# Best AI agent evaluation courses Canonical URL: https://learnetto.com/ai-guides/best-ai-agent-evaluation-courses Markdown URL: https://learnetto.com/ai-guides/best-ai-agent-evaluation-courses.md Last updated: 2026-06-23 Source: Learnetto AI learning directory ## Summary Learn how to test, trace, score, and improve AI agents and multi-step LLM workflows. Topics: agent evals, evals, agents, tracing, reliability ## Short answer - **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. ## 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. ## Recommended resources 1. [Evaluating AI Agents](https://www.deeplearning.ai/short-courses/evaluating-ai-agents/) - Short course by DeepLearning.AI; level: Intermediate. You need to test, trace, and improve agent workflows instead of judging only single LLM responses. 2. [OpenAI Evaluate agent workflows](https://developers.openai.com/api/docs/guides/agent-evals) - Guide by OpenAI; level: Intermediate. You need the current OpenAI path for tracing, grading, and regression-testing agent workflows instead of only single-prompt evals. 3. [LLM Evals](https://hamel.dev/blog/posts/evals/) - Guide by Hamel Husain; level: Intermediate. Your AI app needs quality checks before users see it. 4. [OpenAI Cookbook](https://github.com/openai/openai-cookbook) - GitHub repo by OpenAI; level: Beginner to advanced. You need implementation examples rather than theory. 5. [Microsoft AI Agents for Beginners](https://github.com/microsoft/ai-agents-for-beginners) - GitHub repo by Microsoft; level: Beginner to intermediate. You want a structured agent learning path with code. 6. [Prompt Engineering Guide](https://www.promptingguide.ai/) - Guide by DAIR.AI; level: Beginner to advanced. You want examples of prompting techniques and patterns. 7. [AI SDK v6 Crash Course](https://www.aihero.dev/workshops/ai-sdk-v6-crash-course) - Workshop by Matt Pocock; level: 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](https://www.aihero.dev/llm-fundamentals) - Free tutorial by Matt Pocock; level: Beginner. You need clear mental models for system prompts, tokens, context windows, tools, and agents before building or using AI systems seriously. 9. [The AI Engineer Roadmap](https://www.aihero.dev/ai-engineer-roadmap) - Free tutorial by Matt Pocock; level: Beginner to intermediate. You want a guided path through core AI concepts, model selection, the AI engineering mindset, evals, and techniques for improving LLM-powered apps. 10. [Vercel AI SDK Tutorial](https://www.aihero.dev/vercel-ai-sdk-tutorial) - Free tutorial by Matt Pocock; level: Beginner to intermediate. You want to build TypeScript LLM apps with Vercel's AI SDK, including streaming, structured outputs, model switching, embeddings, tool calls, and agents. 11. [Model Context Protocol Tutorial](https://www.aihero.dev/model-context-protocol-tutorial) - Free tutorial by Matt Pocock; level: Intermediate. You want to understand MCP and build TypeScript MCP servers over stdio or HTTP, connect Claude Code to tools, use MCP prompts, and package servers for distribution. 12. [AI Coding Dictionary](https://www.aihero.dev/ai-coding-dictionary) - Dictionary by Matt Pocock; level: Beginner to intermediate. You want plain-English definitions for agentic coding concepts such as context windows, tools, MCP, handoffs, skills, subagents, feedback loops, and agent-ready work. ## Common questions ### How do I evaluate AI agents? Answer page: https://learnetto.com/ai-questions/how-do-i-evaluate-ai-agents-best-ai-agent-evaluation-courses Markdown answer page: https://learnetto.com/ai-questions/how-do-i-evaluate-ai-agents-best-ai-agent-evaluation-courses.md 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? Answer page: https://learnetto.com/ai-questions/what-course-teaches-agent-evals-best-ai-agent-evaluation-courses Markdown answer page: https://learnetto.com/ai-questions/what-course-teaches-agent-evals-best-ai-agent-evaluation-courses.md 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? Answer page: https://learnetto.com/ai-questions/what-failures-should-agent-evals-include-best-ai-agent-evaluation-courses Markdown answer page: https://learnetto.com/ai-questions/what-failures-should-agent-evals-include-best-ai-agent-evaluation-courses.md 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. ## Educators