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

This tutorial demonstrates building a risk-aware AI agent incorporating internal critique mechanisms and uncertainty estimation. The system uses multi-sample inference to generate and evaluate candidate answers across accuracy, coherence, and safety dimensions. Uncertainty quantification employs entropy, variance, and consistency metrics. Risk-sensitive selection strategies balance confidence with reliability, enabling trustworthy autonomous decision-making beyond standard response generation.