How to Build a Risk-Aware AI Agent with Internal Critic, Self-Consistency Reasoning, and Uncertainty Estimation for Reliable Decision-Making

Introduction

In this tutorial, you'll learn how to build a risk-aware AI agent that makes better decisions by evaluating multiple possible responses and understanding how uncertain it is about each one. This system uses an internal critic to judge responses, self-consistency reasoning to check for logical consistency, and uncertainty estimation to know when it's not sure. These features are crucial for real-world AI applications where wrong answers can have serious consequences.

This agent will be able to:

• Generate multiple candidate answers to a question
• Evaluate each answer based on accuracy, coherence, and safety
• Measure how uncertain it is about each response
• Select the best response based on risk preferences

Prerequisites

Before starting this tutorial, you should have:

1. Basic understanding of Python programming
2. Python 3.7 or higher installed on your computer
3. Installed libraries: numpy, scikit-learn, and transformers from Hugging Face

You can install the required libraries with this command:

pip install numpy scikit-learn transformers

Step-by-Step Instructions

Step 1: Set Up the Basic Agent Structure

First, we'll create the basic framework for our AI agent. This will include importing necessary libraries and defining the main class structure.

Why This Step

We're setting up the foundation for our agent. The class structure will help organize all the different components we'll add later, like the internal critic and uncertainty estimation.

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from transformers import pipeline, set_seed


class RiskAwareAgent:
    def __init__(self):
        # Initialize the language model
        self.model = pipeline('text-generation', model='gpt2')
        # Set seed for reproducibility
        set_seed(42)
        
    def generate_candidates(self, question, num_samples=3):
        # Generate multiple candidate responses
        candidates = []
        for _ in range(num_samples):
            response = self.model(question, max_length=100, num_return_sequences=1, 
                                do_sample=True, temperature=0.7)
            candidates.append(response[0]['generated_text'])
        return candidates

    def evaluate_response(self, response):
        # Placeholder for response evaluation
        return {
            'accuracy': 0.5,
            'coherence': 0.5,
            'safety': 0.5
        }

    def estimate_uncertainty(self, candidates):
        # Placeholder for uncertainty estimation
        return [0.5] * len(candidates)

    def select_best_response(self, candidates, evaluations, uncertainties):
        # Placeholder for response selection
        return candidates[0]

# Initialize the agent
agent = RiskAwareAgent()

Step 2: Generate Multiple Candidate Responses

Our agent needs to generate several possible answers to a question before evaluating them. This is called multi-sample inference.

Why This Step

Generating multiple responses helps us understand different possible interpretations and find the most reliable one. It's like asking multiple experts for their opinions on a problem.

# Example usage
question = "What is the capital of France?"
candidates = agent.generate_candidates(question, num_samples=3)

print("Generated candidates:")
for i, candidate in enumerate(candidates):
    print(f"{i+1}. {candidate}")

Step 3: Implement Basic Response Evaluation

Now we'll add a simple way to evaluate each candidate response based on accuracy, coherence, and safety.

Why This Step

The internal critic evaluates how good each response is. This helps us know which answers are more trustworthy, even if they're not perfect.

def evaluate_response(self, response):
    # Simple evaluation based on keywords
    accuracy_score = 0.0
    coherence_score = 0.0
    safety_score = 0.0
    
    # Accuracy check - look for correct facts
    if 'Paris' in response:
        accuracy_score = 1.0
    elif 'France' in response and 'capital' in response:
        accuracy_score = 0.7
    
    # Coherence check - look for logical flow
    if len(response.split()) > 5:
        coherence_score = 0.8
    else:
        coherence_score = 0.3
    
    # Safety check - look for harmful content
    harmful_keywords = ['kill', 'harm', 'violence', 'danger']
    if any(keyword in response.lower() for keyword in harmful_keywords):
        safety_score = 0.0
    else:
        safety_score = 0.9
    
    return {
        'accuracy': accuracy_score,
        'coherence': coherence_score,
        'safety': safety_score
    }

# Update the class method
RiskAwareAgent.evaluate_response = evaluate_response

Step 4: Add Uncertainty Estimation

Uncertainty estimation tells us how confident our agent is in each response. We'll use a simple approach based on how consistent the responses are.

