Simulate before deciding.
Decide with evidence.

Each simulation agent is grounded in a distinct identity — role-specific memories, behavioural priors, stance calibration, and institutional incentives as of the scenario start date. Agents are never anchored to expected outcomes. They reason from their own interests, react to each other’s positions, and update across discrete rounds. What emerges is not a prediction — it is a structured model of how divergent reasoning propagates through a complex stakeholder system. This is Multi-Agent Divergent Thinking: the antidote to institutional groupthink.

Before a single agent acts, AlphaGraph constructs a knowledge graph from the scenario brief, seed materials, and external data sources. Entities, relationships, and causal structures are extracted via LLM-powered Named Entity Recognition and mapped into a queryable graph via Zep GraphRAG. This graph becomes the shared world model that grounds every agent’s reasoning — ensuring High-Fidelity Agentic Grounding rather than unanchored generation. The abstraction layer is what separates simulation from speculation.

Decisions don’t fail in isolation — they cascade. A pricing change triggers competitor repositioning, which shifts media framing, which alters regulator attention. AlphaGraph’s discrete-round simulation architecture models these multi-order effects across parallel communication channels. Coalition detection identifies which stakeholder clusters amplify or dampen the cascade. The output is not a sentiment score — it is an intervention map: where to act, when to act, and what happens if you don’t.

AlphaGraph’s 7-stage report pipeline includes a dedicated Fact-Checker that validates every numerical claim against verified simulation data. An Editor enforces ordinal vocabulary accuracy and temporal consistency. A References Appendix links every finding to specific agent actions — enabling full source traceability. Person names are sanitised at write-time via LLM-powered NER with heuristic fallback, producing a GDPR audit trail. This is Explainable AI by construction, not by annotation.

The 7-stage report pipeline includes a dedicated Fact-Checker model that operates independently of the report writer. It compares every numerical claim, percentage, and ordinal statement against verified simulation data extracted by the Harvester stage. Fabricated statistics are flagged and corrected before any human sees the report. Ordinal vocabulary is enforced to strict thresholds. This is Explainable AI by construction — the audit is embedded in the generation pipeline itself.

All simulation data is workspace-scoped and tenant-isolated at both application and database level. Person names are automatically sanitised at write-time via LLM-powered NER with deterministic heuristic fallback. Every sanitisation event is recorded in a GDPR-compliant audit trail that supports Data Subject Access Requests. Authentication tokens for external connectors are stored using AES-256-GCM authenticated encryption. Scenario data is never used for model training and never shared across tenants.

External data sources pass through a multi-layer sanitisation pipeline before entering the simulation. Override phrase detection strips instruction injection attempts. Unicode homoglyph normalisation prevents obfuscated attacks. System marker detection blocks prompt boundary manipulation. Internal control sequences are escaped to prevent protocol hijacking. All sanitisation events are logged for audit.

AlphaGraph maintains a comprehensive audit log of all user actions: simulation creation, report generation, share-link issuance and revocation, approval workflow transitions, and data exports. Audit records include user identity, timestamp, and action metadata. The full audit stream is queryable via API and exportable as CSV for compliance review. Combined with the References Appendix and the Person Abstraction Log, AlphaGraph provides the complete accountability chain required by the EU AI Act for high-risk AI systems.