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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 MarkTechPost

 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’ Systems Feel Impressive in Demos and then Completely Fall Apart in Real Use appeared first on MarkTechPost. Read More  

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