Enterprise-JEPA: Learning Latent Tool-Use Dynamics for Efficient Agent Planning

Enterprise workflows require tool-using agents to coordinate actions across applications, maintain state over long horizons, and avoid costly execution errors. World models can improve planning by predicting the consequences of candidate tool actions, but existing LLM-based approaches rely on autoregressive generation of imagined observations, making repeated planning computationally expensive. We introduce Enterprise-JEPA, the first action-conditioned latent world model for enterprise tool-using environments. Given the task context, interaction history, and a candidate action, Enterprise-JEPA predicts the latent representation of the resulting tool observation and decodes seven interpretable operational signals: action type, execution status, error, progress, side effects, information sufficiency, and task completion. A two-stage training procedure first learns latent dynamics from unlabeled trajectories and then fits operational-state heads using annotated transitions. Scaling unlabeled pretraining improves structured next-state prediction and surpasses a similarly sized LLM-based world model. Enterprise-JEPA also delivers strong downstream performance. With beam search, it consistently outperforms a no-world-model baseline across all five benchmarks, while its latent rollout mechanism is substantially faster than an LLM-based world model. These results demonstrate that latent world modeling enables more effective and efficient planning for enterprise tool-using agents, improving decision quality while reducing inference latency. Code is available at https://anonymous.4open.science/r/enterprise_jepa-8421.