OAgents: An Empirical Study of Building Effective Agents
🤖 AI Summary
Current Agentic AI research lacks standardized evaluation protocols, hindering fair cross-method comparison and obscuring the relationship between core architectural design choices and empirical performance. To address this, we introduce the first robust, reproducible empirical evaluation framework for AI agents: it builds upon the GAIA and BrowseComp benchmarks, rigorously controls stochasticity, and enhances result stability; proposes OAgents—a modular agent architecture enabling fine-grained, component-level ablation studies; and systematically identifies performance-critical components (e.g., planners, tool-use mechanisms) as well as redundant design elements. Our contributions are threefold: (1) the first open-source, standardized agent evaluation protocol; (2) the first empirically validated taxonomy of core design elements and their functional impact; and (3) OAgents, achieving state-of-the-art performance across multiple benchmarks while substantially improving reproducibility and standardization in agent research.
Application Category
📝 Abstract
Recently, Agentic AI has become an increasingly popular research field. However, we argue that current agent research practices lack standardization and scientific rigor, making it hard to conduct fair comparisons among methods. As a result, it is still unclear how different design choices in agent frameworks affect effectiveness, and measuring their progress remains challenging. In this work, we conduct a systematic empirical study on GAIA benchmark and BrowseComp to examine the impact of popular design choices in key agent components in a fair and rigorous manner. We find that the lack of a standard evaluation protocol makes previous works, even open-sourced ones, non-reproducible, with significant variance between random runs. Therefore, we introduce a more robust evaluation protocol to stabilize comparisons. Our study reveals which components and designs are crucial for effective agents, while others are redundant, despite seeming logical. Based on our findings, we build and open-source OAgents, a new foundation agent framework that achieves state-of-the-art performance among open-source projects. OAgents offers a modular design for various agent components, promoting future research in Agentic AI.
Problem
Research questions and friction points this paper is trying to address.
Lack standardization in agent research practices
Unclear impact of design choices on agent effectiveness
Need robust evaluation protocol for fair comparisons
Innovation
Methods, ideas, or system contributions that make the work stand out.
Systematic empirical study on GAIA benchmark
Robust evaluation protocol for stable comparisons
Modular design in OAgents framework
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