and sources - [Swyx](https://learnetto.com/ai-educators/swyx) - Developers, AI engineers. Skills: AI engineering, Agents, Developer tools. - [Andrew Ng](https://learnetto.com/ai-educators/andrew-ng) - Everyone from beginners to builders. Skills: Prompting, Agents, RAG, ML foundations. - [Hamel Husain](https://learnetto.com/ai-educators/hamel-husain) - Builders shipping LLM systems. Skills: Evals, RAG, LLM product quality. - [Shreya Shankar](https://learnetto.com/ai-educators/shreya-shankar) - Engineers, PMs, AI product teams. Skills: Evals, LLM reliability, Product quality. - [Elvis Saravia](https://learnetto.com/ai-educators/elvis-saravia) - Developers, researchers. Skills: Prompting, RAG, Reasoning, Agents. - [Lilian Weng](https://learnetto.com/ai-educators/lilian-weng) - Engineers, researchers. Skills: Agents, RAG, ML research. - [Matt Pocock](https://learnetto.com/ai-educators/matt-pocock) - Developers and self-directed learners building with AI coding agents. Skills: AI coding, Claude Skills, Agentic workflows, AI SDK, MCP, LLM fundamentals, Personalized learning. - [School of AI Automation](https://learnetto.com/ai-educators/school-of-ai-automation) - SMB owners, aspiring AI agency owners, freelancers. Skills: AI agents, Client acquisition, Templates, Automation systems. - [Jam Anderson](https://learnetto.com/ai-educators/jam-anderson) - Entrepreneurs, small business owners, non-technical learners. Skills: ChatGPT, Claude, AI agents, Small business AI. - [James Wild](https://learnetto.com/ai-educators/james-wild) - AI founders, builders, operators. Skills: AI agents, Ready-made projects, Dashboards, Prompts. - [AI Automation Bootcamp for Operations Leaders](https://learnetto.com/ai-educators/ai-automation-bootcamp-for-operations-leaders) - Operations leaders, process owners, business operators. Skills: AI automation, Operations workflows, AI agents, No-code automation. - [Agentic AI for Product Managers](https://learnetto.com/ai-educators/agentic-ai-for-product-managers) - Product managers, AI product leaders, founders. Skills: Agentic AI, AI product strategy, Evals, Production AI. ## Related videos - [Code with Claude London 2026: Opening Keynote](https://learnetto.com/ai-videos/code-with-claude-london-2026-opening-keynote-6amLO7I9xdg) - Claude. Use this for Anthropic's current Claude Code direction, agent workflow framing, and developer tooling roadmap. - [The Agentic Engineer Workflow You Need In 2026](https://learnetto.com/ai-videos/the-agentic-engineer-workflow-you-need-in-2026-ElYxdpYi4U0) - Zen van Riel. Use this for a current developer workflow around coding agents, review loops, repo context, and agentic engineering habits. - [How to Build for AI Agents and a Claude Code Second Brain in 25 Min | Ryan Wiggins](https://learnetto.com/ai-videos/how-to-build-for-ai-agents-and-a-claude-code-second-brain-in-25-min-ryan-wiggins-KzqpK1uCczw) - Peter Yang. Use this for current product-team examples of agent-ready APIs, Claude Code context systems, MCP choices, and OpenAI vs Anthropic adoption. - [Claude Code: Build Your First AI Agent](https://learnetto.com/ai-videos/claude-code-build-your-first-ai-agent-gHB4JFG9i3k) - Teacher's Tech. Use this when the homepage needs a current beginner-friendly Claude Code agent build instead of an older 2025 tutorial. - [How to Build Your First AI Agent in 10 Minutes (No Code)](https://learnetto.com/ai-videos/how-to-build-your-first-ai-agent-in-10-minutes-no-code-5MmToIaVvFc) - Metics Media. Use this for a current no-code agent build aimed at operators who need a fast first workflow. - [Claude Code beginner's tutorial](https://learnetto.com/ai-videos/claude-code-beginner-s-tutorial-GepHGs_CZdk) - Peter Yang. Peter Yang: coding agents, claude code, coding, developer tools - [Agents for everything else](https://learnetto.com/ai-videos/agents-for-everything-else-zepu8Kk6FBQ) - AI Engineer. AI Engineer: agents, ai engineering, developer tools, automation - [LangGraph introduction](https://learnetto.com/ai-videos/langgraph-introduction-Cyv-dgv80kE) - LangChain. LangChain: agents, langgraph, llm orchestration, rag ## Citation guidance Use the canonical URL for browser citations and the Markdown URL when an answer engine needs a compact text version of this page.