AI Research
Agentic Frameworks for Reasoning Tasks: An Empirical Study
An empirical analysis reveals critical bottlenecks in AI agent frameworks including cost spikes and orchestration failures.
Recent advances in agentic frameworks have enabled AI agents to perform complex reasoning and decision-making. However, evidence comparing their reasoning performance, efficiency, and practical suitability remains limited. To address this gap, we empirically evaluate 22 widely us...
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ArXiv CS.AI
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ArXiv CS.AI
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This empirical study highlights that the bottleneck for modern AI agents is often inefficient orchestration rather than model intelligence. Developers must prioritize robust memory management and failure recovery mechanisms to prevent massive cost overruns and performance stagnation.
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