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rakesh kumar
rakesh kumar

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Agentic AI Architecture Explained: How AI Agents Think, Plan, Use Tools, and Take Action

Agentic AI is an AI system that can understand a goal, make a plan, take decisions, use tools or APIs, perform actions, check the results, and continue working until the task is completed.

In very simple words:

Agentic AI = AI that can think, plan, act, and complete a goal with less human intervention.
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Example:

A normal chatbot:

User asks → AI gives an answer
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Agentic AI:

User gives a goal → AI plans → searches data → calls APIs/tools → performs actions → checks results → completes the goal
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Example:

“Find the best hospital, compare prices, book an appointment, and send me confirmation.”

An Agentic AI system could potentially handle the whole workflow instead of only telling you what to do

Agentic ai architecture

The easiest way to understand your diagram is

:

Input → Understand/Plan → Think → Remember → Retrieve Knowledge → Use Tools → Execute Workflow → Collaborate → Govern/Monitor → Produce Action
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One important point: sections 4–9 are not always executed one time in a strict sequence. The Agent Orchestration Layer can repeatedly move between memory, RAG, tools, workflows, and other agents until the goal is completed

Agentic AI Architecture — Section 1 to 10

INPUT / CHANNELS

This is where the request enters the Agentic AI system.

Your architecture supports multiple entry points:

User / Business Goal
Chat UI
Voice
Web App / Dashboard
API Request
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For example, a user says:

"Find the best hospital for knee surgery in Delhi, compare prices, and send me a report."

That request might come from:

Chat
Voice
Mobile App
Website
REST API
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All of them eventually become an input request/context.

Flow

User / Business Goal
        ↓
Chat / Voice / Web / API
        ↓
Agentic AI System
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The important concept is:

Different channels, same Agentic AI backend.

AGENT ORCHESTRATION LAYER

This is effectively the control center / manager of the entire Agentic AI system.

It decides:

What does the user want?
What should happen next?
Which agent should work?
Which tool should be called?
Does RAG need to be used?
Do we need human approval?
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Your architecture includes eight important components.

Intent Understanding

Determines what the user actually wants.

Example:

User:

"Find hospitals in Delhi and compare knee surgery costs."
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Intent becomes:

Goal:
Hospital research + cost comparison
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Reasoning

The agent thinks about how to solve the request.

Need hospitals
Need treatment information
Need pricing
Need comparison
Need final report
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Planning

Creates an execution plan.

Step 1 → Find hospitals
Step 2 → Search internal data
Step 3 → Search web if necessary
Step 4 → collect prices
Step 5 → compare hospitals
Step 6 → generate report
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Task Decomposition

A large goal is divided into smaller tasks.

Main Goal
   │
   ├── Hospital Search
   ├── Treatment Research
   ├── Cost Research
   ├── Review Analysis
   └── Report Generation
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This becomes extremely useful for multi-agent systems.

Routing

Routing determines:

Who should perform each task?
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For example:

Hospital research → Research Agent


Pricing → API / Database


Comparison → Analyst Agent


Final validation → Reviewer Agent
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State Manager

Tracks the current progress of the job.

Example:

Hospital search        ✓ Completed
Price collection       ✓ Completed
Review analysis        ⏳ Running
Report generation      Pending
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Without state management, long-running agents can lose track of what they have already completed.

Session Context

Maintains the current conversation.

Example:

User:
Find hospitals in Delhi.


User later:
Only show NABH accredited ones.
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The agent understands that:

"ones"
=
Delhi hospitals found earlier.
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Human-in-the-Loop
Sometimes AI should not automatically perform the final action.

For example:


Agent generates ₹1,00,000 payment
        ↓
Human approval required
        ↓
Approved
        ↓
Payment executed
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This is especially useful for:

payments
deleting records
financial decisions
medical workflows
account changes
high-risk operations
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CORE AGENT BRAIN

Now the orchestration layer sends the task to the Core Agent Brain.

Think of this as:

The intelligence engine of your Agentic AI system.
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Your diagram contains:

LLM / Foundation Model
Prompting
Structured Output
Reflection / Self-Check
Response Generation
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LLM / Foundation Model

The LLM provides language understanding and reasoning capabilities.

Examples could include models from OpenAI, Anthropic, Google, open-source models, etc.

