Efficient Agents: Building Effective Agents While Reducing Cost
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.
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Ningning Wang
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Xavier Hu
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Pai Liu
689385f0741a16f544fbcd8f
He Zhu
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Yue Hou
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Heyuan Huang
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Shengyu Zhang
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Jian Yang
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Jiaheng Liu
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Ge Zhang
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2025-08-13T07:28:14.744Z
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Ge Zhang
user
zhangysk
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Changwang Zhang
claimed_verified
2025-09-06T10:57:35.635Z
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Changwang ZHANG
user
mLeoKing
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Jun Wang
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Yuchen Eleanor Jiang
689385f0741a16f544fbcd99
Wangchunshu Zhou
Efficient Agents: Building Effective Agents While Reducing Cost
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.
A study on the efficiency-effectiveness trade-off in LLM-driven agent systems identifies optimal agent framework design to reduce costs while maintaining performance.
Large Language Model LLM agent systems efficiency-effectiveness trade-off agentic tasks agent framework GAIA benchmark LLM backbone test-time scaling strategies cost-of-pass Efficient Agents OWL