RAG Architectures: Query vs Agentic Patterns

Introduction

Retrieval-Augmented Generation (RAG) has become the foundation for building knowledge-grounded AI systems, enabling language models to access external information sources for more accurate and contextually relevant responses. However, the evolution from simple query-based RAG to sophisticated tool-augmented agentic systems represents a fundamental shift in how we architect AI applications.

This article explores the spectrum of RAG architectures, from naive implementations to advanced agentic patterns. We’ll examine the strengths and limitations of each approach, provide decision frameworks for selecting the right pattern, and demonstrate practical implementation strategies that can guide your architectural decisions.

Part 1: Understanding Naive Query-Based RAG

The Basic RAG Pattern

Query-based RAG follows a straightforward three-step pipeline:

1. User Query: Accept a natural language question
2. Retrieval: Query a vector database or knowledge store to find relevant documents
3. Generation: Feed the retrieved context and original query to an LLM for synthesis

User Query → Vector Embedding → Retrieve Top-K Documents →
LLM(query + context) → Generated Response

This pattern works remarkably well for straightforward question-answering tasks. A user asks “What are the quarterly earnings for Q3 2024?” and the system retrieves relevant financial documents, then synthesizes an answer from that context.

Why Naive RAG Succeeds

Query-based RAG provides several compelling advantages:

• Simplicity: Minimal moving parts reduce operational complexity and debugging surface area
• Predictability: Single-step retrieval makes latency characteristics easy to understand
• Cost Efficiency: One embedding lookup and one LLM call per user query keeps inference costs low
• Determinism: The retrieval result set is consistent across executions with the same query
• Easy Integration: Straightforward to integrate with existing LLM infrastructure

These characteristics make query RAG ideal for customer-facing Q&A systems, documentation search, and knowledge base applications where accuracy and speed matter more than adaptability.

Critical Limitations of Query-Based RAG

Despite its advantages, naive RAG stumbles when facing real-world complexity:

Static Retrieval Problem: The system makes retrieval decisions based only on the initial user query. If “quarterly earnings” retrieves documents about revenue but not expenses, the system cannot course-correct.

Context Window Mismatch: Relevant information may exist across multiple documents, but a single query can only surface a limited set. Some answers require multi-document synthesis that single-shot retrieval cannot provide.

No Tool Intelligence: RAG systems cannot reason about whether to search for financial data, market comparisons, or trend analysis—they retrieve the same way regardless of the query’s actual intent.

Temporal Reasoning Failure: Queries like “Compare our product strategy from 2023 to now” require understanding what changed, when it changed, and why—capabilities absent in static retrieval.

Hallucination Risk: When retrieved context doesn’t contain the answer, the LLM fabricates information. Query-based systems have no mechanism to recognize insufficient context and reformulate the search.

Part 2: Tool-Augmented Agentic RAG Systems

From Retrieval to Reasoning

Agentic RAG systems invert the traditional relationship between reasoning and retrieval. Rather than retrieve-then-answer, they reason-then-retrieve, adapting their search strategy based on understanding the problem.

A tool-augmented agentic system might reason: “This question asks for comparative analysis. I should retrieve Q3 2023 earnings, Q3 2024 earnings, and competitor data. Then I’ll synthesize trends from all three sources.”

The ReAct Pattern: Reasoning + Acting

ReAct (Reasoning + Acting) provides a structured framework for agentic behavior:

Thought: Analyze what information is needed
Action: Call a tool (search, calculate, retrieve) with specific parameters
Observation: Receive tool result
Thought: Reason about the observation and next steps
Action: Call next tool or generate final answer
Observation: Tool result
... (repeat until complete)
Final Answer: Synthesize all observations into response

Here’s a practical Python example of ReAct in action:

