Evaluating Your AI Agent
summary
A running wine-recommendation agent demonstrates tool calls (Google Maps, retrieval/RAG), dialogue turns and planning failures, and evaluation highlights three concrete views: Final Response, Trajectory, Single Step. Evaluation uses curated datasets mixing common and risky cases, trace-based observability (trace logs), benchmarks like AgentBench and τ-bench, and LLM-as-judge calibration to catch biases (position, verbosity, self-enhancement). Manual labeling seeds a judge split train/test; initial judge agreement hits ~63% so iterative refinement and few-shot prompts plus explanation requirements increase trust before production.
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chapters (6)
- 0:00Why evaluation matters
- 8:35Building evaluation dataset
- 16:24End-to-end testing and traces
- 24:13Judge model and biases
- 28:05Analysis and optimization
- 31:59Summary and final recommendations
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