RAG System Showdown: Is It Really Better to Let AI Make Its Own Decisions? A Comprehensive Real-World Test of Enhanced vs. Agentic!
Research Background
Imagine asking ChatGPT a question, and it not only searches its own "brain" for an answer but also scours an external knowledge base before replying. This is what RAG (Retrieval-Augmented Generation) systems do. But the question is: should the system follow a fixed, step-by-step process, or should the AI act as a "project manager," autonomously deciding what to do at each step?
This paper aims to answer that question. The research team divided RAG systems into two main camps:
- Enhanced RAG: Like a meticulously designed assembly line, with specialized modules like a "query rewriter" and a "document ranker," each performing its own function.
- Agentic RAG: Let the large language model be the commander-in-chief, deciding autonomously whether to retrieve, whether to rewrite the query, and controlling the process entirely.
Currently, the industry has its proponents for each approach, but which one is truly better? Which should be chosen for which scenarios? How to balance cost and performance? These questions have no clear answers. Thus, the research team decided to conduct a comprehensive, head-to-head comparison.
Their core contributions are twofold: first, they evaluated the actual performance of both systems from four key dimensions; second, they provided a detailed analysis of the differences in cost and computation time, offering highly practical references for real-world applications.
Related Work: The Evolution of RAG Technology
The concept of RAG was first proposed by Lewis et al. in 2020. The initial design was very simple: receive a query → retrieve relevant documents → feed both the documents and the query to the model → generate an answer. However, this "naïve RAG" (as referred to in the paper) had many problems: sometimes it retrieved unnecessarily, wasting resources; sometimes the retrieved documents were of low quality and irrelevant; and the mismatch in phrasing between user questions and knowledge base documents led to poor matching.
Thus, Enhanced RAG emerged, and researchers began adding various "enhancement modules" to this assembly line:
- Query rewriting module (e.g., HyDE technique, which rewrites the question into a hypothetical answer paragraph for matching).
- Semantic routing module (judging whether a query actually needs retrieval).
- Re-ranking module (re-ranking retrieved documents by relevance).
Meanwhile, with the surge in reasoning capabilities of models like GPT-4, Agentic RAG began to emerge. The core idea of this approach is: since models are so smart now, why not let them decide the workflow themselves? Consequently, various Agent frameworks have sprung up: LangGraph, LlamaIndex, CrewAI, etc.
Interestingly, despite both technical routes being very popular, the academic community has not yet conducted systematic comparative experiments. Neha and Bhati proposed some theoretical distinctions in 2025 but did not conduct actual testing. This paper aims to fill that gap.
Core Method: A "Flesh-and-Blood" Comparison Across Four Dimensions
The research team selected four key dimensions to pit these two systems against each other, each corresponding to a pain point of naïve RAG.
1. User Intent Handling: The Judgment of Whether to Retrieve
Problem Scenario: When a user asks "What's the weather like today?", the system should not scour the knowledge base for documents. But when asking "What are the key data points in the company's Q3 sales report?", retrieval is necessary. This judgment capability is crucial.
Enhanced Approach: Use the semantic-router framework, prepare a set of examples of "valid questions" and "invalid questions" in advance, and compare the similarity of new questions to these examples to determine which category they belong to.
Agentic Approach: Let GPT-4o decide for itself; it can choose to "call the RAG tool" or "answer directly."
Testing Method: Prepare 500 valid queries and 500 invalid queries each on three datasets: FIQA (Financial QA), FEVOR (Fact Verification), and CQADupStack (Forum QA), and see which system judges more accurately.
2. Query Rewriting: Making Questions and Documents "Speak the Same Language"
Problem Scenario: When a user asks "What are the tax implications of freelancing?", the documents in the knowledge base might be written as "Freelancers need to pay the following taxes..." The phrasing is different, so direct matching is ineffective.
Enhanced Approach: Enforce HyDE rewriting—rewrite the question into a hypothetical answer, such as "Freelancers need to pay specific taxes...", and then use this text to match the knowledge base.
Agentic Approach: The prompt tells the Agent it can rewrite the query, but the Agent decides for itself whether and how to rewrite it.
Evaluation Metric: Use NDCG@10 (Normalized Discounted Cumulative Gain) to measure retrieval quality, which is the gold standard in the information retrieval field.
Where:
is the relevance label of the document.
3. Document List Optimization: Further Refinement After Retrieval
Problem Scenario: The first retrieval might yield 20 documents, but some are not very relevant and need further filtering.
Enhanced Approach: Use an ELECTRA-based re-ranking model to re-rank the 20 documents and select the most relevant 10.
Agentic Approach: The Agent can call the retrieval tool multiple times, adjusting the query strategy each time to iterate and optimize on its own.
4. Impact of the Underlying Model: How Much Does Changing the "Brain" Affect Performance?
Experimental Design: Test using four models from the Qwen3 series (0.6B, 4B, 8B, 32B parameters) to see if the impact of model size is consistent across both systems.
Evaluation Method: Use Selene-70B as the "AI Judge" to evaluate the quality of generated answers. This model ranks highly in the LLM-as-a-Judge arena and shows high consistency with human evaluations on financial QA tasks.
