RAG vs Agentic: What Should You Actually Build for Enterprise Use Cases?

The AI architecture debate of 2025: RAG vs Agentic workflows.

Everyone's building something. But which approach is right for your use case?

• RAG (Retrieval-Augmented Generation): Fetch relevant docs, send to LLM
• Agentic: Give LLM tools, let it figure out what to do

Both work. Both have tradeoffs. And most real-world systems need both.

Let me help you decide what to build.

What Is RAG?

RAG = Retrieval-Augmented Generation

Pattern:

1. User asks question
2. System retrieves relevant documents from knowledge base
3. Documents sent to LLM as context
4. LLM generates answer based on provided documents

Example:

User: "What's our return policy for electronics?"

System:
1. Embeds question
2. Searches vector database
3. Retrieves: "Electronics Return Policy" document
4. Sends to LLM: "Based on this policy: [document], answer: [question]"
5. LLM responds: "Electronics can be returned within 14 days..."

Key characteristic: The system retrieves, the LLM reads and summarizes.

What Is Agentic?

Agentic = LLM-driven agent with tool access

Pattern:

1. User asks question
2. LLM reasons about what to do
3. LLM calls tools/APIs to gather information
4. LLM synthesizes answer from tool results

Example:

User: "What's the status of my recent order?"

Agent reasoning:
"I need to find this user's recent order"
→ Calls: get_recent_orders(user_id)
→ Gets: Order #12345

"Now I need to check its status"
→ Calls: get_order_status(order_id="12345")
→ Gets: Status "shipped", tracking "ABC123"

"User might want tracking info"
→ Calls: get_tracking_details("ABC123")
→ Gets: "Out for delivery, ETA today 5pm"

→ Synthesizes answer: "Your order #12345 was shipped and is out for
   delivery. Expected delivery: today by 5pm. Tracking: ABC123"

Key characteristic: The LLM decides what to do, calls tools, reasons about results.

RAG vs Agentic: Quick Comparison

Dimension	RAG	Agentic
Control	High (you control retrieval)	Low (LLM decides)
Predictability	High (same query → same docs)	Low (LLM may try different approaches)
Latency	Fast (1 LLM call)	Slower (multiple LLM calls)
Cost	Lower (fewer LLM calls)	Higher (multiple calls + tool executions)
Complexity	Simple (retrieve → generate)	Complex (reasoning loops, tool orchestration)
Flexibility	Limited (can only answer from docs)	High (can take actions, combine data)
Debugging	Easy (clear retrieval + generation)	Hard (non-deterministic reasoning)
Best for	Q&A over static documents	Dynamic tasks requiring actions

When to Use RAG

Use Case #1: Document Q&A

Scenario: You have a knowledge base (docs, FAQs, policies) and want to answer questions.

Why RAG wins:

• Fast (one retrieval, one LLM call)
• Predictable (same question → same docs → similar answer)
• Transparent (you can show which docs were used)
• Reliable (95%+ accuracy with good retrieval)

Example:

• Internal documentation chatbot
• Customer support FAQ assistant
• Policy/compliance Q&A

Implementation:

def rag_qa(question):
    # Retrieve relevant docs
    relevant_docs = vector_db.similarity_search(question, k=3)

    # Build context
    context = "\n\n".join([doc.content for doc in relevant_docs])

    # Generate answer
    prompt = f"""
    Answer based on these documents:

    {context}

    Question: {question}

    Answer:
    """

    answer = llm.generate(prompt)
    return answer, relevant_docs  # Return docs for transparency

Use Case #2: Research/Summarization

Scenario: Summarize information from multiple documents.

Why RAG wins:

• Retrieves all relevant docs upfront
• LLM sees full context at once
• More coherent summaries than multi-step agent
• Cheaper (one LLM call vs many)

Example:

• "Summarize all customer feedback from Q1"
• "What are the main themes in these research papers?"
• "Give me a competitive analysis based on these reports"

Use Case #3: Compliance/Audit Requirements

Scenario: Need to cite sources, show evidence, maintain audit trail.

