Unlike traditional LLMs that generate responses reactively, agentic AI systems are built to act with intention. They use memory, feedback loops, and decision-making frameworks to navigate tasks across tools and environments. Imagine ChatGPT with goals, recall, and the ability to write code, send emails, fill out forms, and monitor results without being told what to do at each step.
1import time2 3class SimpleAgent:4 def __init__(self, name):5 self.name = name6 self.memory = []7 8 def receive_task(self, task):9 print(f"[{self.name}] Received task: {task}")10 self.memory.append(f"Task: {task}")11 12 def plan(self):13 plan_steps = ["Understand intent", "Search for info", "Generate response"]14 print(f"[{self.name}] Planning steps...")15 for step in plan_steps:16 print(f" - {step}")17 self.memory.append(f"Planned: {step}")18 return plan_steps19 20 def execute(self, steps):21 print(f"[{self.name}] Executing plan...")22 for step in steps:23 print(f" > Performing: {step}")24 time.sleep(class="number">1)25 self.memory.append(f"Executed: {step}")26 print(f"[{self.name}] Task complete.")27 28 def report(self):29 print(f"\n[{self.name}] Memory Log:")30 for entry in self.memory:31 print(f" • {entry}")32 33# Simulate an agent performing a task34if __name__ == "__main__":35 agent = SimpleAgent("TaskBot-01")36 agent.receive_task("Draft email to user about overdue invoice")37 steps = agent.plan()38 agent.execute(steps)39 agent.report()

1class PlannerAgent:2 def __init__(self):3 self.steps = []4 5 def generate_plan(self, task):6 print(f"[Planner] Generating plan for: {task}")7 self.steps = [8 f"Analyze task: {task}",9 "Break into subtasks",10 "Assign priorities",11 "Send to executor"12 ]13 return self.steps14 15 16class ExecutorAgent:17 def __init__(self):18 self.log = []19 20 def execute_steps(self, steps):21 print(f"[Executor] Executing steps...")22 for step in steps:23 print(f" > {step}")24 self.log.append(f"Completed: {step}")25 26 def show_log(self):27 print(f"\n[Executor] Execution Log:")28 for entry in self.log:29 print(f" • {entry}")30 31 32# Simulation33if __name__ == "__main__":34 task_description = "Generate daily report and email to team"35 36 planner = PlannerAgent()37 executor = ExecutorAgent()38 39 plan = planner.generate_plan(task_description)40 executor.execute_steps(plan)41 executor.show_log()
Companies using agentic systems are reporting:
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40 to 60 percent reduction in manual operations
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Faster time to resolution in support and ITSM
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Doubling of output in marketing teams with AI-powered campaigns