**I built a .NET AI agent that runs entirely on a phone — your data stays on the device**

What if your AI agent didn’t need to send your prompts, files, conversation history, or personal data to a cloud AI service?  
This demo is running **TensorSharp + a local Qwen3.5 9B IQ4\_XS model directly on a phone**.  
I simply ask:  
“Please get 10 stocks with most gains today.”  
The agent handles the rest.  
It reasons about the task, generates Python code, executes it locally, retrieves the requested public market data, and returns the result.  
Then I ask:  
“Convert it to a PDF.”  
The interesting part is that there isn’t a hard-coded “convert to PDF” workflow.  
The agent discovers the relevant **Skill**, reads its SKILL.md, figures out how to complete the task, writes the necessary code, executes it, generates the PDF, and returns the file — all from the phone.  
So the stack shown in the video is essentially:  
**Local LLM inference → agent reasoning → Skills → tool use → code generation → local execution → file generation**  
And the important part for mobile use:  
**Your prompts, conversations, files, and private on-device data stay on the phone.**  
The model itself runs locally. An external connection is only made when the task explicitly requires public external information — such as fetching stock prices in this demo. Your local data does not need to be uploaded to an AI provider.  
This is one of the directions I’m exploring with **TensorSharp**: making .NET not just a way to call an AI API, but a runtime capable of running the **model and the agent locally**, including on mobile devices.  
Repo:  
https://github.com/zhongkaifu/TensorSharp  
I think fully local agents could be particularly interesting on phones, where privacy, latency, offline capability, and cloud cost matter a lot.  
Would love to hear what the .NET community thinks about this direction.

I built a .NET AI agent that runs entirely on a phone — no cloud, no data leaving the device