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How do I evaluate AI agents?

Short answer from Learnetto's Best AI agent evaluation courses guide.

Short answer

Evaluate the full trajectory: tool calls, source use, intermediate decisions, final answer, and stopping behavior. Agent evals need traces and scenario datasets, not just final-response scoring.

Context from the full guide

Start with Evaluating AI Agents if you need a course, then use OpenAI agent evals, Hamel Husain, Phoenix, or Promptfoo to build practical traces, graders, regression tests, and red-team checks.

Read the full guide

Useful resources

  1. Evaluating AI Agents

    Short course · DeepLearning.AI · Intermediate

    You need to test, trace, and improve agent workflows instead of judging only single LLM responses.

  2. OpenAI Evaluate agent workflows

    Guide · OpenAI · Intermediate

    You need the current OpenAI path for tracing, grading, and regression-testing agent workflows instead of only single-prompt evals.

  3. LLM Evals

    Guide · Hamel Husain · Intermediate

    Your AI app needs quality checks before users see it.

  4. OpenAI Cookbook

    GitHub repo · OpenAI · Beginner to advanced

    You need implementation examples rather than theory.

  5. Microsoft AI Agents for Beginners

    GitHub repo · Microsoft · Beginner to intermediate

    You want a structured agent learning path with code.

  6. Prompt Engineering Guide

    Guide · DAIR.AI · Beginner to advanced

    You want examples of prompting techniques and patterns.

  7. AI SDK v6 Crash Course

    Workshop · Matt Pocock · Intermediate

    You want a structured AI SDK v6 course that covers model choice, text and object generation, UI streams, agents, persistence, context engineering, evals, and advanced app patterns.

  8. LLM Fundamentals

    Free tutorial · Matt Pocock · Beginner

    You need clear mental models for system prompts, tokens, context windows, tools, and agents before building or using AI systems seriously.

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