🚀 Mastering Agentic AI: Running Multiple AI Agents in Parallel

Why You Need Agentic AI

Imagine having a fleet of AI assistants working in parallel — each handling a specific task without blocking each other.

One AI reviews your code, another fetches emails, a third monitors system performance, and yet another fetches real-time market trends — all happening simultaneously!

This is the power of Agentic AI. 🚀

a-photo-of-a-human-interacting-with-an-agent by https://ideogram.ai/

In this article, we’ll build a system where multiple AI agents execute simple but useful tasks in parallel and continuously produce visible outputs.

Let’s dive in. 🔥

💡 What is Agentic AI?

Agentic AI refers to AI-driven agents that operate autonomously, perceiving the environment, reasoning about information, and executing tasks without human intervention.

For example, in software development:

• A code review agent can scan PRs and suggest improvements.
• A performance monitoring agent can analyze CPU load.
• A news-fetching agent can retrieve real-time stock market trends.

Now, let’s create a simple, visible, and parallel AI system. 🔧

🚀 Building Multiple AI Agents That Run in Parallel

We’ll create multiple AI agents that execute simple tasks independently and concurrently using Python’s multiprocessing.

Each agent will:

• Print outputs every 10 seconds (ensuring real-time visibility).
• Run without blocking other agents.
• Work as a parallel AI system.

🚀 Understanding the AI Agents in the Agentic AI System

This Agentic AI system consists of five independent AI agents, each running simultaneously to perform a unique task. Below is a detailed breakdown of the five agents:

1️⃣ Number Generator AI — Random Number Simulation

🧐 Description:

The Number Generator AI generates a random integer between 1 and 100 every 10 seconds. This mimics real-world random data generation, commonly used in simulations and testing.

def number_generator_agent():
    """Generates a random number every 10 seconds."""
    while True:
        number = random.randint(1, 100)
        print(f"🎲 [Number Generator AI] Generated Number: {number}")
        time.sleep(10)

📌 Use Cases:

✅ Statistical Simulations — Helps in probability and statistics experiments.
✅ Gaming Applications — Generates random events or lucky draws.
✅ AI Model Training — Simulates random datasets for machine learning.

2️⃣ Logger AI — Real-Time System Monitoring

🧐 Description:

The Logger AI continuously logs system messages, helping in real-time monitoring.

def logger_agent():
    """Logs a random message every 10 seconds."""
    messages = ["Processing logs...", "Analyzing patterns...", "Checking system health..."]
    while True:
        print(f"📜 [Logger AI] {random.choice(messages)}")
        time.sleep(10)

📌 Use Cases:

✅ System Monitoring — Tracks system health and activity.
✅ Debugging & Troubleshooting — Identifies software issues in real-time.
✅ Security Logging — Monitors unauthorized access attempts.

3️⃣ Timer AI — Countdown Simulation

🧐 Description:

The Timer AI functions as a countdown timer, starting at 10 seconds and decrementing each second until resetting to 10.

def timer_agent():
    """Prints a countdown message every 10 seconds."""
    counter = 10
    while True:
        print(f"⏳ [Timer AI] Countdown: {counter} seconds remaining...")
        counter -= 1
        if counter == 0:
            counter = 10
        time.sleep(10)

📌 Use Cases:

✅ Task Scheduling — Simulates event-based countdowns.
✅ Pomodoro Productivity Timer — Can be used for time management tools.
✅ Event Triggers — Used for notifications, reminders, or sports tracking.

4️⃣ Weather AI — Simulated Weather Forecasting

🧐 Description:

The Weather AI randomly selects a weather condition from a list every 10 seconds, simulating real-time weather updates.

def weather_agent():
    """Simulates random weather conditions every 10 seconds."""
    conditions = ["Sunny ☀️", "Rainy 🌧️", "Cloudy ☁️", "Windy 🌬️", "Stormy ⛈️"]
    while True:
        print(f"🌍 [Weather AI] Current Weather: {random.choice(conditions)}")
        time.sleep(10)

📌 Use Cases:

✅ Weather Forecasting Simulations — Generates weather-based datasets for AI models.
✅ Gaming & Virtual Worlds — Simulates changing weather conditions.
✅ IoT Climate Control Systems — Helps in smart home automation.

5️⃣ Quote Generator AI — Daily Motivation

🧐 Description:

The Quote Generator AI selects a motivational quote from a predefined list and prints it every 10 seconds.

def quote_agent():
    """Displays an inspirational quote every 10 seconds."""
    quotes = [
        "🚀 Keep pushing forward!",
        "💡 Every problem has a solution.",
        "🎯 Focus on what matters.",
        "🔥 Stay motivated!"
    ]
    while True:
        print(f"📝 [Quote AI] Inspiration: {random.choice(quotes)}")
        time.sleep(10)

📌 Use Cases:

✅ Daily Productivity Bots — Provides inspiration for professionals & students.
✅ Mental Health & Well-being — Helps with positivity and focus.
✅ AI-Powered Learning Assistants — Enhances e-learning platforms.

