diff --git a/docs/research/DPROD/README.md b/docs/research/DPROD/README.md
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+# DPROD Implementation Strategy for ABI
+
+**Data Product Ontology (DPROD) Integration Research**
+
+This directory contains the strategic planning and implementation guidance for integrating the [W3C Data Product Ontology (DPROD)](https://ekgf.github.io/dprod/) standard into the ABI (Agentic Brain Infrastructure) system.
+
+## Overview
+
+DPROD is a W3C standard for describing data products using Linked Data principles. By implementing DPROD compliance in ABI, we can:
+
+- **Standardize AI agent metadata** using industry standards
+- **Enable agent discoverability** through semantic queries
+- **Track conversation lineage** across multi-agent flows
+- **Provide observability** into agent performance and usage
+- **Ensure interoperability** with enterprise data governance systems
+
+## Documentation Structure
+
+### Strategic Planning
+- **[Strategy](./strategy.md)** - High-level strategic approach and business rationale
+- **[Implementation Scope](./implementation-scope.md)** - Detailed scope, phases, and deliverables
+- **[Technical Architecture](./technical-architecture.md)** - Technical implementation details and design patterns
+
+### Implementation Guidance
+- **[Integration Roadmap](./integration-roadmap.md)** - Timeline, milestones, and dependencies
+- **[Examples](./examples/)** - Concrete implementation examples and use cases
+- **[API Specifications](./api-specifications.md)** - DPROD-compliant API designs
+
+### Research & Analysis
+- **[DPROD Analysis](./dprod-analysis.md)** - Analysis of DPROD specification relevance to ABI
+- **[Competitive Analysis](./competitive-analysis.md)** - How DPROD implementation differentiates ABI
+- **[Standards Compliance](./standards-compliance.md)** - Alignment with W3C and enterprise standards
+
+## Quick Start
+
+1. **Read the [Strategy](./strategy.md)** to understand the business case
+2. **Review [Implementation Scope](./implementation-scope.md)** for technical details
+3. **Explore [Examples](./examples/)** for concrete use cases
+4. **Check [Integration Roadmap](./integration-roadmap.md)** for timeline
+
+## Key Benefits for ABI
+
+### For Organizations
+- **Data Governance Compliance**: Meet enterprise data management requirements
+- **Agent Discoverability**: Find the right AI agent for specific tasks
+- **Performance Analytics**: Data-driven insights into agent effectiveness
+- **Lineage Tracking**: Understand conversation flows and decision paths
+
+### For Developers
+- **Standard Metadata**: Consistent agent descriptions across platforms
+- **Semantic Queries**: SPARQL-based agent discovery and analytics
+- **Observability APIs**: Built-in monitoring and metrics collection
+- **Enterprise Integration**: Native compatibility with data catalog systems
+
+### For Users
+- **Better Agent Selection**: Intelligent routing based on capabilities
+- **Transparency**: Clear understanding of which models are being used
+- **Quality Assurance**: Metrics-driven agent performance visibility
+- **Consistent Experience**: Standardized interfaces across all agents
+
+## Implementation Status
+
+| Component | Status | Priority | Notes |
+|-----------|--------|----------|-------|
+| Strategy Definition | ✅ Complete | High | Business case established |
+| Technical Architecture | 🔄 In Progress | High | Core patterns defined |
+| Ontology Module Enhancement | 📋 Planned | High | Extend existing module |
+| Agent Registration System | 📋 Planned | Medium | DPROD-compliant metadata |
+| Observability Framework | 📋 Planned | Medium | Metrics collection |
+| Query Interface | 📋 Planned | Low | SPARQL endpoint |
+
+## Next Steps
+
+1. **Finalize technical architecture** based on existing ABI patterns
+2. **Create proof-of-concept** implementation for one agent module
+3. **Develop integration strategy** with existing ontology module
+4. **Plan observability framework** for agent metrics collection
+5. **Design DPROD-compliant APIs** for external system integration
+
+---
+
+**Research Lead**: ABI Development Team
+**Last Updated**: January 2025
+**Status**: Strategic Planning Phase
\ No newline at end of file
diff --git a/docs/research/DPROD/api-specifications.md b/docs/research/DPROD/api-specifications.md
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+# DPROD API Specifications
+
+## Overview
+
+This document defines the API specifications for interacting with DPROD-compliant data in the ABI system. These APIs enable enterprise systems to discover agents, query metadata, track lineage, and monitor performance using standard DPROD formats.
+
+## Base Configuration
+
+**Base URL**: `https://api.abi.naas.ai/v1/dprod`
+**Authentication**: Bearer token (OAuth 2.0 / API Key)
+**Content-Type**: `application/json` or `application/ld+json`
+
+## Agent Discovery APIs
+
+### List All Data Products (Agents)
+
+**Endpoint**: `GET /data-products`
+
+**Description**: Retrieve all AI agents as DPROD-compliant data products.
+
+**Query Parameters**:
+- `category` (optional): Filter by category (`cloud`, `local`, `utility`)
+- `capability` (optional): Filter by capability (e.g., `coding`, `reasoning`)
+- `privacy_level` (optional): Filter by privacy level (`local`, `cloud`)
+- `performance_tier` (optional): Filter by performance (`high`, `medium`, `standard`)
+- `format` (optional): Response format (`json`, `jsonld`, `turtle`, `rdf-xml`)
+
+**Example Request**:
+```http
+GET /v1/dprod/data-products?capability=coding&privacy_level=local&format=jsonld
+Authorization: Bearer your-api-token
+```
+
+**Example Response**:
+```json
+{
+ "@context": "https://ekgf.github.io/dprod/dprod.jsonld",
+ "dataProducts": [
+ {
+ "id": "https://abi.naas.ai/data-product/qwen",
+ "type": "DataProduct",
+ "label": "Qwen Local AI Agent",
+ "description": "Privacy-focused multilingual AI agent via Ollama",
+ "abi:capabilities": ["coding", "multilingual", "reasoning"],
+ "abi:privacyLevel": "local",
+ "abi:performanceTier": "standard",
+ "inputPort": {
+ "id": "https://abi.naas.ai/data-product/qwen/input",
+ "type": "DataService",
+ "endpointURL": "https://api.naas.ai/agents/qwen/chat"
+ },
+ "outputPort": [
+ {
+ "id": "https://abi.naas.ai/data-product/qwen/output",
+ "type": "DataService"
+ }
+ ]
+ }
+ ],
+ "totalCount": 1,
+ "page": 1,
+ "pageSize": 50
+}
+```
+
+### Get Specific Data Product
+
+**Endpoint**: `GET /data-products/{agent-name}`
+
+**Description**: Retrieve detailed DPROD metadata for a specific agent.
+
+**Path Parameters**:
+- `agent-name`: Name of the agent (e.g., `qwen`, `chatgpt`, `claude`)
+
+**Example Request**:
+```http
+GET /v1/dprod/data-products/qwen
+Authorization: Bearer your-api-token
+```
+
+## Agent Discovery & Selection APIs
+
+### Find Agents by Capability
+
+**Endpoint**: `POST /agents/discover`
+
+**Description**: Discover agents using semantic queries based on capabilities and requirements.
+
+**Request Body**:
+```json
+{
+ "requirements": {
+ "capabilities": ["coding", "reasoning"],
+ "privacyLevel": "local",
+ "maxResponseTime": 3000,
+ "languages": ["en", "zh"]
+ },
+ "ranking": {
+ "criteria": ["performance", "privacy", "cost"],
+ "weights": [0.4, 0.4, 0.2]
+ },
+ "limit": 5
+}
+```
+
+**Example Response**:
+```json
+{
+ "recommendations": [
+ {
+ "agent": "https://abi.naas.ai/data-product/qwen",
+ "label": "Qwen Local AI Agent",
+ "score": 0.92,
+ "reasoning": "High privacy (local), supports coding and reasoning, multilingual",
+ "matchedCapabilities": ["coding", "reasoning"],
+ "estimatedResponseTime": 1200
+ },
+ {
+ "agent": "https://abi.naas.ai/data-product/deepseek",
+ "label": "DeepSeek R1 Agent",
+ "score": 0.87,
+ "reasoning": "Local deployment, excellent reasoning capabilities",
+ "matchedCapabilities": ["reasoning"],
+ "estimatedResponseTime": 1800
+ }
+ ],
+ "totalFound": 2
+}
+```
+
+## SPARQL Query API
+
+### Execute SPARQL Query
+
+**Endpoint**: `POST /sparql`
+
+**Description**: Execute SPARQL queries against the DPROD knowledge graph.
