rakesh kumar

Posted on Aug 19

LLM vs RAG vs AI Agents vs Agentic AI: Architecture, Workflow, Key Differences, Use Cases, and How They Connect

LLM vs RAG vs AI Agent vs Agentic AI
What is an LLM?
How an LLM processes a prompt
What is RAG?
Why RAG is added to an LLM
What is an AI Agent?
How an AI Agent uses LLM + Tools + APIs
What is Agentic AI?
AI Agent vs Agentic AI
LLM vs RAG vs AI Agent vs Agentic AI comparison
How they connect in one architecture
When to use each
Real-world example using all four
Developer learning roadmap: LLM → RAG → Agents → Agentic AI

LLM vs RAG vs AI Agent vs Agentic AI

The easiest relationship to remember is:

LLM
↓
RAG
↓
AI Agent
↓
Agentic AI

But this does not mean one always replaces the previous one.

A better understanding is:

LLM = language intelligence

RAG = LLM + external knowledge

AI Agent = LLM + tools + actions

Agentic AI = agents + planning + memory + tools + feedback + autonomy

What is an LLM?
How an LLM processes a prompt
What is RAG?
Why RAG is added to an LLM
What is an AI Agent?
How an AI Agent uses LLM + Tools + APIs
What is Agentic AI?
AI Agent vs Agentic AI
LLM vs RAG vs AI Agent vs Agentic AI comparison
How they connect in one architecture
When to use each
Real-world example using all four
Developer learning roadmap: LLM → RAG → Agents → Agentic AI

What is an LLM?

LLM stands for Large Language Model

An LLM is an AI model trained on a huge amount of text so it can:

understand language
answer questions
generate text
summarize
translate
write code
explain concepts

reason over information in its context

Examples of LLM families include GPT-style models, Llama-style models, Gemini models, and Claude models.

Simple example

You ask:

Explain photosynthesis in simple words.

The LLM generates:

Photosynthesis is the process plants use
to make food using sunlight, water,
and carbon dioxide.

Main purpose

LLM
=
Understand language
+
Generate language

How does an LLM process a prompt?

Suppose the user asks:

Explain photosynthesis simply.

The simplified processing flow is:

User Prompt
    ↓
Tokenization
    ↓
Tokens
    ↓
Embeddings
    ↓
Transformer Layers
    ↓
Attention
    ↓
Learned Parameters / Weights
    ↓
Next-token probabilities
    ↓
Temperature / Top-P
    ↓
Select Next Token
    ↓
Repeat
    ↓
Generated Answer

Step 1 — Prompt

User writes:

Explain photosynthesis simply.

Step 2 — Tokenization

Text is broken into smaller units.

Conceptually:

Explain
photo
synthesis
simply

Step 3 — Tokens

Each token is converted to an ID.

Example:

Explain → 421
photo → 982
synthesis → 1637
Step 4 — Embeddings

Token IDs are converted into numerical vectors representing information the neural network can process.

Token
↓
Vector

Step 5 — Transformer

The Transformer processes those representations through many layers.

Step 6 — Attention

Attention helps determine which words/tokens are related.

Example:

The plant uses sunlight to make its food.

The word:

its

should relate strongly to:

plant
Step 7 — Parameters / Weights

The model uses the numerical patterns learned during training.

Step 8 — Next-token prediction

The model predicts probabilities for the next token.

Example:

Plants use ______

Possible predictions:

sunlight     65%
water        15%
energy       10%
oxygen        5%
other         5%

Step 9 — Repeat

The selected token is added to the context.

Then the model predicts the next token again.

Token
↓
Next Token
↓
Next Token
↓
Next Token
↓
Final Response

What is RAG?

RAG stands for Retrieval-Augmented Generation.

RAG allows an LLM to use information from external sources before generating an answer.

External sources can include:

PDF
Word Documents
Company Database
Website
Knowledge Base
Product Data
Hospital Records
API Data
Technical Documentation

A typical RAG workflow is:

Documents
   ↓
Chunking
   ↓
Embeddings
   ↓
Vector Database

Then when the user asks something:

User Question
   ↓
Query Embedding
   ↓
Vector Search
   ↓
Relevant Chunks
   ↓
Question + Retrieved Information
   ↓
LLM
   ↓
Grounded Answer

Example

Company policy says:

Employees get 12 casual leaves annually.

User asks:

How many casual leaves do employees get?

RAG retrieves the policy section.

Then:

Question
+
Relevant Policy
↓
LLM
↓
12 casual leaves per year

Why is RAG added to an LLM?

An LLM does not automatically know your private or constantly changing information.

For example, an LLM may not know:

your company policy
your private database
today's inventory
your project documentation
your hospital records
internal APIs
recently updated PDFs

Therefore we add retrieval.

Without RAG:

Question
↓
LLM
↓
Answer using training knowledge

With RAG:

Question
↓
Retrieve relevant information
↓
Question + Information
↓
LLM
↓
Grounded Answer

RAG is useful because it can provide

private knowledge
domain-specific information
newer information
document-based answers
better factual grounding

So:

LLM
=
General intelligence


RAG
=
General intelligence
+
Relevant external knowledge

What is an AI Agent?

