Synthesis Through Simulation: Generating Coherent Enterprise Data via Scalable Agent-System Interaction

Hugging Face Daily Papers Papers
synthetic-data tool-calling-agents enterprise-ai data-synthesis llm-agents simulation

Summary

提出了一种名为 Synthesis Through Simulation (STS) 的无模式数据合成范式，通过让 LLM 智能体在模拟企业环境中调用策略强制 API 来生成结构上天然有效的数据，其 Generalist Populator 智能体在十个环境中达到 0.88 的平均分布保真度和 100% 的约束满足率，且无需访问数据库 schema。

Tool-calling agents have become central to enterprise AI, yet training and evaluating them at scale remains severely constrained due to business and legal restrictions on enterprise systems, data, and database schemas. Tabular data synthesis offers a natural alternative, but its effectiveness is fundamentally limited by structural validity and schema availability, while procedure-based approaches yield the opposite weakness, typically lacking distributional fidelity without per-domain authoring. We introduce **Synthesis Through Simulation** (STS), a **schema--free** data synthesis paradigm in which an LLM agent generates data by executing operations against policy-enforcing APIs within simulated enterprise environments. Because data is generated through the same environment that defines what is valid, STS guarantees structural validity by construction while decoupling validity enforcement from distribution modeling, allowing each to be addressed independently. The **Generalist Populator** (GP), STS's domain-agnostic agent, addresses the remaining challenges of distributional fidelity and synthesis scalability: GP achieves **0.88** average marginal fidelity and **100\% constraint satisfaction** across all ten environments *without access to DB schemas*, while statistical synthesizers are inapplicable to seven due to necessary seed data requirements, and schema-privileged agents fail 82\% of trajectories on airline environment's tightly coupled workflows due to brittle task composition. We open-source the full framework, all ten environments, and generated datasets at https://github.com/SAP/synthesis-through-simulation.
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Source: https://huggingface.co/papers/2610.10549

Abstract

Tool-callingagentshavebecomecentraltoenterpriseAI,yettrainingandevaluatingthematscaleremainsseverelyconstrainedduetobusinessandlegalrestrictionsonenterprisesystems,data,anddatabaseschemas.Tabulardatasynthesisoffersanaturalalternative,butitseffectivenessisfundamentallylimitedbystructuralvalidityandschemaavailability,whileprocedure-basedapproachesyieldtheoppositeweakness,typicallylackingdistributionalfidelitywithoutper-domainauthoring.Weintroduce**SynthesisThroughSimulation**(STS),a**schema--free**datasynthesisparadigminwhichanLLMagentgeneratesdatabyexecutingoperationsagainstpolicy-enforcingAPIswithinsimulatedenterpriseenvironments.Becausedataisgeneratedthroughthesameenvironmentthatdefineswhatisvalid,STSguaranteesstructuralvaliditybyconstructionwhiledecouplingvalidityenforcementfromdistributionmodeling,allowingeachtobeaddressedindependently.The**GeneralistPopulator**(GP),STS’sdomain-agnosticagent,addressestheremainingchallengesofdistributionalfidelityandsynthesisscalability:GPachieves**0.88**averagemarginalfidelityand**100\%constraintsatisfaction**acrossalltenenvironments*withoutaccesstoDBschemas*,whilestatisticalsynthesizersareinapplicabletosevenduetonecessaryseeddatarequirements,andschema-privilegedagentsfail82\%oftrajectoriesonairlineenvironment’stightlycoupledworkflowsduetobrittletaskcomposition.Weopen-sourcethefullframework,alltenenvironments,andgenerateddatasetsathttps://github.com/SAP/synthesis-through-simulation.

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