Efficient Agents: Building Effective Agents While Reducing Cost

TL;DR

This paper systematically studies the efficiency-effectiveness trade-off in LLM-driven agents, proposing a new framework that maintains high performance while significantly reducing operational costs, thus enhancing scalability and accessibility.

Contribution

It introduces the Efficient Agents framework, optimizing agent complexity and design to balance performance and cost, supported by empirical analysis on the GAIA benchmark.

Findings

Efficient Agents retains 96.7% of OWL's performance.

Operational costs are reduced from $0.398 to $0.228.

Achieves a 28.4% improvement in cost-of-pass.

Abstract

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.