Turbulence is rarely that simple. It’s too scattered, too mercurial, too easily triggered by weather patterns that trigger other patterns in an endless cascade. “It’s not just one thing that’s going on,” Bob Sharman, an atmospheric scientist at NCAR, told me. “It’s not just atmospheric convection. It’s not just wind flowing over mountains. It’s everything going on all the time and interacting.” Sharman is one of the country’s preëminent authorities on turbulence prediction. The computer models that he has built can predict where rough air is most likely to arise. “The problem is,” he said, “when we go to meetings with the airline industry and suggest a probabilistic approach, a pilot will stand up and say, ‘No! I want you to tell me if there will be turbulence at this place, at this time.’ ” Sharman threw up his hands. “Nobody knows that. I understand that, in theory, you would want that. But, in practice, that is just not possible.”
Where tracing platforms evaluate turn by turn, Cekura evaluates the full session. Imagine a banking agent where the user fails verification in step 1, but the agent hallucinates and proceeds anyway. A turn-based evaluator sees step 3 (address confirmation) and marks it green - the right question was asked. Cekura's judge sees the full transcript and flags the session as failed because verification never succeeded.Try us out at https://www.cekura.ai - 7-day free trial, no credit card required. Paid plans from $30/month.We also put together a product video if you'd like to see it in action: https://www.youtube.com/watch?v=n8FFKv1-nMw. The first minute dives into quick onboarding - and if you want to jump straight to the results, skip to 8:40.Curious what the HN community is doing - how are you testing behavioral regressions in your agents? What failure modes have hurt you most? Happy to dig in below!
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