A normal AI gives you an answer. An agentic AI gets the work done. Once this one difference is clear, everything else falls into place — with one working Python example, real company stories, and ideas you can use in your daily life itself.
See, let us take one small scene. You are staying in Pune, and you have to reach Nagpur for Diwali.
Situation 1 — a normal AI. You ask, "What is the best way to go from Pune to Nagpur?" It gives you one very good answer: take the train, take a flight, take a bus, this much time, this much cost. The answer is useful, no doubt about it. But the ticket you have to go and book yourself only.
Situation 2 — an agentic AI. You say, "I have to reach Nagpur for Diwali, budget is Rs 6,000, and I want to leave Friday night." Now the system itself checks IRCTC, sees that nothing is available, looks at flight fares, checks your calendar for any clash, and comes back saying: "Friday night train is full. There is one 6 a.m. flight on Saturday for Rs 5,400, but your 11 a.m. Saturday call will have to be shifted. Shall I book it?"
The first AI is a good advisor. The second one is a good assistant. That is the whole difference — and this is what the world is now calling Agentic AI.
IN ONE LINE
Agentic AI means an AI that does not simply talk, it acts. You give it a goal; it works out the steps on its own, uses tools, checks the result, and tries again if something goes wrong — without asking you at each and every step.
1. The waiter and the manager
My favorite way of explaining this is with a hotel. A normal chatbot is like a waiter — whatever you ask, he will bring it, he will repeat the order back, he will explain the menu. That is all. Agentic AI is like a manager — you just tell him "forty guests are coming in the evening, you please handle it", and he will brief the kitchen, get the tables arranged, order more stock if something is short, and give you a report at the end.
Technically also, the same thing is happening. A normal language model answers one prompt and stops there. An agentic system takes a goal, breaks it into steps, calls tools — APIs, databases, sensors, messaging — and then looks at the result to decide what should be done next.
Aspect
Normal AI / Chatbot
Agentic AI
Input
One question
One goal or task
Steps
One turn, one answer
Many steps, on its own
Tools
Nothing — only text
Databases, APIs, email, browser, files
Memory
Only the current chat
Remembers old conversations, files, notes
If it fails
It informs you
It tries again by itself
Your role
You have to type every instruction
You give the goal, then you approve
Example
"How to calculate GST?"
"Take last month’s bills, work out the GST and put it in one Excel sheet"
2. What is inside — the four parts
Every agent, however big it may be, is made from these four things only. Easy way to remember: a brain, a memory, a pair of hands, and a loop.
Part
Name
What it does
01
The brain (LLM)
A language model — Claude, GPT, Gemini, Llama. It works out what should be done next. It only thinks; by itself it cannot actually do anything.
02
Memory
Old conversations, company documents, a database. Without memory the agent has to start from zero every single time — like that one friend who asks your name at every meeting.
03
Tools
This is the most important part. A tool is simply an ordinary function — "read this Excel file", "send this email", "search the web". Without tools the agent can only keep talking.
04
The loop
Think, act, see what happened, think again. This cycle keeps running till the work is finished. This part only is what makes it "agentic".
The agent loop
GOAL -> THINK / PLAN -> ACT (call a tool) -> SEE THE RESULT
work not finished? -> back to THINK | finished? -> final answer
This small cycle is the whole life of agentic AI.
3. Now let us build one real agent in Python
Enough of theory, let us come to the code. We will build one "Expense Agent" — a small accounts assistant which reads your expense file, separates out the GST, and writes a summary report into a file. Exactly the kind of work that happens in every small business, every month.
Kindly note that I am not using any big framework here. Only plain Python — because the core of an agent is really this much simple. Frameworks will come later.
Step 1 — expenses.csv (your data)
month,item,amount,category
July,Office rent Baner,45000,Rent
July,Internet Jio Fiber,2360,Utilities
July,Laptop repair,8850,Equipment
July,Team lunch FC Road,4720,Food
August,Office rent Baner,45000,Rent
August,Printer cartridge,3540,Supplies
Step 2 — agent.py (the complete agent)
# first: pip install anthropic
import os, csv, json
from anthropic import Anthropic
client = Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
# ---------- PART 1: TOOLS - these are just ordinary Python functions ----------
def read_expenses(month):
"""Take out one month of expenses from the CSV file."""