Conceptually:

Prompt + Context + Knowledge
              ↓
             LLM
              ↓
       Reasoned response
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Prompting

The system gives the LLM instructions.

Example:

You are a hospital research agent.

Find hospitals matching:
- Delhi
- Knee surgery
- NABH accredited


Return:
hospital_name
location
estimated_cost
rating
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Structured Output

Instead of returning random text, the model can generate predictable structured data.

Example:

{
  "hospital": "ABC Hospital",
  "city": "Delhi",
  "procedure": "Knee Replacement",
  "estimated_cost": 250000
}
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This is critical for production systems.

Reflection / Self-Check

Before accepting the answer, the agent can check:

Did I complete every task?
Did I use reliable data?
Did the API fail?
Is information missing?
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Does the output match the requested format?

If not:

Reflect
   ↓
Retry
   ↓
Retrieve more information
   ↓
Call another tool
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This creates an agent loop.

Response Generation

Finally, all information is combined into a useful response.

But before reaching this stage, the Core Agent Brain may interact with sections 4, 5, 6, 7, and 8 many times.

MEMORY & STATE

The agent often needs memory.

Your architecture has five types.

Short-Term Memory
Long-Term Memory
User Profile / Preferences
Conversation State
Scratchpad / Working Memory
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Short-Term Memory

Stores information from the current task/session.
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Example:

User asked for:
Delhi hospitals
Knee surgery
Budget ₹4 lakh
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Long-Term Memory

Stores information that may be useful across sessions.
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For example:

Preferred city = Delhi
Preferred language = English
Preferred hospital type = NABH-accredited
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User Profile / Preferences

Stores personalization information.
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User
 ├── Preferred language
 ├── Currency
 ├── Location
 └── Preferences
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Conversation State

Tracks the ongoing conversation.
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Question 1
   ↓
Answer
   ↓
Follow-up question
   ↓
Previous context reused
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Scratchpad / Working Memory

Temporary information required while solving the problem.

For example:

Hospital A score = 8.7


Hospital B score = 9.1


Hospital C score = 8.3


Best candidate currently = Hospital B
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Your diagram correctly shows:

Agent
 ↕
read/write state
 ↕
Memory
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and

Memory
   ↓
recall context
   ↓
Agent
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KNOWLEDGE & RETRIEVAL

The LLM does not necessarily know current or company-specific information.

Therefore your agent uses RAG — Retrieval-Augmented Generation.

Your flow is roughly:

Query
 ↓
RAG Pipeline
 ↓
Embeddings
 ↓
Retriever
 ↓
Re-ranker
 ↓
Relevant Knowledge
 ↓
Agent
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Documents / PDFs / Knowledge Base

Example sources:


Company policies
Product documentation
Hospital information
PDF reports
Training manuals
FAQs
Internal Wiki
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Embeddings

Documents are converted into numerical vector representations.

Conceptually:

"Knee replacement cost"
        ↓
Embedding Model
        ↓
[0.19, -0.32, 0.71 ...]
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Vector Database

Those vectors are stored in something like:

Vector DB
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The system can then perform semantic search.

Retriever

Finds relevant chunks.

Example query:

"Knee surgery package Delhi"
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Retriever might return:

Hospital A package
Hospital B package
Hospital C package
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Re-ranker

The retriever may return 20 results.

The re-ranker decides:

Most relevant
      ↓
#1 Result
#2 Result
#3 Result
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Only the strongest evidence is sent to the LLM.

Web Search

If internal knowledge isn't enough:

Agent
 ↓
Web Search
 ↓
Current information
 ↓
Agent
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So this part of your architecture means:

The agent does not rely only on the LLM's internal knowledge.

TOOLS, APIs & EXTERNAL SYSTEMS

This is one of the biggest differences between a normal chatbot and an AI agent.

A chatbot mainly:

Question
 ↓
LLM
 ↓
Answer
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An agent can:

Question
 ↓
Think
 ↓
Call Tool
 ↓
Do Something
 ↓
Receive Result
 ↓
Continue Reasoning
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Your architecture includes:

Tool Registry
Function Calling
REST APIs
Internal Microservices
Email / Calendar
CRM / ERP
Payment Service
Code Execution
Browser Automation
SQL Database
NoSQL Database
File Storage
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Tool Registry

The agent needs to know which tools exist.