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from typing import Optional

class ReActAgent:
    def __init__(self, llm, tools: dict):
        self.llm = llm
        self.tools = tools  # {"search": fn, "calculate": fn, etc.}

    def execute(self, query: str) -> str:
        history = []
        max_iterations = 10

        for i in range(max_iterations):
            # Get reasoning and action from LLM
            thought_action = self.llm.generate(
                prompt=self._build_prompt(query, history),
                stop_sequences=["Observation:"]
            )

            history.append(f"Thought: {thought_action}")

            # Parse action: [tool_name](params)
            tool_name, params = self._parse_action(thought_action)

            if tool_name == "Final Answer":
                return params

            # Execute tool
            observation = self.tools[tool_name](**params)
            history.append(f"Observation: {observation}")

        return "Max iterations reached"

    def _build_prompt(self, query: str, history: list) -> str:
        return f"""
Question: {query}

Available tools: search(query), retrieve(document_id), calculate(expression)

{chr(10).join(history)}

Thought:"""

Tool Orchestration

The power of agentic systems comes from coordinated tool use:

• Search Tool: Dynamic query reformulation based on reasoning
• Retrieve Tool: Access specific documents by ID when context is known
• Calculate Tool: Perform computations on retrieved data
• Synthesize Tool: Combine information from multiple sources
• Verify Tool: Check claims against known facts

Each tool adds a decision point where the agent can reason about relevance, sufficiency, and next steps.

Advantages of Agentic RAG

• Adaptive Retrieval: Search strategy evolves based on intermediate results
• Multi-step Reasoning: Complex questions decompose into sub-questions
• Error Recovery: Insufficient results trigger new searches rather than hallucination
• Context Awareness: Tool selection changes based on query semantics
• Explainability: Each step is transparent and auditable
• Handling Ambiguity: System can ask clarifying questions or try multiple approaches

Part 3: Decision Frameworks

When to Use Query-Based RAG

Query RAG excels in specific scenarios:

When to Use Agentic RAG

Agentic systems justify added complexity in these contexts:

The Decision Matrix

                        Query RAG          Agentic RAG
────────────────────────────────────────────────────────
Latency Requirement     <500ms             1-10s acceptable
Cost Sensitivity        Very high          Medium
Accuracy Requirement    Good (85%+)        Excellent (95%+)
Query Complexity        Simple → Medium    Medium → Complex
Data Consistency        High               Variable
Context Clarity         Clear              Ambiguous
Domain Complexity       Low → Medium       High
────────────────────────────────────────────────────────
Recommended            Support, FAQ       Analysis, Research
                       Search, Lookup     Reporting, Decisions

Part 4: Performance Trade-offs

Understanding the costs of sophistication is essential for architectural decisions:

Latency Analysis

Query RAG:

• Embedding generation: 50-100ms
• Vector search: 10-50ms
• LLM call: 200-500ms
• Total: 300-700ms (single step)

Agentic RAG:

• Initial LLM reasoning: 200-400ms
• First tool execution: 100-300ms
• Observation processing: 50-100ms
• Second LLM call: 200-400ms
• (Repeat 2-4 times on average)
• Total: 1500-4000ms (multi-step)

The latency multiplier for agentic systems ranges from 3-5x, making them unsuitable for sub-second response requirements.

Cost Analysis

Query RAG per request:

• 1 embedding API call: $0.00001
• 1 LLM call (500 tokens): $0.0015
• Total: ~$0.0016

Agentic RAG per request:

• 3-4 LLM calls (averaging 800 tokens each): $0.0048
• 2-3 embedding calls: $0.00003
• Tool execution overhead: $0.0001
• Total: ~$0.0051 (3x cost)

For high-volume systems, this 3x cost difference becomes significant at scale.