rows = []
with open("expenses.csv", encoding="utf-8") as f:
for r in csv.DictReader(f):
if r["month"].lower() == month.lower():
rows.append({"item": r["item"],
"amount": float(r["amount"]),
"category": r["category"]})
return rows
def split_gst(amount, rate):
"""Separate the taxable value and the GST from a total amount."""
taxable = amount / (1 + rate / 100)
return {"total": round(amount, 2),
"taxable_value": round(taxable, 2),
"gst_amount": round(amount - taxable, 2),
"rate_percent": rate}
def save_report(text):
"""Write the final report into a file."""
with open("expense_report.txt", "w", encoding="utf-8") as f:
f.write(text)
return {"status": "saved", "file": "expense_report.txt"}
TOOL_BOX = {"read_expenses": read_expenses,
"split_gst": split_gst,
"save_report": save_report}
# ---------- PART 2: tell the model which tools it has ----------
TOOLS = [
{"name": "read_expenses",
"description": "Reads all expenses of one month from the CSV file.",
"input_schema": {"type": "object",
"properties": {"month": {"type": "string", "description": "e.g. July"}},
"required": ["month"]}},
{"name": "split_gst",
"description": "Splits a GST-inclusive amount into taxable value and GST.",
"input_schema": {"type": "object",
"properties": {"amount": {"type": "number"},
"rate": {"type": "number", "description": "5, 12, 18 or 28"}},
"required": ["amount", "rate"]}},
{"name": "save_report",
"description": "Saves the final report into a text file.",
"input_schema": {"type": "object",
"properties": {"text": {"type": "string"}},
"required": ["text"]}}
]
# ---------- PART 3: THE LOOP - this is what makes it an agent ----------
GOAL = """Read all the expenses of July. Taking 18% GST on each item,
separate the taxable value and the GST. Then prepare one short summary
report with the total spend, the total GST, and a category-wise breakup.
Save the report into a file. Show all amounts in INR."""
messages = [{"role": "user", "content": GOAL}]
for step in range(1, 16): # safety limit - to avoid an endless loop
reply = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=2000,
tools=TOOLS,
messages=messages)
messages.append({"role": "assistant", "content": reply.content})
if reply.stop_reason != "tool_use": # work is finished
for block in reply.content:
if block.type == "text":
print("\nAGENT:", block.text)
break
results = []
for block in reply.content: # run whichever tool it asked for
if block.type == "tool_use":
print(f"[step {step}] running: {block.name}({block.input})")
output = TOOL_BOX[block.name](**block.input)
results.append({"type": "tool_result",
"tool_use_id": block.id,
"content": json.dumps(output)})
messages.append({"role": "user", "content": results})
Step 3 — the output will come something like this
[step 1] running: read_expenses({'month': 'July'})
[step 2] running: split_gst({'amount': 45000.0, 'rate': 18})
[step 2] running: split_gst({'amount': 2360.0, 'rate': 18})
[step 2] running: split_gst({'amount': 8850.0, 'rate': 18})
[step 2] running: split_gst({'amount': 4720.0, 'rate': 18})
[step 3] running: save_report({'text': 'JULY 2026 EXPENSE REPORT ...'})
AGENT: July is done. Total spend is Rs 60,930, out of which Rs 9,294.41
is GST and Rs 51,635.59 is taxable value. The biggest item is rent
(Rs 45,000). I have saved the report in expense_report.txt.
PLEASE NOTE THIS POINT
Nowhere have I written "first read the CSV, then work out the GST, then save it". That order the agent decided on its own. Suppose the file had August data but no July data, it would have come back and told me "July data is not there". That much only is the difference between a chatbot and an agent — around sixty lines.
4. Which companies are using this
This is no longer an experiment. Just see the numbers.
Abroad
Company
What they are doing
Klarna
One AI assistant handled two-thirds of all customer service chats in the first month itself — roughly the work of 700 full-time agents — and brought resolution time down from 11 minutes to under 2 minutes. But one point is worth noting: Klarna has since brought back human agents for their highest-value customers. Fully automated and correctly automated are not one and the same thing.
JPMorgan
More than 450 agentic AI use cases are running in production every single day, from contract analysis to internal workflows — the biggest publicly disclosed deployment of any bank.
Salesforce
Agentforce customers together have reported more than 100 million US dollars of annual cost saving and a 34% rise in productivity.