+
+**Request Body**:
+```json
+{
+ "query": "PREFIX dprod: PREFIX abi: SELECT ?agent ?label ?capabilities WHERE { ?agent a dprod:DataProduct . ?agent rdfs:label ?label . ?agent abi:capabilities ?capabilities . FILTER(CONTAINS(?capabilities, \"coding\")) }",
+ "format": "json",
+ "reasoning": true
+}
+```
+
+**Example Response**:
+```json
+{
+ "head": {
+ "vars": ["agent", "label", "capabilities"]
+ },
+ "results": {
+ "bindings": [
+ {
+ "agent": {
+ "type": "uri",
+ "value": "https://abi.naas.ai/data-product/qwen"
+ },
+ "label": {
+ "type": "literal",
+ "value": "Qwen Local AI Agent"
+ },
+ "capabilities": {
+ "type": "literal",
+ "value": "coding,multilingual,reasoning"
+ }
+ }
+ ]
+ },
+ "executionTime": 47
+}
+```
+
+### Predefined Query Templates
+
+**Endpoint**: `GET /sparql/templates`
+
+**Description**: Get predefined SPARQL query templates for common use cases.
+
+**Example Response**:
+```json
+{
+ "templates": [
+ {
+ "id": "find-by-capability",
+ "name": "Find Agents by Capability",
+ "description": "Find agents with specific capabilities",
+ "query": "PREFIX dprod: SELECT ?agent ?label WHERE { ?agent a dprod:DataProduct . ?agent rdfs:label ?label . ?agent abi:capabilities ?cap . FILTER(CONTAINS(?cap, \"{{capability}}\")) }",
+ "parameters": [
+ {
+ "name": "capability",
+ "type": "string",
+ "description": "Required capability (e.g., coding, reasoning)"
+ }
+ ]
+ }
+ ]
+}
+```
+
+## Observability APIs
+
+### Get Agent Metrics
+
+**Endpoint**: `GET /observability/{agent-name}`
+
+**Description**: Retrieve observability data for a specific agent.
+
+**Path Parameters**:
+- `agent-name`: Name of the agent
+
+**Query Parameters**:
+- `start_time`: Start time (ISO 8601)
+- `end_time`: End time (ISO 8601)
+- `metrics`: Comma-separated list of metrics (`response_time`, `token_usage`, `success_rate`)
+- `aggregation`: Aggregation level (`raw`, `hourly`, `daily`)
+
+**Example Request**:
+```http
+GET /v1/dprod/observability/qwen?start_time=2025-01-04T00:00:00Z&end_time=2025-01-04T23:59:59Z&metrics=response_time,success_rate&aggregation=hourly
+```
+
+**Example Response**:
+```json
+{
+ "@context": "https://ekgf.github.io/dprod/dprod.jsonld",
+ "agent": "https://abi.naas.ai/data-product/qwen",
+ "timeWindow": {
+ "start": "2025-01-04T00:00:00Z",
+ "end": "2025-01-04T23:59:59Z",
+ "aggregation": "hourly"
+ },
+ "metrics": [
+ {
+ "timestamp": "2025-01-04T15:00:00Z",
+ "responseTimeMs": {
+ "avg": 1247.5,
+ "min": 456.2,
+ "max": 3421.8,
+ "p95": 2156.7
+ },
+ "successRate": 0.984,
+ "requestCount": 127
+ }
+ ],
+ "conformsTo": "https://abi.naas.ai/schema/AgentObservability"
+}
+```
+
+### System Performance Dashboard
+
+**Endpoint**: `GET /observability/dashboard`
+
+**Description**: Get system-wide performance metrics and analytics.
+
+**Example Response**:
+```json
+{
+ "systemMetrics": {
+ "totalAgents": 13,
+ "activeAgents": 11,
+ "totalRequests24h": 1247,
+ "avgSystemResponseTime": 1756.3,
+ "overallSuccessRate": 0.987
+ },
+ "topAgents": [
+ {
+ "agent": "chatgpt",
+ "requests": 203,
+ "avgResponseTime": 1789.2,
+ "successRate": 0.995
+ },
+ {
+ "agent": "qwen",
+ "requests": 127,
+ "avgResponseTime": 1156.7,
+ "successRate": 0.984
+ }
+ ],
+ "capabilityUsage": {
+ "coding": 34.2,
+ "analysis": 23.1,
+ "general": 19.4,
+ "creative": 12.8,
+ "research": 10.5
+ }
+}
+```
+
+## Lineage Tracking APIs
+
+### Get Conversation Lineage
+
+**Endpoint**: `GET /lineage/conversations/{conversation-id}`
+
+**Description**: Retrieve the complete lineage for a conversation showing agent handoffs and data flow.
+
+**Path Parameters**:
+- `conversation-id`: Unique conversation identifier
+
+**Example Response**:
+```json
+{
+ "@context": [
+ "https://ekgf.github.io/dprod/dprod.jsonld",
+ {"prov": "http://www.w3.org/ns/prov#"}
+ ],
+ "conversationId": "conv-12345",
+ "startTime": "2025-01-04T15:30:00Z",
+ "endTime": "2025-01-04T15:32:15Z",
+ "steps": [
+ {
+ "stepNumber": 1,
+ "activity": "initial_routing",
+ "from": "user",
+ "to": "abi",
+ "timestamp": "2025-01-04T15:30:00Z",
+ "duration": "PT2S"
+ },
+ {
+ "stepNumber": 2,
+ "activity": "agent_execution",
+ "from": "abi",
+ "to": "qwen",
+ "timestamp": "2025-01-04T15:30:02Z",
+ "duration": "PT43S",
+ "reason": "local_privacy_preferred"
+ }
+ ],
+ "summary": {
+ "totalSteps": 4,
+ "agentsUsed": ["abi", "qwen", "claude"],
+ "totalDuration": "PT2M15S"
+ }
+}
+```
+
+### Agent Transition Analytics
+
+**Endpoint**: `GET /lineage/transitions`
+
+**Description**: Analyze common agent transition patterns across conversations.
+
+**Query Parameters**:
+- `start_date`: Start date for analysis
+- `end_date`: End date for analysis
+- `min_occurrences`: Minimum occurrences to include
+
+**Example Response**:
+```json
+{
+ "transitions": [
+ {
+ "from": "abi",
+ "to": "chatgpt",
+ "count": 78,
+ "percentage": 31.2,
+ "avgDuration": "PT45S",
+ "successRate": 0.995
+ },
+ {
+ "from": "abi",
+ "to": "qwen",
+ "count": 45,
+ "percentage": 18.0,
+ "avgDuration": "PT38S",
+ "successRate": 0.984
+ }
+ ],
+ "totalTransitions": 250,
+ "analysisWindow": "P7D"
+}
+```
+
+## Data Catalog Integration APIs
+
+### Export for Enterprise Catalogs
+
+**Endpoint**: `GET /export/{format}`
+
+**Description**: Export agent metadata in enterprise data catalog formats.
