# 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: 2026-10-07 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 - [Don't Trust Your AI Agents. Test Them Like a Scientist — Stephanie Jarmak, Omni Analytics](https://learnetto.com/ai-videos/test-ai-agents-scientifically) - AI Engineer. Stephanie Jarmak explains why teams should treat an agent evaluation as an experiment whose tasks, harness, repeated runs, traces, and measurement match the production decision. - [1,368 AI Failures and What They Teach About Evals — Vishal Srivastava & Tanmay Sah, New York Life](https://learnetto.com/ai-videos/ai-failures-eval-design) - AI Engineer. Vishal Srivastava and Tanmay Sah classify 1,368 public AI incidents and propose a failure-driven build, break, and fix loop for higher-risk systems. - [How to Test AI Agents When No Benchmark Exists — Nathanael Weill, Air Canada](https://learnetto.com/ai-videos/agent-tests-without-benchmark) - AI Engineer. Nathanael Weill shows how to start from a known answer, generate a realistic input, and use planted or removed facts to create mechanically checkable agent cases. - [One Day, $1,000 in Tokens: An AI War Room for Legacy Code — Ford Prior, GEICO](https://learnetto.com/ai-videos/legacy-code-agent-war-room) - AI Engineer. Ford Prior describes a one-day GEICO workshop that combined domain experts, one driver, internal context, and several coding agents around a bounded legacy change. - [A Bank Built Its Own Coding Agent. It Took Off — Felipe Felisola Caso & Francesco Galletta, Monzo](https://learnetto.com/ai-videos/monzo-coding-agent-platform) - AI Engineer. Monzo engineers describe how an internal agent grew from a Slack reaction into a shared platform for code, incidents, and business requests. - [Why AI Agents Will Need Their Own Bank Accounts — Lincoln Murr, Coinbase](https://learnetto.com/ai-videos/agent-bank-accounts) - AI Engineer. Lincoln Murr explains Coinbase's path from wallet-enabled agents to x402 payments and argues for gradually earned financial autonomy. - [Smarter Models Killed the Harness. Here's What's Left — Jediah Katz, SpaceXAI](https://learnetto.com/ai-videos/smarter-models-agent-harness) - AI Engineer. Jediah Katz separates coding tricks that stronger models may absorb from runtime, permissions, context, and cost controls that still belong in an agent harness. - [Why AI Agents Still Won't Pay for Anything — Jan Curn, Apify](https://learnetto.com/ai-videos/agent-payments-funnel) - AI Engineer. Jan Curn walks the agent payment funnel from need to purchase and shows why many competing standards have produced little real payment volume so far. ## 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.