An AI Agent is an AI system that can do more than generate text.

It can:

Understand goal
↓
Decide what to do
↓
Use a tool
↓
Observe result
↓
Take another action
↓
Complete task

An AI Agent usually contains:

LLM
+
Tools
+
APIs
+
Instructions
+
State / Memory

Example

User says:

Find flights from Delhi to Bangalore
for tomorrow under ₹6,000.

A normal LLM might explain where to search.

An agent can potentially:

Understand destination
↓
Call Flight API
↓
Get flight results
↓
Filter by ₹6,000
↓
Compare options
↓
Return best flights
Main purpose
LLM → answer

AI Agent → perform a task

How does an AI Agent use LLM + Tools + APIs?

This is one of the most important concepts.

The LLM works as the reasoning/control component.

Tools provide capabilities.

For example:

LLM
↓
"What should I do?"
↓
Select Tool
↓
API Call
↓
API Result
↓
LLM evaluates result
↓
Next Action

Suppose the user asks:

Check tomorrow's weather in Jaipur
and schedule my outdoor meeting
when the weather is suitable.

The agent could perform:

User Goal
   ↓
LLM understands request
   ↓
Weather API
   ↓
Get forecast
   ↓
Calendar API
   ↓
Check available times
   ↓
LLM chooses suitable slot
   ↓
Calendar Tool
   ↓
Create meeting

The roles are:

LLM
= decides


Tool
= performs capability


API
= connects to external system

Examples of tools:

Google Calendar
Gmail
Search
Database
Calculator
CRM
Weather API
Payment API
Booking API
File system

What is Agentic AI?

Agentic AI is a goal-driven AI system that can plan, act, observe, adjust, and continue working toward a goal.

It may use:

one AI agent
or
multiple AI agents

Agentic AI typically includes:

Goal
↓
Planning
↓
Actions
↓
Tools
↓
Observations
↓
Evaluation
↓
Adjustment
↓
Repeat
↓
Final Goal

Example

Goal:

Create a detailed competitor analysis
for our hospital platform.

Agentic AI might:

Create research plan
↓
Search competitors
↓
Collect data
↓
Analyze pricing
↓
Analyze features
↓
Compare companies
↓
Generate report
↓
Review missing information
↓
Research again
↓
Improve report
↓
Final result

The important concept is:

Plan
→ Act
→ Observe
→ Evaluate
→ Adjust
→ Repeat

AI Agent vs Agentic AI

AI Agent example
Book a doctor appointment.

Flow:

Search doctor
↓
Check availability
↓
Book slot
↓
Done

Agentic AI example

Improve hospital appointment utilization
over the next month.

Flow:

Analyze appointment data
↓
Find unused slots
↓
Analyze patient demand
↓
Recommend schedule changes
↓
Run campaigns
↓
Observe bookings
↓
Analyze results
↓
Adjust strategy
↓
Continue

Therefore:

AI Agent
=
Task executor


Agentic AI
=
Goal-driven autonomous system

LLM vs RAG vs AI Agent vs Agentic AI

How do they connect in one architecture?

A useful architecture is:

 USER
                     ↓
                    Goal
                     ↓
              AGENTIC AI SYSTEM
                     ↓
                Orchestrator
                     ↓
              ┌──────┴──────┐
              ↓             ↓
          AI Agent       AI Agent
              ↓             ↓
            LLM           LLM
              ↓             ↓
       ┌──────┴──────┐     Tools
       ↓             ↓
      RAG           Tools
       ↓
Vector Database
       ↓
Documents / Data

Another way to remember it:

LLM
↓
provides intelligence


RAG
↓
provides knowledge to LLM


AI Agent
↓
uses LLM + knowledge + tools


Agentic AI
↓
coordinates agents, tools, memory,
planning and feedback

The relationship is therefore:

Agentic AI
   │
   ├── AI Agent
   │      │
   │      ├── LLM
   │      ├── RAG
   │      ├── Tools
   │      └── APIs
   │
   ├── Memory
   ├── Planning
   ├── Feedback
   └── Orchestration

When should you use each?

Use an LLM when

You need:

Writing
Summarization
Translation
General Q&A
Coding
Explanation
Brainstorming
Classification

Example:

Explain Kubernetes.

Use:

LLM

Use RAG when

The answer must come from:

PDF
Company data
Documentation
Database
Knowledge base
Product catalog
Hospital data
Private information
Frequently updated content

Example:

According to our employee handbook,
how many casual leaves are allowed?

Use:

RAG + LLM

Use an AI Agent when

The system needs to take actions.

Example:

Find an available doctor and book
an appointment tomorrow.

Use:

LLM
+
Doctor API
+
Calendar
+
Booking Tool

Use Agentic AI when

You have a complex goal requiring:

planning
multiple steps
multiple tools
repeated decisions
memory
feedback
adaptation
possibly multiple agents

Example:

Increase appointment bookings by 20%
during the next three months.

Use:

Agentic AI

Real-world example using all four

Consider a hospital AI system.

Stage 1 — LLM

Patient asks:

What is angioplasty?

