{"slug":"developers/CAPABILITY_FABRIC_IMPLEMENTATION","title":"Capability Fabric Layer - Implementation Guide","section":"developers","sectionLabel":"Developers","status":"unclassified","type":"document","path":"developers/CAPABILITY_FABRIC_IMPLEMENTATION.mdx","bytes":13262,"description":null,"updated":null,"source":"integratewise/Integratewise-docs-wiki","content":"\n\n> **Tier C — Historical.** This document is a point-in-time snapshot preserved for reference. For current architecture, see [CANONICAL_PLATFORM_ARCHITECTURE.md](../architecture/CANONICAL_PLATFORM_ARCHITECTURE.md).\n\n## Overview\n\nThe **Capability Fabric** is the runtime execution layer that transforms organizational capabilities into AI-powered workflows. It's the missing link between architecture (what COULD happen) and execution (what WILL happen).\n\n**Status**: Stage 1-2 Complete (Foundation + Runtime Engine)\n\n---\n\n## What We Built\n\n### 1. Capability Registry (`packages/core/capability-registry/`)\n\n**Purpose**: Central registry of 200+ organizational capabilities with metadata, permissions, and requirements.\n\n**Key Components**:\n- `types.ts` - Type definitions (CapabilityDefinition, CapabilityInvocation, etc.)\n- `registry.ts` - CapabilityRegistryImpl with 3 example capabilities\n- Supports: Sell Deal, Monitor Health, Collect Payment\n\n**Example Capability**:\n```typescript\n{\n  id: 'sell-deal',\n  name: 'Sell Deal',\n  domain: CapabilityDomain.REVENUE,\n  steps: [\n    { id: 'assess-fit', handler: HandlerType.AI, ai_model: 'gpt-4' },\n    { id: 'pricing-analysis', handler: HandlerType.AI, requires_approval: true },\n    { id: 'close-approval', handler: HandlerType.HUMAN },\n    { id: 'create-order', handler: HandlerType.SYSTEM }\n  ],\n  requires_data: [\n    { entity_type: 'Opportunity', fields: ['name', 'arr', 'stage'], must_exist: true },\n    { entity_type: 'Account', fields: ['name', 'industry'], must_exist: true }\n  ],\n  permissions: [\n    { role: 'sales-rep', can_invoke: true, can_approve: false },\n    { role: 'sales-manager', can_invoke: true, can_approve: true, can_execute: true }\n  ]\n}\n```\n\n### 2. Capability Engine (`packages/core/capability-engine/`)\n\n**Purpose**: Executes capability workflows through a state machine.\n\n**State Flow**:\n```\npending → ai_processing → human_review → execution → completed\n                                ↓\n                            (if AI fails)\n                           escalates\n```\n\n**Key Features**:\n- Step-by-step execution (AI, human, system, hybrid)\n- Event-driven architecture for state transitions\n- Metrics collection (duration, success, confidence)\n- Mock AI integration (ready for real LLM)\n\n**Usage**:\n```typescript\nconst engine = new CapabilityEngine(config);\nconst invocation = await engine.execute(invocation, capability, context);\n```\n\n### 3. Workflow Router (`packages/core/workflow-router/`)\n\n**Purpose**: Context-aware routing decisions (AI → human → system).\n\n**Routing Logic**:\n- **High confidence + no governance**: Route to AI (auto-execute)\n- **Medium confidence**: Route to hybrid (AI rec + human approval)\n- **Low confidence**: Route to human (escalate)\n- **Governance requirements**: Always route to human\n\n**Context-Aware**:\n- Same capability executes differently for managers vs individual contributors\n- High-value/urgent always requires human review\n- Data access levels (own/team/org) control visibility\n- Load balancing assigns to least-busy team member\n\n**Example**:\n```typescript\nconst router = new WorkflowRouter();\nconst decision = await router.route(capability, context, aiConfidence);\n// Returns: { handler: 'human', assigned_to: 'sales-manager-1', reason: '... }\n```\n\n### 4. Capability Context (`packages/core/capability-context/`)\n\n**Purpose**: Assembles all data needed for capability execution.\n\n**Context Layers**:\n1. **Entity Data** - Primary entities from Spine (accounts, opportunities, etc.)\n2. **Signals** - Engagement, risk, opportunity scores with trends\n3. **Historical** - Past executions of same capability\n4. **AI Context** - LLM-formatted summary with interactions & outcomes\n5. **User Context** - Role, permissions, team\n\n**Example**:\n```typescript\nconst context = await builder.build(\n  capabilityId,\n  executionId,\n  userId,\n  userRole,\n  inputData,\n  capability\n);\n\n// Returns: {\n//   entities: { Opportunity: {...}, Account: {...} },\n//   signals: { engagement: { score: 72, trend: 'improving' } },\n//   history: { past_executions: [...], success_rate: 0.95 },\n//   ai_context: { summary: '...', recent_interactions: [...] }\n// }\n```\n\n### 5. Execution Orchestrator (`packages/core/execution-orchestrator/`)\n\n**Purpose**: Chains capabilities across domains into multi-step workflows.