Advanced Evaluation Techniques for AI Agents
summary
Claims a practical evaluation stack for LLM agents: a simple LM router -> skill -> router architecture (90% of deployments), function calling as the core primitive, and frequent failures from bad parameter extraction. Recommends tracing with Phoenix open-source tracing and OpenTelemetry, experiments in CI and online evals, and evaluations that combine LM-as-judge plus human annotation with categorical metrics, explanations, and automated scenario generation; notes a single line of code can spawn ~50 distributed calls.
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chapters (12)
- 0:00Agent architectures
- 3:37State and process design
- 4:47System complexity and tracing
- 5:20Implementation challenges
- 6:46Router design case study
- 10:02Framework ecosystem
- 11:11Moving beyond demos
- 11:37Agent evaluation strategies
- 14:48Convergence evaluation
- 15:07Practical evaluation tools
- 16:22Iterating with experiments
- 17:22Q&A and best practices
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