🤖 AI Summary
This study addresses the limitations of traditional, static, model-centric evaluation methods in effectively assessing the behavioral reliability and trustworthiness of dynamic, tool-using agents. Moving beyond the prevailing paradigm centered on static benchmarks and aggregated scores, the work uncovers hidden failure modes in current evaluation practices and proposes a novel assessment framework tailored for non-deterministic agent systems. This framework emphasizes continuous, transparent monitoring of behavioral performance, reconceptualizing evaluation not as a one-time performance test but as an ongoing measurement discipline that supports trust formation, system iteration, and governance. The research demonstrates that high benchmark scores are often misleading and advocates for behavioral trustworthiness as a core metric, offering both theoretical foundations and practical pathways toward building trustworthy and governable agent systems.
📝 Abstract
Evaluation is no longer a final checkpoint in the machine learning lifecycle. As AI systems evolve from static models to compound, tool-using agents, evaluation becomes a core control function. The question is no longer"How good is the model?"but"Can we trust the system to behave as intended, under change, at scale?". Yet most evaluation practices remain anchored in assumptions inherited from the model-centric era: static benchmarks, aggregate scores, and one-off success criteria. This paper argues that such approaches are increasingly obscure rather than illuminating system behavior. We examine how evaluation pipelines themselves introduce silent failure modes, why high benchmark scores routinely mislead teams, and how agentic systems fundamentally alter the meaning of performance measurement. Rather than proposing new metrics or harder benchmarks, we aim to clarify the role of evaluation in the AI era, and especially for agents: not as performance theater, but as a measurement discipline that conditions trust, iteration, and governance in non-deterministic systems.