AgenticRAG: Agentic Retrieval for Enterprise Knowledge Bases
📰 ArXiv cs.AI
Learn how AgenticRAG enhances enterprise knowledge base retrieval with a lightweight harness and reasoning LLM, improving search efficiency and accuracy
Action Steps
- Build an AgenticRAG pipeline using existing enterprise search infrastructure
- Configure a reasoning LLM to interact with the knowledge base
- Test the AgenticRAG system with sample queries to evaluate its performance
- Apply AgenticRAG to real-world enterprise knowledge bases to improve retrieval efficiency
- Compare the results with traditional RAG pipelines to measure the benefits of AgenticRAG
Who Needs to Know This
Data scientists, software engineers, and enterprise architects can benefit from AgenticRAG to improve knowledge base retrieval and analysis, enhancing overall business decision-making
Key Insight
💡 AgenticRAG reduces overdependence on traditional search stacks by layering a lightweight harness on top of existing infrastructure, enabling more efficient and accurate retrieval
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💡 Introducing AgenticRAG: Enhance enterprise knowledge base retrieval with a lightweight harness and reasoning LLM! 🚀
Key Takeaways
Learn how AgenticRAG enhances enterprise knowledge base retrieval with a lightweight harness and reasoning LLM, improving search efficiency and accuracy
Full Article
Title: AgenticRAG: Agentic Retrieval for Enterprise Knowledge Bases
Abstract:
arXiv:2605.05538v1 Announce Type: new 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 searc
Abstract:
arXiv:2605.05538v1 Announce Type: new 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 searc
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