Agent Evaluation Beyond Win-Rates: Comprehensive Assessment Frameworks

Current AI agent evaluation overwhelmingly relies on win-rates and aggregate performance scores that obscure crucial dimensions of agent behavior. This reductive approach poses significant risks when agents operate in complex, real-world environments where multiple objectives matter simultaneously. This paper introduces a multidimensional evaluation paradigm that assesses agent performance across eight critical dimensions: calibration, robustness, goal alignment, uncertainty handling, fairness, interpretability, adaptability, and resource efficiency. I propose Agent Evaluation Cards—standardized reporting templates that make agent capabilities and limitations transparent to developers, users, and stakeholders. Case studies in navigation, healthcare triage, and multi-agent games demonstrate how comprehensive evaluation can support responsible agent deployment while enabling beneficial innovation.