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

1. Problem Statement

Retrieval-Augmented Generation (RAG) has evolved from a basic pipeline (Naïve RAG) into two dominant paradigms:

• Enhanced RAG: A fixed pipeline where dedicated modules (routers, rewriters, rerankers) are inserted to mitigate specific weaknesses like noisy retrieval or out-of-scope queries.
• Agentic RAG: A dynamic system where an LLM acts as an orchestrator, autonomously deciding whether to retrieve, rewrite queries, or generate answers based on the task state.

Despite the rapid adoption of Agentic RAG, there is a lack of empirical evidence regarding which approach is superior under specific conditions and the associated trade-offs in performance versus cost. The paper addresses the research question: Should practitioners adopt Agentic architectures or more traditional (Enhanced) pipelines?

2. Methodology

The authors conducted a controlled, empirical comparison across four key dimensions relevant to production environments. They utilized four datasets representing different domains and task types:

• Datasets: FIQA (Finance QA), NQ (General QA), FEVER (Fact Verification), and CQADupStack-English (Grammar/IR).
• Implementations:
  • Enhanced: Implemented using a semantic router, Hyde-based query rewriting (via GPT-4o), and an ELECTRA-based reranker.
  • Agentic: Implemented using the PocketFlow framework. The agent (LLM) decides at each step whether to call a retrieval tool, rewrite the query, or generate an answer. The system was constrained to a single tool (RAG) to ensure a fair comparison with Enhanced pipelines.
• Models: Evaluated using various Qwen3 models (0.6B to 32B) and GPT-4o for evaluation/judging.
• Metrics:
  • Intent Handling: F1 score and Recall (distinguishing valid vs. invalid queries).
  • Retrieval Quality: NDCG@10 (Normalized Discounted Cumulative Gain).
  • Answer Quality: LLM-as-a-Judge (Selene-70B) using classification or pairwise ranking metrics.
  • Cost/Time: Token consumption (input/output) and end-to-end latency.

3. Key Contributions

1. Empirical Comparison: A head-to-head benchmark of Enhanced vs. Agentic RAG across four critical dimensions: User Intent Handling, Query Rewriting, Document List Refinement, and Underlying LLM impact.
2. Cost Analysis: A detailed breakdown of the computational costs and latency differences, quantifying the overhead of Agentic reasoning.
3. Practical Guidelines: A set of actionable recommendations for developers on when to choose one paradigm over the other or how to hybridize them.

4. Key Results

A. User Intent Handling (Routing)

• Agentic Performance: Superior in well-defined domains (Finance, Grammar) where the agent can clearly distinguish between in-scope and out-of-scope queries.
• Enhanced Performance: More reliable in broader, noisier domains (e.g., FEVER fact-checking). The Agentic approach suffered from low recall (49.3%) in FEVER, often retrieving even when unnecessary, likely due to the difficulty of defining "valid" factual queries.
• Conclusion: Enhanced RAG with a dedicated semantic router is more robust for ambiguous or broad domains.

B. Query Rewriting

• Agentic Performance: Outperformed Enhanced RAG by an average of +2.8 NDCG@10.
• Reasoning: The Agentic system's ability to dynamically decide whether to rewrite and how to rewrite (case-by-case) proved more effective than the forced rewriting in Enhanced pipelines. This was particularly evident in tasks where user queries differed significantly from document structures (e.g., NQ).

C. Document List Refinement (Reranking)

• Enhanced Performance: Significantly outperformed Agentic RAG. The dedicated ELECTRA reranker effectively filtered noise.
• Agentic Limitation: The Agentic system showed no benefit from iterating retrieval. In 10% of cases where the agent re-attempted retrieval, 53% of the documents remained identical. The agent struggled to "change its mind" effectively once a decision was made, leading to redundant or ineffective loops.

D. Underlying LLM Impact

• Scalability: Both paradigms exhibited similar performance scaling patterns. As the model size increased (from 0.6B to 32B), performance improved at comparable rates for both Enhanced and Agentic systems.
• Implication: The choice of architecture does not fundamentally alter the dependency on model capability; a stronger generator benefits both equally.

E. Cost and Latency

• Agentic Overhead: Agentic RAG is systematically more expensive.
  • Tokens: Requires 3.3x more input tokens and 1.9x more output tokens on average.
  • Latency: Takes 1.5x more time to process queries.
  • Max Cost: In some scenarios, Agentic RAG was up to 3.6x more expensive than Enhanced RAG due to repeated reasoning steps and tool calls.

5. Significance and Recommendations

The paper concludes that neither paradigm is universally superior. The choice depends on the specific constraints of the application:

• When to use Agentic RAG:

  • When User Intent Handling is critical and the domain is well-defined.
  • When Query Rewriting flexibility is needed to bridge the gap between user queries and document structures.
  • Note: It eliminates the need for manually crafted examples for routing.

• When to use Enhanced RAG:

  • When Document Refinement/Reranking is the primary bottleneck (Agentic agents are poor at iterative refinement).
  • When Cost and Latency are strict constraints.
  • When operating in broad, noisy domains where intent detection is difficult.

• Hybrid Recommendation:
  The authors suggest a hybrid approach for optimal results:

  1. Use the Agentic approach for the initial intent routing and query rewriting stages to maximize relevance.
  2. Integrate an explicit, dedicated reranking step (from Enhanced RAG) into the Agentic pipeline to filter retrieved documents before generation.
  3. This combination leverages the flexibility of agents while mitigating their cost and refinement weaknesses.

Final Verdict: Agentic RAG offers superior adaptability in query formulation but incurs a significant cost penalty and struggles with iterative refinement. Enhanced RAG remains a highly efficient, reliable baseline, particularly for reranking and handling ambiguous intents.