Evals, Testing & Reliability
Know your agent works — with evidence
Stop guessing. Build eval sets, grade with code and LLM judges, trace every run, and systematically debug agent failures.
- Lessons
- 5
- Reading time
- 56 min
- Level
- Advanced
- Access
- Full course
Lessons in this module
- 8.18 min
Why Evals Are Your Superpower
Evals turn 'it seems better' into numbers. Learn the types of evals and the minimum viable eval for any agent.
- 8.213 min
Building Your First Eval Set
Collect realistic test cases, define success criteria, and write a small eval harness that runs in minutes.
- 8.312 min
LLM-as-Judge: Grading at Scale
Use models to grade open-ended outputs with rubrics — and calibrate the judge so you can trust its scores.
- 8.412 min
Tracing and Observability
Capture every model call, tool call, and decision so you can see exactly what your agent did and why.
- 8.511 min
Debugging Agent Failures
A systematic playbook for the most common agent failures: loops, wrong tools, hallucinated facts, and premature stops.
About this module
Evals are repeatable tests for AI systems. An eval set is a collection of realistic inputs, each with criteria for what a good result looks like, and a harness runs your agent on every case and grades the output. Instead of judging a change by trying a few examples and deciding it seems better, you get a score you can compare across prompts, models, and versions.
Agents are especially hard to test by eye. The same input can produce different runs, a fix for one case can quietly break another, and a correct final answer can hide a wasteful or risky path to get there. Evaluation, tracing, and a disciplined debugging process are what let teams change agents with confidence instead of hoping each change helps.
This part starts with why evals matter: the main types of evals, why you should usually grade outcomes rather than exact paths, and how to read results without fooling yourself. You will then build a first eval set for a realistic task, collecting test cases, writing success criteria a grader can check, writing code-based graders, and running a small harness in TypeScript.
Next you will use LLM-as-a-judge to grade open-ended outputs with rubrics, calibrated against your own labels so you know how far to trust it. You will instrument an agent loop with OpenTelemetry tracing so every model call and tool call is visible, and finish with a systematic playbook for debugging the most common failures, including loops, wrong tool calls, hallucinated facts, and premature stops, then locking in each fix with a new eval case.
Questions about Evals, Testing & Reliability
What are evals in AI?
Evals are repeatable tests that measure how well an AI system performs on a set of representative tasks. Each case has an input and criteria for success, and a grader scores the output so you can compare results across versions.
How do you evaluate an AI agent?
Collect realistic test cases, define what success means for each, and run the agent on all of them with automated graders. Use code-based checks where answers can be verified exactly and rubric-based LLM grading for open-ended output. Grade the final outcome first, and inspect traces to understand failures.
What is LLM-as-a-judge?
LLM-as-a-judge means using a language model to grade another model's output against a rubric. It makes scoring open-ended responses practical at scale, but judges have biases, so check their scores against human labels before trusting them.
How many test cases does an eval set need?
You can start small: a few dozen realistic cases will catch many regressions and is far better than none. Grow the set over time by adding cases from real failures, and make sure it covers your most important and most common scenarios.
What is AI agent observability?
Agent observability is the ability to see what an agent did on each run: every model call, tool call, input, output, and duration. It is usually implemented with tracing, often using the OpenTelemetry standard, so runs can be inspected and debugged after the fact.
Why does my AI agent get stuck in a loop?
Loops usually happen when the agent lacks the information it needs to make progress, such as an unhelpful error message, a tool result that looks the same each time, or an unclear goal. Clearer tool errors, better instructions, and a hard turn limit address most cases.