Agentic RAG vs Simple Retrieve-and-Generate: Choosing the Right Architecture

When implementing Retrieval-Augmented Generation (RAG) systems, one of the most important architectural decisions you'll face is whether to use a simple retrieve-and-generate pattern or adopt an agentic approach. This choice significantly impacts your system's complexity, cost, latency, and capability to handle complex queries.

The Core Distinction

Simple RAG

Simple RAG follows a straightforward, deterministic pipeline:

User Query → Embedding → Vector Similarity Search → Top-K Chunks → LLM(Query + Chunks) → Response

The process is linear and predictable. You embed the query, retrieve the most similar chunks from your vector store, stuff them into the LLM's context window, and generate a response. There's no reasoning about whether retrieval is needed or if the results are sufficient.

Agentic RAG

Agentic RAG treats the LLM as a reasoning agent that can:

Decide when and how to retrieve information
Decompose complex queries into sub-tasks
Iteratively refine searches based on intermediate results
Reason about whether retrieved information is sufficient
Route queries to different knowledge sources

The LLM actively plans and executes a retrieval strategy rather than following a fixed pipeline.

When Simple RAG is Sufficient

Simple RAG shines in these scenarios:

Well-Defined Knowledge Domains

If your knowledge base is focused and queries map predictably to relevant content, simple RAG works beautifully. For example:

Internal API documentation
Product specifications
FAQ systems
Policy and compliance documents

Straightforward Factual Questions

When users ask direct questions that can be answered from a single context window:

"What is our return policy for electronics?"
"How do I configure SSL certificates?"
"What are the side effects of medication X?"

Latency-Critical Applications

If users expect sub-second responses, simple RAG is often the only viable option. Real-time chat support, live coding assistants, and interactive tools need speed.

Cost Sensitivity

For high-volume applications where you're processing thousands of queries per hour, the 5-10x cost multiplier of agentic approaches becomes prohibitive.

Example Use Case

An internal documentation chatbot where engineers ask specific questions about your codebase. Queries like "How do I authenticate with the payments API?" have clear answers in specific documents. Simple RAG retrieves the relevant documentation and generates a focused response.

When to Consider Agentic RAG

Agentic RAG becomes valuable when query complexity increases:

Multi-Step Reasoning Required

When answering a question requires synthesizing information from multiple sources or performing logical reasoning:

"Compare our Q3 performance across all regions and explain the main drivers of variance"
"What dependencies would break if we upgrade package X to version 2.0?"
"Find all customer complaints related to feature Y and identify common themes"

Multiple Knowledge Sources

When you need to query different databases, APIs, or knowledge bases:

SQL databases for structured data
Vector stores for unstructured documents
External APIs for real-time information
Code repositories for implementation details

Query Decomposition Needs

Complex queries that should be broken into sub-questions:

"How has our ML model performance changed over time and what infrastructure changes correlate with improvements?"

This requires:

Retrieving model metrics over time
Retrieving infrastructure change logs
Correlating the two datasets
Synthesizing findings

Iterative Refinement

When initial retrieval might miss context and the system needs to adapt:

User: "Tell me about the incident last month"
Agent: Realizes "last month" is ambiguous, retrieves incident log
Agent: Finds multiple incidents, retrieves details for each
Agent: Synthesizes comprehensive response

Technical Implementation Patterns

Simple RAG Implementation

python

# Pseudocode for simple RAG
def simple_rag(query: str) -> str:
# 1. Embed query
query_embedding = embed(query)

# 2. Retrieve top-k similar chunks
chunks = vector_store.similarity_search(
query_embedding,
k=5
)

# 3. Construct prompt with context
context = "\n\n".join([c.text for c in chunks])
prompt = f"""Answer the question based on the context below.

