How to Create an AI Agent From Scratch Using Python- 2025

Do you want to know how to create an AI agent from scratch? If yes, you’re in the right place! AI agents are changing the world by automating tasks, making smart decisions, and improving efficiency. They help businesses, power trading bots, and even play complex games better than humans.

I’m Aqsa Zafar, and in this guide, I’ll take you through the entire process step by step. We’ll start with the basics, explore the tools you need, and then build a fully functional AI agent using Python. By the end, you’ll have a smart system that can understand its environment, make decisions, and take action on its own.

Now, without further ado, let’s get started and understand “how to create an AI agent from scratch”-

How to Create an AI Agent From Scratch Using Python

What is an AI Agent?

An AI agent is a software program designed to perceive its environment, analyze data, make decisions, and take actions to achieve a goal. It can process information, learn from past experiences, and continuously improve its performance.

Some AI agents follow predefined rules to execute tasks, like basic chatbots, while others use machine learning and deep learning to adapt and make smarter decisions over time.

AI agents operate in various environments. A chatbot interacts with users through text, a self-driving car navigates roads, and a stock trading bot analyzes market trends. No matter the task, every AI agent follows a basic cycle:

1. Perception – Collects information from its surroundings (text input, images, sensor data, etc.).
2. Processing & Decision-Making – Analyzes the data and determines the best action to take.
3. Action Execution – Performs the task based on its analysis.
4. Learning & Adaptation (for advanced agents) – Improves over time by learning from past actions.

Resources to Learn AI Agents

Types of AI Agents

AI agents can be classified into different types based on their intelligence level and capabilities.

1. Reactive Agents (Basic AI)

Reactive agents respond to inputs in real time but do not remember past experiences. They follow simple rule-based logic and cannot adapt or learn.

Example: A basic chatbot that responds with predefined answers.

If you ask a chatbot, “What’s the weather today?” and it simply fetches today’s forecast without remembering your location history, that is a reactive agent.

2. Proactive Agents (Intelligent AI)

Proactive agents go beyond reacting to inputs; they anticipate user needs and take the initiative based on past patterns.

Example: Google Assistant or Siri setting reminders based on your schedule.

If you usually order food at 8 PM, a proactive agent might suggest placing your order around that time before you even think about it.

3. Learning Agents (Self-Improving AI)

These agents learn from past experiences and improve their decision-making over time using machine learning algorithms.

Example: Self-driving cars that get better at recognizing obstacles with each drive.

Tesla’s Autopilot learns from previous driving data to make better decisions, ensuring a safer driving experience.

4. Autonomous Agents (Fully Independent AI)

Autonomous agents function with minimal or no human intervention. They make real-time decisions based on their environment and adapt dynamically to new situations.

Example: Industrial robots in manufacturing that adjust their tasks without manual input.

A warehouse robot that scans shelves, picks up items, and packs orders without human assistance is an example of an autonomous AI agent.

Real-World Applications of AI Agents

AI agents are used in almost every industry today. Here are some common examples:

1. Chatbots & Virtual Assistants

• Customer Support: AI-powered chatbots like ChatGPT and Rasa assist businesses in answering customer queries at any time.
• Voice Assistants: Google Assistant, Alexa, and Siri help users set reminders, search the web, and control smart devices.

2. AI in Robotics

• Industrial Robots: Companies like Boston Dynamics and Tesla use AI-driven robots in factories to automate assembly and quality control tasks.
• Self-Driving Cars: AI-powered vehicles such as those by Tesla and Waymo use computer vision and deep learning to navigate roads safely.

3. AI in Finance & Trading

• Stock Trading Bots: AI agents analyze market trends, predict stock movements, and execute trades automatically using real-time data. Examples include Kavout and Alpaca.
• Fraud Detection: AI-powered systems monitor bank transactions in real time to detect and prevent fraudulent activities.

4. AI in Cybersecurity & Automation

Automating IT Operations: AI automates tasks such as server monitoring, network optimization, and system updates, reducing the need for manual intervention.

Threat Detection: AI agents detect suspicious activity and prevent cyberattacks by monitoring networks. Companies like Darktrace AI specialize in this field.

