Aakash Gupta argues eval adoption fails when teams treat evals as a late-stage checkbox; Braintrust built the eval first for Loop, let every model fail, then iterated until Claude 3.7 could meet the bar.
The three mistakes that kill eval adoption at AI teams:
Running evals only at the end. Only having evals that pass. Siloing evals to engineers.
Braintrust shipped their agent product Loop by building the eval before any model could pass it. Every model failed. Then Claude 3.7
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