FactVerse AI Agent
FactVerse AI Agent connects FactVerse digital twins, operational data, simulation services, knowledge, and governed execution paths. Implementation teams use it to build agent workflows for facilities, equipment, maintenance, simulation-ready assets, and industrial operations.
Use this page to choose the right document for the task in front of you.
Implementation flow
Choose your path
| I need to | Start with | Then use |
|---|---|---|
| Connect an AI client for the first time | Getting Started | MCP Integration Guide |
| Plan endpoints, API keys, scopes, and approvals | Access and Scope Planning | MCP Scope Matrix |
| Check whether the workflow has enough usable data | Data Readiness | DFS overview |
| Build a facility operations workflow | Facility Operations Workflow Guide | Facility Operations use case |
| Build a predictive maintenance workflow | Predictive Maintenance Workflow Guide | Predictive Maintenance use case |
| Build a Physical AI workflow | Physical AI Workflow Guide | Physical AI use case |
| Review request and output patterns | Examples | Workflow Run Record |
| Diagnose a failed client, scope, data, or write action | Troubleshooting | MCP Errors and Audit |
| Move a workflow from pilot to regular use | Validation and Handover | Workflow Guides |
| Look up available tools and scopes | Tool Reference | Scope Reference |
Operating model
A production workflow should be built in this order:
- Connect a read-only client and confirm runtime tool discovery.
- Verify tenant, site, asset, equipment, scene, and source-system boundaries.
- Check source freshness, signal quality, document context, scene version, and review ownership.
- Add compute or simulation steps only after the inputs and assumptions are visible.
- Add draft write actions behind explicit human approval and audit records.
- Capture final evidence, reviewer decisions, operator feedback, and accepted corrections.
Use Core Concepts for the vocabulary behind this operating model. Use Architecture and Capability Map when planning a larger deployment.
Product context
| Product area | Role in an Agent workflow |
|---|---|
| FactVerse Platform | Tenant, asset, permission, and product context. |
| Data Fusion Services | Source connection, mapping, quality checks, governed datasets, fusion, review, and BI-ready data. |
| FactVerse Designer | Digital twin scenes, simulation-ready assets, layout planning, and scenario preparation. |
| DataMesh Inspector | Runtime visualization, alarms, work orders, inspection records, and facility operations context. |
| MCP endpoints | Governed tool access for AI clients and enterprise agent runtimes. |
Expected implementation output
For each Agent workflow, keep these artifacts visible:
| Artifact | Purpose |
|---|---|
| Workflow contract | Defines role, boundary, inputs, output, reviewer, and approval path. |
| Access package | Lists MCP endpoint, scopes, key owner, rotation rule, and client runtime. |
| Data readiness note | Records source systems, mappings, freshness, quality issues, and missing evidence. |
| Validation run | Shows the first read-only output with source references and reviewer comments. |
| Run record | Captures tool calls, evidence, assumptions, final decision, and feedback. |
Naming
Use FactVerse AI Agent as the product name for agent workflows, customer-facing guidance, and integration documentation.