Practical Implementation: Building an Agentic AI System 🏗️👨‍💻

Creating a functional agentic AI involves integration of perception, reasoning, and action modules. Here's a simplified workflow:

# Pseudocode for a basic agentic system
class Agent:
    def __init__(self):
        self.knowledge = KnowledgeBase()
        self.goal = 'navigate'

    def perceive(self, environment):
        self.sensory_data = environment.get_data()

    def reason(self):
        if self.goal == 'navigate':
            path = self.plan_path(self.sensory_data)
            return path

    def act(self, decision):
        environment.execute(decision)

    def plan_path(self, data):
        # Use A* or RL for path planning
        return 'next_move'

# Instantiate and run the agent
agent = Agent()
while True:
    environment = get_environment()
    agent.perceive(environment)
    decision = agent.reason()
    agent.act(decision)

This demonstrates the core loop: perceive, reason, act, iteratively enabling autonomous decision-making.