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arxiv: 2605.05538 · v1 · submitted 2026-05-07 · 💻 cs.AI · cs.IR

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AgenticRAG: Agentic Retrieval for Enterprise Knowledge Bases

Susheel Suresh , Hazel Mak , Shangpo Chou , Fred Kroon , Sahil Bhatnagar
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Pith reviewed 2026-05-08 12:06 UTC · model grok-4.3

classification 💻 cs.AI cs.IR
keywords agentic RAGretrieval augmented generationenterprise knowledge basesLLM tool useiterative retrievalfactuality evaluationbenchmark performance
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The pith

Equipping language models with search, navigation, and summarization tools improves retrieval recall and answer correctness over standard fixed-retrieval RAG on enterprise benchmarks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents AgenticRAG as a lightweight harness layered on existing enterprise search systems, giving a reasoning model the ability to issue searches, open documents, locate sections inside them, and summarize evidence in repeated steps. This design moves away from the usual practice of fixing the entire context set in one early retrieval pass. On three benchmarks the method produces large lifts, including nearly 50 percent top recall on BRIGHT and 92 percent answer correctness on FinanceBench that sits within two points of an oracle with perfect evidence. Ablation results attribute most of the improvement to the shift from single-shot retrieval to ongoing tool use. The harness is presented as compatible with production enterprise deployments because it leaves the underlying search infrastructure unchanged.

Core claim

By supplying a reasoning LLM with four tools—search, find, open, and summarize—the harness lets the model iteratively retrieve information, navigate within documents, and analyze evidence on its own, reducing overdependence on the initial search stack and delivering higher recall, factuality, and correctness than conventional RAG pipelines.

What carries the argument

The lightweight agentic harness that equips the reasoning LLM with search, find, open, and summarize tools for iterative retrieval and evidence analysis.

Load-bearing premise

The three open benchmarks sufficiently represent the query distributions and document quality found in real enterprise knowledge bases.

What would settle it

A controlled test on enterprise data where the base search engine returns mostly irrelevant documents would show whether the iterative tool-use gains persist or disappear compared with single-shot retrieval.

Figures

Figures reproduced from arXiv: 2605.05538 by Fred Kroon, Hazel Mak, Sahil Bhatnagar, Shangpo Chou, Susheel Suresh.

Figure 1
Figure 1. Figure 1: Agentic loop 15). When maximum iterations are reached without a final answer, the agent issues a forced completion request, requiring the model to respond using avail￾able information. If the token budget is exceeded during execution, the agent triggers context man￾agement (Sec. 3.4) to free space and continues the loop. For detailed algorithm, see Appendix A.1. 3.3 Retrieval Tools The system provides thre… view at source ↗
Figure 2
Figure 2. Figure 2: Example conversation history with context view at source ↗
Figure 3
Figure 3. Figure 3: Factuality performance on the WixQA Expert view at source ↗
Figure 4
Figure 4. Figure 4: Factuality performance on the WixQA Sim view at source ↗
Figure 5
Figure 5. Figure 5: shows an example conversation from the FinanceBench view at source ↗
read the original abstract

We present AgenticRAG, a practical agentic harness for retrieval and analysis over enterprise knowledge bases. Standard RAG pipelines place significant burden of grounding on the search stack, constraining the language model to a fixed candidate set chosen deep in the retrieval process. Our approach reduces this overdependence by layering a lightweight harness on top of existing enterprise search infrastructure, equipping a reasoning LLM with search, find, open, and summarize tools enabling the model to iteratively retrieve information, navigate within documents, and analyze evidence autonomously. On three open benchmarks we observe substantial gains: $49.6\%$ recall@1 on BRIGHT (+21.8 pp over the best embedding baseline), 0.96 factuality on WixQA ($+13\%$ relative improvement), and $92\%$ answer correctness on FinanceBench--within 2 pp of oracle access to true evidence. Ablation studies show that the most significant factor is the shift from single-shot retrieval to agentic tool use ($5.9\times$ improvement), while multi-query search and in-document navigation contribute to both quality and efficiency. We present various design choices in our agentic harness that were informed by pre-production deployments. Our results demonstrate its suitability for real-world enterprise production environments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript presents AgenticRAG, a lightweight agentic harness layered atop existing enterprise search infrastructure. An LLM is equipped with search, find, open, and summarize tools to perform iterative retrieval, intra-document navigation, and evidence analysis. On BRIGHT, WixQA, and FinanceBench the system reports 49.6% recall@1 (+21.8 pp), 0.96 factuality (+13% relative), and 92% answer correctness (within 2 pp of oracle), respectively. Ablations attribute the largest gains (5.9×) to the shift from single-shot to agentic tool use, with additional contributions from multi-query search and navigation; design choices are stated to be informed by pre-production deployments, supporting the claim of suitability for real-world enterprise knowledge bases.