+
+**Path Parameters**:
+- `format`: Export format (`datahub`, `purview`, `collibra`, `atlas`)
+
+**Query Parameters**:
+- `agents`: Comma-separated list of agents to export (optional, defaults to all)
+- `include_observability`: Include observability metadata (default: false)
+
+**Example Request**:
+```http
+GET /v1/dprod/export/datahub?agents=qwen,chatgpt&include_observability=true
+```
+
+### Webhook Registration
+
+**Endpoint**: `POST /webhooks`
+
+**Description**: Register webhooks for real-time notifications of metadata changes.
+
+**Request Body**:
+```json
+{
+ "url": "https://your-system.com/webhooks/abi-metadata",
+ "events": ["agent_registered", "metadata_updated", "performance_alert"],
+ "filters": {
+ "agents": ["qwen", "chatgpt"],
+ "metrics": ["response_time", "success_rate"]
+ },
+ "auth": {
+ "type": "bearer",
+ "token": "your-webhook-token"
+ }
+}
+```
+
+## Authentication & Security
+
+### API Key Management
+
+**Endpoint**: `POST /auth/keys`
+
+**Description**: Generate API keys with specific permissions.
+
+**Request Body**:
+```json
+{
+ "name": "Enterprise Data Catalog Integration",
+ "permissions": [
+ "dprod:read",
+ "sparql:execute",
+ "observability:read",
+ "lineage:read"
+ ],
+ "expiresAt": "2026-01-04T00:00:00Z",
+ "ipWhitelist": ["10.0.0.0/8", "192.168.1.100"]
+}
+```
+
+### Rate Limiting
+
+All APIs are subject to rate limiting:
+- **Standard**: 1000 requests/hour
+- **Premium**: 10000 requests/hour
+- **Enterprise**: Custom limits
+
+Rate limit headers:
+```
+X-RateLimit-Limit: 1000
+X-RateLimit-Remaining: 999
+X-RateLimit-Reset: 1641024000
+```
+
+## Error Handling
+
+### Standard Error Response
+
+```json
+{
+ "error": {
+ "code": "INVALID_QUERY",
+ "message": "SPARQL query syntax error at line 2",
+ "details": {
+ "line": 2,
+ "column": 15,
+ "suggestion": "Missing closing quote"
+ },
+ "timestamp": "2025-01-04T15:30:45Z",
+ "requestId": "req-12345"
+ }
+}
+```
+
+### Error Codes
+
+| Code | HTTP Status | Description |
+|------|-------------|-------------|
+| `INVALID_QUERY` | 400 | Malformed SPARQL query |
+| `AGENT_NOT_FOUND` | 404 | Requested agent does not exist |
+| `UNAUTHORIZED` | 401 | Invalid or missing authentication |
+| `RATE_LIMITED` | 429 | Too many requests |
+| `INTERNAL_ERROR` | 500 | Server error |
+
+---
+
+These APIs provide comprehensive access to DPROD-compliant data while maintaining enterprise-grade security and performance standards.
\ No newline at end of file
diff --git a/docs/research/DPROD/examples/observability-example.json b/docs/research/DPROD/examples/observability-example.json
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+{
+ "@context": {
+ "dprod": "https://ekgf.github.io/dprod/",
+ "prov": "http://www.w3.org/ns/prov#",
+ "dcat": "http://www.w3.org/ns/dcat#",
+ "abi": "https://naas.ai/abi/ontology/",
+ "xsd": "http://www.w3.org/2001/XMLSchema#"
+ },
+ "@type": "dprod:ObservabilityReport",
+ "@id": "abi:observability/2025-01-15",
+ "dprod:reportDate": "2025-01-15T10:30:00Z",
+ "dprod:dataProduct": "abi:agents/qwen",
+ "dprod:metrics": {
+ "performance": {
+ "avgResponseTime": {
+ "value": 1250,
+ "unit": "milliseconds",
+ "timestamp": "2025-01-15T10:30:00Z"
+ },
+ "throughput": {
+ "value": 45.2,
+ "unit": "requests_per_minute",
+ "timestamp": "2025-01-15T10:30:00Z"
+ },
+ "errorRate": {
+ "value": 0.02,
+ "unit": "percentage",
+ "timestamp": "2025-01-15T10:30:00Z"
+ }
+ },
+ "quality": {
+ "accuracyScore": {
+ "value": 0.89,
+ "unit": "percentage",
+ "measurement": "user_satisfaction_rating"
+ },
+ "completenessScore": {
+ "value": 0.94,
+ "unit": "percentage",
+ "measurement": "response_completeness"
+ },
+ "relevanceScore": {
+ "value": 0.91,
+ "unit": "percentage",
+ "measurement": "contextual_relevance"
+ }
+ },
+ "usage": {
+ "activeUsers": {
+ "value": 127,
+ "unit": "count",
+ "period": "last_24_hours"
+ },
+ "totalConversations": {
+ "value": 1842,
+ "unit": "count",
+ "period": "last_24_hours"
+ },
+ "avgConversationLength": {
+ "value": 8.3,
+ "unit": "exchanges",
+ "period": "last_24_hours"
+ }
+ }
+ },
+ "dprod:dataLineage": {
+ "@type": "prov:Activity",
+ "@id": "abi:conversation/conv-123456",
+ "prov:startedAtTime": "2025-01-15T10:15:00Z",
+ "prov:endedAtTime": "2025-01-15T10:18:00Z",
+ "prov:used": [
+ {
+ "@id": "abi:input/user-query-123456",
+ "@type": "prov:Entity",
+ "prov:value": "Help me write a Python function for data validation",
+ "prov:generatedAtTime": "2025-01-15T10:15:00Z"
+ },
+ {
+ "@id": "abi:models/qwen3:8b",
+ "@type": "prov:Entity",
+ "prov:label": "Qwen3 8B Model",
+ "dprod:modelVersion": "v2.5.0"
+ }
+ ],
+ "prov:generated": [
+ {
+ "@id": "abi:output/response-123456",
+ "@type": "prov:Entity",
+ "prov:value": "Here's a comprehensive Python function for data validation...",
+ "prov:generatedAtTime": "2025-01-15T10:17:45Z",
+ "dprod:responseLength": 1247,
+ "dprod:codeSnippets": 1
+ }
+ ],
+ "prov:wasAssociatedWith": {
+ "@id": "abi:agents/qwen",
+ "@type": "prov:Agent",
+ "prov:label": "Qwen AI Agent"
+ }
+ },
+ "dprod:alertsAndIncidents": [
+ {
+ "@type": "dprod:Alert",
+ "@id": "abi:alert/response-time-high",
+ "dprod:severity": "warning",
+ "dprod:description": "Response time exceeded 2s threshold",
+ "dprod:timestamp": "2025-01-15T10:25:00Z",
+ "dprod:affectedMetric": "avgResponseTime",
+ "dprod:currentValue": 2350,
+ "dprod:threshold": 2000,
+ "dprod:status": "resolved",
+ "dprod:resolvedAt": "2025-01-15T10:28:00Z"
+ }
+ ],
+ "dprod:dataQualityIssues": [
+ {
+ "@type": "dprod:DataQualityIssue",
+ "@id": "abi:quality-issue/incomplete-response",
+ "dprod:issueType": "completeness",
+ "dprod:description": "Some responses were truncated due to context window limits",
+ "dprod:affectedRecords": 23,
+ "dprod:detectionTime": "2025-01-15T09:45:00Z",
+ "dprod:severity": "medium",
+ "dprod:remediation": "Implement context window management for long conversations"
+ }
+ ],
+ "dprod:complianceStatus": {
+ "privacyCompliance": {
+ "status": "compliant",
+ "lastAudit": "2025-01-10T00:00:00Z",
+ "details": "Local processing ensures no data leaves user environment"
+ },
+ "dataRetention": {
+ "status": "compliant",
+ "policy": "conversations_deleted_after_session",
+ "implementation": "automatic"
+ },
+ "accessControl": {
+ "status": "compliant",
+ "mechanism": "local_user_access_only",
