Evaluating AI Agents: Building Single-Turn Tool-Selection Evals

Building reliable AI agents requires systematic testing to ensure they select the right tools for the job. Without proper evaluations, subtle changes to tool descriptions or prompts can silently break your agent's decision-making flow. This text-only course guides you through the foundational concepts of AI agent evaluations, showing you how to design, write, and execute single-turn evaluations to measure and optimize your agent's tool-selection capabilities. What you'll learn: - Understand the foundational concepts of AI agent tool-calling and evaluation metrics - Create structured test datasets using input-output pairs to validate agent decisions - Build mock tools to simulate real-world API behaviors without external dependencies - Develop a custom single-turn eval executor to run automated validation checks - Optimize system prompts and tool descriptions to improve selection accuracy - Apply modern evaluation patterns to continuously monitor agent performance The course starts with essential terminology and the core theory of tool-calling, then progresses to designing structured test files, coding mock tools, and running your own single-turn evaluation suite. This training is designed for software developers and AI enthusiasts who want to build more reliable AI systems, with no advanced machine learning background required. Start building robust evaluation pipelines for your AI agents today.