A coding guide to designing an agent AI system using a control plane architecture for secure, modular and scalable tool-driven reasoning workflows

In this tutorial, we’ll create an advanced Agentic AI using the Control Plane design pattern and walk through each component step by step as we implement it. We view the control plane as the central orchestrator that coordinates tools, manages security rules, and structures the reasoning loop. We also set up a miniature retrieval system, defined modular tools, and integrated an agentic reasoning layer that plans and executes actions dynamically. Finally, we observe the entire system behaving like a disciplined, tool-aware AI, capable of retrieving knowledge, assessing understanding, updating learner profiles, and logging all interactions through a unified, scalable architecture. Check this out FULL CODES here.

import subprocess
import sys


def install_deps():
   deps = ('anthropic', 'numpy', 'scikit-learn')
   for dep in deps:
       subprocess.check_call((sys.executable, '-m', 'pip', 'install', '-q', dep))


try:
   import anthropic
except ImportError:
  ...