Trust Is Not a Strategy

When people talk about AI agent safety, they usually frame it as a trust problem. "Can you trust the agent to do the right thing?" But trust is the wrong frame. The right question is: can you verify what the agent actually did?

Trust is a feeling. Verification is a system.

What Verification Looks Like

A verifiable AI agent produces a complete record of its actions. Every click, every keystroke, every file modification, every API call - logged and reviewable. After the agent completes a task, you can scrub through an action replay and see exactly what happened.

This isn't just a debug tool. It's a fundamental design requirement. When an agent can send emails, modify code, move files, and interact with web services on your behalf, you need to be able to check its work.

Why Screenshots Matter

Screenshots taken during agent execution serve as visual proof of what the agent saw and did. They're harder to fake than text logs and easier to review quickly. You can glance at a sequence of screenshots and immediately spot if something went wrong - the agent clicked the wrong button, filled in the wrong field, or navigated to an unexpected page.

The Audit Log

A good audit log captures more than just what the agent did. It captures why - the reasoning behind each action, the decision points, and the alternatives it considered. When something goes wrong, you want to understand the agent's logic, not just replay its clicks.

Building Verification In

Verification can't be an afterthought. It needs to be part of the agent's architecture from the start. Every action goes through a logging layer. Every state change is recorded. The overhead is minimal on modern hardware, and the payoff is enormous - you get an agent you can actually hold accountable.

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