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