AI Engineer · October 5, 2026

Evaluate agent tool results beyond HTTP success

video Model Evaluation AI Engineering

Evaluate agent tool results beyond HTTP success video thumbnail

Why it matters

Laurie Voss’s toy-store exercise shows an agent receiving successful HTTP responses while returning poor results because search handling, age filters and price fields are inadequate. Tool traces expose the gap between transport success and task correctness. The later working demonstration copies an existing completed application, so it is not a controlled test of an autonomous repair system or an equal-budget performance comparison. The transferable method is to define task-level checks, inspect failed tool interactions and rerun evaluations after changing the implementation.

My takeaway: Create fixtures for empty searches, missing prices and age-filter boundaries. Rerun the same queries with the same budgets before and after a fix, include older passing cases, and verify returned product data rather than accepting status codes or a fluent final answer.

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