Best AI Agent Projects For FREE 

1. Multi AI Agent Systems with crewAI Project
2.  Building Your Own Database Agent
3. AI Agents in LangGraph
4. AI Agentic Design Patterns with AutoGen

Breaking Down the Components of an AI Agent

Building an AI agent requires multiple components working together to perceive information, make decisions, and take actions. Let’s take a closer look at these components and how they contribute to an AI agent’s functionality.

1. Perception: How AI Agents Gather Information

Before an AI agent can make decisions, it must first understand its environment. This process is known as perception, where the AI collects data from different sources. The type of perception an AI agent uses depends on the problem it is solving.

Sources of Input for AI Agents

A. Sensors (for physical AI agents like robots)

Robots rely on various sensors to collect real-world data:

• Cameras – Help AI agents see and recognize objects (used in self-driving cars, surveillance, and robotics).
• Microphones – Capture sound for speech recognition in virtual assistants like Siri and Alexa.
• Motion Detectors – Detect movement for applications like security systems and gesture-based controls.
• Lidar & Radar – Used in self-driving cars to detect obstacles and measure distances.

B. APIs (for web-based AI agents)

Many AI agents interact with online data through Application Programming Interfaces (APIs), allowing them to fetch real-time information.

• Example: A stock trading bot uses APIs to collect market data and make trading decisions.
• Example: A weather chatbot fetches live weather updates using OpenWeather API.

C. Databases (for knowledge-based AI systems)

AI agents store and retrieve structured and unstructured data from databases to make informed decisions.

• Structured Databases: Use SQL databases like MySQL and PostgreSQL to store organized data in tables.
• Unstructured Databases: Use NoSQL databases like MongoDB and Firebase to store large amounts of text, images, or video data.

Perception is the first step of an AI agent’s workflow. Once the agent gathers information, it needs to analyze and make decisions based on the data it receives.

2. Decision-Making: How AI Agents Process Information

Once an AI agent gathers data, it needs to process it, analyze patterns, and decide what action to take next. The way an AI agent makes decisions depends on its complexity.

Types of Decision-Making Methods

A. Rule-Based Systems (Traditional AI)

• These systems rely on predefined rules written by humans.
• They follow an if-then logic, meaning they only act when specific conditions are met.
• Example: A basic chatbot that replies with predefined responses. If you ask, “What’s your name?”, it always responds with “I’m Chatbot AI.”

Limitations: These agents cannot learn or improve over time.

B. Machine Learning Models (Data-Driven AI)

• These agents learn from data instead of following fixed rules.
• They use statistical models to detect patterns and make predictions.
• Example: A spam filter trained on thousands of emails learns to classify messages as spam or not spam.

Types of machine learning models used in AI agents:

• Supervised Learning: Learns from labeled examples (e.g., email spam detection).
• Unsupervised Learning: Finds hidden patterns in data without labeled examples (e.g., customer segmentation in marketing).
• Deep Learning: Uses neural networks to process large-scale data like images and speech (e.g., facial recognition, language translation).

C. Reinforcement Learning (Self-Improving AI)

• In this approach, the AI learns by trial and error.
• It receives rewards for good actions and penalties for bad actions, improving its behavior over time.
• Example: A reinforcement learning agent playing chess learns by experimenting with moves and refining its strategy after thousands of games.

Reinforcement learning is commonly used in robotics, self-driving cars, and game-playing AI systems like AlphaGo.

3. Action Execution: How AI Agents Perform Tasks

After an AI agent gathers information and makes a decision, it must execute an action. The way an AI executes actions depends on whether it’s a software-based AI or a physical AI system.

A. APIs: Interacting with External Services

• AI agents often communicate with other applications through APIs.
• Example: A chatbot sending a message via WhatsApp API.
• Example: A voice assistant controlling smart home devices like lights and thermostats through API integration.

B. Automation: AI-Driven Process Execution

• AI is used in process automation to handle repetitive business tasks without human intervention.
• Example: Robotic Process Automation (RPA) automates data entry in businesses, saving time and reducing human errors.

