Ensure AI Agents Work: Evaluation Frameworks for Scaling Success
summary
Claims a concrete evaluation stack: routers, skills, memory instrumented as traces; metrics include router correctness rate, whether skill calls pass the right parameters, chunk-level relevance and answer correctness, and convergence (step count) where the same skill surprisingly takes wildly different numbers of steps across LLMs. Voice agents add audio-chunk quality and transcription accuracy. Demonstrates an eval product dogfooding its own co‑pilot that runs per‑trace checks, surfaces which component failed, and shows observability patterns that catch production regressions.
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chapters (8)
- 0:00Introduction
- 1:35Textbased agents
- 2:12Multimodal agents
- 3:43Routers
- 5:16Memory
- 5:54Traces
- 8:25Router
- 13:02Example
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