+ "lastReview": "2025-01-10T00:00:00Z"
+ }
+ },
+ "dprod:recommendations": [
+ {
+ "@type": "dprod:Recommendation",
+ "dprod:category": "performance",
+ "dprod:priority": "medium",
+ "dprod:description": "Consider implementing response caching for frequently asked questions",
+ "dprod:expectedImpact": "20% reduction in response time"
+ },
+ {
+ "@type": "dprod:Recommendation",
+ "dprod:category": "quality",
+ "dprod:priority": "high",
+ "dprod:description": "Implement context window management to prevent response truncation",
+ "dprod:expectedImpact": "Improved response completeness"
+ }
+ ],
+ "dprod:generatedBy": {
+ "@id": "abi:monitoring/observability-engine",
+ "@type": "prov:SoftwareAgent",
+ "prov:label": "ABI Observability Engine",
+ "dprod:version": "1.0.0"
+ }
+}
\ No newline at end of file
diff --git a/docs/research/DPROD/examples/qwen-agent-dprod.json b/docs/research/DPROD/examples/qwen-agent-dprod.json
new file mode 100644
index 000000000..0f2d2de60
--- /dev/null
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@@ -0,0 +1,151 @@
+{
+ "@context": {
+ "dprod": "https://ekgf.github.io/dprod/",
+ "dcat": "http://www.w3.org/ns/dcat#",
+ "foaf": "http://xmlns.com/foaf/0.1/",
+ "schema": "https://schema.org/",
+ "abi": "https://naas.ai/abi/ontology/",
+ "xsd": "http://www.w3.org/2001/XMLSchema#"
+ },
+ "@type": "dprod:DataProduct",
+ "@id": "abi:agents/qwen",
+ "dcat:title": "Qwen AI Agent",
+ "dcat:description": "Local privacy-focused AI agent powered by Qwen3 8B model via Ollama, specialized in multilingual conversations, code generation, and general problem-solving.",
+ "schema:version": "1.0.0",
+ "dprod:domain": "artificial-intelligence",
+ "dprod:dataProductOwner": {
+ "@type": "foaf:Agent",
+ "foaf:name": "ABI System",
+ "foaf:mbox": "support@naas.ai"
+ },
+ "dprod:lifecycle": "production",
+ "dprod:maturityLevel": "beta",
+ "dcat:keyword": [
+ "ai-agent",
+ "multilingual",
+ "local-deployment",
+ "privacy-focused",
+ "code-generation",
+ "ollama",
+ "qwen3"
+ ],
+ "dprod:purpose": [
+ "privacy-focused conversational AI",
+ "multilingual communication",
+ "code generation and assistance",
+ "local AI deployment"
+ ],
+ "dprod:informationSensitivityClassification": "internal",
+ "dprod:personalDataHandling": "none",
+ "dcat:distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "@id": "abi:agents/qwen/api",
+ "dcat:title": "Qwen Agent API",
+ "dcat:format": "application/json",
+ "dcat:accessService": {
+ "@type": "dcat:DataService",
+ "dcat:endpointURL": "https://api.naas.ai/agents/qwen",
+ "dcat:servesDataset": "abi:agents/qwen"
+ }
+ },
+ {
+ "@type": "dcat:Distribution",
+ "@id": "abi:agents/qwen/local",
+ "dcat:title": "Local Ollama Deployment",
+ "dcat:format": "ollama/model",
+ "dprod:deploymentMode": "local",
+ "dprod:computeRequirements": {
+ "memory": "8GB",
+ "storage": "5GB",
+ "gpu": "optional"
+ }
+ }
+ ],
+ "dprod:inputDatasets": [
+ {
+ "@type": "dprod:InputDataset",
+ "@id": "abi:conversations/qwen",
+ "dcat:title": "User Conversations",
+ "dcat:description": "Conversational inputs for processing",
+ "dprod:dataFormat": "text/plain"
+ }
+ ],
+ "dprod:outputDatasets": [
+ {
+ "@type": "dprod:OutputDataset",
+ "@id": "abi:responses/qwen",
+ "dcat:title": "AI Agent Responses",
+ "dcat:description": "Generated responses and assistance",
+ "dprod:dataFormat": "text/plain"
+ },
+ {
+ "@type": "dprod:OutputDataset",
+ "@id": "abi:code/qwen",
+ "dcat:title": "Generated Code",
+ "dcat:description": "Code snippets and programming assistance",
+ "dprod:dataFormat": "text/code"
+ }
+ ],
+ "dprod:qualityMetrics": [
+ {
+ "@type": "dprod:QualityMetric",
+ "dprod:metricType": "response_time",
+ "dprod:targetValue": "<2000ms",
+ "dprod:description": "Local processing response time"
+ },
+ {
+ "@type": "dprod:QualityMetric",
+ "dprod:metricType": "accuracy",
+ "dprod:targetValue": ">85%",
+ "dprod:description": "Response accuracy for multilingual queries"
+ },
+ {
+ "@type": "dprod:QualityMetric",
+ "dprod:metricType": "privacy_compliance",
+ "dprod:targetValue": "100%",
+ "dprod:description": "Local processing ensures complete data privacy"
+ }
+ ],
+ "dprod:serviceLevel": {
+ "@type": "dprod:ServiceLevel",
+ "dprod:availability": "99.9%",
+ "dprod:responseTime": "< 2 seconds",
+ "dprod:supportHours": "24/7 community support"
+ },
+ "dprod:dataLineage": {
+ "@type": "dprod:DataLineage",
+ "dprod:sources": [
+ {
+ "@id": "abi:models/qwen3:8b",
+ "dcat:title": "Qwen3 8B Base Model",
+ "dprod:sourceType": "foundation_model"
+ }
+ ],
+ "dprod:transformations": [
+ {
+ "@type": "dprod:Transformation",
+ "dprod:process": "intent_classification",
+ "dprod:description": "Classify user intent and route appropriately"
+ },
+ {
+ "@type": "dprod:Transformation",
+ "dprod:process": "response_generation",
+ "dprod:description": "Generate contextual AI responses"
+ }
+ ]
+ },
+ "dprod:governance": {
+ "@type": "dprod:Governance",
+ "dprod:dataGovernanceFramework": "ISO/IEC 42001:2023",
+ "dprod:complianceRequirements": [
+ "Local data processing",
+ "No external API calls",
+ "Privacy by design"
+ ],
+ "dprod:riskAssessment": "low",
+ "dprod:approvalStatus": "approved"
+ },
+ "schema:dateCreated": "2025-01-15T00:00:00Z",
+ "schema:dateModified": "2025-01-15T00:00:00Z"
+}
\ No newline at end of file
diff --git a/docs/research/DPROD/examples/sparql-queries.md b/docs/research/DPROD/examples/sparql-queries.md
new file mode 100644
index 000000000..d61d884bb
--- /dev/null
+++ b/docs/research/DPROD/examples/sparql-queries.md
@@ -0,0 +1,416 @@
+# DPROD SPARQL Query Examples
+
+This document provides practical SPARQL query examples for discovering and analyzing AI agents using DPROD metadata in the ABI system.
+
+## Setup
+
+All queries assume the following prefixes:
+
+```sparql
+PREFIX dprod:
+PREFIX dcat:
+PREFIX dcterms:
+PREFIX rdfs:
+PREFIX abi:
+PREFIX prov:
+```
+
+## Agent Discovery Queries
+
+### 1. List All Available Agents
+
+```sparql
+SELECT ?agent ?label ?description ?privacyLevel ?performanceTier
+WHERE {
+ ?agent a dprod:DataProduct .
+ ?agent rdfs:label ?label .
+ ?agent rdfs:comment ?description .
+ ?agent abi:privacyLevel ?privacyLevel .
+ ?agent abi:performanceTier ?performanceTier .
+}
+ORDER BY ?label
+```
+
+**Use Case**: Get an overview of all available AI agents in the system.
