提出高效智能体框架，成本降30%性能仅损失3.3%
Efficient Agents: Building Effective Agents While Reducing Cost
- 基于实证分析优化智能体架构复杂度
- 新框架成本降低28.4%，性能保留96.7%以上
- 适合追求高性价比AI系统的开发者
大型语言模型驱动的智能体虽能处理复杂多步任务，但成本飙升威胁其可扩展性。本文首次系统研究现代智能体系统中的效率-性能权衡问题，针对三个核心问题展开：任务本身需要多少复杂度？何时增加模块会边际收益递减？高效框架设计能带来多大效率提升？在GAIA基准上的实证分析表明，通过选择合适的LLM主干、优化智能体框架设计与测试时扩展策略，可量化评估各维度的效率-性能权衡。基于此，我们提出Efficient Agents框架，在保持96.7%的OWL（领先开源框架）性能的同时，将运营成本从$0.398降至$0.228，成本/轮次指标提升28.4%。研究成果为构建高效高性能智能体系统提供可操作指导，推动AI解决方案的可访问性与可持续性。
原文摘要 · Abstract (English)
The remarkable capabilities of Large Language Model (LLM)-driven agents have enabled sophisticated systems to tackle complex, multi-step tasks, but their escalating costs threaten scalability and accessibility. This work presents the first systematic study of the efficiency-effectiveness trade-off in modern agent systems, addressing the critical need for cost-effective designs without sacrificing performance. We investigate three key questions: (1) How much complexity do agentic tasks inherently require? (2) When do additional modules yield diminishing returns? (3) How much efficiency can be gained through the design of efficient agent frameworks? Through an empirical analysis on the GAIA benchmark, we evaluate the impact of LLM backbone selection, agent framework designs, and test-time scaling strategies. Using the cost-of-pass metric, we quantify the efficiency-performance trade-off across these dimensions. Our findings inform the development of Efficient Agents , a novel agent framework that has an optimal complexity to task requirements. Efficient Agents retains 96.7% of the performance of OWL, one leading open-source agent framework, while reducing operational costs from $0.398 to $0.228, resulting in a 28.4% improvement in cost-of-pass. Our work provides actionable insights for designing efficient, high-performing agent systems, advancing the accessibility and sustainability of AI-driven solutions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。