Ensure AI Agents Work: Evaluation Frameworks for Scaling Success — Aparna Dhinkaran, CEO Arize
Apr 23, 2025 · 15:28
Aparna Dhinakaran, CEO of Arize AI, explains that evaluating AI agents requires testing three core components—routers, skills, and memory—each at different trace levels. Routers must be checked for correct skill selection and parameter passing; skills need LLM-as-judge or code-based evals for chunk relevance and answer correctness; and convergence measures whether the agent takes a consistent number of steps to complete a task. For voice agents, additional evaluations on audio chunks—sentiment, speech-to-text accuracy, and tone consistency—are necessary. Dhinakaran demonstrates Arize's own Copilot, where evals run at every trace step (router choice, argument passing, task completion) to isolate failures. She argues that observability-driven evaluation frameworks, with multiple eval layers, transform experimental agents into production-ready enterprise tools.
