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[2602.18029] Towards More Standardized AI Evaluation: From Models to Agents
Summary
This paper discusses the evolution of AI evaluation from static models to dynamic agents, emphasizing the need for standardized evaluation practices that foster trust and governance in AI systems.
Why It Matters
As AI systems transition from static models to complex agents, traditional evaluation methods become inadequate. This paper highlights the importance of developing new evaluation frameworks that ensure AI systems behave reliably and can be trusted in real-world applications, addressing a critical gap in AI governance and safety.
Key Takeaways
- Evaluation must evolve from static benchmarks to dynamic assessments.
- Trust in AI systems hinges on effective evaluation practices.
- Traditional metrics can obscure system performance and reliability.
- New evaluation frameworks are necessary for non-deterministic AI systems.
- The role of evaluation should focus on governance and iterative improvement.
Computer Science > Computation and Language arXiv:2602.18029 (cs) [Submitted on 20 Feb 2026] Title:Towards More Standardized AI Evaluation: From Models to Agents Authors:Ali El Filali, Inès Bedar View a PDF of the paper titled Towards More Standardized AI Evaluation: From Models to Agents, by Ali El Filali and In\`es Bedar View PDF HTML (experimental) 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. Comments: Subjects: Computation and Language (c...
