This episode of Techsplainers explores AI agent evaluation - the systematic approaches used to assess the performance, capabilities, and limitations of autonomous AI systems. Unlike simpler AI models, agents require multidimensional evaluation frameworks that examine task performance, reasoning quality, safety, adaptability, efficiency, and user experience. We discuss various evaluation methodologies including benchmark testing, simulation-based evaluation, and human assessment, along with specific metrics organizations use to measure agent effectiveness. The episode also addresses the unique challenges of evaluating multi-agent systems, open-ended tasks, and ethical dimensions of agent behavior. Listeners will learn about emerging trends in agent evaluation, including automated assessment tools and sophisticated observability mechanisms that provide insight into agent decision-making processes. As AI agents become more capable and widely deployed, robust evaluation practices become increasingly essential for ensuring these systems perform reliably, safely, and effectively across diverse contexts. Find more information at https://www.ibm.biz/techsplainers-podcast Narrated by Cole Stryker