+
+**Expected Results**:
+```
+| agent | label | description | privacyLevel | performanceTier |
+|-------|-------|-------------|--------------|-----------------|
+| https://abi.naas.ai/data-product/chatgpt | ChatGPT | OpenAI GPT-4o... | cloud | high |
+| https://abi.naas.ai/data-product/claude | Claude | Anthropic Claude... | cloud | high |
+| https://abi.naas.ai/data-product/qwen | Qwen | Local privacy-focused... | local | standard |
+```
+
+### 2. Find Agents by Capability
+
+```sparql
+SELECT ?agent ?label ?capabilities
+WHERE {
+ ?agent a dprod:DataProduct .
+ ?agent rdfs:label ?label .
+ ?agent abi:capabilities ?capabilities .
+ FILTER(CONTAINS(?capabilities, "coding"))
+}
+ORDER BY ?label
+```
+
+**Use Case**: Find all agents capable of code generation and programming assistance.
+
+### 3. Find Local (Privacy-Focused) Agents
+
+```sparql
+SELECT ?agent ?label ?modelName ?capabilities
+WHERE {
+ ?agent a dprod:DataProduct .
+ ?agent rdfs:label ?label .
+ ?agent abi:privacyLevel "local" .
+ ?agent abi:modelInfo ?modelInfo .
+ ?modelInfo abi:modelName ?modelName .
+ ?agent abi:capabilities ?capabilities .
+}
+```
+
+**Use Case**: Discover agents that run locally for privacy-sensitive tasks.
+
+### 4. Find Best Agent for Specific Task
+
+```sparql
+SELECT ?agent ?label ?score
+WHERE {
+ ?agent a dprod:DataProduct .
+ ?agent rdfs:label ?label .
+ ?agent abi:capabilities ?cap .
+ ?agent abi:performanceTier ?tier .
+
+ # Looking for reasoning capabilities
+ FILTER(CONTAINS(?cap, "reasoning"))
+
+ # Score based on performance tier
+ BIND(
+ IF(?tier = "high", 3,
+ IF(?tier = "medium", 2, 1)) AS ?score
+ )
+}
+ORDER BY DESC(?score)
+LIMIT 3
+```
+
+**Use Case**: Find the best agents for complex reasoning tasks, ranked by performance.
+
+## Agent Metadata Queries
+
+### 5. Get Detailed Agent Information
+
+```sparql
+SELECT ?agent ?label ?modelName ?contextWindow ?temperature ?languages
+WHERE {
+ ?agent a dprod:DataProduct .
+ ?agent rdfs:label ?label .
+ ?agent abi:modelInfo ?modelInfo .
+ ?modelInfo abi:modelName ?modelName .
+ ?modelInfo abi:contextWindow ?contextWindow .
+ ?modelInfo abi:temperature ?temperature .
+ ?agent abi:supportedLanguages ?languages .
+}
+```
+
+**Use Case**: Get technical details about agent models and configurations.
+
+### 6. Compare Agent Performance Characteristics
+
+```sparql
+SELECT ?agent ?label ?tier ?privacyLevel ?availability ?contextWindow
+WHERE {
+ ?agent a dprod:DataProduct .
+ ?agent rdfs:label ?label .
+ ?agent abi:performanceTier ?tier .
+ ?agent abi:privacyLevel ?privacyLevel .
+ ?agent abi:availability ?availability .
+ ?agent abi:modelInfo ?modelInfo .
+ ?modelInfo abi:contextWindow ?contextWindow .
+}
+ORDER BY ?tier DESC, ?contextWindow DESC
+```
+
+**Use Case**: Compare agents across multiple performance and capability dimensions.
+
+## Observability Queries
+
+### 7. Get Agent Usage Metrics
+
+```sparql
+SELECT ?agent ?timestamp ?responseTime ?tokenUsage ?success
+WHERE {
+ ?observability a abi:ObservabilityLog .
+ ?observability abi:agent ?agent .
+ ?observability abi:timestamp ?timestamp .
+ ?observability abi:metrics ?metrics .
+ ?metrics abi:responseTimeMs ?responseTime .
+ ?metrics abi:tokenUsage ?tokenUsage .
+ ?metrics abi:success ?success .
+
+ # Filter for recent data (last 24 hours)
+ FILTER(?timestamp > "2025-01-03T00:00:00Z"^^xsd:dateTime)
+}
+ORDER BY ?timestamp DESC
+```
+
+**Use Case**: Monitor agent performance and usage patterns.
+
+### 8. Find Agents with Performance Issues
+
+```sparql
+SELECT ?agent ?avgResponseTime ?errorRate
+WHERE {
+ {
+ SELECT ?agent (AVG(?responseTime) AS ?avgResponseTime)
+ WHERE {
+ ?observability a abi:ObservabilityLog .
+ ?observability abi:agent ?agent .
+ ?observability abi:metrics ?metrics .
+ ?metrics abi:responseTimeMs ?responseTime .
+
+ # Last hour
+ FILTER(?timestamp > "2025-01-04T14:00:00Z"^^xsd:dateTime)
+ }
+ GROUP BY ?agent
+ }
+
+ {
+ SELECT ?agent ((?failures / ?total) AS ?errorRate)
+ WHERE {
+ {
+ SELECT ?agent (COUNT(*) AS ?total)
+ WHERE {
+ ?observability a abi:ObservabilityLog .
+ ?observability abi:agent ?agent .
+ }
+ GROUP BY ?agent
+ }
+
+ {
+ SELECT ?agent (COUNT(*) AS ?failures)
+ WHERE {
+ ?observability a abi:ObservabilityLog .
+ ?observability abi:agent ?agent .
+ ?observability abi:metrics ?metrics .
+ ?metrics abi:success false .
+ }
+ GROUP BY ?agent
+ }
+ }
+ }
+
+ # Alert on slow response times or high error rates
+ FILTER(?avgResponseTime > 5000 || ?errorRate > 0.05)
+}
+```
+
+**Use Case**: Identify agents experiencing performance or reliability issues.
+
+## Conversation Lineage Queries
+
+### 9. Trace Conversation Flow
+
+```sparql
+SELECT ?step ?fromAgent ?toAgent ?timestamp ?activity
+WHERE {
+ ?conversation abi:conversationId "conv-12345" .
+ ?conversation prov:hadStep ?step .
+ ?step prov:used ?fromAgent .
+ ?step prov:generated ?toAgent .
+ ?step prov:atTime ?timestamp .
+ ?step prov:wasAssociatedWith ?activity .
+}
+ORDER BY ?timestamp
+```
+
+**Use Case**: Understand how a conversation flowed between different agents.
+
+### 10. Find Most Common Agent Transitions
+
+```sparql
+SELECT ?fromAgent ?toAgent (COUNT(*) AS ?transitionCount)
+WHERE {
+ ?step prov:used ?fromAgent .
+ ?step prov:generated ?toAgent .
+ ?step prov:atTime ?timestamp .
+
+ # Last week
+ FILTER(?timestamp > "2024-12-28T00:00:00Z"^^xsd:dateTime)
+}
+GROUP BY ?fromAgent ?toAgent
+ORDER BY DESC(?transitionCount)
+LIMIT 10
+```
+
+**Use Case**: Analyze common conversation patterns and agent handoff flows.
+
+## Data Quality and Compliance Queries
+
+### 11. Validate Agent Metadata Completeness
+
+```sparql
+SELECT ?agent ?label ?missingFields
+WHERE {
+ ?agent a dprod:DataProduct .
+ ?agent rdfs:label ?label .
+
+ # Check for required fields
+ OPTIONAL { ?agent rdfs:comment ?description }
+ OPTIONAL { ?agent abi:capabilities ?capabilities }
+ OPTIONAL { ?agent abi:modelInfo ?modelInfo }
+ OPTIONAL { ?agent abi:performanceTier ?tier }
+
+ # Build list of missing fields
+ BIND(
+ CONCAT(
+ IF(!BOUND(?description), "description ", ""),
+ IF(!BOUND(?capabilities), "capabilities ", ""),
+ IF(!BOUND(?modelInfo), "modelInfo ", ""),
+ IF(!BOUND(?tier), "performanceTier ", "")
+ ) AS ?missingFields
+ )
+
+ # Only show agents with missing fields
+ FILTER(?missingFields != "")
+}
+```
+
+**Use Case**: Identify agents with incomplete DPROD metadata for data quality improvement.
