Evaluating AI Agents with Arize AI - Part 3: Agent as a Judge

This 42-minute webinar from Data Science Dojo explores advanced evaluation strategies for AI agent systems, focusing on understanding agent reasoning processes rather than just outcomes. Dive into techniques for analyzing how agents think, plan, and collaborate, including reasoning path analysis, role effectiveness assessment, and agent-as-judge methodologies. Learn how to implement transparent evaluation methods for both single and multi-agent frameworks using Arize Phoenix. The session covers evaluating reasoning processes behind agent decisions, measuring convergence and reasoning quality, analyzing collaboration in multi-agent systems, assessing planning quality in hierarchical teams, leveraging self-evaluation and peer review techniques, and implementing scalable evaluation workflows for production environments. The comprehensive content is organized into clear segments, starting with an introduction and series recap, then progressing through various evaluation methodologies, including a demonstration of agent-as-judge functionality in Arize Phoenix, before concluding with practical application guidance.