Is Agentic RAG worth it? An experimental comparison of RAG approaches

Abstract: Retrieval-Augmented Generation (RAG) systems are usually defined by the combination of a generator and a retrieval component that extracts textual context from a knowledge base to answer user queries. However, such basic implementations exhibit several limitations, including noisy or suboptimal retrieval, misuse of retrieval for out-of-scope queries, weak query-document matching, and variability or cost associated with the generator. These shortcomings have motivated the development of "Enhanced" RAG, where dedicated modules are introduced to address specific weaknesses in the workflow. More recently, the growing self-reflective capabilities of LLMs have enabled a new paradigm, which we refer to as "Agentic" RAG. In this approach, the LLM orchestrates the entire process-deciding which actions to perform, when to perform them, and whether to iterate-thereby reducing reliance on fixed, manually engineered modules. Despite the rapid adoption of both paradigms, it remains unclear which approach is preferable under which conditions. In this work, we conduct an extensive, empirically driven evaluation of Enhanced and Agentic RAG across multiple scenarios and dimensions. Our results provide practical insights into the trade-offs between the two paradigms, offering guidance on selecting the most effective RAG design for real-world applications, considering both costs and performance.

• The paper demonstrates that Agentic RAG improves retrieval adaptability through dynamic LLM management while incurring higher computational costs compared to Enhanced RAG.
• Methodologically, the evaluation employed FIQA and CQADupStack-English datasets to assess query rewriting, document alignment, and iterative refinement capabilities.
• The study suggests that integrating dynamic agentic features with structured enhancements could balance performance gains and cost efficiency in RAG systems.

An Experimental Comparison of RAG Approaches

Introduction

The paper "Is Agentic RAG worth it? An experimental comparison of RAG approaches" (2601.07711) addresses the challenges associated with the traditional and enhanced Retrieval-Augmented Generation (RAG) systems, and introduces Agentic RAG as a potentially superior paradigm. RAG systems, integral for LLMs to integrate external document-based knowledge, rely on retrieval components to fetch relevant contextual data. However, basic implementations often suffer from suboptimal retrieval and management issues. This paper embarks on a comprehensive empirical assessment comparing Enhanced and Agentic RAG approaches across various dimensions in real-world applications.

Enhanced vs. Agentic RAG

Enhanced RAG systems are structured with sequential modules designed to optimize various stages of the pipeline, including query rewriting and document reranking. This fixed architecture enhances retrieval efficacy but may limit flexibility in handling out-of-scope queries or adjusting strategies dynamically.

Figure 1: Enhanced RAG system uses a structured sequence of modules whereas Agentic RAG allows the LLM to dynamically manage the complete process.

By contrast, Agentic RAG leverages the self-reflective nature of LLMs, enabling them to orchestrate the entire retrieval and generation pipeline autonomously. This agentic approach is characterized by its iterative, non-linear workflow, where LLMs decide actions dynamically, potentially leading to improved adaptability across varying query types.

Methodology and Evaluation Dimensions

The paper explores four key evaluation dimensions that signify limitations in Naïve RAG systems:

1. User intent handling: This dimension tests the capability of the system to discern when retrieval is necessary, thereby avoiding unnecessary computational cost. Enhanced RAG uses a semantic router while Agentic RAG relies on intrinsic LLM judgment.
2. Query-documents alignment: Evaluates the effectiveness of systems in transforming user queries to match the structure and semantics of the target documents using techniques like Hyde-based rewriting.
3. Retrieved documents adjustment: Focuses on improving the initial retrieval list through methods such as reranking for Enhanced RAG and iterative refinement for Agentic RAG.
4. Impact of LLM quality: Assesses robustness to changes and variances in LLM capabilities.

Experimental Analysis

The experiments utilized FIQA (Financial QA) and CQADupStack-English datasets to test RAG systems in QA and IR/E tasks. Results showed Agentic RAG's prowess in flexible query rewriting, improving retrieval quality, while Enhanced RAG maintained reliability in well-defined domains, excelling in reranking retrieved documents.

Figure 2: Performance comparison of Enhanced and Agentic RAG across various LLM configurations, showing impact on user queries.

The impact of different LLMs was also evaluated, with Agentic RAG demonstrating adaptability but incurring higher computational costs, emphasizing the need for optimization in cost-critical applications.

Cost and Computational Implications

Enhanced and Agentic RAG systems were assessed for their computational and runtime expenses. Agentic RAG exhibited higher token usage due to its repeated reasoning steps leading to increased financial costs compared to Enhanced settings.

Figure 3: Computational cost and token usage for each model under Agentic settings, highlighting efficiency and runtime considerations.

This analysis underscores that while Agentic RAG might provide performance gains, Enhanced RAG could be preferable for scenarios demanding resource efficiency.

Conclusion

This investigation reveals that Agentic RAG holds promise in adaptive query handling and retrieval, though its computational cost remains a significant consideration for scalability. Enhanced RAG, albeit less dynamic, offers a more structured approach with clear cost benefits in static environments.

Advanced RAG systems will benefit from integrating agentic dynamic capabilities with enhanced structured components to achieve balanced, efficient implementations that cater to diverse real-world needs. Future research should focus on refining hybrid approaches that leverage the strengths of both paradigms.