Accuracy Trade-offs

Query RAG:

• Recall: 70-85% (depends on document quality)
• Precision: 60-75% (retrieval noise)
• Hallucination rate: 15-25%

Agentic RAG:

• Recall: 85-95% (multi-search strategy)
• Precision: 80-90% (reasoning filters noise)
• Hallucination rate: 5-10% (self-correction loops)

Part 5: Hybrid Architectures

The most practical systems often combine both patterns strategically:

Fast-Path + Fallback Pattern

User Query
  ↓
[Fast Path] Is this simple?
  ├─ YES → Query RAG → Response (90% of queries)
  └─ NO → Agentic RAG → Response (10% of queries)

Route simple queries through fast query RAG, escalate complex queries to agentic systems. This preserves low latency for the common case while providing accuracy for difficult questions.

Agentic Planning with Query Execution

Let the agent plan the retrieval strategy, then execute with optimized query RAG components:

Agentic System Plans:
  "I need financial data (Tool A) + competitor analysis (Tool B) + trend data (Tool C)"
  ↓
Parallel Query RAG Execution:
  Tool A: Retrieve financial docs
  Tool B: Retrieve competitor docs
  Tool C: Retrieve trend docs
  ↓
Synthesis: Combine all results for final answer

This separates reasoning (agentic) from execution (optimized retrieval), gaining flexibility without sacrificing performance.

Confidence-Based Switching

Query RAG includes a confidence score. If confidence is low (e.g., <70%), escalate to agentic system:

Query RAG Response → Confidence Score?
  ├─ >80% → Return immediately
  ├─ 60-80% → Augment with agentic verification
  └─ <60% → Run full agentic pipeline

Part 6: Implementation Patterns

Query RAG Implementation Skeleton

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from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOpenAI

def query_rag(user_query: str) -> str:
    # Retrieve
    docs = vector_store.similarity_search(user_query, k=5)
    context = "\n".join([d.page_content for d in docs])

    # Generate
    prompt = f"Context: {context}\n\nQuestion: {user_query}\n\nAnswer:"
    response = llm.predict(prompt)

    return response

Agentic RAG with LangGraph

LangGraph provides a graph-based framework for building agentic systems:

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from langgraph.graph import StateGraph, END
from typing import TypedDict, List

class State(TypedDict):
    query: str
    thoughts: List[str]
    documents: List[str]
    answer: str

def reasoning_step(state: State) -> State:
    thought = llm.generate(
        f"What approach should I take? Query: {state['query']}"
    )
    state['thoughts'].append(thought)
    return state

def retrieval_step(state: State) -> State:
    # Parse thought to determine search strategy
    search_query = parse_intent(state['thoughts'][-1])
    docs = vector_store.search(search_query)
    state['documents'].extend(docs)
    return state

def synthesis_step(state: State) -> State:
    context = "\n".join(state['documents'])
    answer = llm.generate(
        f"Synthesize an answer: {state['query']}\nContext: {context}"
    )
    state['answer'] = answer
    return state

# Build graph
graph = StateGraph(State)
graph.add_node("reason", reasoning_step)
graph.add_node("retrieve", retrieval_step)
graph.add_node("synthesize", synthesis_step)

graph.add_edge("reason", "retrieve")
graph.add_edge("retrieve", "synthesize")
graph.set_entry_point("reason")
graph.add_edge("synthesize", END)

executor = graph.compile()

Conclusion

The evolution from query-based to agentic RAG represents a spectrum of trade-offs between simplicity and sophistication. Query RAG systems excel when speed and cost matter; agentic systems shine when accuracy and adaptability are paramount.

The most effective RAG architectures don’t choose one pattern exclusively. Instead, they layer query and agentic approaches strategically: using fast query retrieval as a default, escalating to agentic reasoning for complex cases, and employing hybrid patterns that combine the strengths of both.

Your choice depends on your specific constraints: latency budgets, accuracy requirements, data characteristics, and operational complexity tolerance. Start with query RAG for simplicity, instrument carefully to identify cases where agentic patterns add value, then implement hybrid architectures that optimize for your actual workload distribution.

By understanding both patterns deeply, you’ll design RAG systems that deliver speed where it matters and accuracy where it counts.