AI

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

Read full article at source ↗

Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/10/2026, 04:03:24 UTC.

Briefings

This article presents Synthesis Through Simulation (STS), a schema-free data synthesis paradigm using LLM agents to execute operations against policy-enforcing APIs within simulated enterprise environments. The Generalist Populator (GP) agent achieves 0.88 average marginal fidelity and 100% constraint satisfaction across ten environments without access to database schemas, addressing the structural validity limitations of traditional approaches. The framework is open-sourced via GitHub, with all ten environments and generated datasets available for research.

Key points

• The Generalist Populator (GP) agent achieves 0.88 average marginal fidelity and 100% constraint satisfaction across all ten simulated environments without access to database schemas.
• STS decouples validity enforcement from distribution modeling, allowing each to be addressed independently through policy-enforcing API execution within simulated environments.

Context and limitations

The study covers ten specific simulated enterprise environments; statistical synthesizers were inapplicable to seven environments due to necessary seed data requirements, and schema-privileged agents failed 82% of trajectories on the airline environment due to brittle task composition.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 09/10/2026, 04:45:34 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

View archived briefing on IPFS
bafybeiftfor4nngeoqqy5226tkjuq7rieiumnyupwejtmntxxygqn5kjha