Example:

Tools Available
│
├── search_hospital()
├── send_email()
├── create_booking()
├── get_payment_status()
├── create_calendar_event()
└── generate_report()
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Function Calling

The LLM decides:

I need hospital information.

Then it may call:


search_hospital(
 city="Delhi",
 speciality="Orthopedics"
)
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REST APIs

For external services:

Agent
 ↓
REST API
 ↓
External System
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Internal Microservices

Very important for microservice applications.

Example:

AI Agent
   ↓
Hospital Microservice
   ↓
Doctor Microservice
   ↓
Quote Microservice
   ↓
Payment Microservice
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Email / Calendar

Agent could execute:

Send email
Create meeting
Update calendar
Schedule appointment
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CRM / ERP

Example:

Create customer
Update lead
Retrieve order
Update support case
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Payment Service

For example:

Create payment request
Check transaction
Process refund
Verify payment
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Normally sensitive actions should use human approval.

Code Execution

The agent could write and execute code.

Example:

Generate Python analysis
 ↓
Execute
 ↓
Get result
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Browser / Web Automation

Example:

Open website
 ↓
Search data
 ↓
Fill form
 ↓
Click button
 ↓
Get result
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SQL / NoSQL

Agent can interact with databases.

Agent
 ↓
SQL Query
 ↓
Database
 ↓
Result
 ↓
Agent
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The key loop shown in your diagram is:

Agent
 ↓
Call Tool / Execute Action
 ↓
External System
 ↓
Return Result
 ↓
Agent
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This loop might run several times.

WORKFLOW EXECUTION

A real business operation usually contains multiple steps.

That's why your architecture includes a Workflow Execution Layer.

Workflow Engine
      ↓
Approval Steps
      ↓
Business Rules
      ↓
Background Jobs
      ↓
Event Triggers
      ↓
Notifications
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Workflow Engine

Controls the overall execution.

Example:

Patient Request
 ↓
Find hospitals
 ↓
Get quotes
 ↓
Compare quotes
 ↓
Patient approval
 ↓
Booking
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Approval Steps

Some processes require approval.

AI Recommendation
       ↓
Human Approval
       ↓
Continue Workflow
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Business Rules

Example:

IF amount > ₹50,000
THEN require manager approval

or:

IF doctor unavailable
THEN find next available doctor
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Background Jobs

Tasks may run asynchronously.

Examples:

Generate report
Process large document
Send bulk notifications
Run analytics
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Event Triggers

Something happens and activates the agent.

Example:

Payment Completed
        ↓
Event
        ↓
Agent Workflow Starts
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or:

New Support Ticket
       ↓
AI Agent
       ↓
Classify ticket
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Notifications

Finally:

Email
SMS
Push Notification
WhatsApp
Dashboard Notification
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can be sent.

MULTI-AGENT COLLABORATION

Instead of one AI agent doing everything, your architecture supports specialized agents.

You have:

Coordinator / Orchestrator Agent
             ↓
 ┌───────────┼───────────┐
 ↓           ↓           ↓
Planner   Research     Tool
Agent      Agent       Agent
             ↓
         Analyst
             ↓
         Reviewer
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A more complete interpretation:

Coordinator Agent
       │
       ├── Planner Agent
       ├── Research Agent
       ├── Tool Agent
       ├── Analyst Agent
       └── Reviewer Agent
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Planner Agent

Creates the plan.

Goal
 ↓
Tasks
 ↓
Execution sequence
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Research Agent

Searches:

RAG
Documents
Web
Databases
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Tool Agent

Handles:

APIs
Databases
Microservices
Code
External systems
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Analyst Agent

Processes the results.

Example:

Hospital A ₹3.2L
Hospital B ₹2.7L
Hospital C ₹3.5L
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It can analyze:

Cost
Rating
Distance
Facilities
Risk
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Reviewer Agent

Checks:

Accuracy
Completeness
Compliance
Quality
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Then:

All agent results
        ↓
Coordinator
        ↓
Aggregate Results
        ↓
Synthesize
        ↓
Final Output
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That is what the arrows in your Multi-Agent Collaboration block represent.

SAFETY, GOVERNANCE & MONITORING

This section is a little different.

It does not happen only after step 8.

It surrounds and controls the entire Agentic AI system.

