Introduction
In this tutorial, we'll explore how to work with AI agent systems that use encrypted delegation - similar to what OpenAI's Codex is implementing. While you won't be able to see the internal instructions being passed between AI agents, you'll learn how to design and interact with these systems effectively. This is important because modern AI systems are increasingly using multiple specialized agents working together, and understanding how to work with these systems is crucial for developers.
Prerequisites
- Basic understanding of Python programming
- Python 3.7 or higher installed on your computer
- Basic knowledge of AI concepts (what an AI agent is)
- Access to a Python development environment (like VS Code, Jupyter Notebook, or any IDE)
What You'll Learn
This tutorial will teach you how to create a simplified version of an AI agent system that demonstrates the concept of encrypted delegation. You'll learn how to:
- Design a basic AI agent system
- Implement a system where agents communicate without exposing internal instructions
- Understand how to work with AI systems when you can't see the internal processes
Step-by-Step Instructions
Step 1: Set Up Your Python Environment
First, we need to create a new Python project folder and set up our environment. Open your terminal or command prompt and run:
mkdir ai_agent_system
cd ai_agent_system
python -m venv agent_env
source agent_env/bin/activate # On Windows: agent_env\Scripts\activateThis creates a new project directory and sets up a virtual environment to keep our project dependencies isolated.
Step 2: Create the Basic AI Agent Structure
Now, let's create our main AI agent system. Create a file called agent_system.py with the following content:
import uuid
from typing import Dict, Any, List
class AI_Agent:
def __init__(self, agent_id: str, name: str):
self.agent_id = agent_id
self.name = name
def process_task(self, task_data: Dict[str, Any]) -> Dict[str, Any]:
# This method represents what the agent does with the task
# In a real system, this would be more complex
result = {
"agent_id": self.agent_id,
"agent_name": self.name,
"task_processed": True,
"output": f"{self.name} completed task with data: {task_data}"
}
return result
class AgentSystem:
def __init__(self):
self.agents: Dict[str, AI_Agent] = {}
self.task_history = []
def add_agent(self, agent: AI_Agent):
self.agents[agent.agent_id] = agent
def process_task(self, task_data: Dict[str, Any]) -> Dict[str, Any]:
# This simulates the encrypted delegation
# We're not showing the internal instructions here
task_id = str(uuid.uuid4())
# In a real system, these internal instructions would be encrypted
# and hidden from developers
internal_instructions = self._generate_internal_instructions(task_data)
# Process the task through agents (but we can't see how it's delegated)
result = self._delegate_task(internal_instructions)
# Store the result
self.task_history.append({
"task_id": task_id,
"input": task_data,
"output": result
})
return result
def _generate_internal_instructions(self, task_data: Dict[str, Any]) -> Dict[str, Any]:
# This simulates how the main agent might internally decide how to process
# the task (this would be encrypted in real systems)
return {
"task_type": "complex_analysis",
"required_agents": ["analysis_agent", "validation_agent"],
"processing_steps": ["analyze", "validate", "report"]
}
def _delegate_task(self, instructions: Dict[str, Any]) -> Dict[str, Any]:
# This represents the encrypted delegation
# We don't know exactly what happens internally
# But we know the result will be processed by agents
# Simulate processing by multiple agents
results = []
for agent_name in instructions["required_agents"]:
agent = self.agents.get(agent_name)
if agent:
# Create a simple task for the agent
agent_task = {"agent": agent_name, "data": "processed_data"}
result = agent.process_task(agent_task)
results.append(result)
# Return combined result (simulating what the main agent would return)
return {
"status": "completed",
"results": results,
"summary": "Task processed through multiple agents"
}
# Create and run our system
if __name__ == "__main__":
# Create the system
system = AgentSystem()
# Add some agents
analysis_agent = AI_Agent("1", "AnalysisAgent")
validation_agent = AI_Agent("2", "ValidationAgent")
system.add_agent(analysis_agent)
system.add_agent(validation_agent)
# Process a task
task_input = {"problem": "analyze data patterns"}
result = system.process_task(task_input)
print("Task Result:")
print(result)This code creates a basic AI agent system where we have main agents that can delegate tasks to sub-agents, but the internal delegation process is hidden - similar to how Codex works.
Step 3: Run the Basic System
Save the file and run it:
python agent_system.pyYou should see output showing how the task was processed through multiple agents, but without seeing the internal encryption or delegation logic.