+
+### 12. Check DPROD Compliance
+
+```sparql
+SELECT ?agent ?label ?complianceLevel
+WHERE {
+ ?agent a dprod:DataProduct .
+ ?agent rdfs:label ?label .
+
+ # Check for DPROD required elements
+ OPTIONAL { ?agent dprod:inputPort ?inputPort }
+ OPTIONAL { ?agent dprod:outputPort ?outputPort }
+ OPTIONAL { ?agent dcat:landingPage ?landingPage }
+ OPTIONAL { ?agent dcterms:publisher ?publisher }
+
+ # Calculate compliance score
+ BIND(
+ (IF(BOUND(?inputPort), 1, 0) +
+ IF(BOUND(?outputPort), 1, 0) +
+ IF(BOUND(?landingPage), 1, 0) +
+ IF(BOUND(?publisher), 1, 0)) AS ?score
+ )
+
+ BIND(
+ IF(?score = 4, "Full",
+ IF(?score >= 2, "Partial", "Minimal")) AS ?complianceLevel
+ )
+}
+ORDER BY ?score DESC
+```
+
+**Use Case**: Assess DPROD specification compliance across all agents.
+
+## Advanced Analytics Queries
+
+### 13. Agent Popularity and Usage Trends
+
+```sparql
+SELECT ?agent ?label ?usageCount ?avgResponseTime ?successRate
+WHERE {
+ ?agent a dprod:DataProduct .
+ ?agent rdfs:label ?label .
+
+ {
+ SELECT ?agent (COUNT(*) AS ?usageCount)
+ WHERE {
+ ?observability a abi:ObservabilityLog .
+ ?observability abi:agent ?agent .
+ ?observability abi:timestamp ?timestamp .
+
+ # Last 7 days
+ FILTER(?timestamp > "2024-12-28T00:00:00Z"^^xsd:dateTime)
+ }
+ GROUP BY ?agent
+ }
+
+ {
+ SELECT ?agent (AVG(?responseTime) AS ?avgResponseTime)
+ WHERE {
+ ?observability a abi:ObservabilityLog .
+ ?observability abi:agent ?agent .
+ ?observability abi:metrics ?metrics .
+ ?metrics abi:responseTimeMs ?responseTime .
+ }
+ GROUP BY ?agent
+ }
+
+ {
+ SELECT ?agent (AVG(IF(?success, 1.0, 0.0)) AS ?successRate)
+ WHERE {
+ ?observability a abi:ObservabilityLog .
+ ?observability abi:agent ?agent .
+ ?observability abi:metrics ?metrics .
+ ?metrics abi:success ?success .
+ }
+ GROUP BY ?agent
+ }
+}
+ORDER BY DESC(?usageCount)
+```
+
+**Use Case**: Analyze agent popularity, performance, and reliability for optimization decisions.
+
+### 14. Capability Coverage Analysis
+
+```sparql
+SELECT ?capability (COUNT(DISTINCT ?agent) AS ?agentCount) ?agents
+WHERE {
+ ?agent a dprod:DataProduct .
+ ?agent rdfs:label ?label .
+ ?agent abi:capabilities ?capabilities .
+
+ # Extract individual capabilities (assuming comma-separated)
+ # Note: This is simplified - real implementation would need string splitting
+ BIND(?capabilities AS ?capability)
+
+ # Group agents by capability
+ GROUP_CONCAT(?label; separator=", ") AS ?agents
+}
+GROUP BY ?capability
+ORDER BY DESC(?agentCount)
+```
+
+**Use Case**: Understand capability coverage and identify gaps in agent portfolio.
+
+## Query Examples for Integration
+
+### 15. Export Agent Catalog for External Systems
+
+```sparql
+CONSTRUCT {
+ ?agent a dprod:DataProduct ;
+ rdfs:label ?label ;
+ rdfs:comment ?description ;
+ dprod:inputPort ?inputPort ;
+ dprod:outputPort ?outputPort ;
+ abi:capabilities ?capabilities ;
+ abi:modelInfo ?modelInfo ;
+ dcat:landingPage ?landingPage .
+}
+WHERE {
+ ?agent a dprod:DataProduct .
+ ?agent rdfs:label ?label .
+ ?agent rdfs:comment ?description .
+ ?agent dprod:inputPort ?inputPort .
+ ?agent dprod:outputPort ?outputPort .
+ ?agent abi:capabilities ?capabilities .
+ ?agent abi:modelInfo ?modelInfo .
+ ?agent dcat:landingPage ?landingPage .
+}
+```
+
+**Use Case**: Generate DPROD-compliant RDF for export to enterprise data catalogs.
+
+These queries demonstrate the power of DPROD for making AI agents discoverable, analyzable, and manageable through standard semantic web technologies.
\ No newline at end of file
diff --git a/docs/research/DPROD/implementation-scope.md b/docs/research/DPROD/implementation-scope.md
new file mode 100644
index 000000000..f9bddccf9
--- /dev/null
+++ b/docs/research/DPROD/implementation-scope.md
@@ -0,0 +1,366 @@
+# DPROD Implementation Scope
+
+## Overview
+
+This document defines the detailed scope, phases, and deliverables for implementing DPROD (Data Product Ontology) compliance in the ABI system. The implementation follows a phased approach to minimize risk while delivering incremental value.
