Agent-Orchestrated Adaptive RAG: A Comparative Study on Structured and Multi-Hop Retrieval

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Expert, extended

Summary

An Agent-Orchestrated Adaptive RAG framework introduces dynamic query decomposition, iterative retrieval, and a bounded self-reflective evaluation loop to enhance Large Language Models (LLMs). The system, built on a local, privacy-first inference stack using Llama-3.1-8B-Instruct (4-bit GGUF) and BGE-base-en-v1.5 embeddings with FAISS, was evaluated on a domain-specific DevOps knowledge base (80 documents, ~10,000 words) and the multi-hop MuSiQue benchmark. Query decomposition consistently improved performance in the structured DevOps domain (overall score +0.04, MRR +0.17), but degraded ranking precision on MuSiQue (MRR from 0.469 to 0.102), while doubling latency (21s to 48s on DevOps). The reflection mechanism improved citation accuracy but incurred substantial latency costs, increasing response time sixfold on MuSiQue (17s to 104s) for inconsistent quality gains. These results highlight that agentic enhancements are not universally beneficial and require selective, cost-aware application.

Key takeaway

For Machine Learning Engineers designing RAG systems, carefully evaluate the domain and query complexity before implementing agentic features. Your decision to use query decomposition or self-reflection should be adaptive and cost-aware, as these enhancements can significantly increase latency for inconsistent or even detrimental quality changes, especially in multi-hop scenarios. Prioritize simpler RAG for most queries and apply complex strategies only when warranted.

Key insights

Agentic RAG enhancements are domain-dependent and cost-intensive, necessitating selective, adaptive orchestration.

Principles

• Adaptive orchestration is essential.
• Decomposition is domain-dependent.
• Reflection adds significant latency.

Method

The Agent-Orchestrated Adaptive RAG system uses a Query Classifier, Decomposer, Answer Evaluator, and Orchestrator with rule-based logic to dynamically route queries for direct retrieval, decomposition, or reflection.

In practice

• Use metadata-aware filtering for structured data.
• Employ 600-token chunks with 100-token overlap.
• Run LLM inference locally with 4-bit GGUF.

Topics

• Agentic RAG
• Query Decomposition
• Multi-hop Retrieval
• LLM Hallucination
• DevOps Knowledge Bases
• RAG Latency Tradeoffs
• Self-reflection

Best for: AI Architect, Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.