Context:
{context}

Question: {query}

Answer:"""

# 4. Generate response
response = llm.generate(prompt)
return response

Agentic RAG Patterns

1. ReAct-Style Agent

The LLM reasons about what to do, acts, and observes results:

python

def react_agent(query: str) -> str:
conversation_history = []
max_iterations = 5

for i in range(max_iterations):
# LLM decides next action
prompt = build_react_prompt(query, conversation_history)
response = llm.generate(prompt)

# Parse action
if "SEARCH:" in response:
search_query = extract_search_query(response)
results = vector_store.search(search_query)
conversation_history.append({
"thought": response,
"action": "search",
"observation": results
})
elif "ANSWER:" in response:
return extract_answer(response)
else:
# Agent is confused, provide guidance
conversation_history.append({
"error": "Invalid action format"
})

return "Unable to answer after maximum iterations"

2. Query Decomposition

Break complex queries into simpler sub-queries:

python

def decomposition_agent(query: str) -> str:
# Ask LLM to break down query
sub_queries = llm.generate(f"""Break this complex query into simpler sub-queries:
{query}

Return as numbered list.""")

# Retrieve for each sub-query
all_context = []
for sub_q in parse_sub_queries(sub_queries):
chunks = vector_store.search(sub_q)
all_context.extend(chunks)

# Synthesize final answer
synthesis_prompt = f"""Answer the original query using the context from sub-queries:

Original Query: {query}

Context:
{format_context(all_context)}

Synthesized Answer:"""

return llm.generate(synthesis_prompt)

3. Conditional Retrieval

Only retrieve when necessary:

python

def conditional_rag(query: str) -> str:
# First, ask if retrieval is needed
decision_prompt = f"""Can you answer this query with your training knowledge alone?
Query: {query}

Respond with: NEED_RETRIEVAL or CAN_ANSWER"""

decision = llm.generate(decision_prompt)

if "CAN_ANSWER" in decision:
# Direct answer
return llm.generate(f"Answer this query: {query}")
else:
# Use retrieval
return simple_rag(query)

The Cost-Benefit Analysis

Latency Impact

Simple RAG: Embedding (20-50ms) + Retrieval (30-100ms) + LLM generation (100-300ms) = 150-450ms total
Agentic RAG: Planning (200ms) + Multiple retrievals (3 × 100ms) + Multiple LLM calls (3 × 200ms) + Synthesis (300ms) = 1,500-5,000ms+ total

For every agentic turn, you're adding substantial latency. This compounds with query complexity.

Token Cost Calculation

Assuming GPT-4 pricing (~$0.03/1K input tokens, ~$0.06/1K output tokens):

Simple RAG per query:

Input: ~2,000 tokens (system + context + query) = $0.06
Output: ~300 tokens = $0.018
Total: ~$0.08/query

Agentic RAG per query:

Planning call: ~500 tokens in/out = $0.045
3 reasoning calls: ~3,000 tokens in/out each = $0.27
Synthesis call: ~2,500 tokens in/out = $0.15
Total: ~$0.47/query

At 10,000 queries/day, that's $800/day (simple) vs $4,700/day (agentic) = $1.4M/year difference.

Quality Improvement

The key question: does agentic RAG provide enough quality improvement to justify 5-10x higher cost?

For simple factual queries: No. Simple RAG achieves 85-95% quality at a fraction of the cost.

For complex analytical queries: Potentially yes. If agentic RAG improves quality from 60% to 90% on complex queries, and those queries represent high-value use cases, the ROI may be positive.

Middle Ground Approaches

You don't have to choose exclusively. Here are hybrid strategies:

1. Query Routing

Classify queries by complexity and route accordingly:

python

def hybrid_rag(query: str) -> str:
complexity = classify_query_complexity(query)

if complexity == "simple":
return simple_rag(query)
elif complexity == "moderate":
return query_expansion_rag(query) # Expand query, single retrieval
else:
return agentic_rag(query)

Use a small classifier model (or even regex patterns) to route 80% of simple queries to the fast path.

2. Progressive Enhancement

Start simple, escalate if needed:

python

def progressive_rag(query: str) -> str:
# Try simple RAG first
result = simple_rag(query)

# Check if result is confident/complete
confidence = evaluate_response_confidence(result)

if confidence > 0.8:
return result
else:
# Escalate to agentic approach
return agentic_rag(query)

3. Constrained Agency

Give the agent limited autonomy:

python

def constrained_agent(query: str) -> str:
# Allow agent to refine query but limit iterations
refined_query = llm.generate(f"Rephrase for better retrieval: {query}")

# Do 2 retrievals max
results_1 = vector_store.search(refined_query)

if needs_more_context(results_1):
follow_up = generate_follow_up_query(query, results_1)
results_2 = vector_store.search(follow_up)
all_results = results_1 + results_2
else:
all_results = results_1

return generate_answer(query, all_results)

This gives you some agentic benefits (query refinement, multi-retrieval) without full complexity.

