Not long ago, AI agents were still in a nascent stage. Most of the industry wasn’t quite sure what they were, what they could realistically do, or whether they were just another marketing buzzword. Fast forward to today, and the picture looks very different. Almost every other engineer seems to be building, experimenting with, or at least talking about agentic AI in some form. What was once a vague concept has quickly become a hands-on playground for technologists. I remember when I first started hearing the term “AI agents” , it felt like one of those concepts which everyone nodded to but very few could clearly explain. Even I wasn’t sure whether it was a real shift or just another buzzword in the market. But as time passed and the more I explored; the more I realized that this is something that is something that will stick around and something that every data engineer should learn and comprehend. One thing I noticed during my learning phase is that many of the existing blogs, explanations on this topic focus either on the theoretical foundation or on hands-on frameworks and tooling. Also, the thing that often gets less attention is why agentic AI exists in the first place and when it actually makes sense to use it. why In this article, I try to answer these foundational questions for a target audience who already have a tech background but haven’t worked with AI agents yet. already have a tech background haven’t worked with AI agents yet By the end, you’ll: Understand what Agentic AI actually means
Learn the core concepts and terms
Build a simple Agentic AI “Hello World” project you can run locally Understand what Agentic AI actually means what Agentic AI actually means Learn the core concepts and terms core concepts and terms Build a simple Agentic AI “Hello World” project you can run locally simple Agentic AI “Hello World” project What Is Agentic AI? An AI agent is like an AI system that doesn’t just answer questions, but can also take actions, make decisions, and work toward a goal. AI agent answer questions take actions make decisions work toward a goal. Think of a normal AI chatbot like someone you ask: AI chatbot What’s the best restaurant nearby? What’s the best restaurant nearby? and it replies with suggestions. But an AI agent is like a real assistant who can do much more than just suggestions; like AI agent Look up restaurants
Check ratings
Book a table
Add it to your calendar
Set remind Look up restaurants Check ratings Book a table Add it to your calendar Set remind So instead of just giving information, it acts on your behalf. acts on your behalf A simple way to think about it would be with the following analogy: LLM = Brain
Agent = Brain + Memory + Tools + Decision Loop LLM = Brain LLM = Brain Agent = Brain + Memory + Tools + Decision Loop Agent = Brain + Memory + Tools + Decision Loop If an LLM answers questions, an agent gets things done. agent gets things done Why Do We Even Need Agents? Because real problems aren’t one-shot prompts. In continuation to the example we saw in previous section, here is another example to to make things clear on how AI agents are different for the usual AI system and what specific need or gap they fulfil Let’s say we need to know how today’s weather looks like and based on that decide whether to go for a run or not This would require: Fetching data
Reasoning about it
Making a decision
Producing an action Fetching data Reasoning about it Making a decision Producing an action An agent in this case can be helpful with: Deciding which tool to call
Handling intermediate steps
Retry if something fails
Stop when the task is done Deciding which tool to call which tool to call Handling intermediate steps Retry if something fails Stop when the task is done Core Agentic AI Concepts I hope by now you are pretty clear on what AI agents are and why would we even need it. Next, let’s dive into the world of Agentic AI and try to build some basic vocabulary that would help simplify learning of more advanced concepts. 1. Agent An agent is an AI system that: agent Has a goal
Can take actions
Can reason over steps or action items
Can use tools Has a goal goal Can take actions take actions Can reason over steps or action items reason over steps or action items Can use tools use tools Think of it as a long-running task executor, not a chatbot. long-running task executor 2. Goal / Objective This is the north star for the agent. north star Examples: “Summarize a document”
“Answer a customer question using internal docs”
“Monitor logs and alert on anomalies” “Summarize a document” “Answer a customer question using internal docs” “Monitor logs and alert on anomalies” Agents don’t just respond — they work towards a goal. work towards a goal 3. Tools Tools are external capabilities the agent can call. external capabilities Examples: APIs (weather, payments, search)
Databases
File system
Functions you write in Python/JS APIs (weather, payments, search) Databases File system Functions you write in Python/JS Agents decide when and how to use them. when how 4. Memory Agents often need context beyond one prompt. Memory can be: Short-term (conversation history)
Long-term (stored facts, embeddings, vector DBs) Short-term (conversation history) Short-term Long-term (stored facts, embeddings, vector DBs) Long-term For a basic agent, short-term memory is enough. 5. Planning / Reasoning Loop This is the heart of agentic behavior. Typical loop: Observe current state
Think about next step
Choose an action
Execute the action
Observe result
Repeat or stop Observe current state Think about next step Choose an action Execute the action Observe result Repeat or stop You’ll often see this called ReAct or Plan-Act-Observe. ReAct Plan-Act-Observe Multi-agent System Multi-agent System Multi-agent System A multi-agent system is a setup where multiple AI agents work together, each with its own role and responsibility, to solve a problem collaboratively like a team. multi-agent system multiple AI agents work together Environment Environment Environment An environment is where the agent operates, like a : browser
file system
calendar
business tools
apps browser file system calendar business tools apps Agent Framework Agent Framework Agent Framework These are the tools used to build agents. Some of the most commonly used frameworks are as below LangChain
AutoGPT
CrewAI
LlamaIndex LangChain AutoGPT CrewAI LlamaIndex Time to get our hands dirty… Enough of theory, let’s jump on to build a basic AI agent that would help clear up any doubts that you still may have. We will build a Task Agent that: Task Agent Takes a task from the user
Decides whether it needs a tool
Uses the tool
Returns a final answer Takes a task from the user Decides whether it needs a tool Uses the tool Returns a final answer What This Agent Will Do Example task: “What’s the current time and should I grab a coffee?” “What’s the current time and should I grab a coffee?” The agent will: Decide it needs the current time
Call a time tool
Reason about coffee
Respond Decide it needs the current time Call a time tool Reason about coffee Respond Step 1: Basic Setup We will use Python and an OpenAI-compatible API. Run the following command in your terminal or command prompt: pip install openai pip install openai Step 2: Define a Tool We will define the tool as a simple Python function in a single file, let’s say agent_hello_world.py. For a real project, you would likely organize tools into separate modules, but keeping everything together makes it easier to see how the agent loop works end to end. from datetime import datetime

