Latent Workflow Discovery for Enterprise World Models: Grounding General-Purpose Agents in Organizational Context

Enterprise agents must connect user goals to organization-specific records, tool dependencies, and workflow prerequisites. We formulate experience reuse as latent workflow discovery and propose Graph World Models (GWMs), combining a workflow graph, language model, and software harness. GWM builds a world model for a target enterprise from interaction histories, storing inferred workflow states, observed successors, outcomes, support counts, and examples. The harness retrieves evidence for model calls that advise the policy or select its candidates. We evaluate within-benchmark, cross-benchmark, and cross-model reuse on customer-relationship-management tasks in CRMArena-Pro and enterprise-operation tasks in EnterpriseOps-Gym. By combining graph-structured workflow memory, advice, and candidate selection, GWM improves CRM task success over direct generation for both Qwen and Gemma without policy retraining. Qwen-derived experience supports same-model and cross-model guidance, and the large-graph configuration further improves Gemma success over the small-graph configuration. Controlled comparisons characterize the incremental effects of historical evidence and candidate selection. Our benchmark-agnostic analysis separates abstraction loss, candidate availability, and selection error; in historical CRM pools, missing successful candidates account for more failures than misranking available ones. We release GWM as a vLLM extension with loadable workflow-graph adapters, supporting graph construction and online/offline inference at https://github.com/iclr-gwm/gwm