C. Robotics: AI Controlling Physical Machines

• AI-powered robots perform complex tasks in manufacturing, logistics, and healthcare.
• Example: AI-driven robotic arms assembling cars in Tesla factories.
• Example: Delivery robots navigating streets to bring food or packages to customers.

This action execution step completes the AI agent cycle, allowing it to continuously interact with its environment and refine its responses over time.

Choosing the Right Tools & Technologies for AI Agents

If you want to build your own AI agent, you need the right tools and technologies. Here are the most important ones:

1. Programming Language

• Python is the most popular language for AI because of its simplicity and extensive libraries.

2. AI Libraries & Frameworks

A. Natural Language Processing (NLP) for Text-Based AI Agents

• OpenAI GPT – Generates human-like text responses.
• LangChain – Helps build AI-powered chatbots and virtual assistants.

B. Chatbot Development

• Rasa – Open-source chatbot framework for advanced conversational AI.

C. Reinforcement Learning

• Stable-Baselines3 – A Python library for training reinforcement learning agents.
• OpenAI Gym – Provides environments for testing and training AI agents.

D. Deep Learning

• TensorFlow & PyTorch – Used for training neural networks in AI agents.

E. Model Deployment

• Flask & FastAPI – Web frameworks for deploying AI models as APIs.

Now, let’s see the how to create an AI agent from scratch with step-by-step approach-

pip install jupyterlab

pip install openai langchain rasa gym flask fastapi requests numpy pandas

import openai

def get_response(prompt):
    openai.api_key = "your-api-key"
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are a helpful AI assistant."},
            {"role": "user", "content": prompt}
        ]
    )
    return response["choices"][0]["message"]["content"]

if __name__ == "__main__":
    while True:
        user_input = input("You: ")
        if user_input.lower() == "exit":
            break
        response = get_response(user_input)
        print("AI: ", response)

from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory()

def get_response_with_memory(prompt):
    memory.save_context({"input": prompt}, {"output": get_response(prompt)})
    return memory.load_memory_variables({})

from flask import Flask, request, jsonify
import openai

app = Flask(__name__)
openai.api_key = "your-api-key"

@app.route("/chat", methods=["POST"])
def chat():
    data = request.get_json()
    user_input = data.get("message")
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are a helpful AI assistant."},
            {"role": "user", "content": user_input}
        ]
    )
    return jsonify({"response": response["choices"][0]["message"]["content"]})

if __name__ == "__main__":
    app.run(debug=True)

import unittest

class TestChatbot(unittest.TestCase):
    def test_response(self):
        user_input = "Hello, how are you?"
        response = get_response(user_input)
        self.assertIsInstance(response, str)
        self.assertGreater(len(response), 0)

if __name__ == "__main__":
    unittest.main()

from transformers import Trainer, TrainingArguments

def train_model():
    training_args = TrainingArguments(output_dir="./results", num_train_epochs=3, per_device_train_batch_size=8)
    trainer = Trainer(model=model, args=training_args, train_dataset=train_data)
    trainer.train()

from functools import lru_cache

@lru_cache(maxsize=100)
def cached_response(prompt):
    return get_response(prompt)

from multiprocessing import Pool

prompts = ["Hello!", "Tell me a joke", "What's the weather like?"]

with Pool(4) as p:
    responses = p.map(get_response, prompts)
    print(responses)

Challenges & Best Practices in AI Agent Development

Building an AI agent comes with its own set of challenges. Here’s how to address them effectively:

1. Ethical Considerations in AI

AI should be developed with fairness and transparency in mind. Some key best practices include:

• Transparency: Ensure that AI decision-making processes are understandable and explainable. Provide clear documentation on how the AI model works.
• Fairness: Avoid biases by ensuring diverse representation in training data. AI should work equally well for different demographics.
• Privacy & Security: Implement encryption and anonymization techniques to protect user data. Follow ethical guidelines such as GDPR and HIPAA compliance.
• Responsible AI Usage: AI should not generate harmful, misleading, or unethical content. Implement safeguards to prevent misinformation.

