Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding their responses in external knowledge, but conventional pipelines rely on static, single-step retrieval that limits performance on complex queries. This paper presents an Agent-Orchestrated Adaptive RAG framework that introduces dynamic query decomposition, iterative retrieval, and a bounded self-reflective evaluation loop. We evaluate the system across two complementary datasets: a domain-specific DevOps knowledge base and the multi-hop reasoning benchmark MuSiQue. Using metrics that include overall score, citation accuracy, mean reciprocal rank, and topic coverage, we find that query decomposition yields consistent gains in the structured domain (overall score $+0.04$, MRR $+0.17$ on DevOps) but degrades ranking precision on the multi-hop benchmark, while the reflection mechanism improves citation accuracy at a substantial latency cost. These contrasting results show that agentic enhancements are not universally beneficial and must be applied selectively according to query and domain characteristics. Our findings argue for adaptive, cost-aware orchestration rather than uniformly aggressive reasoning pipelines.

翻译：检索增强生成（RAG）通过将大语言模型（LLM）的响应锚定于外部知识来提升其性能，但传统流水线依赖静态单步检索，限制了处理复杂查询的能力。本文提出一种Agent编排的自适应RAG框架，引入动态查询分解、迭代检索及有界自反思评估循环。我们在两个互补数据集上评估系统：领域特定的DevOps知识库与多跳推理基准MuSiQue。采用总体评分、引文准确性、平均倒数排名及主题覆盖率等指标，我们发现查询分解在结构化领域产生持续增益（DevOps上总体评分+0.04，MRR+0.17），但在多跳基准上降低了排序精度；反思机制虽提升引文准确性却带来显著延迟成本。这些对比结果表明，Agent增强并非普遍适用，必须根据查询与领域特性选择性应用。我们的研究主张采用自适应、感知成本的编排策略，而非统一的激进推理流水线。