Enterprise Agents Need Runtime Context, Not Just Learned World Models
A recent study published on arXiv questions the belief that enterprise agents are required to develop world models using historical data. The researchers contend that in enterprise environments, where transition dynamics are dictated by tenant-specific business logic that changes with each deployment and evolves, models based on previous transitions become fragile during shifts in deployment. They suggest the use of enterprise discovery agents that analyze system configurations during runtime to capture relevant dynamics, enhancing offline training. Additionally, they present CascadeBench, a benchmark focused on reasoning for this context. The paper, arXiv:2605.12178, explores whether agents must learn dynamics when rules can be accessed during inference, showing that runtime discovery effectively grounds predictions in the current system instance.
Key facts
- Paper arXiv:2605.12178 challenges the need for learned world models in enterprise systems.
- Enterprise dynamics are defined by tenant-specific business logic that varies and evolves.
- Models trained on historical transitions become brittle under deployment shift.
- Authors propose enterprise discovery agents that read configuration at runtime.
- Runtime discovery complements offline training by grounding predictions in the active instance.
- CascadeBench is introduced as a reasoning-focused benchmark.
- The paper asks if agents need to learn dynamics when rules are readable at inference time.
- Empirical results show runtime discovery improves prediction accuracy.
Entities
Institutions
- arXiv