Most Agents Have No Privacy Boundaries

Here's what happens with most AI agent frameworks: the model gets access to your screen, your files, your applications, and it sends all of that data to a remote server for processing. There's no granular control over what it can see. There's no way to exclude sensitive applications. It's all or nothing.

For personal use on a throwaway machine, maybe that's fine. For anyone working with client data, financial records, medical information, or proprietary code, it's a non-starter.

What Privacy-First Actually Means

A privacy-first agent processes screen data locally. The raw pixels from your display never leave your machine. Text extraction, UI element detection, and action planning all happen on-device. The only data that goes to an LLM API is the structured text and commands - not your raw screen content.

This distinction matters. Sending "click the Submit button in the Expenses app" to an API is fundamentally different from sending a full screenshot that includes your bank balance, email contents, and Slack messages visible in other windows.

Granular Controls Change the Dynamic

Good privacy controls go beyond just local processing. They let you specify which applications the agent can interact with. You might want it to handle Finder and Chrome but never touch your password manager or banking app. You might allow it to read documents but not modify them without confirmation.

These aren't theoretical features - they're practical requirements for anyone who wants to use AI agents in a professional context. An agent without access controls is a liability, no matter how capable it is.

The Trust Equation

Users adopt AI agents when the perceived benefit exceeds the perceived risk. Better capabilities increase the benefit. Better privacy controls decrease the risk. Most frameworks focus entirely on capabilities and ignore the risk side of the equation.

Open source helps here too. When the code is public, anyone can audit exactly what data the agent accesses and where it goes. Combined with local-first processing and granular permissions, you get an agent that people actually feel comfortable running on their work machine.

Fazm is an open source macOS AI agent. Open source on GitHub.