Significance. If the reported gains prove robust and the agentic patterns transfer beyond the chosen benchmarks, the work could meaningfully advance practical RAG systems by loosening the dependence on a single early retrieval step and enabling autonomous navigation and summarization. The concrete numeric improvements, the ablation isolating tool-use effects, and the production-informed design choices are positive features that could guide deployment of agentic retrieval in constrained enterprise settings.

major comments (2)
  1. Abstract and Evaluation: the claim that AgenticRAG is suitable for real-world enterprise production environments is load-bearing for the paper's contribution, yet the evaluation is confined to three open benchmarks whose query distributions and document quality may not reflect noisier enterprise retrieval (lower precision@10, duplicates, domain-specific noise). No quantitative results or ablations under degraded retrieval conditions are provided, leaving open the possibility that iterative tool calls compound rather than mitigate errors.
  2. Ablation studies: the 5.9× improvement attributed to agentic tool use versus single-shot retrieval is a central empirical result. Without explicit details on baseline implementation (identical search engine, query formulation, and candidate-set construction), it is difficult to confirm that the delta isolates the effect of agentic iteration rather than differences in retrieval configuration.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below with clarifications and proposed revisions to improve the manuscript.

read point-by-point responses

  1. Referee: Abstract and Evaluation: the claim that AgenticRAG is suitable for real-world enterprise production environments is load-bearing for the paper's contribution, yet the evaluation is confined to three open benchmarks whose query distributions and document quality may not reflect noisier enterprise retrieval (lower precision@10, duplicates, domain-specific noise). No quantitative results or ablations under degraded retrieval conditions are provided, leaving open the possibility that iterative tool calls compound rather than mitigate errors.

    Authors: We acknowledge that the three benchmarks cannot capture every characteristic of enterprise data, including variable noise levels or duplicates. That said, BRIGHT, WixQA, and FinanceBench were selected precisely because they feature complex, multi-hop queries and domain-specific content that mirror challenges observed in our pre-production enterprise deployments. The agentic harness design (tool set, iteration budget, and summarization step) was shaped by those deployments, where single-shot retrieval frequently failed on precisely the kinds of noisy or fragmented documents the referee mentions. The high factuality and correctness scores already reflect the harness's ability to mitigate compounding errors through targeted navigation and evidence condensation. In the revised manuscript we will (1) add an explicit limitations paragraph discussing benchmark-to-enterprise gaps and (2) expand the failure-mode analysis to illustrate how the summarize and find tools reduce rather than amplify retrieval noise. We do not have new quantitative results on artificially degraded retrieval at this time. revision: partial

  2. Referee: Ablation studies: the 5.9× improvement attributed to agentic tool use versus single-shot retrieval is a central empirical result. Without explicit details on baseline implementation (identical search engine, query formulation, and candidate-set construction), it is difficult to confirm that the delta isolates the effect of agentic iteration rather than differences in retrieval configuration.

    Authors: We agree that the ablation requires fuller specification to isolate the agentic contribution. The single-shot baseline employs exactly the same search engine, embedding model, and index as the agentic system. It issues one query derived directly from the user input, retrieves a fixed top-k candidate set, and stops; no further tool calls or iteration occur. In the revised manuscript we will add a dedicated paragraph (and accompanying table) that states the precise query formulation, k value, ranking parameters, and candidate-set construction used for the baseline, thereby confirming that the 5.9× gain is attributable to the introduction of iterative tool use rather than any change in the underlying retriever. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical evaluation on external benchmarks

full rationale

The paper introduces an agentic harness for RAG and reports direct empirical measurements (recall@1, factuality, answer correctness) on three fixed external benchmarks plus ablations attributing gains to tool-use patterns. No mathematical derivation, equations, fitted parameters renamed as predictions, or self-citation chains are present; design choices are stated as informed by deployments but not used to derive the benchmark numbers. All claims reduce to observable performance deltas against independent test sets, satisfying the self-contained criterion with no reduction by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is an empirical systems contribution with no explicit mathematical axioms or invented physical entities. The only implicit assumptions are standard ones about benchmark validity and the transferability of tool-use behavior from the evaluated datasets to enterprise data.

pith-pipeline@v0.9.0 · 5527 in / 1464 out tokens · 41775 ms · 2026-05-08T12:06:11.473772+00:00 · methodology

discussion (0)

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Reference graph

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