The authors create the first large-scale dataset and taxonomy of failure modes in multi-agent LLM systems to explain their limited performance gains.
Specifications: The miss- ing link to making the development of LLM systems an engineering discipline
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AI agents should shift from on-the-fly plan synthesis to invoking pre-engineered, tested, and reusable workflows stored in an AI Workflow Store to gain reliability and security.
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Why Do Multi-Agent LLM Systems Fail?
The authors create the first large-scale dataset and taxonomy of failure modes in multi-agent LLM systems to explain their limited performance gains.
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Engineering Robustness into Personal Agents with the AI Workflow Store
AI agents should shift from on-the-fly plan synthesis to invoking pre-engineered, tested, and reusable workflows stored in an AI Workflow Store to gain reliability and security.