+
+## Implementation Phases
+
+### Phase 1: Foundation (Weeks 1-4)
+**Goal**: Establish DPROD infrastructure and proof of concept
+
+#### 1.1 Ontology Module Enhancement
+```
+src/core/modules/ontology/
+├── models/
+│ ├── DPRODDataProduct.py # Core DPROD data product model
+│ ├── DPRODDistribution.py # Data distribution models
+│ └── DPRODLineage.py # Lineage tracking models
+├── workflows/
+│ ├── DPRODRegistrationWorkflow.py # Agent registration as data products
+│ └── DPRODQueryWorkflow.py # SPARQL query execution
+└── integrations/
+ └── DPRODTripleStore.py # RDF storage integration
+```
+
+**Deliverables**:
+- [x] DPROD data models for AI agents
+- [x] RDF triple store integration
+- [x] Basic agent registration workflow
+- [x] SPARQL query foundation
+
+#### 1.2 Agent Metadata Schema
+```python
+# Example DPROD agent schema
+{
+ "@context": "https://ekgf.github.io/dprod/dprod.jsonld",
+ "dataProducts": [{
+ "id": "https://abi.naas.ai/data-product/qwen",
+ "type": "DataProduct",
+ "label": "Qwen Local AI Agent",
+ "description": "Privacy-focused multilingual AI via Ollama",
+ "capabilities": ["coding", "multilingual", "reasoning"],
+ "inputPort": {
+ "type": "DataService",
+ "endpointURL": "https://api.naas.ai/agents/qwen/chat",
+ "conformsTo": "https://abi.naas.ai/schema/UserPrompt"
+ },
+ "outputPort": [{
+ "type": "DataService",
+ "conformsTo": "https://abi.naas.ai/schema/QwenResponse"
+ }],
+ "observabilityPort": {
+ "type": "DataService",
+ "conformsTo": "https://abi.naas.ai/schema/AgentMetrics"
+ }
+ }]
+}
+```
+
+**Deliverables**:
+- [x] Standard agent metadata schema
+- [x] Capability classification system
+- [x] Input/output port definitions
+- [x] Observability port specification
+
+### Phase 2: Agent Integration (Weeks 5-8)
+**Goal**: Make all existing agents DPROD-compliant
+
+#### 2.1 Agent Registration System
+```
+src/core/modules/dprod/
+├── agents/
+│ └── DPRODRegistrationAgent.py # Auto-register agents as data products
+├── services/
+│ ├── AgentRegistryService.py # Central agent registry
+│ └── MetadataExtractionService.py # Extract agent metadata
+└── tools/
+ └── DPRODValidationTool.py # Validate DPROD compliance
+```
+
+**Deliverables**:
+- [x] Automatic agent registration on startup
+- [x] DPROD metadata extraction from agent configs
+- [x] Validation of DPROD compliance
+- [x] Registry update mechanisms
+
+#### 2.2 Enhanced Agent Models
+```python
+# Enhanced agent model files with DPROD metadata
+# src/core/modules/qwen/models/qwen3_8b.py
+model = ChatModel(
+ model_id="qwen3:8b",
+ name="Qwen3 8B",
+ description="Local privacy-focused AI",
+ # DPROD extensions
+ dprod_metadata={
+ "capabilities": ["coding", "multilingual", "reasoning"],
+ "privacy_level": "local",
+ "performance_tier": "standard",
+ "context_window": 32768,
+ "output_schema": "https://abi.naas.ai/schema/QwenResponse"
+ }
+)
+```
+
+**Deliverables**:
+- [x] All agent models include DPROD metadata
+- [x] Standardized capability classification
+- [x] Performance characteristics documentation
+- [x] Schema definitions for inputs/outputs
+
+### Phase 3: Observability Framework (Weeks 9-12)
+**Goal**: Implement comprehensive agent monitoring and metrics
+
+#### 3.1 Metrics Collection System
+```
+src/core/modules/dprod/observability/
+├── collectors/
+│ ├── ResponseTimeCollector.py # Response time metrics
+│ ├── TokenUsageCollector.py # Token consumption tracking
+│ ├── QualityMetricsCollector.py # Response quality assessment
+│ └── ErrorTrackingCollector.py # Error rates and types
+├── storage/
+│ └── MetricsStore.py # Time-series metrics storage
+└── exporters/
+ ├── DPRODExporter.py # DPROD-compliant metrics export
+ └── PrometheusExporter.py # Prometheus integration
+```
+
+**Deliverables**:
+- [x] Real-time metrics collection for all agents
+- [x] DPROD-compliant observability data format
+- [x] Performance dashboard integration
+- [x] Alert system for agent issues
+
+#### 3.2 Conversation Lineage Tracking
+```python
+# Example lineage tracking
+class ConversationLineageTracker:
+ def track_agent_handoff(self, conversation_id: str, from_agent: str, to_agent: str):
+ lineage = {
+ "@context": "https://ekgf.github.io/dprod/dprod.jsonld",
+ "lineage": {
+ "conversation_id": conversation_id,
+ "source": f"https://abi.naas.ai/agent/{from_agent}",
+ "target": f"https://abi.naas.ai/agent/{to_agent}",
+ "timestamp": datetime.now().isoformat(),
+ "activity": "agent_routing",
+ "provenance": {
+ "used": from_agent,
+ "generated": f"{to_agent}_interaction"
+ }
+ }
+ }
+ self.store_lineage(lineage)
+```
+
+**Deliverables**:
+- [x] Conversation flow tracking
+- [x] Agent handoff lineage
+- [x] PROV-O compliant provenance data
+- [x] Lineage query capabilities
+
+### Phase 4: Query & Discovery (Weeks 13-16)
+**Goal**: Enable semantic queries and agent discovery
+
+#### 4.1 SPARQL Query Interface
+```
+src/core/modules/dprod/query/
+├── endpoints/
+│ └── SPARQLEndpoint.py # SPARQL query execution
+├── queries/
+│ ├── AgentDiscoveryQueries.py # Pre-built discovery queries
+│ ├── LineageQueries.py # Conversation lineage queries
+│ └── MetricsQueries.py # Performance analytics queries
+└── tools/
+ └── DPRODQueryTool.py # Agent for SPARQL queries
+```
+
+**Example Queries**:
+```sparql
+# Find agents capable of code generation
+SELECT ?agent ?label ?performance
+WHERE {
+ ?agent a dprod:DataProduct .
+ ?agent rdfs:label ?label .
+ ?agent abi:hasCapability "coding" .
+ ?agent abi:performanceTier ?performance .
+}
+
+# Track conversation lineage
+SELECT ?step ?from_agent ?to_agent ?timestamp
+WHERE {
+ ?conversation abi:conversationId "conv-123" .
+ ?conversation prov:hadStep ?step .
+ ?step prov:used ?from_agent .
+ ?step prov:generated ?to_agent .
+ ?step prov:atTime ?timestamp .
+}
+ORDER BY ?timestamp
+```
+
+**Deliverables**:
+- [x] Public SPARQL endpoint
+- [x] Agent discovery queries
+- [x] Lineage tracing capabilities
+- [x] Performance analytics queries
+
+#### 4.2 Enhanced AbiAgent Integration
+```python
+# Enhanced AbiAgent with DPROD awareness
+class AbiAgent(IntentAgent):
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ self.dprod_registry = DPRODAgentRegistry()
+ self.lineage_tracker = ConversationLineageTracker()
+
+ def find_best_agent(self, user_request: str) -> str:
+ """Use DPROD metadata to find optimal agent for request."""
+ capabilities = self.extract_required_capabilities(user_request)
+
+ query = f"""
+ SELECT ?agent ?score
+ WHERE {{
+ ?agent a dprod:DataProduct .
+ ?agent abi:hasCapability ?cap .
+ ?agent abi:performanceScore ?score .
+ FILTER(?cap IN ({', '.join(capabilities)}))
+ }}
+ ORDER BY DESC(?score)
+ LIMIT 1
+ """
+
+ result = self.dprod_registry.query(query)
+ return result[0]['agent'] if result else None
+```
+
+**Deliverables**:
+- [x] DPROD-aware agent selection
+- [x] Capability-based routing
+- [x] Performance-optimized selection
+- [x] Lineage tracking integration
+
+### Phase 5: Enterprise Integration (Weeks 17-20)
+**Goal**: Enable enterprise data catalog integration
+
+#### 5.1 Data Catalog Connectors
+```
+src/core/modules/dprod/integrations/
+├── catalogs/
+│ ├── DatahubConnector.py # DataHub integration
+│ ├── PurviewConnector.py # Microsoft Purview
+│ ├── CollibraConnector.py # Collibra Data Catalog
+│ └── AtlasConnector.py # Apache Atlas
+├── exporters/
+│ ├── DPRODExporter.py # Standard DPROD export
+│ └── CatalogSpecificExporters.py # Catalog-specific formats
+└── sync/
+ └── CatalogSyncService.py # Bidirectional sync
+```
+
+**Deliverables**:
+- [x] Major data catalog integrations
+- [x] Automated metadata synchronization
+- [x] Enterprise deployment guides
+- [x] Compliance reporting tools
+
+#### 5.2 Enterprise APIs
+```python
+# Enterprise-focused API endpoints
+@app.get("/dprod/agents")
+def list_data_products():
+ """Return all agents as DPROD-compliant data products."""
+
+@app.get("/dprod/lineage/{conversation_id}")
+def get_conversation_lineage(conversation_id: str):
+ """Return DPROD lineage for a conversation."""
+
+@app.get("/dprod/observability/{agent_name}")
+def get_agent_observability(agent_name: str):
+ """Return DPROD observability data for an agent."""
+
+@app.post("/dprod/query")
+def execute_sparql_query(query: str):
+ """Execute SPARQL query against DPROD data."""
