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What failures should agent evals include?

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

Short answer

Include wrong tool choice, bad retrieval, stale data, unsafe actions, loops, missing clarification, and cases where the agent should stop. These are the failures that polished demos usually hide.

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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