Ho to Test and Evaluate AI Agents - an introduction

What You’ll Learn

Artificial Intelligence agents are no longer static chatbots, they plan, reason, and act autonomously. This course teaches you how to systematically test, measure, and validate AI agent behavior using the latest tools and frameworks.

Through real-world Python examples and structured exercises, you’ll learn how to evaluate both functional and non-functional aspects of AI systems; from goal completion and plan accuracy to efficiency and bias detection.

By the end of this course, you’ll know how to design robust AI evaluation pipelines, implement RAG (Retrieval-Augmented Generation) tests, and confidently report metrics that reflect true agent performance.

Course Modules

1. Understand the Fundamentals of AI Agent Testing
   Learn what makes AI agents unique — from autonomy and planning to tool-use and decision-making.

2. Design and Execute Systematic AI Agent Tests
   Build a repeatable test strategy using structured test cases, reproducible results, and automated evaluation scripts.

3. Implement RAG (Retrieval-Augmented Generation) Evaluation
   Evaluate how effectively an agent retrieves and integrates external knowledge sources.

4. Understand Functional Testing of AI Agents
   Test accuracy, correctness, and behavior alignment with expected outcomes.

5. Understand Non-Functional Testing of AI Agents
   Measure efficiency, robustness, reliability, and responsiveness in complex or dynamic environments.

6. Evaluate Key Agent Metrics

   • Goal Completion

   • Task Execution

   • Plan Creation

   • Cost and Efficiency

7. Compare Deterministic vs. Agentic vs. Autonomous Systems
   Understand the testing implications across AI system maturity levels.

   
   

Tools & Frameworks Covered:

• DeepEval and GEval for metric-based evaluation

• RAGAS for assessing retrieval-based systems

• Python for implementing automated test pipelines

  
  

By the End of This Course, You Will Be Able To:

• Design a complete AI agent testing strategy from scratch

• Implement functional and non-functional AI validation frameworks

• Apply objective metrics for task, goal, and efficiency evaluation

• Test RAG pipelines for retrieval and answer accuracy

• Distinguish between deterministic, agentic, and autonomous systems

• Build a portfolio project that demonstrates your AI testing expertise