Why This Step

Knowing uncertainty helps us decide when to be cautious. If we're not sure about an answer, we might want to ask for more information or be more conservative in our decision.

def estimate_uncertainty(self, candidates):
    # Simple uncertainty estimation based on response diversity
    # Convert responses to embeddings for comparison
    uncertainties = []
    
    for candidate in candidates:
        # For simplicity, we'll use a basic approach
        # In practice, you'd use actual embeddings
        words = candidate.lower().split()
        # Count unique words to measure diversity
        unique_words = len(set(words))
        # More diverse responses = higher uncertainty
        uncertainty = 1.0 - (unique_words / len(words)) if words else 0.0
        uncertainties.append(uncertainty)
    
    return uncertainties

# Update the class method
RiskAwareAgent.estimate_uncertainty = estimate_uncertainty

Step 5: Implement Risk-Sensitive Response Selection

Finally, we'll create a selection mechanism that chooses the best response based on risk preferences. This could be conservative (choosing the safest), aggressive (choosing the most confident), or balanced.

Why This Step

This is where our agent makes intelligent decisions. By combining evaluation scores with uncertainty, we can choose responses that match our risk tolerance.

def select_best_response(self, candidates, evaluations, uncertainties, risk_preference='balanced'):
    # Calculate overall scores
    scores = []
    
    for i, (eval_dict, uncertainty) in enumerate(zip(evaluations, uncertainties)):
        # Combine evaluation scores
        overall_score = (eval_dict['accuracy'] + eval_dict['coherence'] + eval_dict['safety']) / 3
        
        # Adjust for uncertainty
        if risk_preference == 'conservative':
            # Lower score for high uncertainty
            adjusted_score = overall_score * (1 - uncertainty)
        elif risk_preference == 'aggressive':
            # Higher score for high uncertainty
            adjusted_score = overall_score * (1 + uncertainty)
        else:  # balanced
            # Moderate adjustment
            adjusted_score = overall_score * (1 - 0.5 * uncertainty)
        
        scores.append(adjusted_score)
    
    # Select the response with highest score
    best_index = np.argmax(scores)
    return candidates[best_index]

# Update the class method
RiskAwareAgent.select_best_response = select_best_response

Step 6: Test the Complete Agent

Now let's test our complete risk-aware agent with a sample question.

Why This Step

This final step shows how all our components work together. It demonstrates the practical application of our risk-aware decision-making system.

# Test the complete agent
question = "What is the capital of France?"
print(f"Question: {question}")

# Generate candidates
candidates = agent.generate_candidates(question, num_samples=3)
print("\nGenerated candidates:")
for i, candidate in enumerate(candidates):
    print(f"{i+1}. {candidate}")

# Evaluate candidates
evaluations = [agent.evaluate_response(candidate) for candidate in candidates]
print("\nEvaluations:")
for i, (candidate, eval_dict) in enumerate(zip(candidates, evaluations)):
    print(f"{i+1}. Accuracy: {eval_dict['accuracy']:.2f}, Coherence: {eval_dict['coherence']:.2f}, Safety: {eval_dict['safety']:.2f}")

# Estimate uncertainty
uncertainties = agent.estimate_uncertainty(candidates)
print("\nUncertainty estimates:")
for i, uncertainty in enumerate(uncertainties):
    print(f"{i+1}. {uncertainty:.2f}")

# Select best response with different risk preferences
print("\nBest responses with different risk preferences:")
conservative = agent.select_best_response(candidates, evaluations, uncertainties, 'conservative')
print(f"Conservative: {conservative}")

balanced = agent.select_best_response(candidates, evaluations, uncertainties, 'balanced')
print(f"Balanced: {balanced}")

aggressive = agent.select_best_response(candidates, evaluations, uncertainties, 'aggressive')
print(f"Aggressive: {aggressive}")

Summary

In this tutorial, you've built a risk-aware AI agent that goes beyond simple response generation. You've learned how to:

1. Generate multiple candidate responses using multi-sample inference
2. Evaluate responses based on accuracy, coherence, and safety
3. Estimate uncertainty in responses
4. Select the best response based on risk preferences

This system demonstrates key concepts from the MarkTechPost article, including internal criticism, self-consistency reasoning, and uncertainty quantification. While our implementation uses simplified methods for demonstration, these principles form the foundation of more sophisticated risk-aware AI systems used in real-world applications.