Do Enterprise Systems Need Learned World Models? The Importance of Context to Infer Dynamics
Summary
This paper investigates whether enterprise agents need learned world models or can instead rely on runtime discovery of system configurations. It introduces 'enterprise discovery agents' and a benchmark called CascadeBench, demonstrating that reading runtime context yields better robustness against deployment shifts in dynamic enterprise environments.
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Paper page - Do Enterprise Systems Need Learned World Models? The Importance of Context to Infer Dynamics
Source: https://huggingface.co/papers/2605.12178 Authors:
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Abstract
Enterprise discovery agents that read system configuration at runtime outperform traditional world models in configurable environments where dynamics change over time.
World modelsenable agents to anticipate the effects of their actions by internalizing environment dynamics. Inenterprise systems, however, these dynamics are often defined by tenant-specific business logic that varies across deployments and evolves over time, making models trained on historical transitions brittle underdeployment shift. We ask a question the world-models literature has not addressed: when the rules can be read at inference time, does an agent still need to learn them? We argue, and demonstrate empirically, that in settings wheretransition dynamicsare configurable and readable,runtime discoverycomplements offline training by grounding predictions in the active system instance. We proposeenterprise discovery agents, which recover relevanttransition dynamicsat runtime by reading the system’s configuration rather than relying solely on internalized representations. We introduceCascadeBench, a reasoning-focused benchmark for enterprise cascade prediction that adopts the evaluation methodology ofWorld of Workflowson diverse synthetic environments, and use it together with deployment-shift evaluation to show that offline-trainedworld modelscan perform well in-distribution but degrade as dynamics change, whereas discovery-based agents are more robust under shift by grounding their predictions in the current instance. Our findings suggest that, in configurable enterprise environments, agents should not rely solely on fixed internalized dynamics, but should incorporate mechanisms for discovering relevant transition logic at runtime.
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