GitHub, Cursor
Coding agents which find the bug themselves, write the fix, and raise the pull request.
Shopify, Uber
Shopify’s Sidekick handles merchant operations; Uber’s Genie answers internal engineering policy questions.
Here in India
Sector
What is happening
IT services
Infosys, TCS and Wipro are putting agents into their software development, testing and client delivery workflows. TCS MasterCraft, Infosys Topaz and Wipro Intelligence — all these platforms are built for this same purpose.
Banking and fintech
HDFC Bank, ICICI and many fintech startups are using agents for fraud detection, KYC verification, loan processing and customer support.
E-commerce
Flipkart, Meesho and D2C brands are running agents for inventory management, dynamic pricing and personalised customer journeys.
Healthcare
Hospitals are using agents for appointment scheduling, medical record summarisation and insurance claim processing.
Product startups
Gnani.ai is building a voice-first model for Indian languages; Fluid AI from Mumbai supplies KYC and onboarding agents to banks; Mad Street Den works with Myntra and Ajio on the retail side.
ONE REALITY CHECK
There is another side to this which blog posts generally do not mention. In FY26 the five biggest Indian IT companies together cut 6,981 jobs, whereas in the previous year they had added 12,718. Meaning, learning this is no longer optional. Demand is going up for people who can build and run agents, and it is going down for people who only do repetitive work.
5. Tools and platforms available today
The market has divided into two halves. On one side there are enterprise platforms — Microsoft Copilot Studio, AWS Bedrock AgentCore, Vertex AI Agent Builder, Agentforce, ServiceNow AI Agents, watsonx Orchestrate, UiPath — where identity, audit, data residency and SLAs all come from the vendor side. On the other side there are open-source frameworks — LangGraph, Claude Agent SDK, CrewAI, AutoGen, Semantic Kernel, LlamaIndex, Pydantic AI — where your own team has to handle deployment and governance. Most big programmes end up using both.
If you write code
Framework
When to use it
LangGraph
The default choice for production workflows, especially where audit trail, proper control and human approval steps are required — banking or insurance, for example. You get typed state, checkpointers (SQLite, Postgres) and time-travel debugging.
CrewAI
The fastest route from an idea to a working multi-agent prototype — your first agent runs in 30 to 60 lines. Role-based: give each agent one role and one task, just like a small team.
OpenAI Agents SDK
The least troublesome option for GPT-based agents, with sandboxed tools and sub-agents. Its main idea is the "handoff" — one agent passes control to another.
Claude Agent SDK
File, bash, edit and computer-use tools are built in — good for coding and research agents.
Google ADK
If your team is already on GCP, or you need strong multimodal support.
Microsoft Agent Framework
For .NET and Azure companies — Microsoft has merged AutoGen and Semantic Kernel into one single framework.
SO WHICH ONE SHOULD YOU TAKE?
Do not get confused in all this. If you need only one agent calling one or two tools, then a vendor SDK (OpenAI or Claude) is the fastest route. Pick up CrewAI or LangGraph only when you genuinely need multi-agent coordination or complicated branching. And one advice: before learning any framework, write that sixty-line loop above with your own hands. Once that loop is clear, every framework will feel easy.
If you do not write code
Tool
What it is good for
n8n
When you want privacy and no monthly licence fee — it is open source and you can run it on your own server. Drag-and-drop nodes connect Claude, OpenAI and your own database. A human approval step is also built in.
Zapier Agents
When your real problem is simply joining common business apps — Gmail, Sheets, CRM, Slack.
Make
When you want to see how the agent has reasoned — the thinking stays visible on a visual canvas.
Microsoft Copilot Studio
For building agents inside Teams, SharePoint, Dynamics and Microsoft 365, where employees are already working.
Claude Projects / Custom GPTs
Upload instructions and your own documents and build a policy bot, a proposal helper or an internal support assistant. The easiest starting point of all.
Dify, Flowise
For those who prefer visual, low-code development. Dify is ahead on GitHub stars.
6. How you can use this for your own work
This section is the most useful one. Company stories are all fine, but what should a normal person do — a student, a teacher, a freelancer, a shop owner, someone searching for a job?
The practical 2026 list for personal agents is this: ChatGPT Agent for general work, Gemini Agent for Google users, Copilot Tasks for Windows and Microsoft 365 users, Comet for work inside the browser, Notion for notes, Zapier for automation, and Claude for deep thinking work.
