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Sayash Kapoor

What Sayash Kapoor thinks about AI agents

@sayash-kapoor · 20 positions · On ppll ↗

PhD candidate in computer science at Princeton's Center for Information Technology Policy; researches the societal impact of AI and co-writes AI as Normal Technology with Arvind Narayanan.

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Only Sayash Kapoor on AI agents. See everyone on AI agents

3 dated positions, 2026, in their own words. Our reading of what Sayash Kapoor has said — not written or endorsed by them.

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

  1. Our readingConducting open-ended research remains challenging for frontier AI agents.

    Their wordsOur results suggest that conducting open-ended research remains challenging for frontier AI agents.

    ↗AI agents can't yet do open-ended AI researchnormaltech.ai 1st of 2 in this piece

  2. 5 months earlier

  3. Our readingReliability lags capability in AI agents, and will remain a barrier to deployment unless effort goes into improving it as a separate dimension from accuracy.

    Their wordsOur view is that reliability lags capability, and that reliability will remain a barrier to deployment unless researchers and developers focus effort on improving reliability as a separate dimension from accuracy.

    ↗New Paper: Towards a science of AI agent reliabilitynormaltech.ai 1st of 3 in this piece

  5. Our readingAutonomous AI agents in high-stakes contexts need 99.9% to 99.999% accuracy before reliability stops being an issue, and LLM-based agents are not on track to reach that.

    LLMs

    Their wordsOur view is that for autonomous operation in high-stakes contexts, we need 3-5 "nines" of performance - 99.9% to 99.999% accuracy - in order for reliability to become a non-issue, and we don't think LLM-based agents are on track to reach such a threshold.

    ↗New Paper: Towards a science of AI agent reliabilitynormaltech.ai 2nd of 3 in this piece