agent-artifex:foundations

The AI testing pyramid evaluates LLM development across three layers: Deterministic, Recorded Replay, and Probabilistic. It provides a structured methodology to assess AI agent performance and response accuracy by validating the causal chain from tool descriptions to outputs.

To design robust AI agents, apply a structured framework covering seven design areas. This approach establishes foundational design principles that map tool descriptions directly to response accuracy, ensuring reliable AI agent performance before implementation begins.

Key metrics for evaluating AI agent performance involve a six-component rubric and two-tier grading system. These metrics measure response accuracy and the effectiveness of the causal chain linking tool descriptions to final AI outputs.

No specific dependencies are required to use this AI testing framework. It serves as a comprehensive reference for design principles and testing methodologies, making it accessible for engineers seeking foundational knowledge on evaluating AI agent performance.

Use deterministic testing for fixed outputs and probabilistic testing for variable AI responses. The AI testing pyramid outlines these layers, allowing you to perform impact and effort analysis to determine the appropriate validation strategy for your LLM development lifecycle.

Inconsistent AI agent responses often stem from gaps in the causal chain between tool descriptions and outputs. Applying the framework's seven design areas and the six-component rubric helps identify structural weaknesses and standardize response accuracy evaluation.