Evals Must Test Decisions and Outcomes, Not Just Polished Answers
hamostaf04 · x · 2026-08-02
The article argues that most current AI agent evals are flawed because they grade whether an agent sounds helpful, not whether it does the job right. For instance, a support agent might approve an over-limit refund, or a research agent might cite wrong sources smoothly. Effective evals must test decisions, tool use, permissions, and real business outcomes, rather than just the final response.
Related event: Rethinking AI Agent Evals: Business Decisions Matter More Than Text(4 posts)→
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