AI Agent Evaluation

Benchmarks, automated evaluation methods, trajectory analysis, and production monitoring for AI agents.

Curriculum

A structured path through the course content.

Core concepts, challenges, and frameworks for evaluating AI agents.

Major benchmarks, leaderboards, and evaluation datasets.

LLM-as-judge, rubric-based scoring, and automated metrics.

Analyzing agent reasoning chains and decision processes.

Statistical rigor, confidence intervals, and significance testing.

Balancing evaluation cost, quality, and speed.

Red teaming, safety benchmarks, and alignment testing.

Frameworks, platforms, and infrastructure for evaluation.

Online evaluation, A/B testing, and production metrics.

Open problems and emerging directions in agent evaluation.