Practical Decision Framework

Use this flowchart to decide:

START: Analyze your query distribution

↓

Question 1: What percentage of queries require multi-hop reasoning or multiple knowledge sources?

< 20%: Lean toward Simple RAG
20-50%: Consider Hybrid
50%: Consider Agentic

↓

Question 2: What are your latency requirements?

< 500ms: Simple RAG only
< 2s: Hybrid with query routing
2s acceptable: Agentic is viable

↓

Question 3: What's your query volume and budget?

10K queries/day + cost sensitive: Simple RAG or selective hybrid
Moderate volume or high value per query: Agentic is viable

↓

Question 4: How critical is accuracy for complex queries?

Mission-critical (legal, medical, safety): Agentic might be worth it
Best-effort: Start with Simple RAG

↓

RECOMMENDATION: Choose your architecture

Real-World Gotchas

Agentic Systems Can Hallucinate Actions

LLMs sometimes "invent" tool calls or search queries that don't make sense:

Agent: SEARCH: quantum entanglement corporate policies
# Your company has no quantum physics policies...

You need robust error handling and guardrails.

Debugging is Exponentially Harder

With simple RAG, you can log: query → retrieved chunks → response. Clear failure points.

With agentic RAG, you need to trace:

Planning decisions
Each retrieval action
Intermediate reasoning steps
Synthesis logic

Non-deterministic behavior makes reproduction difficult.

Token Costs Can Spiral

On complex queries, poorly designed agents might loop unnecessarily:

3 iterations planned → 8 iterations executed → budget blown

Always implement:

Hard limits on iterations
Cost tracking per query
Circuit breakers for runaway loops

Users May Not Understand Latency

If 80% of queries return in 300ms (simple RAG) but 20% take 5+ seconds (agentic), users will notice the inconsistency and may perceive the system as slow or unreliable.

Consider:

Progress indicators for long-running queries
Async patterns (return partial results, then refine)
Setting expectations ("This is a complex query, analyzing...")

Framework Considerations

If you're building agentic RAG, don't roll your own unless you have a compelling reason. Consider:

LangGraph

Pros: Explicit state management, good debugging, flexible Cons: Steeper learning curve, more verbose

LlamaIndex Agents

Pros: High-level abstractions, quick to prototype Cons: Less control over agent behavior, framework lock-in

Semantic Kernel

Pros: Strong typed planning, good for enterprise Cons: More opinionated, C#-first (Python support improving)

Custom Implementation

Pros: Full control, no framework overhead Cons: You're implementing error handling, state management, retries, observability from scratch

Most teams should start with a framework and only go custom if they hit limitations.

Recommendations

Start with Simple RAG Unless you have proven need for agentic capabilities, start simple. You can always add complexity later. Get your chunking strategy, retrieval quality, and evaluation pipeline solid first.

Measure Before Optimizing Instrument your simple RAG system to understand:

What percentage of queries fail or produce low-quality results?
What types of queries fail?
Would agentic reasoning help those cases?

Implement Hybrid Routing Once you identify complex query patterns, implement routing:

80% of queries → simple RAG (fast, cheap)
20% of queries → agentic RAG (slower, better for complexity)

Monitor Costs Religiously Agentic RAG costs can surprise you. Track:

Token usage per query
Average iterations per query
Cost per query type
Total daily/monthly spend

Set budgets and alerts before deploying to production.

Invest in Evaluation With agentic systems, you need robust evaluation:

Unit tests for agent components
Integration tests for full flows
Regression tests for common query patterns
Human evaluation for quality assessment

The non-determinism of agentic systems makes this even more critical.

Conclusion

The choice between simple and agentic RAG isn't binary. Most production systems will benefit from a hybrid approach: simple RAG as the fast path for straightforward queries, with agentic capabilities reserved for complex cases where the quality improvement justifies the cost.

Start simple, measure relentlessly, and add complexity only where it provides clear value. Your users—and your infrastructure budget—will thank you.