agent-evals

sidebar_position	0
title	Build Your Evals Skill
description	Create your agent-evals skill from official documentation before learning evaluation concepts. This skill-first approach ensures you build production-ready evaluation capabilities grounded in authoritative sources.
keywords	["agent evaluation","evals","AI testing","graders","error analysis","regression testing","evaluation frameworks"]
chapter	47
lesson	0
duration_minutes	25
skills	[{"name":"Understanding Evaluation Fundamentals","proficiency_level":"A2","category":"Conceptual","bloom_level":"Remember","digcomp_area":"Information Literacy","measurable_at_this_level":"Student can identify the difference between TDD (code testing) and evals (agent evaluation) and explain why evals produce probabilistic scores rather than PASS/FAIL outcomes"},{"name":"Writing Learning Specifications","proficiency_level":"A2","category":"Technical","bloom_level":"Understand","digcomp_area":"Problem-Solving","measurable_at_this_level":"Student can articulate what they want to learn about agent evaluation in a structured LEARNING-SPEC.md document with clear success criteria"},{"name":"Building Skills from Documentation","proficiency_level":"B1","category":"Applied","bloom_level":"Apply","digcomp_area":"Digital Content Creation","measurable_at_this_level":"Student can use AI tools to fetch official documentation and create an initial skill structure following the SKILL.md format"}]
learning_objectives	[{"objective":"Identify why systematic evaluation distinguishes good agents from great ones","proficiency_level":"A2","bloom_level":"Remember","assessment_method":"Recognition of Andrew Ng's core thesis about disciplined evaluation processes"},{"objective":"Create a LEARNING-SPEC.md document articulating evaluation learning goals","proficiency_level":"A2","bloom_level":"Apply","assessment_method":"Written specification with clear intent, success criteria, and questions to answer"},{"objective":"Build an initial agent-evals skill structure using AI-assisted documentation fetching","proficiency_level":"B1","bloom_level":"Apply","assessment_method":"Functional SKILL.md file that passes basic structure validation"}]
cognitive_load	{"new_concepts":4,"assessment":"4 concepts (evals vs TDD distinction, LEARNING-SPEC pattern, skill structure, documentation-first approach) within A2-B1 limit (7-10 concepts)"}
differentiation	{"extension_for_advanced":"Research OpenAI's Evals framework and Google ADK's evaluation module; compare grader patterns across frameworks","remedial_for_struggling":"Focus on the LEARNING-SPEC.md creation only; defer skill building to next session"}

Build Your Evals Skill

You have built agents that generate outputs, make decisions, and take actions. But how do you know if those outputs are actually good? How do you measure whether your agent made the right decision, not just a decision? This is where most developers struggle, and it separates effective AI builders from the rest.

Andrew Ng, one of the pioneers of modern AI, identified the critical pattern: "One of the biggest predictors for whether someone is able to build agentic workflows really well is whether or not they're able to drive a really disciplined evaluation process." The developers who master evaluation build agents that improve systematically. The developers who skip evaluation build agents that seem to work until they fail in production.

This lesson follows the Skill-First pattern. Instead of learning evaluation concepts first and building a skill later, you will create your agent-evals skill immediately. Why? Because building the skill forces you to engage with official documentation from the start. Your skill becomes grounded in authoritative sources rather than general knowledge. Every lesson in this chapter will then test and improve your skill, and by the capstone, you will own a production-ready evaluation capability.

Why Skill-First for Evals

The traditional approach to learning agent evaluation looks like this:

1. Read about evaluation concepts
2. See some examples
3. Maybe build something later
4. Forget most of it within a week

The Skill-First approach inverts this:

1. Define what you want to learn (specification)
2. Build a skill structure from official documentation
3. Learn concepts by improving your skill
4. End the chapter owning a valuable asset

The difference is ownership. In traditional learning, knowledge lives in your head (temporarily). In Skill-First learning, knowledge lives in a reusable skill that you can apply to every future agent project.

For evaluations specifically, this matters more than most topics. Evaluation requires discipline, and discipline requires structure. By encoding evaluation methodology into a skill, you create a forcing function that ensures you actually do the work rather than skipping it when deadlines pressure you.

Clone skills-lab Fresh

Every chapter starts with a clean slate. This prevents confusion from leftover files and ensures reproducible results.

Open your terminal and navigate to your workspace directory:

cd ~/workspace

If you have a previous skills-lab directory, remove it:

rm -rf skills-lab