LLM-Based Agent Evaluation Needs a Unified Framework

A recent study published on arXiv (2602.03238) contends that the current methods for assessing LLM-based agents are inconsistent and inadequate. The authors note that benchmarks are influenced by irrelevant elements such as system prompts, tool configurations, and environmental variations. There is significant inconsistency in how prompts are engineered for reasoning and tool application, complicating the ability to link performance directly to the model. Furthermore, the absence of standardized environmental data results in errors that are difficult to trace and outcomes that cannot be replicated, leading to issues of fairness and transparency. The authors advocate for a comprehensive evaluation framework as crucial for meaningful progress in agent assessment.