A composable, deployable on-device Agent runtime that connects models, orchestration, memory, audio infrastructure, MCP and device tools in one trusted execution flow.
This is more than an LLM wrapper. What an agent can do, remember, access and safely complete is designed as a bounded system capability.
Coordinate lightweight on-device and cloud models, selecting a route based on task, network, device resources and privacy. Each model has a role; not every task is forced to run locally.
The Agent runtime follows Task, Plan, Execute and Verify, with task state, multi-agent collaboration, recovery, risk controls and observable tool execution.
Long-term memory and retrieval, real-time voice, context management and model services are shared infrastructure for the Agent runtime—and can also be integrated independently.
Expose executable capabilities to agents while preserving native protocols: business tools use MCP/API, while real-time audio, hardware inference and system calls stay on their appropriate SDKs and local interfaces.
Every real action needs a clear source, permission and feedback. A completed tool action must be distinguished from a request that was merely sent.
Voice · Text · Device Events
Session · Memory · Constraints
Agent Model · Planner
ToolGate · MCP · Device
Execution Receipts · Permission Checks
Trace · Governed Memory Write-back
Existing engineering work covers Android AI Service, AIDL/Binder, Qwen GGUF and LiteRT routing, memory/RAG and tool receipts.
Android 12 device compatibility has been tested, including AI Service SDK, model runtime and external Binder calls.
Android Engineering ValidationQwen GGUF and LiteRT models are routed to their respective runtimes; unknown formats are validated before loading.
Dual RuntimeToolGate, AppBridge, tool results and execution states distinguish planned actions from completed ones.
Trace TraceableHelp device makers preserve their hardware and product experience while integrating on-device Agent capabilities faster.