EnterpriseWorld: A Realistic World for Enterprise Long-horizon Agents

In enterprise workflows, agents must carry the results, records, and state changes of earlier operations into later ones, often across systems. Existing benchmarks increasingly capture complex, stateful and long-horizon tasks, but rarely make the dependencies between tasks explicit, verified and controllable, limiting both diagnosis of workflow failures and controlled evaluation of changing task relationships. We therefore introduce EnterpriseFlow, a programmatic framework for constructing enterprise workflows around four design principles: realistic enterprise operations, long-horizon coordination, structural control over task dependencies, and verifiable local and cross-task outcomes. Using this framework, we construct EnterpriseWorld, a benchmark of 200 cross-system workflows with 20 operations each in a shared executable environment. Dependencies are explicitly grounded in results and state changes produced by earlier operations, verified by tracing input origins and testing whether upstream errors affect downstream success, and further supported by a human audit with 97.2% agreement. We also construct paired workflow variants that either modify the required dependency topology or add task/tool distractors while preserving it. Our evaluation measures local outcomes, progress along valid dependency chains, and workflow completion, enabling process-level diagnosis of where execution breaks down. Across five frontier models, GPT-6 Astra completes only 46.5% of full workflows, while completion drops by 32-46 points as execution extends across longer dependency chains. Ablation analysis further shows that supplying the correct intermediate results from earlier operations in the dependency chain helps substantially more than exposing the dependency structure, with most dependency failures originating when those results are produced; matched perturbations also break 14-26% of previously successful workflows.