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

arXiv: https://arxiv.org/abs/2610.05034 · HTML: https://mlsanity.com/paper/2610.05034/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation.
- First posted on arXiv 4 Oct 2026.

## Placed in

- Retrieval-augmented generation / [Agentic RAG, agents and memory](https://mlsanity.com/rag/agentic-rag-agents-and-memory/)
- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Abstract (arXiv)

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. parmedskipnoindentKeywords: Agentic RAG; Evaluation budget; Generalizability theory; Repeated sampling.