LLM AI Agent Evaluations and Observability with Galileo AI

Designed for software engineers, data scientists, and AI practitioners, this comprehensive course on LLM AI Agent Evaluations and Observability with Galileo AI bridges the gap between prototyping and building production-grade AI systems. Instructed by Henry Habib, the curriculum dives deep into the complexities of monitoring, evaluating, and debugging multi-step AI agents. As AI agents move from simple single-call applications to complex, autonomous systems that leverage tool-use, planning, and external retrieval, the risk of silent failures, regressions, and drift increases significantly. This course equips you with the practical skills needed to design robust LLM observability plans, instrument agent workflows, and structure detailed traces. You will learn how to leverage Galileo AI to construct highly realistic evaluation datasets, build custom metrics for groundedness and safety, and implement LLM-as-a-judge patterns to systematically minimize evaluator bias. By mastering production monitoring, error tracing, and continuous integration of evaluation workflows, you will ensure your AI agents remain safe, reliable, and highly performant in real-world environments.