Evaluating AI Agents: A Practical Framework

Hello AI Enabled Academy learners!

So recently I noticed that in many organizations, an agent that nails the demo can still fall apart on its first real user. The fix is not more prompting or a better model. It is a short list of test cases with clear pass criteria, so a failure tells you what broke and where. Skip that and every model or prompt change is a guess, and you find out from your users.

## What you'll learn

⚡Define Your Agent's Goal, Scope, and Key Scenarios
Learn to write down what your agent should handle, decline, and treat as success before you build or test anything.

⚡Write Test Cases With Clear Acceptance Criteria
Learn to turn each key scenario into a test prompt with specific pass criteria, like an HR bot citing the PTO policy.

⚡Run a Baseline and Fix Failures in a Loop
Learn to run your test cases, sort failures by type, and repeat evaluate, analyze, improve until you hit your targets.

⚡Expand Into Four Types of Test Cases
Learn to add core, variation, architecture, and edge case tests so you can tell what broke and where it broke.

⚡Set a Cadence That Catches Drift Early
Learn to decide when to rerun your suite after a model, prompt, or knowledge base change, before users notice.