AI

SynthCorp: A Platform for Creating Benchmarks for Data Agents

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 13/11/2026, 07:48:24 UTC.

Briefings

SynthCorp is a new platform that simulates how business events create and modify corporate data over time, generating customizable benchmarks for LLM-driven data agents. The system synthesizes reusable event types into executable programs, schedules events on a virtual calendar to maintain data consistency, and executes simulations to produce verified gold programs. Initial evaluations tested seven data estates with up to 1,000 operational tables and 256 tasks across five domains, achieving near-zero error rates on deterministic pipeline checks while introducing cross-asset complexities. Three models were evaluated across four data-context settings, demonstrating that the benchmarks successfully separate data contexts from model tiers. Auditing candidate queries against causal provenance in the simulation log allows verification of gold programs without manual annotation, including on complex multi-event tasks that expose reasoning failures in state-of-the-art agents.

Key points

• SynthCorp generates initial evaluation benchmarks comprising seven data estates with up to 1,000 operational tables and 256 tasks across five domains.
• The platform separates data contexts and model tiers by evaluating three models across four data-context settings using verified gold programs derived from simulation logs.

Context

This research addresses the saturation of existing benchmarks for LLM-driven data agents, which cannot target specific capabilities. SynthCorp fills this gap by simulating business events to produce and modify corporate data over time, enabling the creation of challenging, enterprise-grade benchmarks.

Limitations

The evidence is limited to a single preprint (arXiv:2611.14765v1) from November 2026, which may not reflect final implementation or long-term performance. The study uses synthetic data estates with deterministic checks, so real-world error rates and cross-asset complexities may differ. Evaluation results are based on initial testing with three models and four data-context settings, limiting generalizability to other agent architectures or domains.

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