Destroyed servers and DoS attacks: What can happen when OpenClaw AI agents interact

Introduction

In this tutorial, you'll learn how to create and test AI agents that can interact with each other in a controlled environment, similar to the OpenClaw AI system mentioned in recent research. Understanding how AI agents interact is crucial for developing robust systems, as demonstrated by the catastrophic failures observed when agents interact in uncontrolled ways. This hands-on approach will help you build a basic multi-agent system and observe how interactions can lead to system instability.

Prerequisites

• Basic understanding of Python programming
• Python 3.7 or higher installed
• Required Python libraries: numpy, matplotlib, random
• Text editor or IDE for writing code

Step-by-Step Instructions

1. Set up your development environment

First, create a new directory for this project and install the required dependencies:

mkdir multi_agent_system
cd multi_agent_system
pip install numpy matplotlib

This creates a dedicated workspace and installs the necessary libraries for our agent simulation.

2. Create the basic agent class

Let's start by creating a simple AI agent that can interact with others:

import numpy as np
import random
import matplotlib.pyplot as plt


class SimpleAgent:
    def __init__(self, agent_id, initial_energy=100):
        self.id = agent_id
        self.energy = initial_energy
        self.position = np.random.rand(2) * 100  # Random 2D position
        self.interactions = []
        
    def move(self):
        # Agents move randomly
        self.position += np.random.randn(2) * 2
        self.energy -= 0.1
        
    def interact_with(self, other_agent):
        # Simulate interaction with another agent
        if self.energy > 10 and other_agent.energy > 10:
            # Energy transfer during interaction
            transfer_amount = min(self.energy, other_agent.energy) * 0.1
            self.energy -= transfer_amount
            other_agent.energy += transfer_amount
            self.interactions.append(f'Agent {other_agent.id} interacted with {self.id}')
            return True
        return False
    
    def is_alive(self):
        return self.energy > 0

This agent class represents a basic AI agent with energy, position, and interaction capabilities. The agent moves randomly and can interact with other agents to transfer energy.

3. Create the simulation environment

Now let's build a system that can manage multiple agents and observe their interactions:

class AgentEnvironment:
    def __init__(self, num_agents=10):
        self.agents = [SimpleAgent(i) for i in range(num_agents)]
        self.time_steps = 0
        self.energy_history = []
        
    def step(self):
        self.time_steps += 1
        
        # Move all agents
        for agent in self.agents:
            agent.move()
            
        # Random interactions
        for i in range(len(self.agents)):
            if random.random() < 0.3:  # 30% chance of interaction
                other_idx = random.choice([j for j in range(len(self.agents)) if j != i])
                self.agents[i].interact_with(self.agents[other_idx])
                
        # Record energy levels
        total_energy = sum(agent.energy for agent in self.agents)
        self.energy_history.append(total_energy)
        
    def run_simulation(self, steps=100):
        for _ in range(steps):
            self.step()
            
        # Plot results
        self.plot_results()
        
    def plot_results(self):
        plt.figure(figsize=(10, 6))
        plt.plot(self.energy_history)
        plt.title('Total Energy in System Over Time')
        plt.xlabel('Time Steps')
        plt.ylabel('Total Energy')
        plt.grid(True)
        plt.show()
        
    def get_alive_agents(self):
        return [agent for agent in self.agents if agent.is_alive()]

This environment class manages all agents and simulates their interactions over time. It tracks energy levels and visualizes how the system evolves.

4. Run the simulation to observe interactions

Let's create a main script to run our simulation:

def main():
    # Create environment with 15 agents
    env = AgentEnvironment(num_agents=15)
    
    print("Starting simulation with 15 agents...")
    print(f"Initial alive agents: {len(env.get_alive_agents())}")
    
    # Run simulation for 200 time steps
    env.run_simulation(steps=200)
    
    print(f"Final alive agents: {len(env.get_alive_agents())}")
    print("Simulation completed.")

if __name__ == "__main__":
    main()

This script runs a simulation where agents interact and move over time, allowing us to observe how system dynamics change.

5. Test extreme interaction scenarios

To understand catastrophic failures like those in the OpenClaw research, let's create a scenario with more aggressive interactions:

class AggressiveAgent(SimpleAgent):
    def __init__(self, agent_id, initial_energy=100):
        super().__init__(agent_id, initial_energy)
        
    def interact_with(self, other_agent):
        # Aggressive interaction - takes more energy
        if self.energy > 5 and other_agent.energy > 5:
            # Take more energy during interaction
            transfer_amount = min(self.energy, other_agent.energy) * 0.3
            self.energy -= transfer_amount
            other_agent.energy -= transfer_amount  # Both lose energy
            self.interactions.append(f'Aggressive interaction: {self.id} took from {other_agent.id}')
            return True
        return False


class AggressiveEnvironment(AgentEnvironment):
    def __init__(self, num_agents=10):
        # Create mix of normal and aggressive agents
        self.agents = []
        for i in range(num_agents):
            if random.random() < 0.3:  # 30% aggressive agents
                self.agents.append(AggressiveAgent(i))
            else:
                self.agents.append(SimpleAgent(i))
        self.time_steps = 0
        self.energy_history = []

This aggressive version shows how interactions can become destructive when agents are designed to consume more resources than they provide.

6. Compare system stability

Let's create a comparison between normal and aggressive systems:

def compare_systems():
    # Normal system
    normal_env = AgentEnvironment(num_agents=20)
    normal_env.run_simulation(steps=150)
    
    # Aggressive system
    aggressive_env = AggressiveEnvironment(num_agents=20)
    aggressive_env.run_simulation(steps=150)
    
    print("Comparison completed - observe the differences in energy consumption")

This comparison helps demonstrate how different interaction patterns can lead to vastly different system outcomes.

Summary

This tutorial demonstrated how to build a basic multi-agent system that simulates AI agent interactions. By creating different types of agents and observing their behavior over time, we can understand how system failures can occur when interactions are not properly managed. The key lesson from the OpenClaw research is that uncontrolled agent-to-agent interactions can lead to catastrophic system failures, as seen in our simulation where aggressive interactions caused rapid energy depletion. This understanding is crucial for developing robust AI systems that can handle complex agent interactions without collapsing under their own dynamics.