# GuruRMM Development Principles

**Created:** 2026-04-29
**Authority:** Mike Swanson (owner)
**Location:** Documented in `projects/msp-tools/guru-rmm/docs/DESIGN.md`

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## Holistic Feature Development (MANDATORY)

When planning or implementing ANY GuruRMM feature, the complete stack must be considered and built:

### Required Components for Every Feature:
1. **Backend/Agent Logic** — core capability implementation
2. **API Endpoints** — control and monitoring interfaces
3. **UI/UX** — dashboard configuration, status display, management interface
4. **Documentation** — user guides and operational docs
5. **Scalability Design** — architected for future expansion

### Example: Network Discovery Node
A complete implementation includes:
- Agent-side scanning capability (ICMP, ARP, SNMP)
- Server-side data storage and API endpoints
- Dashboard UI for:
  - Designating which agent is the discovery node
  - Viewing discovered devices
  - Configuring scan schedules
  - Setting IP ranges and exclusions
- Status indicators (discovery progress, last scan time)
- Future-proof data model supporting multiple discovery methods

### Why This Matters:
- **Completeness:** Features without UI are unusable by non-API-expert admins
- **User Experience:** Configuration should be intuitive, not require documentation diving
- **Consistency:** Every feature should feel native to the product
- **No Dead Ends:** Design decisions shouldn't block obvious next steps

**Features shipped without their UI/configuration interfaces are incomplete and will be rejected.**

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## AI-Optional Operation

GuruRMM must be fully functional without requiring AI agents (Claude, autonomous analysis tools) to operate.

### Core Requirements:
- All functionality accessible via traditional dashboard/API
- Configuration and management through standard interfaces
- Usable by MSP techs with zero AI/ML knowledge
- Deterministic, reliable operation for production environments

### AI Features Are Enhancements:
- **Agentic analysis** (AI-powered log analysis, anomaly detection, troubleshooting) — planned enhancement
- **Agentic command routing** (intelligent decision-making about command execution) — planned enhancement
- Users choose whether to enable AI features
- Product does not mandate AI usage

### Why This Matters:
- Real MSPs need deterministic, reliable systems
- AI features can break, hallucinate, or be unavailable
- Core operations cannot depend on AI availability
- Production stability over experimental features

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## Application to Development

### When Adding Features:
1. ✅ Design the complete stack before starting implementation
2. ✅ Include UI mockups in feature planning
3. ✅ Consider future expansion in data model design
4. ✅ Ensure feature works via dashboard without API knowledge
5. ✅ Never assume AI availability for core functionality

### When Reviewing Features:
1. ❌ Reject backend-only implementations without UI
2. ❌ Reject features that require API expertise to configure
3. ❌ Reject designs that paint into architectural corners
4. ❌ Reject features that require AI to function

### Planning Questions:
- "How does an admin configure this in the dashboard?"
- "What does the status display look like?"
- "How do we expand this in v2/v3?"
- "Does this work if AI services are unavailable?"

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**These principles apply to ALL features — past, present, and future.**