\n\n**Workflows Supported**:\n- **Sequential**: Sell Deal → Onboard Customer → Create Project → Notify Finance\n- **Parallel**: Independent capabilities run together\n- **Dependent**: One capability must complete before next starts\n- **With Fallback**: Alternative capability if primary fails\n\n**Example**:\n```typescript\nconst orchestrator = new ExecutionOrchestrator(engine);\nconst plan = {\n  steps: [\n    { id: 'sell', capability: sellDeal },\n    { id: 'onboard', capability: onboardCustomer, dependsOn: ['sell'] },\n    { id: 'create-project', capability: createProject, dependsOn: ['onboard'] }\n  ]\n};\nconst results = await orchestrator.orchestrate(plan, context);\n```\n\n### 6. Capability Metrics (`packages/core/capability-metrics/`)\n\n**Purpose**: Tracks execution metrics and extracts learning insights.\n\n**Metrics Collected**:\n- Success rate, execution time, AI accuracy\n- Human override rate, confidence scores\n- Business impact (revenue, time saved, etc.)\n- Trend analysis (improving/stable/degrading)\n\n**Learning Patterns**:\n- Identifies slow capabilities (optimization needed)\n- High-override capabilities (AI needs retraining)\n- Low-accuracy recommendations (context gaps)\n- Improving patterns (learning loop working)\n\n**Example**:\n```typescript\nconst metrics = new CapabilityMetricsService();\nmetrics.recordExecution({\n  capability_id: 'sell-deal',\n  execution_time_ms: 3600000,\n  success: true,\n  ai_confidence: 0.87,\n  human_override: false,\n  business_impact: { revenue: 500000, time_saved_hours: 2 }\n});\n\nconst insights = metrics.generateLearningInsights();\n// Returns patterns for Twin: slow caps, high overrides, low accuracy\n```\n\n### 7. Capability Shell UI (`apps/live/components/capability-shell.tsx`)\n\n**Purpose**: Generic React component for rendering any capability.\n\n**Features**:\n- Adaptive UI based on execution state (pending → AI → human → completed)\n- Entity context visualization with signals\n- AI recommendation panel with confidence scores\n- Result/error display\n- Approve/reject/execute actions\n\n**States Rendered**:\n- `pending`: Ready to execute button\n- `ai_processing`: Loading state\n- `human_review`: AI recommendation panel with approve/reject\n- `executing`: In-progress animation\n- `completed`: Success result\n- `failed`: Error message\n\n**Usage**:\n```tsx\n<CapabilityShell\n  capabilityId=\"sell-deal\"\n  capabilityName=\"Sell Deal\"\n  domain=\"revenue\"\n  state={executionState}\n  entityName=\"Acme Corp\"\n  signals={signals}\n  aiRecommendation={recommendation}\n  onApprove={handleApprove}\n  onExecute={handleExecute}\n/>\n```\n\n### 8. Capabilities Dashboard (`apps/live/app/(app)/capabilities/page.tsx`)\n\n**Purpose**: Homepage showing available capabilities with quick execution.\n\n**Features**:\n- List view: Grid of capabilities filtered by role\n- Detail view: Full capability execution with shell\n- Signals visualization: Real-time health indicators\n- Action history: Track recent executions\n- Learning insights: Recommendations for improvements\n\n---\n\n## Architecture: The Unified Lifecycle\n\nEvery capability execution follows this flow:\n\n```\n1. Capability Invoked\n   User says \"sell this deal\" or clicks \"Execute\"\n   ↓\n2. Context Assembly\n   CapabilityContext fetches entities, signals, historical data\n   ↓\n3. Routing Decision\n   WorkflowRouter decides: AI? Human? System? Hybrid?\n   ↓\n4. AI Processing (if applicable)\n   CapabilityEngine invokes AI with full context\n   ↓\n5. Human Review (if needed)\n   Route to human if confidence low or governance requires\n   ↓\n6. Execution\n   Execute steps via system APIs\n   ↓\n7. Outcome Tracking\n   CapabilityMetrics records results\n   ↓\n8. Learning\n   Extract patterns, improve AI recommendations, update Twin\n```\n\n---\n\n## Next Steps: Stage 3 (Metrics & Learning) + Stage 4 (Integration)\n\n### Stage 3: Metrics & Learning (Weeks 5-6)\n\n**3.1 Capability Metrics Dashboard**\n- Per-capability performance graphs\n- Success rate trends (30-day)\n- AI accuracy by recommendation type\n- Human override heatmaps\n- Cost per execution analysis\n\n**3.2 Learning Service**\n- Extract patterns from execution history\n- Identify improvement opportunities\n- Generate Twin-ready insights\n- Recommend model retraining\n\n**3.3 Twin Integration**\n- Feed metrics back to Digital Twins\n- Update Twin confidence based on feedback\n- Twin-driven capability optimization\n\n**Files to create**: 3 files, ~500 