Troubleshooting AI Agent Regressions: Foundations of AI Engineering

When an AI agent suddenly stops behaving as expected, finding the root cause can feel like chasing a moving target. Is it a change in the underlying language model, a prompt engineering issue, or a simple bug in your application logic? This course teaches you how to systematically troubleshoot and resolve regressions in AI-powered applications. You will learn how to isolate variables, analyze system behavior, and confidently restore your agent's performance. What you'll learn: Understand the core differences between deterministic code bugs and probabilistic model regressions; Isolate behavioral anomalies in AI agents, such as sudden failures in structured formatting or tool calling; Establish basic evaluation pipelines to measure and track agent performance over time; Apply systematic debugging workflows to pin down regressions without guessing; Configure structured outputs and schema validations to prevent formatting failures; Implement version control and regression testing practices for system prompts. The course begins with foundational concepts of AI agent architectures and the nature of non-deterministic bugs. You will then progress through written walkthroughs, analyzing mock regression scenarios and learning how to set up systematic tests to isolate the root cause. This course is designed for beginner AI engineers, software developers, and tech enthusiasts looking to build reliable AI systems, with no advanced machine learning background required. Start mastering the art of AI agent troubleshooting and build more resilient systems today.