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    Best frontier AI agent reportedly passes barely more than a third of database benchmark queries

    Weaviate Podcast describes a benchmark built by Shreya Shankar and colleagues with 54 queries spanning 12 datasets and four database systems, testing how AI agents answer questions across databases.

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    TLDR

    Weaviate Podcast says Shreya Shankar counted 25-plus data-agent benchmarks and found none that tested querying multiple databases. It describes the Data Agent Benchmark she built with colleagues at UC Berkeley, Washington University and Hasura to address that gap: 54 queries spanning 12 datasets and four database systems. According to the podcast, the best frontier AI agent passes barely more than a third of the queries. It also describes agents avoiding unfamiliar SQL dialects by exporting tables to flat files and doing the work in Pandas instead.

    Combined views

    10.6K

    4 Sources, first seen 16d ago

    Combined views

    10.6K

    4 Sources, first seen 16d ago

    31 likes
    16d ago
    first seen 16d ago
    31 likes
    8 comments
    18 saves
    10 reposts

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    8 comments
    18 saves
    10 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    4 Sources

    @weaviatepodcastA data agent is an AI agent that answers questions over your databases. Often all of them at once, and that plurality is a big part of the challenge. Enterprises spread their data across dozens of systems, yet Shreya Shankar (@sh_reya) counted 25+ data agent benchmarks and found none that tested querying multiple databases. 🤖 The Data Agent Benchmark she built with colleagues at UC Berkeley, Washington University, and Hasura fills that gap with 54 queries spanning 12 datasets and 4 database systems, and the best frontier agent passes barely more than a third of them. The failure modes expose intrinsic biases in how LLMs query databases. For example, they dodge unfamiliar SQL dialects by dumping tables to flat files and doing everything in Pandas. 🐼 Full conversation on Weaviate Podcast #135: https://www.youtube.com/watch?v=C-fNVPYZrVg
    @CShorten30RT @weaviatepodcast: A data agent is an AI agent that answers questions over your databases. Often all of them at once, and that plurality…
    @sh_reyaDid this podcast a couple of months ago but data agents are more timely than ever! Since then, OpenAI has released a data agent product, and I’m sure more LLM companies will follow suit…and, @ruiyingm1120 will present our paper at EMNLP next month 🎉

    4 Sources

    @weaviatepodcastA data agent is an AI agent that answers questions over your databases. Often all of them at once, and that plurality is a big part of the challenge. Enterprises spread their data across dozens of systems, yet Shreya Shankar (@sh_reya) counted 25+ data agent benchmarks and found none that tested querying multiple databases. 🤖 The Data Agent Benchmark she built with colleagues at UC Berkeley, Washington University, and Hasura fills that gap with 54 queries spanning 12 datasets and 4 database systems, and the best frontier agent passes barely more than a third of them. The failure modes expose intrinsic biases in how LLMs query databases. For example, they dodge unfamiliar SQL dialects by dumping tables to flat files and doing everything in Pandas. 🐼 Full conversation on Weaviate Podcast #135: https://www.youtube.com/watch?v=C-fNVPYZrVg
    @CShorten30RT @weaviatepodcast: A data agent is an AI agent that answers questions over your databases. Often all of them at once, and that plurality…
    @sh_reyaDid this podcast a couple of months ago but data agents are more timely than ever! Since then, OpenAI has released a data agent product, and I’m sure more LLM companies will follow suit…and, @ruiyingm1120 will present our paper at EMNLP next month 🎉