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.