OrgForge: A Multi-Agent Simulation Framework for Verifiable Synthetic Corporate Corpora

TL;DR

OrgForge is an open-source multi-agent simulation framework that creates verifiable synthetic organizational data by modeling processes rather than documents, improving fidelity over LLM-generated corpora.

Contribution

It introduces a deterministic simulation engine enforcing a physics-cognition boundary, generating traceable organizational artifacts without hallucination artifacts.

Findings

Achieves 0.46 improvement in prose-to-ground-truth fidelity over LLM baselines.

Simulates organizational processes producing cross-artifact causal cascades.

Identifies a hallucination failure mode in chained LLM document generation.

Abstract

Building and evaluating enterprise AI systems requires synthetic organizational corpora that are internally consistent, temporally structured, and cross-artifact traceable. Existing corpora either carry legal constraints or inherit hallucination artifacts from the generating LLMs, silently corrupting results when timestamps or facts contradict across documents and reinforcing those errors during training. We present OrgForge, an open-source multi-agent simulation framework that enforces a strict physics-cognition boundary: a deterministic Python engine maintains a SimEvent ground-truth bus while LLMs generate only surface prose. OrgForge simulates the organizational processes that produce documents, not the documents themselves. Engineers leave mid-sprint, triggering incident handoffs and CRM ownership lapses. Knowledge gaps emerge when under-documented systems break and recover through organic documentation and incident resolution. Customer emails fire only when simulation state warrants contact; silence is verifiable ground truth. A live CRM state machine extends the physics-cognition boundary to the customer boundary, producing cross-system causal cascades spanning engineering incidents, support escalation, deal risk flagging, and SLA-adjusted invoices. The framework generates fifteen interleaved artifact categories traceable to a shared immutable event log. Four graph-dynamic subsystems govern organizational behavior independently of any LLM. An embedding-based ticket assignment system using the Hungarian algorithm makes the simulation domain-agnostic. An empirical evaluation across ten incidents demonstrates a 0.46 absolute improvement in prose-to-ground-truth fidelity over chained LLM baselines, and isolates a consistent hallucination failure mode in which chaining propagates fabricated facts faithfully across documents without correcting them.