AI agent evaluation involves assessing large language model (LLM) systems that use external tools to perform tasks, and it is crucial for identifying failures in AI systems, which can occur either at an end-to-end level or a component level. The evaluation process requires setting up LLM tracing to track and apply metrics to various components of an AI agent, enabling the analysis of whether an agent can complete a task correctly. Single-turn agents, which complete tasks with one interaction, and multi-turn agents, which require several interactions, demand different evaluation strategies and metrics, like task completion and argument correctness. Tools like DeepEval and Confident AI offer platforms to facilitate this process by providing metrics, datasets, and observability features for more efficient evaluations. By using curated datasets and a mix of component-level and end-to-end metrics, AI teams can benchmark and improve their agents' performance, ensuring they meet user demands effectively.