def get_current_time():
    return datetime.now().strftime("%H:%M") from datetime import datetime

def get_current_time():
    return datetime.now().strftime("%H:%M") Step 3: Define the Agent Prompt This is where agent behavior is designed. Add this to your python file created in previous step. designed SYSTEM_PROMPT = """
You are a simple AI agent.

You can:
- Think step by step
- Decide when to call tools
- Use the tool: get_current_time

When the task is complete, respond with a final answer.
""" SYSTEM_PROMPT = """
You are a simple AI agent.

You can:
- Think step by step
- Decide when to call tools
- Use the tool: get_current_time

When the task is complete, respond with a final answer.
""" Step 4: Agent Reasoning Loop Next, we will add the reasoning logic to our code. Below snippet starts by sending the user’s task, along with a system prompt, to GPT-4 through the ChatCompletion API. It then enters a loop where it keeps receiving the model’s responses. If the model’s reply includes a specific trigger like USE_TOOL:get_current_time, the agent calls a helper function to get the current time. Adds both the tool request and the tool result back into the message history and continues the conversation. If the reply doesn’t request a tool, the agent assumes the task is complete and returns the final response. USE_TOOL:get_current_time import os
import openai
from openai import 

openai.api_key = "YOUR_API_KEY"

def run_agent(task):
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": task}
    ]

    while True:
        response = openai.ChatCompletion.create(
            model="gpt-4",
            messages=messages
        )

        reply = response["choices"][0]["message"]["content"]

        if "USE_TOOL:get_current_time" in reply:
            time = get_current_time()
            messages.append({"role": "assistant", "content": reply})
            messages.append({"role": "tool", "content": time})
        else:
            return reply import os
import openai
from openai import 

openai.api_key = "YOUR_API_KEY"

def run_agent(task):
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": task}
    ]

    while True:
        response = openai.ChatCompletion.create(
            model="gpt-4",
            messages=messages
        )

        reply = response["choices"][0]["message"]["content"]

        if "USE_TOOL:get_current_time" in reply:
            time = get_current_time()
            messages.append({"role": "assistant", "content": reply})
            messages.append({"role": "tool", "content": time})
        else:
            return reply Step 5: Test It Finally, add below lines of code in your script and execute to test it. task = "What time is it right now, and should I grab a coffee?"
result = run_agent(task)
print(result) task = "What time is it right now, and should I grab a coffee?"
result = run_agent(task)
print(result) Congratulation! You’ve just built a basic agent. basic agent Not fancy, but real one by yourself. We just implemented a basic local AI agent with: Goal-driven behavior
Tool usage
A reasoning loop
Stateful decision-making Goal-driven behavior Tool usage A reasoning loop Stateful decision-making This is the core of every agent framework out there. core of every agent framework LangChain, AutoGen, CrewAI - all of them abstract this exact pattern. this exact pattern Final Thoughts To me, agentic AI isn’t some overnight revolution but It is more like a slow and steady shift in the same direction we have been moving for years which is to automate and make systems more intelligent. When you strip away the buzzwords, it is still just a system running in a loop, holding context, making decisions, and calling tools when needed. The only new part is that the “thinking” piece can now be done by an LLM. thinking I have found that agents make a lot more sense when you treat them like yet another engineering component and not like a futuristic sci-fi concept. Build them the way you would build any other system and you would realize that not every problem needs an agent, sometimes the simplest workflow is still the best one. not every problem needs an agent

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Getting Started with Agentic AI: Concepts, Terminology, and a Python Hello World

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