Why RAG wins:

• Clear provenance (which docs were used)
• Deterministic (same input → same output)
• Easy to audit ("Answer came from doc X, section Y")

Example:

• Legal document analysis
• Medical diagnosis support
• Financial reporting

When to Use Agentic

Use Case #1: Multi-Step Workflows

Scenario: Task requires multiple actions in sequence.

Why agentic wins:

• Agent orchestrates steps automatically
• Can adapt based on intermediate results
• Handles dependencies between steps

Example:

• "Book me a flight to NYC next week"
  • Agent checks calendar
  • Searches flights
  • Compares prices
  • Books ticket
  • Adds to calendar
  • Sends confirmation

RAG couldn't do this: No documents to retrieve, need to take actions.

Use Case #2: Dynamic Data Queries

Scenario: Answer requires querying databases, APIs, or live systems.

Why agentic wins:

• Can call multiple data sources
• Combines information dynamically
• Handles complex queries

Example:

• "Which customers haven't ordered in 90 days but have high lifetime value?"
  • Agent queries customer database
  • Filters by last order date
  • Calculates lifetime value
  • Returns list

RAG couldn't do this: No static documents, need live queries.

Use Case #3: Personalized Recommendations

Scenario: Need to consider user context, preferences, history.

Why agentic wins:

• Can fetch user-specific data
• Reasons about preferences
• Adapts recommendations

Example:

• "Recommend products for me"
  • Agent gets user purchase history
  • Gets browsing history
  • Checks current promotions
  • Reasons about preferences
  • Suggests personalized products

Use Case #4: Problem-Solving Tasks

Scenario: Open-ended tasks requiring reasoning and iteration.

Why agentic wins:

• Can explore different approaches
• Iterates based on results
• Handles ambiguity

Example:

• "Debug why our website is slow"
  • Agent checks server logs
  • Queries database performance
  • Checks CDN status
  • Analyzes metrics
  • Identifies bottleneck

RAG couldn't do this: Not a document lookup problem.

The Hybrid Approach (Most Common)

Reality: Most enterprise use cases need both.

Pattern #1: RAG for Knowledge, Agentic for Actions

Use RAG when: Answering questions from documents Use agentic when: Taking actions based on answers

Example: Customer Support Agent

def handle_customer_query(query, user_id):
    # Step 1: Classify intent
    intent = classify(query)

    if intent == "question":
        # Use RAG for Q&A
        answer, sources = rag_qa(query)
        return {"answer": answer, "sources": sources}

    elif intent == "action":
        # Use agent for actions
        result = agent.execute(query, user_id=user_id)
        return result

Queries:

• "What's your return policy?" → RAG (document lookup)
• "Process a return for order #12345" → Agent (action)

Pattern #2: Agentic with RAG as a Tool

Agent has RAG as one of its tools:

class HybridAgent:
    def __init__(self):
        self.tools = [
            search_knowledge_base,  # RAG tool
            lookup_order,           # Database tool
            process_refund,         # Action tool
            send_email,             # Action tool
        ]

    def search_knowledge_base(self, query):
        """RAG tool: Search docs and return answer"""
        return rag_qa(query)

Example conversation:

User: "I want to return my laptop. What's the policy?"

Agent: [Calls search_knowledge_base("laptop return policy")]
Agent: "Laptops can be returned within 30 days. Do you want to start a return?"

User: "Yes, order #12345"

Agent: [Calls lookup_order("12345")]
Agent: [Calls process_refund("12345")]
Agent: "Return processed! You'll receive refund in 5-7 days."

Benefit: Agent decides when to use RAG vs other tools.

Pattern #3: RAG for Context, Agentic for Reasoning

Retrieve context, let agent reason:

def hybrid_workflow(query, user_id):
    # Step 1: Retrieve relevant context (RAG)
    relevant_docs = vector_db.search(query, k=5)
    user_data = db.get_user(user_id)
    recent_orders = db.get_recent_orders(user_id)

    # Step 2: Give all context to agent (Agentic)
    context = {
        "docs": relevant_docs,
        "user": user_data,
        "orders": recent_orders
    }

    agent_response = agent.run(query, context=context)
    return agent_response

Example:

User: "Recommend a laptop for video editing"

System:
- Retrieves: Product docs, laptop specs, video editing requirements (RAG)
- Fetches: User's budget, past purchases (Database)
- Agent reasons:
  "User bought budget mouse, likely cost-conscious"
  "Video editing needs 16GB RAM minimum"
  "Within budget: Model X at $1,299"
- Recommends Model X with justification

Decision Framework

Question 1: Is this primarily Q&A over static documents?

