C-World: A Computer Use Agent Environment Creator

TL;DR

C-World is a versatile environment creation system that enables scalable, on-demand generation of agent environments for improved planning and reasoning in large language models.

Contribution

It introduces a comprehensive environment creation framework with both realistic and synthesized modes, facilitating scalable testing and training of LLM-based agents.

Findings

Planning ability is strong across state-of-the-art LLMs.

Execution remains the main bottleneck in agent performance.

C-World's World Engine correlates highly with real execution and outperforms baselines with limited data.

Abstract

To close the gap between LLM-based agents and humans in planning and reasoning, agents need large-scale, diverse environments for continuous learning -- yet building such environments is itself prohibitively expensive. We present C-World, an environment creation system that enables users to build agent environments on demand. We define a complete agent environment through four components: an Action Space of 5,571 format-unified tools across 204 common applications, a Task Distribution engine that synthesizes long-horizon workflows with wild constraints, a Transition Function implemented as a state controller that injects realistic failures and perturbations, and a Reward Signal combining verifiable metrics with LLM-based judgment. C-World operates in two modes: a realistic mode grounded in live API execution, and a synthesized mode powered by the World Engine, which approximates tool behavior without live service access, enabling scalable environment creation -- including environments for domains and tools that do not yet exist in the real world. Evaluation of nine state-of-the-art LLMs reveals that planning ability is uniformly strong but execution remains the bottleneck, and that constraint following -- not tool invocation -- is the dominant failure mode. The World Engine achieves Spearman $\rho = 0.883$ ranking correlation with real execution, and fine-tuning on just 1,170 C-World trajectories outperforms baselines trained on 119k samples, demonstrating C-World's dual value as a rigorous evaluation environment and a scalable data engine. Our code and data are available at https://ziqiao-git.github.io/C-World/