Debugging AI Agents: Data Analysis for Better Reliability

AI agents often generate incorrect answers with absolute certainty, making system reliability a major challenge in modern software. Understanding why these errors happen is the first step toward building systems users can trust. This text-based course teaches you how to diagnose AI agent failures using structured data analysis techniques. You will learn to distinguish between genuine hallucinations and logic errors, enabling you to systematically improve agent performance and decision-making. What you'll learn: Understand the fundamental differences between AI hallucinations and standard logic errors; Apply data analysis techniques to trace and evaluate agent decision pathways; Analyze prompt performance and retrieval-augmented generation inputs to find failure points; Implement basic confidence-scoring methods to catch incorrect agent outputs; Design simple evaluation datasets to measure agent reliability over time. You will start with core definitions of agent architectures and common failure modes before moving into step-by-step analysis workflows and practical text-based debugging exercises. This course is designed for beginners, data analysts, and aspiring AI developers with no advanced machine learning background required. Start reading today to make your AI applications more predictable and reliable.