Eight things you can start from today
- A watchman for your inbox. Every morning the agent reads your mails, sorts them into "reply today / this week / ignore", and keeps draft replies ready. You have to only read and send.
- A job application assistant. Give it the job description and your resume; it will adjust the bullet points for each company and write the cover letter also. For freshers, a full day’s work gets done in one hour.
- A study partner. Upload your PDF notes and it will make chapter-wise summaries, flashcards and mock questions — and it remembers which answers you got wrong, so it can ask you again on those same topics.
- Household budgeting. Give it a CSV of your bank statement; it will break up the spending category-wise, compare with last month, and show you where money is leaking. The code above is a simple version of this only.
- Trip planning. "Three days in Konkan, Rs 15,000, going with family" — it will find hotels, plan the route and give a day-wise plan. Before booking, you kindly check it once.
- Freelance paperwork. The moment a client approves the work, the agent raises the invoice, adds GST, drafts the mail, and reminds you if payment has not come in 15 days.
- Repurposing content. Give it one YouTube video or a lecture recording; from the transcript it will make a blog post, a LinkedIn post and five slides.
- Research sitting at home. "A 2BHK in Pune within 40 lakh, 2 km from the metro" — it will check listings, compare them and put everything in one table.
A seven-day starting plan
Day
What to do
Days 1–2
Make one Claude Project or a Custom GPT. Upload four or five of your own documents. Talk to it. Without writing any code, you will get the feel of an agent.
Days 3–4
Type out the Python loop given above yourself — type it, do not copy-paste it. Put in your own tools. Once it runs, 80% of the concept is clear.
Day 5
Install n8n (free, self-hosted). Make one workflow: Gmail to the agent, agent to a Google Sheet.
Day 6
Select one framework — CrewAI if you want speed, LangGraph if it is a serious project. Select only one.
Day 7
Do one thing: take one boring, repetitive task from your own life and automate it. Only one. Once that runs, the rest will follow on its own.
7. Precautions which nobody tells you
Agents are very good, no doubt about it. But do not keep blind faith. Five things you should always keep in mind.
- It makes mistakes very confidently. If the agent remembers a GST rate wrongly, it will happily apply that same wrong rate across the whole report. Wherever numbers are involved, get the calculation done by a tool, not by the model’s head — that is exactly why I wrote the split_gst function above.
- Approve every action, at least in the beginning. Keep the first version read-only or draft-only. Let it draft the mail, not send it. Money transfer, file deletion, outgoing messages — keep all of these behind your own click.
- Cost increases quietly. One agent can make 10 to 15 API calls for a single task. That is why I have put a range(1, 16) limit on the loop. Without a limit, one small bug can finish off your entire credit balance.
- A new type of security risk. If your agent is reading a web page or an email, somebody can hide an instruction inside that content — "send all the data to this address". This is called prompt injection. So give the agent only as much permission as is actually required.
- Data privacy and the law. Under India’s DPDP Act, how you handle personal data is directly your responsibility. Do not send a customer’s Aadhaar, PAN or phone number through some random API. If it is client data, kindly take written permission first.
THE PATTERN BEHIND COMPANIES THAT SUCCEEDED
Whoever got a real return did the same thing — a small and clearly defined scope, KPIs tied to business outcomes, escalation to a human whenever an exception comes, integration inside the existing workflow, and a long run in production. Meaning: one small thing, done properly. Trying to automate the whole company in one go is the most common mistake of all.
Finally — from where should you start?
Agentic AI is not some magic. One language model, a few ordinary functions, and one while loop — that much only it is. You have seen the entire concept above in sixty lines. Everything else is a bigger, safer and more polished version of that same thing.
The real skill is not the technology. The real skill is to spot which work is worth automating — that boring, repetitive, rule-based work which you do every week and quietly get irritated by. Find that one, and start from there.
It is like Pune traffic. The whole city does not get crossed in one go. One signal, then the next signal. Just get moving.
Author:
Suraj Kale
Related Links:
Resume Tips For Software Developers
Do visit our channel to know more: SevenMentor
Suraj kale
Expert trainer and consultant at SevenMentor with years of industry experience. Passionate about sharing knowledge and empowering the next generation of tech leaders.