What Does It Take to Detect an AI Agent? Minimal Feature Sets for Behavioral Detection under Browser Automation

TL;DR - A three-class framework distinguishes AI browser agents from humans and conventional bots using minimal behavioral features. It shows that browser-automation artifacts enable highly accurate detection, though they identify Playwright-like automation rather than agent reasoning.

• Binary detectors mislabeled 34.5–39.1% of real AI-agent sessions as human because they lacked an agent class.
• Adding an explicit agent class achieved agent F1 of 1.000 across 30 model-and-seed runs.
• Two features—mouse event rate and teleport-click ratio—provided 100% observed agent recall and 0.994 precision across five evasion levels.
• Five features achieved macro-F1 of at least 0.99 across humans, bots, and agents.