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2025-12-24 20:58:02 -

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2025-12-24 20:58:02 -

Stanford and Harvard researchers tackle one of the most frustrating patterns in AI right now: why agentic systems nail the demo but crumble in production. The paper digs into the core issues—unreliable tool use, weak long-term planning, and poor generalization. If you've ever wondered why your AI agent works perfectly in testing then fails spectacularly on real tasks, this explains the mechanics behind it.

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This AI Paper from Stanford and Harvard Explains Why Most ‘Agentic AI’ Systems Feel Impressive in Demos and then Completely Fall Apart in Real Use

Agentic AI systems sit on top of large language models and connect to tools, memory, and external environments. They already support scientific discovery, software development, and clinical research, yet they still struggle with unreliable tool use, weak long horizon planning, and poor generalization. The latest research paper ‘Adaptation of Agentic AI‘ from Stanford, Harvard, UC […] The post This AI Paper from Stanford and Harvard Explains Why Most ‘Agentic AI’ Sys

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