[
    {
        "id": "osp-18588",
        "type": "article-journal",
        "title": "Agentic RAG Evaluation: Budget Allocation Across Questions, Trajectories, and Reads",
        "author": [
            {
                "family": "Ning",
                "given": "Jingjie"
            },
            {
                "family": "Li",
                "given": "Xueqi"
            },
            {
                "family": "Kong",
                "given": "Yibo"
            }
        ],
        "URL": "https://omanscience.com/en/articles/agentic-rag-evaluation-budget-allocation-across-questions-trajectories-and-reads",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Evaluation budgets in agentic retrieval-augmented generation span questions, search trajectories, and repeated answers. We measure allocation precision, reading efficiency, and cost boundaries using a retrieval-feedback comparison on HotpotQA and MuSiQue. At 34.14--34.39M model tokens, broader question coverage lowers standard error by 33\\% versus five reads and 12.6\\% versus three trajectories. Archived nested and Q-only forecasts predict these allocations within 4.0\\% and 3.5\\%, respectively. Depth subsets establish no clear forecasting advantage beyond the two-trajectory audit. One-read variance penalties relative to the fitted optimum at the same token budget are 0--9.9\\%, with substantial Pro uncertainty. Under recorded model fees, more questions beat more trajectories at search prices of \\$0--1 per 1,000 requests; question-versus-read fee rankings remain unresolved. Temperature zero cuts answer disagreement from 14.3\\% to 3.4\\% while comparison precision stays similar. \\par\\medskip\\noindent\\textbf{Keywords:} Agentic RAG; Evaluation budget; Generalizability theory; Repeated sampling."
    }
]