Reading the agent traces is how you make the call your eval can't

In an exploration of AI-driven application development, the text highlights the complexities and challenges of relying on artificial intelligence agents to handle user queries, particularly in the context of a schedule builder for an AI conference. The discussion centers on the limitations of AI models in providing accurate information, as exemplified by an agent incorrectly naming conference speakers based on fabricated data. The author emphasizes the importance of thorough testing and evaluation (evals) to catch such errors, suggesting strategies like groundedness checks to ensure data accuracy. By tracing errors back to their source, developers can make informed decisions on model selection, routing, and prompt writing, balancing cost, quality, and speed. The piece advocates for a proactive approach in monitoring agent interactions through telemetry and highlights the need for human judgment in making trade-offs that automated evaluations cannot address.

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