Simple RAG vs Agentic RAG: What Problem Are You Actually Solving?

Let’s start with a real problem.

“Can I terminate this contract early, and what penalties apply?”

You have:

• A set of contracts (PDFs)
• A user asking a natural-language question
• An LLM-powered application

The question is not:

• “Should I use RAG or agents?”

The real question is:

How much reasoning does this problem actually require?

Step 1: The Simple RAG Approach (And Why It Often Works)

What Simple RAG Looks Like

A typical Simple RAG pipeline:

1. User asks a question
2. Embed the query
3. Retrieve top-K chunks
4. Inject them into the prompt
5. Generate an answer

In code terms (conceptually):

query → retriever → context → prompt → LLM → answer

What Happens in Practice

For many questions, this works surprisingly well:

• “What is the notice period?”
• “When does the contract expire?”
• “Is early termination allowed?”

Why?
Because the answer exists verbatim in the documents.

No planning.
No tool chaining.
No decision-making.

Step 2: Where Simple RAG Starts to Break

Now try this question:

“If I terminate early due to breach, does the penalty still apply?”

Suddenly:

• The answer spans multiple clauses
• Conditions matter
• Exceptions override defaults

What Simple RAG does:

• Retrieves multiple chunks
• Dumps them into context
• Hopes the LLM figures it out

Sometimes it does.
Sometimes it hallucinates confidently.

The failure mode isn’t retrieval — it’s implicit reasoning.

Step 3: Enter Agentic RAG (And Why People Overuse It)

Agentic RAG introduces explicit reasoning steps.

Instead of:

“Answer directly”

The system does:

1. Identify sub-questions
2. Decide which tools to call
3. Retrieve information iteratively
4. Synthesize an answer

Conceptually:

plan → retrieve → evaluate → retrieve → decide → answer

This shines when:

• Questions are multi-hop
• Dependencies exist
• Decisions affect next steps

For example:

• “Check termination clause”
• “Check breach exceptions”
• “Check penalty override”
• “Combine results”

This is real reasoning, not just recall.

Step 4: Where Agentic RAG Becomes a Liability

Now consider this question:

“What is the termination notice period?”

An agent might:

• Plan unnecessarily
• Call tools repeatedly
• Increase latency
• Increase cost
• Introduce new failure modes

You traded:

• A 1-step pipeline for
• A 5-step reasoning loop To answer a lookup question.

This is overengineering.

The Core Insight Most Teams Miss

Agentic RAG is not “better RAG.”
It’s a different tool for a different problem.

The decision is not:

• Simple vs Agentic
  It’s:

• Recall vs Reasoning

A Practical Decision Rule (Use This)

Use Simple RAG when:

• The answer exists verbatim
• Questions are independent
• Latency and cost matter
• Determinism is important

Use Agentic RAG when:

• Answers span multiple sources
• Decisions affect next retrieval
• You need traceable reasoning
• You accept higher cost for correctness

Why Many Systems Fail in Production

Most teams:

• Jump to Agentic RAG too early
• Before fixing ingestion
• Before fixing chunking
• Before understanding attention limits

Agents amplify:

• Bad context
• Poor retrieval
• Weak observability

They don’t fix fundamentals.

Final Takeaway

Simple RAG fails when reasoning is required.
Agentic RAG fails when reasoning is unnecessary.

The best systems:

• Route questions intentionally
• Use agents selectively
• Treat reasoning as a cost, not a default

What’s Next

Next, we’ll go one level deeper:

Prompt Routing & Context Engineering: Letting the System Decide What It Needs

That’s where real production intelligence starts.