Towards Trustworthy and Cost-Efficient Data Integration: From Naïve RAG to Agentic RAG
Merged summary
TL;DR - This paper surveys the evolution from conventional RAG to autonomous, multi-agent RAG for enterprise data integration. It outlines how knowledge grounding and adaptive retrieval could improve verifiability, scalability, and cost efficiency.
- Compares classic RAG with GraphRAG and knowledge graph-based RAG for bridging parametric and contextual knowledge.
- Frames Agentic RAG as agents that dynamically plan, retrieve, refine, and reason across complex integration tasks.
- Defines trustworthiness through evidence-grounded, transparent decisions that resist hallucinations and remain consistent.
- Discusses computational bottlenecks, optimization strategies, and open challenges rather than reporting new empirical results.
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Towards Trustworthy and Cost-Efficient Data Integration: From Naïve RAG to Agentic RAG
TL;DR - This paper surveys the evolution from conventional RAG to autonomous, multi-agent RAG for enterprise data integration. It outlines how knowledge grounding and adaptive retrieval could improve verifiability, scalability, and cost efficiency.
- Compares classic RAG with GraphRAG and knowledge graph-based RAG for bridging parametric and contextual knowledge.
- Frames Agentic RAG as agents that dynamically plan, retrieve, refine, and reason across complex integration tasks.
- Defines trustworthiness through evidence-grounded, transparent decisions that resist hallucinations and remain consistent.
- Discusses computational bottlenecks, optimization strategies, and open challenges rather than reporting new empirical results.