Evaluating agents component by component, not just end to end
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New short course: Evaluating AI Agents! Evals are important for driving AI system improvements, and in this course you'll learn to systematically assess and improve an AI agent’s performance. This is built in partnership with @arizeai and taught by @JohnGilhuly, Head of Developer Relations, and @amankhan, Director of Product. I've often found evals to be a critical tool in the agent development process - they can be the difference between picking the right thing to work on vs. wasting weeks of effort. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically, rather than relying on random trial and error. This course shows you how to structure your evals to assess the performance of each component of an agent and its end-to-end performance. For each component, you select the appropriate evaluators, test examples, and performance metrics. This helps you identify areas for improvement both during development and in production. (If you're familiar with error analysis in supervised learning, think of this as adapting those ideas to agentic workflows.) In this course,…
Context
Evaluating an 's individual steps helps identify what to fix when its final answer disappoints. The Evaluating AI Agents course teaches developers to collect traces of the agent's actions and choose test examples, evaluators, and metrics for separate components. In the announcement, Andrew Ng connects those component checks with end-to-end . Code can check explicitly defined conditions, while model or human judgments can assess more open-ended outputs. Together, these measurements guide experiments with prompts, models, and agent logic.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
- eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
- LLM-as-judge — Using one model to score another's output against a rubric, so quality can be measured at a scale human grading cannot reach.
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