Most AI Evals Are Bullshit: A Practical Guide to Agent Evaluation
hamostaf04 · x · 2026-08-02
The author points out a common pitfall in AI agent evaluation: teams often grade whether the output sounds good rather than evaluating if the agent made the right decision for the business.
The hard part of building useful evals is defining what "good" actually means. Engineers need deep domain expertise—like knowing how to redline a commercial contract—to create reliable evals that accurately measure task success.
Related event: Rethinking AI Agent Evals: Business Decisions Matter More Than Text(4 posts)→
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