Researchers infer LLM agent personality from real behavior, not self-reports
Researchers propose A-B-D, a method to measure AI agent personality traits by analyzing actual behavioral patterns rather than relying on self-reported assessments or expensive human ratings. Using 345,667 real-world trajectories across 80 models and multiple task domains, the work extracts functional and communicative features that reveal how agents genuinely operate in practice. This addresses a critical gap in agent evaluation: existing personality frameworks fail to capture the gap between what models claim about themselves and how they actually behave. The approach scales to diverse scenarios and could reshape how teams benchmark agent reliability and fit for specific workflows.
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