AI agent chatbot

Part 1: Building the AI Agent

1. Understanding the AI Agent Concept

An AI agent is a system that perceives its environment through sensors and acts upon that environment through actuators. For our simple agent, we'll create a rule-based chatbot that can answer questions about specific topics.

2. Choosing the Tech Stack

For this project, we'll use:

Python: The programming language
Flask: Web framework for the interface
NLTK: For basic natural language processing
Rule-based approach: Simple pattern matching

3. Implementation Steps

Step 1: Setting Up the Environment

mkdir simple-ai-agent
cd simple-ai-agent
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
pip install flask nltk

Step 2: Creating the Agent Logic

import re
import random
from nltk.chat.util import Chat, reflections

# Define response pairs
pairs = [
    [
        r"my name is (.*)",
        ["Hello %1! How can I help you today?",]
    ],
    [
        r"what is your name?",
        ["I'm SudhinBot, your AI assistant.",]
    ],
    [
        r"how are you?",
        ["I'm functioning optimally, thank you!", "I'm just a program, but I'm running well!"]
    ],
    [
        r"(.*) (weather|temperature) (.*)",
        ["I'm not connected to weather services, but you can check your favorite weather app!",]
    ],
    [
        r"quit",
        ["Goodbye! It was nice talking to you.", "See you later!"]
    ],
    [
        r"(.*)",
        ["I'm not sure I understand. Can you rephrase that?",]
    ]
]

class SimpleAIAgent:
    def __init__(self):
        self.chatbot = Chat(pairs, reflections)

    def respond(self, input_text):
        return self.chatbot.respond(input_text)

# Example usage
if __name__ == "__main__":
    print("Simple AI Agent initialized. Type 'quit' to exit.")
    agent = SimpleAIAgent()
    while True:
        user_input = input("You: ")
        if user_input.lower() == 'quit':
            break
        response = agent.respond(user_input)
        print("Bot:", response)

Step 3: Building the Web Interface

from flask import Flask, request, jsonify
from agent import SimpleAIAgent

app = Flask(__name__)
agent = SimpleAIAgent()

@app.route('/chat', methods=['POST'])
def chat():
    data = request.json
    user_input = data.get('message', '')
    response = agent.respond(user_input)
    return jsonify({'response': response})

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

Step 4: Creating the Frontend

<!DOCTYPE html>
<html>
<head>
    <title>Simple AI Agent</title>
    <style>
        #chatbox { height: 300px; border: 1px solid #ccc; overflow-y: scroll; padding: 10px; }
        #userInput { width: 80%; }
    </style>
</head>
<body>
    <h1>Simple AI Agent</h1>
    <div id="chatbox"></div>
    <input type="text" id="userInput" placeholder="Type your message...">
    <button onclick="sendMessage()">Send</button>

    <script>
        function sendMessage() {
            const input = document.getElementById('userInput');
            const message = input.value;
            input.value = '';

            // Display user message
            const chatbox = document.getElementById('chatbox');
            chatbox.innerHTML += `<p><strong>You:</strong> ${message}</p>`;

            // Send to backend
            fetch('/chat', {
                method: 'POST',
                headers: { 'Content-Type': 'application/json' },
                body: JSON.stringify({ message: message })
            })
            .then(response => response.json())
            .then(data => {
                chatbox.innerHTML += `<p><strong>Bot:</strong> ${data.response}</p>`;
                chatbox.scrollTop = chatbox.scrollHeight;
            });
        }

        // Allow sending with Enter key
        document.getElementById('userInput').addEventListener('keypress', function(e) {
            if (e.key === 'Enter') {
                sendMessage();
            }
        });
    </script>
</body>
</html>

Step 5: Updating the backend

from flask import Flask, request, jsonify, render_template
from agent import SimpleAIAgent

app = Flask(__name__)
agent = SimpleAIAgent()

@app.route('/')
def home():
    return render_template('index.html')

@app.route('/chat', methods=['POST'])
def chat():
    data = request.json
    user_input = data.get('message', '')
    response = agent.respond(user_input)
    return jsonify({'response': response})

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

Running the Agent

python app.py

Result

Challenges and Learnings

While building this simple agent, I learned:

Rule-based systems are limited but great for learning
Natural language understanding is complex
Even simple AI can feel surprisingly interactive

Next Steps

To improve this agent, you could:

Add machine learning for better understanding
Connect to APIs for real data
Implement memory for context awareness

Conclusion

Building this simple AI agent was a great learning experience. While it's basic, it demonstrates core AI concepts and provides a foundation for more complex projects.

Creating your first Simple AI Agent: End-to-End Guide