Title: ROGA: Scaling Generalist Agents for Office Productivity Tasks via Tool Generation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Generalist agent, Office productivity, Tool generation

Abstract: Automatic tool generation (ATG) has emerged as a key approach to enable the automatic adaptation across diverse tasks within a single generalist agent.
Despite their potential, we argue that current ATG agents fail to effectively adapt to realistic environments requiring long context reasoning steps, particularly in office ecosystems involving Excel, Word, and PowerPoint.
We empirically show that current ATG agents underperform by up to 27.43\% compared to the specialized office agent in these tasks.
This performance degradation stems from three critical limitations of prevailing ATG agent paradigms, including insufficient handling of long file contexts, a lack of context sharing across tool calls leading to wrong tool execution, and inefficient tool reuse leading to repeated error-prone generation.

To address these limitations, we propose ROGA, a novel ATG agent framework that introduces four core innovations: (1) a comprehension–operation paradigm for deep file understanding, (2) a dual-reflection mechanism for reliable tool generation, (3) a state-sharing sandbox supporting seamless multi-step tool execution, and (4) a finite-state machine for full tool lifecycle management. 
Comprehensive experiments on widely used benchmarks show that ROGA consistently outperforms existing ATG agents by up to 13.64\% in Pass@1, even matching specialized agents in challenging table reasoning tasks. 
These results underscore ROGA's potential to advance the ATG paradigm, delivering a practical pathway toward building sustainable generalist agents in realistic environments.