather than relying on a reassuring label.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What information can an AI use from a device?
The exact inventory varies by product and permissions, but the categories below show how a system might reason about context without assuming that every product reads every signal.
Hardware and operating state
- Battery percentage, charging state and power-saving mode.
- Processor, graphics or neural-accelerator load.
- Screen dimensions, input methods and available hardware features.
- Storage or memory pressure that could constrain a local model.
Connectivity and coordination
- Whether Wi-Fi, cellular, Bluetooth or another communication path is available.
- Estimated throughput, latency or congestion.
- Presence of nearby authorized devices that can contribute context or receive an action.
Activity and surroundings
- Motion or activity state, such as stationary, walking or running.
- Lighting and other environmental conditions detected by permitted sensors.
- Calendar, location or preference information supplied by the user or an app.
The first two groups are closer to device and network context. The final group can reveal behavior, routines and relationships, so it deserves stronger scrutiny. A product should explain not only that it uses “context,” but which signals are optional, how they are combined and what an agent can infer from them.
A separate app example: ContextSDK
ContextSDK’s guide shows that device context is also used outside general-purpose personal assistants. It describes an iOS software development kit that processes sensor and device signals locally to help determine when an app should present a prompt. The stated purpose is engagement timing, not a universal personal-AI architecture.
ContextSDK’s own guide reports access to “over 200 real-world signals” and claims that its product can return a context state in “under 2 seconds.” Those are vendor-published figures for that SDK, with no independent validation established here. They should not be treated as measurements of personal AI systems generally, nor as evidence that Liquid Context has the same capabilities, speed or privacy properties.
Benefits and trade-offs of device-level context
Potential benefits
- Better resource decisions: An AI can avoid an expensive local computation when battery or processor capacity is constrained.
- More usable interfaces: Screen size, input capability and current activity can guide presentation choices.
- Faster local reactions: A device may respond to a state change without waiting for a round trip to a server.
- Resilience: Local context can support limited functionality when connectivity is poor.
- Coordinated experiences: Multiple devices and cloud services can contribute different capabilities.
Trade-offs
- Inference risk: Combining harmless-looking signals can reveal routines or sensitive circumstances.
- Permission complexity: Users may struggle to understand which agent receives which context.
- Resource cost: Continuous sensing and local inference can consume battery, memory or processing capacity.
- Wrong assumptions: A system may misread motion, location or availability and take an unwanted action.
- Expanded attack surface: Every connected agent and synchronization path becomes part of the data boundary.
How to assess a device-context feature before using it
- Find the signal list. Look for a product-specific description of sensors, device state, account data and inferred attributes.
- Map each signal to a purpose. A feature should explain why it needs battery state, motion, location or another input instead of grouping everything under “personalization.”
- Check the execution path. Determine what remains local and what is sent to a remote model or service.
- Identify every recipient. “Selected agents” is meaningful only if the product names those agents and shows how access is granted or revoked.
- Review lifecycle controls. Confirm whether context can be inspected, corrected and deleted, and whether retention periods are stated.
- Separate availability from announcement language. A platform demonstration or OEM evaluation path is not the same as a feature enabled on your device.
- Look for independent evidence. Vendor claims about speed, accuracy or battery use should remain attributed unless an independent test supports them.
The practical meaning for personal AI users
Device-level context is best understood as the AI’s awareness of the hardware and its immediate operating constraints. It can make a personal assistant more timely and less wasteful, especially when combined with user, environmental and network context. It does not automatically reveal what a person wants, and it does not automatically protect everything stored on the device.
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The Liquid AI announcement demonstrates a proposed pattern: maintain a context layer locally, then provide relevant portions to chosen local, cloud or hybrid agents under permissions. Whether that pattern is private enough or useful enough depends on implementation details that the announcement does not yet specify. For any real product, inspect the signals, processing locations, recipients and lifecycle controls before treating “on-device” as a privacy conclusion.
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