Agentic RAG Evaluation: Budget Allocation Across Questions, Trajectories, and Reads

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 $0–1 per 1,000 requests; question-versus-read fee rankings remain unresolved. Temperature zero cuts answer disagreement from 14.3% to 3.4% while comparison precision stays similar. \par\medskip\noindent$\mathbf{\text{Keywords:}}$ Agentic RAG; Evaluation budget; Generalizability theory; Repeated sampling.