Python AI Agent Mastery Hub
Python AI Agent Mastery Hub
Master the art of building intelligent agents through interactive learning, practical examples, and real-time visualization
🤖 Understanding AI Agents
Perception
Sensing environment
Decision
Processing & reasoning
Action
Executing response
🟢 Agent Ready
🎮 Interactive Agent Simulations
Rule-Based Agent
Learning Agent
💻 Python Implementation Examples
# Simple AI Agent Example
class SimpleAgent:
def __init__(self):
self.state = "exploring"
self.memory = []
self.knowledge_base = {
"greetings": ["hello", "hi", "hey"],
"farewell": ["bye", "goodbye", "see you"]
}
def perceive(self, input_data):
"""Process environmental input"""
processed_input = input_data.lower().strip()
self.memory.append(processed_input)
return processed_input
def decide(self, perception):
"""Make decisions based on perception"""
for category, keywords in self.knowledge_base.items():
if any(keyword in perception for keyword in keywords):
return f"respond_to_{category}"
return "default_response"
def act(self, decision):
"""Execute action based on decision"""
actions = {
"respond_to_greetings": "Hello! How can I help you?",
"respond_to_farewell": "Goodbye! Have a great day!",
"default_response": "I'm processing your request..."
}
return actions.get(decision, "Unknown action")
# Usage example
agent = SimpleAgent()
user_input = "Hello there!"
# Agent cycle: Perceive -> Decide -> Act
perception = agent.perceive(user_input)
decision = agent.decide(perception)
response = agent.act(decision)
print(f"User: {user_input}")
print(f"Agent: {response}")
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