lines\n- `packages/core/capability-metrics/learning-service.ts`\n- `packages/core/capability-metrics/dashboard-metrics.ts`\n- `apps/live/app/(app)/capabilities/metrics/page.tsx`\n\n### Stage 4: UI & Integration (Weeks 7-8)\n\n**4.1 Capability Invocation Surfaces**\n- Dashboard cards showing next recommended actions\n- Chat-based: \"Sell this deal\" as natural language\n- Mobile: Quick capability access\n- Conversational: Ask AI for capability suggestions\n\n**4.2 Integration with Existing UI**\n- Wire into Workbench (primary workspace)\n- Add to AI Home (daily recommendations)\n- Embed in entity detail pages\n- Mobile app integration\n\n**4.3 Admin Management**\n- Capability CRUD interface\n- Permission policy editor\n- AI model configuration\n- Monitoring & debugging tools\n\n**Files to create**: 8+ files, ~800 lines\n\n---\n\n## Key Design Decisions\n\n### 1. Role-Aware Execution\n**Why**: Same capability behaves differently for manager vs IC\n- Managers always see human review for oversight\n- ICs can auto-execute low-risk actions\n- Data access controls prevent unauthorized access\n- **Result**: Capability Fabric adapts to organization structure\n\n### 2. Confidence-Based Escalation\n**Why**: AI recommendations have varying reliability\n- High confidence (>85%): Auto-execute if permitted\n- Medium confidence (70-85%): Human review with recommendation\n- Low confidence (<70%): Escalate to human\n- **Result**: Balances speed (auto-execute) with safety (human oversight)\n\n### 3. Event-Driven State Machine\n**Why**: Observable, auditable workflow execution\n- Each state transition emits event (step_completed, human_decision, etc.)\n- Listeners can hook into flow for monitoring/alerts\n- Full audit trail of execution\n- **Result**: Governance requirements met, debugging enabled\n\n### 4. Context Assembly Pattern\n**Why**: AI needs full organizational context\n- Data layer (entities from Spine)\n- Signal layer (risk/opportunity scores)\n- Historical layer (past decisions)\n- User layer (role/permissions)\n- **Result**: AI recommendations are contextual, not generic\n\n### 5. Metrics-Driven Continuous Improvement\n**Why**: Capabilities must get better over time\n- Track all metrics (success, speed, accuracy, cost)\n- Extract patterns (slow caps, high overrides, etc.)\n- Feed insights to Twin for model improvement\n- Monitor trends to detect regression\n- **Result**: Learning loop that improves AI and human decisions\n\n---\n\n## How to Use the Capability Fabric\n\n### As a Developer\n\n1. **Define a Capability**\n```typescript\nconst myCap: CapabilityDefinition = {\n  id: 'revenue/upsell-contract',\n  name: 'Upsell Contract',\n  domain: CapabilityDomain.REVENUE,\n  steps: [ /* workflow steps */ ],\n  requires_data: [ /* required entities */ ],\n  permissions: [ /* role permissions */ ]\n};\nregistry.register(myCap);\n```\n\n2. **Execute a Capability**\n```typescript\nconst context = await contextBuilder.build(...);\nconst invocation = await engine.execute(myInvocation, myCap, context);\nconst metrics = await metricsService.recordExecution({\n  capability_id: myCap.id,\n  execution_time_ms: invocation.duration_ms,\n  success: invocation.state === 'completed',\n  ...\n});\n```\n\n3. **Render Capability UI**\n```tsx\n<CapabilityShell\n  capabilityId={capability.id}\n  state={invocation.state}\n  // ... other props\n/>\n```\n\n### As a Business User\n\n1. **Browse available capabilities** in dashboard\n2. **Click to execute** (e.g., \"Sell Deal\")\n3. **Review AI recommendation** if shown\n4. **Approve/execute** to trigger workflow\n5. **Monitor results** in timeline\n\n---\n\n## Testing & Validation\n\n### Unit Tests\n- Registry lookup & validation\n- Router decision logic\n- Engine state transitions\n- Context assembly\n\n### Integration Tests\n- Multi-step orchestration\n- Parallel execution\n- Rollback on failure\n- Metrics collection\n\n### E2E Tests\n- Full capability execution end-to-end\n- UI interactions (approve/reject)\n- Data validation\n- Output verification\n\n---\n\n## Deployment Checklist\n\n- [ ] All capability definitions registered\n- [ ] AI integration tested (real model, not mock)\n- [ ] Metrics collection validated\n- [ ] Learning insights verified\n- [ ] UI shell responsive on mobile/desktop\n- [ ] Capabilities dashboard live\n- [ ] Admin interfaces functional\n- [ ] Monitoring/alerting configured\n- [ ] Documentation complete\n- [ ] Team trained\n\n---\n\n## References\n\n- **Platform Spec**: `docs/00-20/PLATFORM.md`\n- **Operational Spine**: `packages/platform/spine/`\n- **Digital Twin**: `packages/platform/twin/`\n- **Workflow Examples**: `docs/CAPABILITY_EXECUTION_PATTERNS.md`\n"}