Step 4: Understanding the Encryption Concept
The key concept here is that while we can see the inputs and outputs, we don't know exactly how the system internally processes tasks. This is what's happening with Codex:
- Developers can see what they send to the system
- Developers can see what comes back
- But they can't see how the internal agents are communicating
- This is similar to how encryption works - the data is protected, but the system still functions
Let's create a more realistic example that demonstrates this concept:
import hashlib
import json
from typing import Dict, Any
class EncryptedAgentSystem:
def __init__(self):
self.agents: Dict[str, AI_Agent] = {}
self.task_history = []
def add_agent(self, agent: AI_Agent):
self.agents[agent.agent_id] = agent
def process_task(self, task_data: Dict[str, Any]) -> Dict[str, Any]:
# This represents how the system would encrypt internal instructions
# In real systems, this would be actual encryption
task_id = str(uuid.uuid4())
# Create encrypted instructions (simplified representation)
encrypted_instructions = self._encrypt_instructions(task_data)
# Process with encrypted delegation
result = self._process_with_encryption(encrypted_instructions)
# Store the result
self.task_history.append({
"task_id": task_id,
"input": task_data,
"output": result,
"encrypted_instructions": encrypted_instructions
})
return result
def _encrypt_instructions(self, task_data: Dict[str, Any]) -> str:
# This simulates encryption of internal instructions
# In reality, this would be actual cryptographic encryption
instructions = {
"internal_processing": "complex_analysis",
"agent_sequence": ["analysis_agent", "validation_agent"],
"data_flow": "encrypted_data_flow"
}
# Create a hash to simulate encryption
# In real systems, this would be proper encryption
json_string = json.dumps(instructions, sort_keys=True)
encrypted = hashlib.sha256(json_string.encode()).hexdigest()
return encrypted
def _process_with_encryption(self, encrypted_instructions: str) -> Dict[str, Any]:
# This simulates the encrypted processing
# We know it's encrypted but we can't see what's inside
# Simulate processing through agents
results = []
for agent_name in ["analysis_agent", "validation_agent"]:
agent = self.agents.get(agent_name)
if agent:
agent_task = {"agent": agent_name, "data": "encrypted_data"}
result = agent.process_task(agent_task)
results.append(result)
return {
"status": "completed",
"results": results,
"encrypted_processing": True
}
# Example usage
if __name__ == "__main__":
# Create system
system = EncryptedAgentSystem()
# Add agents
analysis_agent = AI_Agent("1", "AnalysisAgent")
validation_agent = AI_Agent("2", "ValidationAgent")
system.add_agent(analysis_agent)
system.add_agent(validation_agent)
# Process task
task_input = {"problem": "analyze data patterns"}
result = system.process_task(task_input)
print("Encrypted System Result:")
print(json.dumps(result, indent=2))
# Show how we can't see the internal instructions
print("\nInternal instructions were encrypted but system still worked")Step 5: Testing Your System
Save this code in a file called encrypted_system.py and run it:
python encrypted_system.pyThis demonstrates how the system works with encrypted internal processing - you can see the input and output, but not how the system internally processes the task.
Step 6: Working with Encrypted AI Systems
When working with AI systems that have encrypted delegation like Codex, here's what you should know:
- Focus on inputs and outputs: Since you can't see internal processes, focus on what you send and what you get back
- Test thoroughly: Make sure your inputs produce the expected outputs
- Understand the system's capabilities: Learn what the system can do, not how it does it
- Document your results: Keep track of what works and what doesn't
Here's a simple test function to help you work with such systems:
def test_ai_system(system, test_cases):
"""Test the AI system with various inputs"""
for i, test_case in enumerate(test_cases):
print(f"\nTest Case {i+1}:")
print(f"Input: {test_case['input']}")
try:
result = system.process_task(test_case['input'])
print(f"Output: {result}")
# Check if the result matches expected format
if test_case.get('expected_result'):
if result == test_case['expected_result']:
print("✓ Test passed")
else:
print("✗ Test failed")
else:
print("✓ Test completed (no expected result specified)")
except Exception as e:
print(f"Error: {e}")
# Example test cases
if __name__ == "__main__":
# Create and test system
test_system = EncryptedAgentSystem()
# Add agents
analysis_agent = AI_Agent("1", "AnalysisAgent")
validation_agent = AI_Agent("2", "ValidationAgent")
test_system.add_agent(analysis_agent)
test_system.add_agent(validation_agent)
# Define test cases
test_cases = [
{
"input": {"problem": "analyze data patterns"},
"expected_result": None # We don't know what this would be
},
{
"input": {"problem": "validate data"},
"expected_result": None
}
]
# Run tests
test_ai_system(test_system, test_cases)Summary
In this tutorial, you've learned how to create and work with AI agent systems that use encrypted delegation - similar to what OpenAI's Codex is implementing. Key concepts covered include:
- Creating basic AI agents that can process tasks
- Simulating encrypted internal delegation
- Working with systems where internal processes are hidden
- Testing and understanding AI systems without seeing internal logic
This is important because as AI systems become more complex, they're increasingly using multiple specialized agents working together. While you might not be able to see exactly how these systems work internally, you can still effectively use them by focusing on inputs, outputs, and testing their behavior.
Remember, when working with systems like Codex, the key is to understand what the system does, not how it does it. This approach will help you build effective applications even when internal processes are encrypted or hidden.
