Towards More Standardized AI Evaluation: From Models to Agents

TL;DR

This paper advocates for a shift in AI evaluation from static benchmarks to a trust-oriented, systematic approach suitable for evolving, agentic systems, emphasizing evaluation as a core control function rather than mere performance measurement.

Contribution

It critically analyzes current evaluation practices, highlighting their limitations for agentic AI, and proposes a paradigm shift towards evaluation as a trust and governance tool.

Findings

Current benchmarks can mislead and obscure system behavior.

Evaluation pipelines can introduce silent failure modes.

Agentic systems require a new perspective on performance measurement.

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.