The first step to building multi-agent systems for any industry. Transform domain knowledge and natural-language requirements into structured, machine-readable models.
Part of the SRAO Framework — a systematic methodology for building multi-agent workflow orchestration across industries. Start here, then chain with SRAO-Agent-Graph and SRAO-Workflow-Orchestrator.
Most multi-agent projects fail not because agents are bad, but because nobody modeled the domain first. Teams jump to building agents before understanding:
- What entities exist and how they relate?
- What workflows actually happen in this industry?
- What does "success" look like in measurable terms?
SRAO Domain Modeler forces you to answer these questions before writing a single line of agent code.
Stage 0 - Domain Modeling
- Build concept dictionaries with entity types, attributes, and relationships
- Create entity-relationship diagrams (text-based, version-controllable)
- Extract parameterized workflow templates from SOPs/process descriptions
Stage 1 - Requirement Structuring
- Convert natural-language intent into Structured Requirement Models (SRM)
- Define KPI targets, constraints, and acceptance criteria
- Inventory available tools, data sources, and human roles
- Describe your industry scenario in natural language
- The modeler generates a domain model (concept dictionary + ER diagram + workflow templates)
- Requirements are structured into SRM documents with measurable goals
- Pass SRM output to SRAO-Agent-Graph for agent capability mapping
`` User: "We want to automate order scheduling in our factory"
Output: Concept Dictionary: Order, Work Order, Production Line, Equipment, Material, QC Record ER Diagram: [Order] --1:N--> [Work Order] --N:1--> [Production Line] Workflow Template: WF-MFG-001 Order Scheduling (4 steps, parallel+serial) SRM: REQ-001 with goals (scheduling <30min), constraints (3-month delivery), acceptance (95% capacity utilization) ``
| Industry | Reference | Key Workflows |
|---|---|---|
| Manufacturing | references/manufacturing.md | Order scheduling, Predictive maintenance, Quality tracing |
| Energy (Wind/Solar) | references/energy.md | Turbine health monitoring, Power forecasting, Drone inspection |
| Healthcare | references/healthcare.md | ER triage, Radiology AI, Surgical robotics |
| Agriculture | references/agriculture.md | Pest early warning, Precision irrigation, Yield prediction |
| Transport/Tunnel | references/transport.md | Rock stability monitoring, Fire robot patrol |
New industries? Start from scratch using the universal flow.
yaml structured_requirement: id: REQ-001 name: "Order Scheduling Automation" domain: Manufacturing goals: - metric: "Scheduling response time" current: "4 hours" target: "<30 minutes" constraints: - "3-month delivery timeline" existing_tools: ["SAP ERP", "MES"] acceptance: - "95% capacity utilization"
+----------------------+ +----------------------+ +-----------------------------+ | SRAO-Domain-Modeler |---->| SRAO-Agent-Graph |---->| SRAO-Workflow-Orchestrator | | (Model & Structure) | SRM | (Decompose & Map) | DAG | (Orchestrate & Monitor) | +----------------------+ +----------------------+ +-----------------------------+
Industry Solution = Domain Knowledge Graph x Agent Capability Graph x Dynamic Orchestration Engine
This repo handles the first term. The other two are handled by the companion repos.
MIT