• Yes → RAG
• No → Go to Q2

Question 2: Does this require taking actions?

• Yes → Agentic
• No → Go to Q3

Question 3: Do you need to query live data or APIs?

• Yes → Agentic
• No → RAG

Question 4: How important is cost?

• Very important → RAG (cheaper)
• Less important → Agentic OK

Question 5: How important is predictability?

• Critical (compliance, legal) → RAG
• Flexible → Agentic OK

Question 6: Is the task open-ended?

• Yes (research, exploration) → Agentic
• No (specific question) → RAG

Cost Comparison

RAG (1,000 queries):

• Embedding: 1,000 queries × 100 tokens × $0.0001/1K = $0.01
• Vector search: ~$0.10
• LLM generation: 1,000 × 3,000 tokens × $0.003/1K = $9
• Total: ~$9.10

Agentic (1,000 queries):

• Tool calling: 1,000 × 5 calls × 2,000 tokens × $0.003/1K = $30
• Reasoning: 1,000 × 5 calls × 500 tokens × $0.015/1K = $37.50
• Total: ~$67.50

Agentic is 7x more expensive for same query volume.

Real-World Examples

RAG Winner: Legal Document Analysis

Task: Answer questions about legal contracts

Why RAG:

• Documents are static
• Need to cite sources (compliance)
• Predictable answers required
• High accuracy critical

Implementation: Vector DB of contracts + RAG pipeline

Agentic Winner: Sales Assistant

Task: Help sales reps with customer outreach

Why agentic:

• Needs to query CRM, calendar, email
• Takes actions (schedule calls, send emails)
• Personalizes based on customer data
• Multi-step workflows

Implementation: LangGraph agent with CRM integration

Hybrid Winner: IT Help Desk

Task: Answer IT questions and resolve tickets

Why hybrid:

• Q&A needs RAG (knowledge base lookup)
• Actions need agent (reset password, create ticket)
• Best of both worlds

Implementation:

• RAG for "How do I...?" questions
• Agent for "Please reset my password"

Common Mistakes

Mistake #1: Using Agentic for Simple Q&A

Wrong: Build an agent to answer FAQ questions

Right: Use RAG

Why: Agents are overkill for document lookup. RAG is faster, cheaper, more reliable.

Mistake #2: Using RAG for Dynamic Tasks

Wrong: Try to RAG "book me a flight"

Right: Use agent

Why: RAG can't take actions or query live data. You need an agent.

Mistake #3: Not Considering Hybrid

Wrong: "We need to choose RAG or agentic"

Right: "Where do we use each?"

Why: Most systems benefit from both.

The Bottom Line

RAG:

• Fast, cheap, predictable
• Best for: Q&A over static documents, research, summaries
• Use when: Retrieval solves the problem

Agentic:

• Flexible, powerful, autonomous
• Best for: Multi-step workflows, dynamic queries, actions
• Use when: Task requires reasoning/decisions/actions

Hybrid (most common):

• RAG for knowledge lookup
• Agentic for actions and dynamic tasks
• Best for: Complex real-world applications

Start with RAG, add agentic capabilities where needed.

Getting Started

If building Q&A system:

1. Start with RAG
2. Measure accuracy
3. Add agent if users need actions

If building task automation:

1. Start with constrained agent (LangGraph)
2. Add RAG tool for knowledge lookup
3. Expand agent capabilities iteratively

If unsure:

1. Prototype both approaches
2. Test on real queries
3. Measure cost, latency, accuracy
4. Choose based on data

Need help choosing the right architecture for your use case? We've built RAG systems, agents, and hybrid approaches for dozens of companies.

Get architecture consultation →

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Related reading:

• Latent Space: RAG vs Agents discussion: https://www.latent.space/p/rag-vs-agents
• LangChain RAG guide: https://python.langchain.com/docs/use_cases/question_answering/