Your architecture correctly labels it:

Cross-Cutting

Meaning:

          Governance
              │
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┌─────────────────────────────────┐
│ Input │
│ LLM │
│ RAG │
│ Memory │
│ Tools │
│ Agents │
│ Workflow │
│ Outputs │
└─────────────────────────────────┘

It contains:

Guardrails / Safety

Protects against:

Harmful requests
Prompt injection
Unsafe output
Invalid actions
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Authentication / Permissions

Checks:

Who is the user?


What are they allowed to do?
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Example:

Student → read course


Trainer → update class


Admin → manage users
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Rate Limits

Prevents excessive API/tool usage.

Example:

100 requests / minute
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Policy Checks

Example:

Can AI issue a refund?


Can this user access patient data?


Can this agent delete a record?
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Observability / Logs

Every important action should be logged.

Agent decision
Tool call
API request
Model response
Latency
Error
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Evaluation / Feedback Loop

Measure:

Was answer correct?


Did agent choose correct tool?


Did workflow succeed?


Was user satisfied?

Results improve future performance.
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Cost / Latency Monitoring
Track:

Token cost
API cost
Tool cost
Response time
Agent execution time
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This becomes extremely important in production Agentic AI.

OUTPUTS / ACTIONS

Finally the system produces something useful.

Your architecture includes two categories:

Information Outputs

Final Answer


Recommendation


Report / Summary
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Example:

Hospital B appears to offer the best combination of price, rating, and facilities.

Real-World Actions

This is where Agentic AI becomes more powerful than simple GenAI.

Your architecture includes:

Workflow Completion
Ticket Update
API Action Executed
Automation Triggered
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For example:

User:
"Book the selected hospital appointment."

Agent
 ↓
Check availability
 ↓
Call Hospital API
 ↓
Create booking
 ↓
Send email
 ↓
Create calendar event
 ↓
Update database
 ↓
Return confirmation
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The output isn't merely:

"Your appointment should be booked."

Instead, the system can actually perform the action.

Complete 1 → 10 flow

Your entire architecture can therefore be understood as:

1. INPUT / CHANNELS
User gives a goal
        ↓


2. AGENT ORCHESTRATION
Understand
Reason
Plan
Decompose
Route
        ↓


3. CORE AGENT BRAIN
LLM thinks and decides
        ↓


4. MEMORY & STATE
Remember context and progress
        ↕
        │
5. KNOWLEDGE & RETRIEVAL
RAG / Vector DB / Documents / Web
        ↕
        │
6. TOOLS & APIs
APIs / DB / Microservices / Email / Browser
        ↕
        │
7. WORKFLOW EXECUTION
Rules / approvals / jobs / events
        ↕
        │
8. MULTI-AGENT COLLABORATION
Planner + Researcher + Tool + Analyst + Reviewer
        ↓


9. SAFETY + GOVERNANCE + MONITORING
Controls ALL layers continuously
        ↓


10. OUTPUT / ACTION
Answer / Report / API Action / Automation
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But the real runtime architecture is more like this:

             ┌──────────── MEMORY ────────────┐
                         │                               │
                         ↓                               │
User → Orchestrator → Agent Brain ←→ RAG                 │
                         ↕                               │
                      Tools / APIs                       │
                         ↕                               │
                      Workflow                           │
                         ↕                               │
                    Other Agents ────────────────────────┘
                         │
                         ↓
                 Reflect / Validate
                         │
                   Need more info?
                    ↙          ↘
                  YES            NO
                   │              │
                   └── LOOP ──→ Output
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That loop is the most important concept in Agentic AI.

A chatbot normally does:

Prompt → LLM → Answer

Whereas your architecture represents:

Goal
 ↓
Understand
 ↓
Plan
 ↓
Reason
 ↓
Retrieve
 ↓
Use Memory
 ↓
Call Tools
 ↓
Execute
 ↓
Observe Result
 ↓
Reflect
 ↓
Need another step?
 ↓ YES
Repeat
 ↓
Complete Goal
 ↓
Final Answer / Real Action
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So the simplest one-line definition of the architecture in your image is:

Agentic AI is an AI system where an LLM is surrounded by orchestration, memory, RAG, tools, APIs, workflows, specialized agents, and governance so it can reason, make decisions, execute actions, observe results, and continue working until a goal is completed.
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