FactVerse AI Agent Capability Map
The MCP tool reference describes individual tools. This map explains how tools, data, digital twins, and product modules combine into capabilities that customers can evaluate, deploy, and govern.
Capability groups
| Capability | Connected components | Typical output | Governance concern |
|---|---|---|---|
| Industrial operations copilot | FactVerse Platform, DFS, Inspector, knowledge stores | Situation summaries, asset answers, procedure guidance, task drafts | Tenant context, source traceability, approval for write actions |
| Predictive maintenance workflow | DFS signals, asset history, Inspector work orders, AI Agent analysis tools | Maintenance risk explanation, suggested checks, work-order preparation | Model confidence, maintenance authority, record retention |
| Facility and energy operations | Operational twins, meter data, asset metadata, inspection records | Energy-use calculation support, abnormal trend review, evidence organization | Data freshness, regulatory wording, human engineering review |
| Simulation and Physical AI workflow | Designer scenes, SimReady assets, simulation services, operational constraints | Layout comparison, process rehearsal, robot or equipment training context | Simulation assumptions, versioned assets, validation boundary |
| Knowledge and document workflow | Manuals, SOPs, customer documents, generated references | Retrieval, comparison, translation support, structured answer drafts | Access control, citation quality, confidential document handling |
| Field execution and feedback | Inspector mobile workflows, alerts, work orders, checklists | Dispatch context, inspection guidance, completion records, feedback loops | Operator approval, audit trail, operational safety |
| Data governance and integration | DFS pipelines, quality checks, source-system connectors | Data readiness review, schema mapping, anomaly flags, integration status | Data ownership, quality gates, pipeline observability |
| Agent integration tooling | MCP endpoints, scopes, tool references, customer AI clients | Controlled agent access, runtime discovery, integration test paths | Scope design, key rotation, monitoring, deprecation handling |
How to read the map
Start from the business workflow, then identify the product modules and tools required to support it. For example, a predictive maintenance workflow normally needs asset context from FactVerse Platform, operational signals from DFS, maintenance history and work-order context from Inspector, and scoped tools exposed through MCP.
Capability assembly pattern
- Define the operational question or decision boundary.
- Attach tenant, asset, and permission context.
- Retrieve relevant data, knowledge, digital twin state, or simulation assets.
- Generate analysis or a proposed task with source references.
- Route write actions through approval and audit.
- Feed completed work, inspection results, and operator feedback back into the system.
Capability naming
In business and integration discussions, use capability names such as "predictive maintenance workflow" or "industrial operations copilot." Use raw MCP tool names for integration behavior, debugging, and API-level references.