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Where do engineers learn reliable AI agent evaluation workflows?

Short answer from Learnetto's Best AI resources for evals and reliability guide.

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

    AI agent security guide · Uber · 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

    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.

  3. The AI Engineer Roadmap

    Free tutorial · Matt Pocock · 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

    Guide · Hamel Husain · Intermediate

    Your AI app needs quality checks before users see it.

  5. Evaluating AI Agents

    Short course · DeepLearning.AI · Intermediate

    You need to test, trace, and improve agent workflows instead of judging only single LLM responses.

  6. Building and Evaluating Advanced RAG Applications

    Short course · DeepLearning.AI · Intermediate

    You already know basic RAG and need better retrieval, evaluation, and production-quality patterns.

  7. Promptfoo

    Open-source eval and red-team framework · OpenAI / Promptfoo · 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

    Pytest-style LLM evaluation framework · Confident AI · Intermediate to advanced

    Engineering teams that want LLM and agent evaluations to behave like software unit tests, with thresholds, assertions, and CI-friendly failures.