2. Avoiding Bias in AI Agents

Bias in AI can lead to unfair or discriminatory results. To minimize bias:

• Use Diverse Datasets: Ensure training data includes a wide range of perspectives, languages, and demographics to prevent discrimination.
• Bias Detection & Auditing: Regularly audit AI outputs using fairness metrics like disparate impact analysis and SHAP (SHapley Additive exPlanations).
• Human-in-the-Loop (HITL) Review: Have humans review AI-generated responses to detect unintended biases. Feedback should be integrated into model improvements.
• Bias Mitigation Techniques: Use techniques such as adversarial debiasing, reweighting, and counterfactual data augmentation to reduce bias in training data.

3. Optimizing Efficiency and Accuracy

AI performance depends on speed and accuracy. To enhance efficiency:

• Model Optimization: Use lightweight models (like DistilBERT instead of GPT-4) to improve response time while maintaining accuracy.
• Parallel Processing: Implement multi-threading and distributed computing to handle multiple AI interactions simultaneously.
• Hardware Acceleration: Use GPUs and TPUs to speed up AI computations. Deploy models on cloud-based AI services (e.g., Google Vertex AI, AWS Sagemaker) for scalability.
• API Rate Limiting & Caching: Reduce redundant API calls by caching frequent queries, minimizing processing overhead.
• Fine-Tuning Models: Instead of using large pre-trained models, fine-tune smaller models on domain-specific data for better accuracy with fewer resources.

Future of AI Agents: What’s Next?

The AI landscape is evolving rapidly. Here are some advancements shaping the future of AI agents:

1. Autonomous AI Systems

AI agents are moving towards full autonomy, where they can:

• Learn and make decisions without human intervention.
• Adapt dynamically to new information and changing environments.
• Automate complex workflows, reducing manual input in industries like customer service and finance.
• Use reinforcement learning to improve over time based on user interactions.

2. Self-Learning AI Models

Future AI models will:

• Continuously improve without retraining, using online learning and self-supervised learning.
• Adapt in real-time by analyzing user feedback and adjusting responses accordingly.
• Leverage transfer learning to apply knowledge from one domain to another, making AI more versatile.

3. AI Agents for Enterprise Automation

Businesses are integrating AI agents for:

• Customer Support: AI-powered chatbots can handle support tickets, reducing response time.
• Process Automation: AI agents can streamline workflows, such as document processing and fraud detection.
• Decision-Making: AI-driven insights can assist in making strategic business decisions, improving operational efficiency.
• AI-Powered Assistants: Digital employees that schedule meetings, summarize emails, and automate repetitive tasks.

Conclusion

Building an AI agent from scratch is an exciting journey, and with the right approach, anyone can do it. The key is to break it down into simple steps—understanding how the AI perceives information, makes decisions, and responds. Once you get a grasp on these basics, how to create an AI agent from scratch becomes much easier.

Choosing the right tools is just as important. Python, TensorFlow, and OpenAI’s GPT models are great starting points. If you want to make your AI scalable, using cloud platforms like AWS or Google Cloud can help. The goal is to create an AI agent that not only works but also performs well, responds quickly, and gives meaningful results.

Another big part of how to create an AI agent from scratch is making sure it’s accurate and fair. AI should be trained on diverse datasets to avoid bias and should be regularly tested to ensure it improves over time. Small tweaks and fine-tuning can make a big difference in how well it understands and responds to different inputs.

AI is changing everything—from chatbots that answer customer queries to automated systems that handle business processes. If you’ve ever wanted to build your own AI, there’s never been a better time to start. Learning how to create an AI agent from scratch can open up so many opportunities, whether you’re interested in automation, machine learning, or just experimenting with AI for fun.

At the end of the day, the best way to learn is by doing. Start simple, test different ideas, and keep improving your AI. The more you experiment, the better you’ll understand how to create an AI agent from scratch, and who knows—you might end up building something groundbreaking!

Happy Learning!

Thank YOU!

Explore more about Artificial Intelligence.

Thought of the Day…

‘ It’s what you learn after you know it all that counts.’

– John Wooden

Written By Aqsa Zafar

Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.