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Agricultural artificial intelligence is commonly compared through benchmark datasets and technical metrics before any field experimentation occurs. Such comparisons can help identify promising models, reveal methodological weaknesses, and organize evidence for further testing, but they cannot establish field readiness, agronomic validity, operational safety, or deployment suitability. This article proposes a conceptual framework for benchmarking agricultural AI without field experiments. The framework separates five linked assessment domains: model readiness, technical validation, data quality, contextual fit, and deployment risk and decision validity. It addresses benchmark-task clarity, metric justification, dataset representativeness, provenance, label and ground-truth uncertainty, structured validation, domain shift, robustness, calibration, uncertainty, error patterns, bias, explainability, human oversight, and monitoring requirements. Non-empirical evaluation is treated as a structured screening process based on documentation, reported evidence, cross-context reasoning, and explicit claim boundaries. The framework produces a conditional benchmark-readiness statement and a field-validation gap register rather than a deployment decision. Its principal contribution is to distinguish benchmark performance from decision validity and deployment readiness while giving researchers, developers, evaluators, and agricultural stakeholders a transparent structure for comparing evidence before field testing. The framework is conceptual, does not replace field experiments, and should be updated as new data, validation evidence, user feedback, and operational observations become available.