LLM AI Agent Evaluations and Observability with Galileo AI

About this course

Important note: Please click the video for more information. This course is hands-on and practical, designed for developers, AI engineers, founders, and teams building real LLM systems and AI agents. It’s also ideal for anyone interested in LLM observability and AI evaluations and who wants to apply these skills to future agentic apps. You should have some knowledge in AI agents and how they are built.

Note this is the complete guide to AI Observability and Evaluations. We go both into theory and practice, using Galileo AI as the AI Agent / LLM monitoring platform. Learners also get access to all resources and the GitHub code / notebooks used in the course.

Why does LLM Observability and Evaluations Matter?

LLMs are powerful, but they are unpredictable. They hallucinate, they fail silently, they behave differently across prompts and versions. There is a big difference between building an AI agentic / LLM system and actually "productionalizing" it. What if the LLM starts producing offensive content? What if tools embedded within agents fail silently? How do you measure model quality degradation? Traditional monitoring and building methods don't work. You need to run experiments, build custom evaluations, and set up alerts that assess subjective measures. Dashboards built to track classification accuracy are not designed for open-ended text generation. Log pipelines created for predictable APIs cannot capture reasoning steps, tool usage, or why an agent failed.

As a result, most teams fall back on manual spot checks, gut feel, and endless prompt tweaking. That approach might work in the beginning, but it does not scale.

What we need instead is a systematic way to measure, monitor, evaluate, and continuously improve LLM and agent systems. That is where observability and structured evaluation come in.

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