The food and agriculture domain generates a large and growing body of scientific knowledge, much of it encoded in journal articles containing equations and metadata critical for process-based modeling and decision support. When field data are sparse or inaccessible, such models become essential for informing research and on-farm management. However, manually extracting predictive equations and associated metadata from the literature is labor-intensive, error-prone, and difficult to scale. While agentic and generative artificial intelligence (AI) systems have shown promise for information extraction, the food and agriculture domain lacks structured benchmarks and evaluation agents to assess their performance in a rigorous, domain-aware manner. We present an agent-to-agent (A2A) evaluation architecture consisting of a purple participant agent and a green evaluator agent, designed to assess equation and metadata extraction from dairy science literature. The purple agent performs extraction tasks, while the green agent orchestrates task dispatch, enforces extraction templates, and evaluates outputs against gold-standard references. This work focuses explicitly on the design and operation of the green agent. The evaluation is executed through a predefined scenario configuration that launches both agents, triggers an assessment request, and processes a fixed set of dairy science papers. The green agent loads XML-formatted Journal of Dairy Science papers, applies predefined extraction templates, dispatches extraction tasks to the purple agent, and evaluates returned structured JSON outputs against gold-standard reference files curated by subject-matter experts. The evaluation tasks include extracting predictive equations, structured equation representations, and associated metadata fields such as variable definitions, units, and contextual assumptions. For demonstration, the purple agent is instantiated using the GPT-4.1-mini model, which processes each paper independently and returns one structured JSON output per paper. The green agent evaluates extraction performance using an automated scoring pipeline that includes exact equation matching, structured field validation, and BERTScore-based semantic similarity metrics computed for extracted metadata fields. Evaluation results are aggregated across all papers and reported as structured JSON artifacts generated by the evaluator. In local scenario runs, BERTScore F1 values for metadata extraction ranged from approximately 0.85 to 0.90 across papers, indicating moderate semantic agreement with gold-standard references. Equation matching results further reveal that while general-purpose language models can identify and reproduce some predictive equations from XML inputs, errors persist in equation completeness, formatting consistency, and associated metadata linkage. Overall, this work introduces a green-agent-centered evaluation framework for agentic AI in food and agriculture, providing a foundation for scalable benchmarking, transparent assessment, and future extensions to more complex scientific reasoning tasks.