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Jeff Dean

What Jeff Dean thinks about AI agents

@jeff-dean · 22 positions · On ppll ↗

Chief Scientist at Google DeepMind and Google Research, at Google since 1999, where he co-created MapReduce, Bigtable, TensorFlow and the TPU.

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Only Jeff Dean on AI agents. See everyone on AI agents

4 dated positions, 2026, in their own words. Our reading of what Jeff Dean has said — not written or endorsed by them.

4 positions so far — this page is not yet offered to search engines.

  1. Our readingAgents can already be run for days or weeks on a single hard problem, and almost nobody has internalised that.

    Their wordsI mean I think um probably one thing is people don't quite realize how possible it is to have you know agent-based systems that can run not just for an hour or two hours on a problem you care about but for some problem domains and with highly capable models underlying them you can get them to run for days or weeks and do really really complicated tasks

    ↗Jeff Dean: The 1% Rule for Building in AIyoutube.com 3rd of 22 in this recording

  3. Our readingYou improve a model you cannot retrain by writing better guidelines and skills for it, not by adjusting its parameters.

    Their wordsAnd often you can actually make the model work better and succeed at that kind of problem by not just adjusting the model parameters which is hard to do from the outside but from you know creating better guidelines for the model you know writing skills for the model to know how to use different tools that would be incredibly useful for solving this particular class of problem.

    ↗Jeff Dean: The 1% Rule for Building in AIyoutube.com 9th of 22 in this recording

  5. Our readingLong-running agents fail because they drift off the distribution they were trained on, and degrade further the farther out they get.

    Their wordsUm, and sometimes that's because the model is trying to do something it doesn't have a lot of experience doing. So it's been trained on a whole set of things and as soon as you get a little bit off the distribution of things it knows how to do then like most machine learning models it will you know its performance will suddenly will start to degrade and the farther you get off the comfort zone of what it knows how to do the the more likely it is to to not work as well.

    ↗Jeff Dean: The 1% Rule for Building in AIyoutube.com 10th of 22 in this recording

  7. Our readingSpending compute at inference time to search over plausible solutions is the general fix for unreliable long agent runs.

    Their wordsUm, and that's a very very useful general technique is you know inference time compute to perform search over plausible ways of solving the problem that can get much much higher performance or much more reliability in longunning agent flows.

    ↗Jeff Dean: The 1% Rule for Building in AIyoutube.com 11th of 22 in this recording