mlsanityData to 7 Oct 2026

Towards Trustworthy and Cost-Efficient Data Integration: From Na"ive RAG to Agentic RAG

arXiv 2607.22319 ↗First posted 24 Jul 2026cs.DB cs.AIcited by 0

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

Abstract (arXiv)

Large language models (LLMs) and AI agents have demonstrated strong potential for data integration in zero-shot and few-shot settings. However, they continue to face significant accuracy and cost challenges in enterprise environments due to a persistent knowledge gap. This paper envisions trustworthy, scalable, and cost-efficient integration through knowledge-grounded LLMs and agents operating within a retrieval-augmented generation (RAG) workflow. Here, trustworthiness refers to evidence-grounded, verifiable reasoning, where integration decisions are transparently supported by retrieved knowledge, robust against hallucination, and consistent across tasks. We trace the evolution from classic RAG to GraphRAG and KG-RAG (knowledge graph-based RAG), highlighting how these paradigms bridge parametric and contextual knowledge. Building on this trajectory, we explore the shift toward Agentic RAG, where autonomous multi-agent systems adaptively plan, retrieve, refine, and reason for complex integration tasks. We examine optimization strategies for cost-efficient integration, addressing computational bottlenecks in large-scale enterprise settings. Finally, we outline open challenges and future directions toward building reliable, explainable, and scalable knowledge-grounded integration systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 33 of its 46 references that are in the map. First posted on arXiv 24 Jul 2026.

Builds on

33 of 46 references.

Closest ancestors · 9

Same direction, newest first

  1. 2026MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation30 May
  2. 2026Agentic Graph Retrieval-Augmented Generation for Auditable Commercial Registry Analysis15 Apr
  3. 2026SCOUT-RAG: Scalable and Cost-Efficient Unifying Traversal for Agentic Graph-RAG over Distributed Domains9 Feb
  4. 2026A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces3 Feb
  5. 2025Cache Mechanism for Agent RAG Systems4 Nov
  6. 2025Agentic RAG with Knowledge Graphs for Complex Multi-Hop Reasoning in Real-World Applications22 Jul
  7. 2025MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoning26 May
  8. 2024A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data8 Dec
  9. 2023Agent-OM: Leveraging LLM Agents for Ontology Matching1 Dec

Other references · 24

  1. 2022Can Foundation Models Wrangle Your Data?cited by 173
  2. 2025Multi-Agent Collaboration via Evolving Orchestrationcited by 102
  3. 2024RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generationcited by 97
  4. 2026MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agentscited by 49
  5. 2025Cache-Craft: Managing Chunk-Caches for Efficient Retrieval-Augmented Generationcited by 36
  6. 2024Tool-Planner: Task Planning with Clusters across Multiple Toolscited by 32
  7. 2023Entity Matching using Large Language Modelscited by 31
  8. 2025ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generationcited by 27
  9. 2025MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree Searchcited by 20
  10. 2024ReMatch: Retrieval Enhanced Schema Matching with LLMscited by 16
  11. 2024Fine-tuning Large Language Models for Entity Matchingcited by 16
  12. 2023ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Modelscited by 15
12 more
  1. 2023Cost-Effective In-Context Learning for Entity Resolution: A Design Space Explorationcited by 15
  2. 2025Knowledge Graph-based Retrieval-Augmented Generation for Schema Matchingcited by 13
  3. 2025Unveiling Challenges for LLMs in Enterprise Data Engineeringcited by 8
  4. 2024RACOON: An LLM-based Framework for Retrieval-Augmented Column Type Annotation with a Knowledge Graphcited by 7
  5. 2025In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Explorationcited by 7
  6. 2026Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Explorationcited by 3
  7. 2022Amalur: Data Integration Meets Machine Learningcited by 2
  8. 2025Schema Matching on Graph: Iterative Graph Exploration for Efficient and Explainable Data Integrationcited by 2
  9. 2025Structured Multi-Step Reasoning for Entity Matching Using Large Language Modelcited by 2
  10. 2026ConStruM: A Structure-Guided LLM Framework for Context-Aware Schema Matchingcited by 2
  11. 2026GATHER: Convergence-Centric Hyper-Entity Retrieval for Zero-Shot Cell-Type Annotationcited by 1
  12. 2026RACT: Retrieval Augmented Column-Table Learning and Prediction for Multi-Table Schema Matchingcited by 1

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  5. 2026FROAV: A Framework for RAG Observation and Agent Verification -- Lowering the Barrier to LLM Agent Research
  6. 2026Securing the Agent: Vendor-Neutral, Multitenant Enterprise Retrieval and Tool Use
  7. 2025SIRAG: Towards Stable and Interpretable RAG with A Process-Supervised Multi-Agent Framework
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