mlsanityData to 7 Oct 2026

AgenticRAG: Agentic Retrieval for Enterprise Knowledge Bases

arXiv 2605.05538 ↗First posted 7 May 2026cs.AI cs.IRcited by 1

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

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× 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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 24 of its 32 references that are in the map. First posted on arXiv 7 May 2026.

Builds on

24 of 32 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Keyword search is all you need: Achieving RAG-Level Performance without vector databases using agentic tool use19 Dec
  2. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG15 Jan

Other references · 22

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  4. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  5. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  6. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  7. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,371
  8. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
  9. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,225
  10. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  11. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,160
  12. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
10 more
  1. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
  2. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  3. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 786
  4. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 663
  5. 2019Passage Re-ranking with BERTcited by 629
  6. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 519
  7. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  8. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 437
  9. 2023FinanceBench: A New Benchmark for Financial Question Answeringcited by 261
  10. 2024PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makerscited by 42

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Fidelity Is Not Enough: Dispatch-Level Instrumentation for Agentic Datasheet Extractioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Corpus2Skill: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG
  2. 2026Orchestrating Specialized Agents for Trustworthy Enterprise RAG
  3. 2026Knowledge Graph RAG: Agentic Crawling and Graph Construction in Enterprise Documents
  4. 2026Beyond Semantic Similarity: Performance and Costs of Agentic Retrieval for Complex Tasks
  5. 2025From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
  6. 2025AgREE: Agentic Reasoning for Knowledge Graph Completion on Emerging Entities
  7. 2026Retrieval as Reasoning: Self-Evolving Agent-Native Retrieval via LLM-Wiki
  8. 2026VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge