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    Rohan Paul summarizes Scale AI paper urging cost-based agent ranking

    Rohan Paul summarizes a Scale AI and UC paper introducing the READY framework for human-AI evaluation.

    RP
    2 Sources, 26d ago, first seen 26d ago

    TLDR

    Rohan Paul posted a summary of a Scale AI and University of California paper. According to his post, two agents can post nearly identical benchmark scores yet demand very different amounts of human review. Paul describes READY as a framework that evaluates the combined agent-plus-oversight system. Per his summary, it measures required reliability, cases an agent can handle alone, review volume needed, and total policy cost. Paul concludes that enterprise teams should rank agents by the expense of reaching reliable deployment instead of raw accuracy numbers.

    Combined views

    5.9K

    2 Sources, first seen 26d ago

    Combined views

    5.9K

    2 Sources, first seen 26d ago

    38 likes
    38 likes
    14 comments
    29 saves
    18 reposts

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    14 comments
    29 saves
    18 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    2 Sources

    @rohanpaul_aiScale AI + Univ of California paper shows 2 agents can score almost the same yet need very different human review, so enterprise teams should rank agents by the cost of reliable deployment, not benchmark accuracy. READY evaluates the agent together with the human review around it. It asks how much oversight the agent needs to reach the reliability your workflow requires. READY argues that enterprise evaluation should measure the human-AI system: what reliability you need, which cases the agent can handle alone, how much human review is required, and what that policy costs.

    2 Sources

    @rohanpaul_aiScale AI + Univ of California paper shows 2 agents can score almost the same yet need very different human review, so enterprise teams should rank agents by the cost of reliable deployment, not benchmark accuracy. READY evaluates the agent together with the human review around it. It asks how much oversight the agent needs to reach the reliability your workflow requires. READY argues that enterprise evaluation should measure the human-AI system: what reliability you need, which cases the agent can handle alone, how much human review is required, and what that policy costs.