# Where do engineers learn reliable AI agent evaluation workflows?

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Full guide Markdown: https://learnetto.com/ai-guides/best-ai-resources-for-evals.md
Last updated: 2026-10-07
Source: Learnetto AI learning directory

## Short answer
Start with an agent-evaluation course that covers trajectories, tool calls, datasets, graders, and traces, then implement a small code-owned regression suite. Learnetto's eval guides compare course material with OpenAI, Phoenix, Promptfoo, and practitioner resources for moving from examples to CI and production monitoring.

## Useful resources
1. [Uber production AI agent security guide](https://www.uber.com/us/en/blog/securing-ai-agents/) - AI agent security guide by Uber; level: Intermediate. Use this guide to turn a production agent's purpose, tools and risk level into enforceable checks before it can take sensitive actions.
2. [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.
3. [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.
4. [LLM Evals](https://hamel.dev/blog/posts/evals/) - Guide by Hamel Husain; level: Intermediate. Your AI app needs quality checks before users see it.
5. [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.
6. [Building and Evaluating Advanced RAG Applications](https://www.deeplearning.ai/short-courses/building-evaluating-advanced-rag/) - Short course by DeepLearning.AI; level: Intermediate. You already know basic RAG and need better retrieval, evaluation, and production-quality patterns.
7. [Promptfoo](https://www.promptfoo.dev/docs/intro/) - Open-source eval and red-team framework by OpenAI / Promptfoo; level: Intermediate to advanced. Declarative regression tests and side-by-side comparisons of prompts, models, RAG systems, and agent configurations, especially when red teaming is also required.
8. [DeepEval](https://deepeval.com/docs/introduction) - Pytest-style LLM evaluation framework by Confident AI; level: Intermediate to advanced. Engineering teams that want LLM and agent evaluations to behave like software unit tests, with thresholds, assertions, and CI-friendly failures.

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