Best course-style starting point: Evaluating AI Agents. DeepLearning.AI short course focused on evaluating agent trajectories, not just final answers. It focuses on testing and improving multi-step agent workflows.
Best official agent-eval reference: OpenAI Evaluate agent workflows. Official OpenAI guidance for traces, graders, and regression testing agent workflows. It covers traces, graders, and regression testing for agent behavior.
Best open-source eval tooling route: Promptfoo Intro. Promptfoo documentation for repeatable prompt, model, and red-team tests. It is useful when you want repeatable red-team and regression checks.
Agent evals are not the same as prompt evals
A single-turn prompt eval checks whether one response is good enough. An agent eval has to judge a trajectory: which tools were called, whether the agent used the right evidence, how it recovered from errors, and whether it stopped at the right time. That is why agent evaluation needs traces, datasets, graders, and scenario design, not just a spreadsheet of expected answers.
The most useful courses teach you to evaluate the workflow, not the model in isolation. If an agent gives a poor answer, the problem might be retrieval, tool descriptions, permission design, missing state, bad routing, weak instructions, or a model mismatch. A good eval course helps you separate those causes instead of repeatedly rewriting prompts.
What a practical eval stack should cover
Start with a course such as Evaluating AI Agents if you want a guided introduction. Then pair it with current docs from OpenAI, Phoenix, Promptfoo, or Hamel Husain's eval writing. You want material that shows traces, human review, automated graders, regression tests, adversarial cases, and examples that fail in realistic ways.
For agent work, your eval set should include tasks with tool errors, stale data, ambiguous instructions, and unsafe actions. It should test whether the agent asks for clarification, refuses actions it should not take, and preserves important context across multiple steps. Without those cases, an agent can look good in demos and still be risky in production.
The mistake most teams make
Teams often wait until after an AI workflow is built to ask how they will measure quality. That usually leads to vague human review, late redesign, and arguments about whether a failure was a prompt issue or a product issue. Better eval courses encourage you to define representative tasks before the implementation hardens.
A useful rule is to write the eval as soon as you can describe the user promise. If the promise is 'research this market and cite sources', test citation quality, source freshness, synthesis, and unsupported claims. If the promise is 'fix this bug in a repo', test whether commands are run, tests are updated, and the final diff actually solves the problem.
Recommended courses and resources
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Evaluating AI Agents
Short course · DeepLearning.AI · Intermediate
You need to test, trace, and improve agent workflows instead of judging only single LLM responses.
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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.
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LLM Evals
Guide · Hamel Husain · Intermediate
Your AI app needs quality checks before users see it.
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OpenAI Cookbook
GitHub repo · OpenAI · Beginner to advanced
You need implementation examples rather than theory.
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Microsoft AI Agents for Beginners
GitHub repo · Microsoft · Beginner to intermediate
You want a structured agent learning path with code.
How to choose
- Pick resources that evaluate whole trajectories, not only final answers.
- Look for examples with traces, datasets, graders, and regression tests.
- Include failure cases such as tool misuse, bad retrieval, and unsafe actions.
Common questions
How do I evaluate AI agents?
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.
What course teaches agent evals?
Evaluating AI Agents is the clearest course-style starting point. Follow it with OpenAI agent eval docs, Phoenix, Promptfoo, or Hamel Husain's eval material for practical implementation patterns.
What failures should agent evals include?
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.
