SynthCorp Generates Enterprise Data-Agent Benchmarks Through Traceable Event Simulation
Summary:SynthCorp creates customizable data estates and benchmarks for LLM-driven data agents by simulating how business events generate and modify enterprise data over time. Each estate combines relational databases, a document store, and a data catalog, accompanied by analytical tasks and executable gold programs. The platform compiles scenarios into event programs, schedules routine and disruption events on a virtual calendar, materializes the resulting state, and records every operation in a causal
Core Idea: Turning Enterprise Evaluation into Replayable Causal Simulation
SynthCorp targets a gap in current evaluations for LLM-driven data agents: static collections of tables, documents, and questions rarely control which capabilities are tested or reproduce how changes in business-event order affect the correct answer. Instead of generating question-answer pairs directly, SynthCorp builds an internally consistent enterprise environment that evolves over time.
Each data estate combines three asset types:
- A relational database for structured operational state;
- A document store for contracts, reports, and other unstructured material;
- A data catalog containing metadata and relationships across tables, fields, and documents.
The platform also produces analytical tasks and executable gold programs. Evaluation can therefore compare not only natural-language responses but also run deterministic reference programs that encode SQL, filtering, aggregation, or cross-asset operations.
How the Simulation Preserves Cross-Asset Consistency
1. Compiling Scenarios into Event Programs
Given a business scenario and requested complexity dimensions, SynthCorp synthesizes reusable event types and compiles each into a program over predefined simulation primitives. This acts like a domain-specific workflow language: scenarios are not turned directly into tables, but pass through an explicit and inspectable event semantics.
The design separates task difficulty from vague prompt engineering. Developers can control complexity through event count, asset coupling, state dependencies, or disruption injection rather than simply asking for harder questions.
2. Scheduling Routine and Disruption Events
Event instances are placed on a virtual calendar. Routine and disruption events are deliberately interleaved so that orders, inventory, payments, customer status, and other fields evolve while references across tables, documents, and catalog entries remain consistent.
The difficult part is not updating one table, but respecting cross-asset transaction boundaries. A single business event may alter a database record, generate a document, and update lineage metadata in the catalog. Independent asset generation would create a random-data benchmark. Preserving event order and causal dependencies is what forces an agent to track state.
3. Materializing State and Recording Causal Provenance
After calendar execution, SynthCorp materializes the relational database, documents, and catalog while recording reads, writes, and derivations. The simulation log is both a debugging artifact and the causal foundation for task generation and answer verification.
Unlike a final-state snapshot, an operation-level log can explain why a field has its current value, which business event created a document, and what upstream state produced an intermediate result. This gives the evaluator a stronger signal than string matching: candidate queries can be audited against the provenance of their claims.
4. Deriving Tasks and Gold Programs from the Log
SynthCorp creates analytical questions from the log and derives gold programs from the executed trajectory. A reference program therefore corresponds to a known, legal, executable state transition rather than to a manually authored answer that may not be reproducible in the generated environment.
This is particularly important for multi-event tasks whose answers depend on ordering, intermediate state, and final aggregation. A complete causal chain provides a more stable criterion than a final answer alone.
How to Interpret the Initial Results
The initial benchmark contains seven data estates and 256 tasks across five domains; the largest estate includes 1,000 operational tables. SynthCorp reports deterministic checks for foreign-key violations, undeclared writes, recurrence violations, and database execution failures, with a near-zero error rate.
These checks can detect structural corruption, such as dangling references, writes not declared in the schema, or events that cannot be replayed. However, passing pipeline validation does not establish realism. Real enterprises contain dirty records, manual updates, access-control boundaries, legacy systems, and incomplete documents, whereas simulation primitives naturally favor well-formed states.
The authors evaluated three models across four data-context settings and report that the benchmark separates context conditions and model tiers. Provenance-based auditing also verifies gold programs without manual annotation, including on composite multi-event tasks. This demonstrates a high degree of evaluation automation, but the abstract does not disclose model identities, scores, confidence intervals, repeated-run variance, or statistical significance. The results should therefore not be interpreted as a definitive model ranking.
Engineering Trade-offs
SynthCorp exchanges realism for controllability, automation, and deterministic verification. Those gains come with meaningful engineering costs:
- Controllability versus realism. Predefined primitives make consistency and verification easier, but a narrow primitive library can make generated tasks resemble the same structural patterns.
- Traceability versus storage and execution cost. Operation-level logs support causal auditing and gold-program derivation, but logging writes and replaying large estates adds storage and compute overhead.
- Automation versus reference bias. Deriving gold programs from simulation trajectories removes manual annotation noise, yet proves consistency with simulated semantics rather than correctness under real business policy.
- Scalable difficulty versus leakage. If generated tasks, templates, and answers enter model-training corpora, models may learn the generator's signatures instead of enterprise reasoning.
Practical Adoption Guidance
Teams should treat SynthCorp as a programmable benchmark factory, not as a direct substitute for real enterprise data.
First, define an event ontology that specifies which state changes have side effects, which operations require atomic updates across assets, and which disruptions alter subsequent query semantics. Second, run gold programs in an isolated database sandbox with explicit permissions and temporal cutoffs. Third, manually audit a sample of tasks for whether they require genuine inference or can be solved by recognizing templates and field names. Finally, separate training and evaluation data at the event-type and template level, while retaining real enterprise samples as an external validation set.
Versioning also matters. Schemas, event primitives, document templates, catalog metadata, and gold programs should all be pinned. Without that discipline, a benchmark update can make performance changes indistinguishable from changes in task distribution.
Potential Impact
If the platform, generation configuration, and benchmark assets are released, SynthCorp could shift data-agent evaluation from fixed question sets toward capability-targeted, causally verified generation. Its primary contribution is not a better model score; it is a way to create regression tests with controlled event complexity, state dependencies, and cross-asset coupling.
The abstract does not disclose the open-source license, repository status, generation time, storage cost per estate, or validation on real enterprises. Until those details are available, SynthCorp is best viewed as a research-oriented benchmark generator. Its path to production depends on task diversity, auditability, and the long-term cost of maintaining the simulation stack.
— END —Source: ArXiv AI (cs.AI) (2026-11-13)
Tags: #SynthCorp #Data Agents #Enterprise Data #Executable Evaluation #Causal Provenance
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