🚀 Running All AI Agents in Parallel

🚀 Running All Agents in Parallel in PyCharm Notebook

import multiprocessing

if __name__ == "__main__":
    agents = [
        multiprocessing.Process(target=logger_agent),
        multiprocessing.Process(target=number_generator_agent),
        multiprocessing.Process(target=timer_agent),
        multiprocessing.Process(target=weather_agent),
        multiprocessing.Process(target=quote_agent),
    ]

    # Start all AI agents
    for agent in agents:
        agent.start()

    # Keep running the processes
    for agent in agents:
        agent.join()

🚀 Running All Agents in Parallel in Jupyter Notebook

### **🚀 Running All Agents in Parallel in Jupyter Notebook**
agents = [
    threading.Thread(target=clock_agent, daemon=True),
    threading.Thread(target=counter_agent, daemon=True),
    threading.Thread(target=random_number_agent, daemon=True),
    threading.Thread(target=greeting_agent, daemon=True),
    threading.Thread(target=math_agent, daemon=True),
]

# Start all agents
for agent in agents:
    agent.start()

# Keep Jupyter Notebook running to see output
while True:
    time.sleep(1)  # Prevents notebook from stopping

🧐 What This Code Does:

✅ Uses multiprocessing to run agents simultaneously.
✅ Starts five independent AI agents.
✅ Ensures they run in parallel without blocking each other.

Full Code: Running AI Agents Simultaneously

import multiprocessing
import time
import random

### **1️⃣ Agent: AI-Powered Logger**
def logger_agent():
    """Logs a random message every 10 seconds."""
    messages = ["Processing logs...", "Analyzing patterns...", "Checking system health..."]
    while True:
        print(f"📜 [Logger AI] {random.choice(messages)}")
        time.sleep(10)

### **2️⃣ Agent: AI Number Generator**
def number_generator_agent():
    """Generates a random number every 10 seconds."""
    while True:
        number = random.randint(1, 100)
        print(f"🎲 [Number Generator AI] Generated Number: {number}")
        time.sleep(10)

### **3️⃣ Agent: AI Timer Countdown**
def timer_agent():
    """Prints a countdown message every 10 seconds."""
    counter = 10
    while True:
        print(f"⏳ [Timer AI] Countdown: {counter} seconds remaining...")
        counter -= 1
        if counter == 0:
            counter = 10
        time.sleep(10)

### **4️⃣ Agent: AI Weather Simulator**
def weather_agent():
    """Simulates random weather conditions every 10 seconds."""
    conditions = ["Sunny ☀️", "Rainy 🌧️", "Cloudy ☁️", "Windy 🌬️", "Stormy ⛈️"]
    while True:
        print(f"🌍 [Weather AI] Current Weather: {random.choice(conditions)}")
        time.sleep(10)

### **5️⃣ Agent: AI Quote Generator**
def quote_agent():
    """Displays an inspirational quote every 10 seconds."""
    quotes = [
        "🚀 Keep pushing forward!",
        "💡 Every problem has a solution.",
        "🎯 Focus on what matters.",
        "🔥 Stay motivated!"
    ]
    while True:
        print(f"📝 [Quote AI] Inspiration: {random.choice(quotes)}")
        time.sleep(10)

### **🚀 Running All Agents in Parallel**
if __name__ == "__main__":
    agents = [
        multiprocessing.Process(target=logger_agent),
        multiprocessing.Process(target=number_generator_agent),
        multiprocessing.Process(target=timer_agent),
        multiprocessing.Process(target=weather_agent),
        multiprocessing.Process(target=quote_agent),
    ]

    # Start all AI agents
    for agent in agents:
        agent.start()

    # Keep running the processes
    for agent in agents:
        agent.join()

🧐 How This Code Works

✅ Each AI agent runs a separate process (ensuring parallel execution).
✅ They continuously print results every 10 seconds, so you always see visible output.
✅ Uses multiprocessing so that tasks don’t block each other.

📊 Example Real-Time Output

📜 [Logger AI] Checking system health...
🎲 [Number Generator AI] Generated Number: 47
⏳ [Timer AI] Countdown: 10 seconds remaining...
🌍 [Weather AI] Current Weather: Sunny ☀️
📝 [Quote AI] Inspiration: 🚀 Keep pushing forward!

(10 seconds later…)

📜 [Logger AI] Processing logs...
🎲 [Number Generator AI] Generated Number: 93
⏳ [Timer AI] Countdown: 9 seconds remaining...
🌍 [Weather AI] Current Weather: Rainy 🌧️
📝 [Quote AI] Inspiration: 🎯 Focus on what matters.

🔥 Pro Tips for Scaling Agentic AI

✅ Use AI-powered APIs: Enhance your agents with OpenAI (for chat assistants), Google APIs (for calendar management), or Twitter API (for real-time news).
✅ Store Data: Save results in a database or log file for analysis.
✅ Add UI Dashboards: Visualize agent outputs using Dash, Flask, or Streamlit.
✅ Deploy in the Cloud: Run agents continuously on AWS Lambda, Google Cloud Functions, or Docker.

🎯 Why You Should Try This Today

If you’re still manually juggling tasks — code review, logging, scheduling, monitoring, and fetching data — it’s time to automate your life with Agentic AI.

With just a few lines of code, you can have an AI-powered team working alongside you 24/7.

Drop a comment 💬if you have any suggestions on how we can further improve image generation using AI!

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Disclaimer: All views expressed here are my own and do not reflect the opinions of any affiliated organization.