Flow:

Patient
↓
LLM
↓
Explanation

LLM handles general medical explanation.

Stage 2 — RAG

Patient asks:

Which treatments does ABC Hospital
provide for heart patients?

Flow:

Patient Question
↓
Search Hospital Knowledge
↓
Retrieve relevant records
↓
LLM
↓
Grounded Answer

Now RAG is being used.

Stage 3 — AI Agent

Patient asks:

Find a cardiologist tomorrow
and book an appointment.

Flow:

User Request
↓
AI Agent
↓
Search Doctors API
↓
Check Availability
↓
Select Doctor
↓
Booking API
↓
Appointment Created

The system now takes an action.

Stage 4 — Agentic AI

Hospital gives the system a goal:

Reduce doctor idle time
and increase appointments.

Flow:

Goal
↓
Analyze booking history
↓
Analyze doctor schedules
↓
Identify empty slots
↓
Find patient demand
↓
Recommend scheduling changes
↓
Send targeted campaigns
↓
Track new appointments
↓
Evaluate performance
↓
Adjust strategy
↓
Repeat

Here:

LLM
= understands and reasons


RAG
= provides hospital knowledge


AI Agent
= performs actions


Agentic AI
= manages the complete goal

Developer Learning Roadmap

For learning, follow this order:

LLM
↓
RAG
↓
AI Agent
↓
Agentic AI

Phase 1 — LLM Fundamentals

Learn:

AI basics
↓
Machine Learning basics
↓
Deep Learning basics
↓
Transformer
↓
Tokens
↓
Tokenization
↓
Embeddings
↓
Attention
↓
Parameters / Weights
↓
Pre-training
↓
Fine-tuning
↓
Inference
↓
Context Window
↓
Temperature
↓
Top-P
↓
Next-token prediction

Build:

Project 0 — LLM Foundations Experiment Lab

Phase 2 — LLM Application Development

Learn:

Prompt engineering
System prompts
Structured outputs
LLM APIs
Function calling basics
Context management

Build:

Project 1 — AI Text Analyzer

For example:

Input text
↓
Summarize
↓
Classify
↓
Extract keywords
↓
Generate response

Phase 3 — RAG

Learn:

Document loading
↓
Chunking
↓
Embeddings
↓
Vector Database
↓
Similarity Search
↓
Retriever
↓
Prompt + Context
↓
LLM

Also learn:

Metadata filtering
Hybrid search
Reranking
RAG evaluation

Build:

Project 2 — PDF / Company Knowledge Assistant

Phase 4 — Tool Calling

Learn:

Function calling
APIs
JSON schemas
Tool execution
Error handling
Authentication

Build:

Project 3 — Tool-Using Assistant

Example:

Weather
Calculator
Search
Database
Email

Phase 5 — AI Agent

Learn:

Agent loop
↓
Reasoning
↓
Tool selection
↓
Action
↓
Observation
↓
Next action

Also learn:

State
Memory
Retries
Human approval
Guardrails

Build:

Project 4 — AI Booking Agent

Phase 6 — Agent Frameworks

Then learn frameworks such as:

LangChain
LangGraph
LlamaIndex
OpenAI Agents SDK
other agent orchestration frameworks

The important concept is not memorizing frameworks.

Understand:

State
Nodes
Edges
Tools
Memory
Routing
Retries
Human-in-the-loop

Build:

Project 5 — Stateful Workflow Agent

Phase 7 — Agentic AI

Learn:

Planning
↓
Task decomposition
↓
Memory
↓
Tool orchestration
↓
Feedback
↓
Reflection
↓
Evaluation
↓
Replanning

Build:

Project 6 — Research Agent

Example:

Topic
↓
Plan
↓
Search
↓
Retrieve
↓
Analyze
↓
Write
↓
Review
↓
Improve

Phase 8 — Multi-Agent Systems

Learn:

Agent roles
Agent communication
Orchestrator
Supervisor
Worker agents
Shared state
Agent handoffs

Architecture:

             Supervisor
                 ↓
      ┌──────────┼──────────┐
      ↓          ↓          ↓
 Researcher   Analyst     Writer
      ↓          ↓          ↓
    Tools       Tools      Tools
      └──────────┼──────────┘
                 ↓
              Reviewer
                 ↓
            Final Output

Build:

Project 7 — Multi-Agent Research System

Final roadmap

The complete learning path becomes:

1. LLM Fundamentals
        ↓
2. Prompt Engineering
        ↓
3. LLM APIs
        ↓
4. Embeddings
        ↓
5. Vector Databases
        ↓
6. RAG
        ↓
7. Function / Tool Calling
        ↓
8. AI Agents
        ↓
9. State + Memory
        ↓
10. Agent Workflows
        ↓
11. Planning + Reflection
        ↓
12. Agentic AI
        ↓
13. Multi-Agent Systems
        ↓
14. Production Agentic AI

The single best sentence to remember the entire blog is:

 LLMs == provide intelligence,
 RAG == provides external knowledge,
 AI Agents == provide actions,
 Agentic AI == combines planning, tools, memory, feedback, and autonomy to achieve complex goals.