Efficient Agents: Building Effective Agents While Reducing Cost
Authors: name: Ningning Wang, name: Xavier Hu, name: Pai Liu, name: He Zhu, name: Yue Hou, name: Heyuan Huang, name: Shengyu Zhang, name: Jian Yang, name: Jiaheng Liu, name: Ge Zhang, name: Changwang Zhang, name: Jun Wang, name: Yuchen Eleanor Jiang, name: Wangchunshu Zhou

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.