Below is a from-scratch, layman-friendly, deeply structured explanation of State, TypedDict, Agent State, Message State, BaseMessage, and other key concepts used in Agentic RAG — explained with real-world analogies, comparisons, and one end-to-end use case.

I’ll explain this like you’re explaining it to:
👉 a non-AI stakeholder,
👉 then slowly level it up to Agentic RAG engineer thinking.

🌈 Agentic RAG — Concepts Explained from Absolute Basics

🧠 First: What is Agentic RAG (In Simple Words)?

Agentic RAG =

An AI system that can remember, decide, retrieve knowledge, and act step-by-step instead of just answering once.

Think of it as:

🏠 The Core Idea You Must Understand FIRST: STATE

🧳 What is State? (Layman Explanation)

State = Everything the AI needs to remember right now

📌 Think of State as a file folder 📂 that contains:

• What is happening

• What has already happened

• What should happen next

🏢 Real-World Example: Hospital File

A patient file contains:

• Patient details

• Symptoms

• Tests done

• Doctor notes

👉 That file = State

Without it:

• Doctor forgets everything

• Starts from zero every time ❌

🤖 In Agentic AI

State stores:

• User question

• Conversation history

• Retrieved documents

• Agent decisions

• Tool outputs

👉 State = AI’s working memory

🧩 Why State is CRITICAL in Agentic RAG

Without State:

User: Explain diabetes
AI: (answers)
User: What about treatment?
AI: Diabetes? Which diabetes? ❌

With State:

AI remembers:
• Topic = diabetes
• Context = explanation
• Now → treatment

🧱 Next Building Block: TypedDict

📦 What is TypedDict? (Very Simple)

TypedDict = A fixed structure for the State

📌 Analogy:

A printed form instead of loose papers

🏢 Example: Hospital Admission Form

You cannot randomly write anything anywhere.

🤖 In Code Terms (Conceptual, not coding)

TypedDict tells AI:

• What keys exist

• What type of data goes where

📌 Example:

State must have:
- question (text)
- documents (list)
- answer (text)

❓ Why TypedDict is Needed?

👉 Agentic systems break without structure

🧠 Agent State (MOST IMPORTANT)

🧑‍💼 What is Agent State?

Agent State = The complete brain memory of an agent at a given moment

It includes:

• Current goal

• Knowledge retrieved

• Decisions made

• Next action

📌 Think of Agent State as:

A to-do list + notes + memory of an employee

🏢 Real-World Example: Research Analyst

An analyst working on a report keeps:

• Research topic

• Articles collected

• Draft notes

• Pending tasks

👉 That entire workspace = Agent State

🤖 Agent State in Agentic RAG

Agent State may contain:

• User intent

• Retrieved documents

• Intermediate reasoning

• Tool results

• Final answer

🔄 Agent State Keeps Updating

State at Step 1 → Question
State at Step 2 → Retrieved Docs
State at Step 3 → Reasoned Answer

💬 Message State (Conversation Memory)

🗣️ What is Message State?

Message State = Chat history stored in structured form

📌 Analogy:

WhatsApp chat history 📱

🏢 Real-World Example: Customer Support

Support agent remembers:

• What customer said

• What agent replied

• What issue is open

Without this → chaos ❌

🤖 In Agentic RAG

Message State stores:

• User messages

• AI messages

• Tool messages

So the AI knows:

“What exactly has been said so far?”

🧱 BaseMessage (Message Building Block)

🧱 What is BaseMessage?

BaseMessage = One single message unit

📌 Analogy:

One WhatsApp message bubble 💬

Types of BaseMessages

🏢 Example Conversation

SystemMessage: You are a medical assistant
HumanMessage: What is diabetes?
AIMessage: Diabetes is...
ToolMessage: Retrieved medical articles

👉 Each line = one BaseMessage

👉 All messages together = Message State

🧠 Relationship Between All Concepts (Big Picture)

Agent State
 ├── Question
 ├── Retrieved Documents
 ├── Reasoning
 ├── Answer
 └── Message State
        ├── BaseMessage (User)
        ├── BaseMessage (AI)
        └── BaseMessage (Tool)

🔍 Now Add RAG into This (Agentic RAG)

📚 What RAG Adds

RAG = Retrieval Augmented Generation

It adds:

• External knowledge

• Documents

• Databases

🏢 Real-World Example: Doctor + Medical Books

Doctor:

• Remembers patient (State)

• Talks to patient (Message State)

• Refers books (RAG)

• Decides treatment (Agent)

🧪 FULL USE CASE: Healthcare Agentic RAG

🎯 Goal

“Explain diabetes treatment based on latest guidelines”

🔁 Step-by-Step Flow

1️⃣ User Asks

HumanMessage: Explain diabetes treatment

2️⃣ State Created

State:
- question
- empty documents
- empty answer

3️⃣ Agent Plans

“I need medical guidelines”

4️⃣ RAG Retrieval

ToolMessage:
- Retrieved WHO diabetes guidelines

5️⃣ State Updated

State:
- documents filled

6️⃣ Reasoning

Agent analyzes:

• Medicines

• Lifestyle

• Diet

7️⃣ Final Answer

AIMessage: Diabetes treatment includes...

🆚 Comparative Summary (Layman Friendly)

🌟 Final One-Line Understanding

Agentic RAG works because STATE keeps everything connected, structured, and intelligent.

Without state:
❌ AI forgets
❌ AI repeats
❌ AI fails

With state:
✅ AI remembers
✅ AI reasons
✅ AI acts like a human worker

🚀 Mentor Note 

This exact understanding is what:

• LangGraph

• AutoGen

• CrewAI

• Advanced Agentic systems

are built upon.

If you master State & Message design, you master Agentic AI architecture.