Experimental Results: Who Is Stronger? It Depends on the Specific Scenario
User Intent Handling: Enhanced is More Stable in Complex Scenarios
The results are interesting: in scenarios with clear domain boundaries, such as FIQA (Finance) and CQADupStack (English Grammar), Agentic RAG performed better, with F1 scores reaching 98.8 and 99.8, respectively. However, in the open-domain task FEVOR (Fact Verification), Agentic's recall was only 49.3%, which is 35 percentage points lower than Enhanced!
The reason is clear: when task boundaries are ambiguous, the Agent often becomes "overly enthusiastic," retrieving even when it shouldn't. In contrast, Enhanced's example-based routing system is more stable in such situations.
Query Rewriting: Agent's Flexibility Wins
Across all datasets, Agentic RAG's retrieval quality was, on average, 2.8 NDCG@10 points higher than Enhanced RAG. Especially on the NQ (Natural Questions) dataset, Agentic reached 51.7, nearly 8 points higher than Enhanced's 43.9.
What does this mean? The Agent can flexibly decide on the rewriting strategy based on the specific question, whereas Enhanced uses a one-size-fits-all forced rewriting, which can sometimes be counterproductive.
Document Optimization: Enhanced's Re-ranking is a Clear Winner
This result was unexpected: Enhanced RAG, through its re-ranking module, improved from 45.0 to 51.0 (a 6-point increase) on FIQA and from 46.0 to 48.0 on CQADupStack.
What about Agentic RAG? Even when allowed to call the retrieval tool multiple times, its performance was worse than the baseline (dropping to 43.4 on FIQA and 44.4 on CQADupStack). It seems that while the Agent can make autonomous decisions, it is still not as reliable as a specially trained re-ranking model when it comes to "carefully selecting documents."
Impact of Model Size: Both Systems Show Similar Trends
Whether Enhanced or Agentic, as the underlying model size increased from 0.6B to 32B, performance improved steadily, and the improvement curves were almost identical. This indicates that the impact of model capabilities is cross-system, and the choice of architecture and model size can be considered independently.
Cost Analysis: The "Luxury Tax" of Agentic Cannot Be Ignored
This section contains data that is likely of greatest concern to practical applications:
Token Consumption Comparison (FIQA Dataset):
- Agentic consumes 2.7 times more input tokens than Enhanced.
- Output tokens are 1.7 times higher.
- Overall time consumption increases by 1.5 times.
The gap is even larger on the CQADupStack dataset:
- Input tokens are 3.9 times higher.
- Output tokens are 2.0 times higher.
Converting this to real money: if you use the OpenAI API, the cost of Agentic RAG could be 3-4 times that of Enhanced. For large-scale applications, this is not a small amount.
Why is this? Because Agentic requires constant "thinking"—at every step, it must reason about whether to call a tool and how to call it, and these intermediate steps consume tokens. Enhanced, on the other hand, follows a fixed process, doing what it needs to do without extra "thinking."
From the distribution chart, it can be seen that Agentic's token consumption and time consumption both exhibit a significant "long-tail" phenomenon—some queries are particularly demanding, requiring the Agent to call the tool multiple times.
Paper Summary: No Silver Bullet, Only Trade-offs
The greatest value of this paper lies in: it debunks the myth that "new technology is always better."
The main findings can be summarized as follows:
Choose Agentic for narrow-domain tasks, Enhanced for open-domain tasks: In scenarios with clear boundaries, such as finance and grammar, the Agent's understanding can play to its strengths. However, in scenarios like FEVOR where "anything can be asked," rule-based routing is more reliable.
Agentic has the advantage in the query rewriting stage: Flexible rewriting strategies can indeed improve retrieval quality, with an average increase of 2.8 NDCG points. This advantage is real and tangible.
Document refinement must use re-ranking: The Agent's strategy of multiple retrievals is not as effective as Enhanced's specialized re-ranking model. This may be the biggest shortcoming of the Agentic architecture. The paper suggests: why not add a re-ranking tool to Agentic as well?
The cost difference cannot be ignored: A 3-4 times increase in cost is unaffordable for many applications. Unless you have extreme performance requirements, an optimized Enhanced RAG may be more cost-effective.
The impact of model size is consistent for both: This means you can choose the architecture first, and then select the model based on your budget. The two decisions are relatively independent.
Practical Advice:
If you are an enterprise developer in a small-scale, budget-limited scenario, Enhanced RAG may be the wiser choice—its performance is sufficient, and costs are controllable.
If you pursue the ultimate user experience, or your application scenario is particularly complex and variable, then the flexibility of Agentic RAG is worth paying for.
However, the ideal solution might be a "hybrid architecture": combining Enhanced's re-ranking module with Agentic's flexible decision-making, leveraging the strengths of both. The research team also admits that their Agentic implementation only used one tool (RAG). If Agent were equipped with a richer toolbox, the results could be completely different.
This showdown has no absolute winner, but it provides a clear reference framework: when choosing an RAG system, consider the scenario, budget, and requirements. Blindly chasing the new is not as good as rational trade-offs.
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