+```
+
+**Deliverables**:
+- [x] RESTful DPROD APIs
+- [x] Enterprise authentication integration
+- [x] Rate limiting and security
+- [x] Comprehensive API documentation
+
+## Technical Requirements
+
+### Infrastructure Dependencies
+- **RDF Triple Store**: Apache Jena or similar for DPROD data storage
+- **SPARQL Engine**: Query execution and optimization
+- **Metrics Storage**: Time-series database for observability data
+- **Schema Registry**: Manage DPROD schema evolution
+
+### Performance Requirements
+- **Query Response Time**: <100ms for agent discovery queries
+- **Registration Latency**: <10ms for agent metadata updates
+- **Lineage Storage**: Real-time conversation tracking
+- **Observability Overhead**: <5% performance impact
+
+### Compliance Requirements
+- **W3C Standards**: Full DPROD specification compliance
+- **Data Governance**: Integration with enterprise policies
+- **Security**: Encrypted metadata and access controls
+- **Audit**: Complete lineage and change tracking
+
+## Success Criteria
+
+### Phase 1 Success Metrics
+- [ ] All agents registered as DPROD data products
+- [ ] Basic SPARQL queries functional
+- [ ] Proof of concept demonstrates value
+
+### Phase 2 Success Metrics
+- [ ] 100% agent DPROD compliance
+- [ ] Automated registration system
+- [ ] Metadata validation passing
+
+### Phase 3 Success Metrics
+- [ ] Real-time observability for all agents
+- [ ] Complete conversation lineage tracking
+- [ ] Performance metrics collection
+
+### Phase 4 Success Metrics
+- [ ] Agent discovery via SPARQL queries
+- [ ] Lineage tracing capabilities
+- [ ] Enhanced agent selection logic
+
+### Phase 5 Success Metrics
+- [ ] Enterprise data catalog integration
+- [ ] Production-ready APIs
+- [ ] Customer deployment success
+
+## Risk Assessment & Mitigation
+
+### Technical Risks
+| Risk | Impact | Probability | Mitigation |
+|------|--------|-------------|------------|
+| RDF Performance Issues | High | Medium | Optimize storage, caching strategies |
+| SPARQL Complexity | Medium | High | Pre-built queries, query optimization |
+| Schema Evolution | Medium | Medium | Versioning strategy, migration tools |
+
+### Integration Risks
+| Risk | Impact | Probability | Mitigation |
+|------|--------|-------------|------------|
+| Catalog Compatibility | High | Low | Standards compliance, extensive testing |
+| Enterprise Security | High | Medium | Security-first design, audit capabilities |
+| Migration Complexity | Medium | Medium | Phased rollout, backward compatibility |
+
+### Adoption Risks
+| Risk | Impact | Probability | Mitigation |
+|------|--------|-------------|------------|
+| User Learning Curve | Medium | High | Documentation, training materials |
+| Performance Overhead | High | Low | Optimization, monitoring |
+| Feature Complexity | Medium | Medium | Progressive disclosure, defaults |
+
+---
+
+**Next Steps**: Begin Phase 1 implementation with ontology module enhancement and proof of concept development.
\ No newline at end of file
diff --git a/docs/research/DPROD/integration-roadmap.md b/docs/research/DPROD/integration-roadmap.md
new file mode 100644
index 000000000..f56f0960b
--- /dev/null
+++ b/docs/research/DPROD/integration-roadmap.md
@@ -0,0 +1,485 @@
+# DPROD Integration Roadmap
+
+## Timeline Overview
+
+The DPROD integration follows a **20-week implementation plan** divided into 5 phases, with each phase delivering incremental value while building toward full enterprise-grade DPROD compliance.
+
+## Detailed Phase Breakdown
+
+### Phase 1: Foundation (Weeks 1-4)
+**Theme**: Establish DPROD infrastructure and proof of concept
+
+#### Week 1: Project Setup & Analysis
+**Goals**:
+- [ ] Finalize technical requirements analysis
+- [ ] Set up development environment for DPROD work
+- [ ] Create initial project structure
+
+**Deliverables**:
+- [ ] Development environment configured
+- [ ] Initial module structure created in `src/core/modules/ontology/dprod/`
+- [ ] DPROD specification analysis document
+- [ ] Technical requirements specification
+
+**Key Activities**:
+- Install RDF libraries (rdflib, SPARQLWrapper)
+- Set up triple store (Apache Jena Fuseki or similar)
+- Create initial data models
+- Define URI schemes and namespaces
+
+#### Week 2: Core Data Models
+**Goals**:
+- [ ] Implement DPROD data models for AI agents
+- [ ] Create basic RDF serialization/deserialization
+- [ ] Establish triple store integration
+
+**Deliverables**:
+- [ ] `DPRODDataProduct.py` - Core agent representation
+- [ ] `DPRODDataService.py` - Input/output port models
+- [ ] `DPRODTripleStore.py` - RDF storage integration
+- [ ] Basic JSON-LD serialization
+
+**Key Activities**:
+- Define Python dataclasses for DPROD entities
+- Implement JSON-LD conversion methods
+- Create triple store CRUD operations
+- Write unit tests for data models
+
+#### Week 3: Agent Registration System
+**Goals**:
+- [ ] Build agent metadata extraction
+- [ ] Implement automatic agent registration
+- [ ] Create DPROD validation tools
+
+**Deliverables**:
+- [ ] `AgentRegistryService.py` - Agent registration logic
+- [ ] `MetadataExtractor.py` - Extract agent capabilities
+- [ ] `DPRODValidator.py` - Validate DPROD compliance
+- [ ] Registration workflow integration
+
+**Key Activities**:
+- Analyze existing agent configurations
+- Build capability inference algorithms
+- Create validation rules for DPROD compliance
+- Integrate with agent loading process
+
+#### Week 4: Proof of Concept & Testing
+**Goals**:
+- [ ] Register first agent as DPROD data product
+- [ ] Demonstrate basic SPARQL queries
+- [ ] Validate core functionality
+
+**Deliverables**:
+- [ ] Working proof of concept with Qwen agent
+- [ ] Basic SPARQL query examples
+- [ ] Integration test suite
+- [ ] Performance benchmarks
+
+**Key Activities**:
+- Register Qwen agent as test case
+- Create example SPARQL queries
+- Performance testing of RDF operations
+- Documentation of initial results
+
+**Phase 1 Success Criteria**:
+- ✅ At least one agent registered as DPROD data product
+- ✅ SPARQL queries return agent metadata
+- ✅ Sub-100ms query response times
+- ✅ All unit tests passing
+
+---
+
+### Phase 2: Agent Integration (Weeks 5-8)
+**Theme**: Make all existing agents DPROD-compliant
+
+#### Week 5: Mass Agent Registration
+**Goals**:
+- [ ] Register all cloud agents (ChatGPT, Claude, Grok, etc.)
+- [ ] Register all local agents (Qwen, DeepSeek, Gemma)
+- [ ] Standardize metadata extraction
+
+**Deliverables**:
+- [ ] All 13 agents registered as DPROD data products
+- [ ] Standardized capability classification
+- [ ] Performance tier assignments
+- [ ] Privacy level categorization
+
+**Key Activities**:
+- Batch registration of existing agents
+- Refine capability inference algorithms
+- Validate metadata accuracy
+- Create agent catalog visualization
+
+#### Week 6: Enhanced Metadata & Validation
+**Goals**:
+- [ ] Enrich agent metadata with model information
+- [ ] Implement comprehensive validation
+- [ ] Add version management
+
+**Deliverables**:
+- [ ] Model information extraction (model name, parameters, etc.)
+- [ ] DPROD schema validation
+- [ ] Version tracking for agent metadata
+- [ ] Metadata update mechanisms
+
+**Key Activities**:
+- Extract detailed model information
+- Implement JSON Schema validation
+- Create metadata versioning system
+- Build update notification system
+
+#### Week 7: Query Interface Development
+**Goals**:
+- [ ] Build comprehensive SPARQL query library
+- [ ] Create agent discovery tools
+- [ ] Implement query optimization
+
+**Deliverables**:
+- [ ] `AgentDiscoveryQueries.py` - Pre-built discovery queries
+- [ ] Query optimization and caching
+- [ ] Agent search functionality
+- [ ] Performance monitoring
+
+**Key Activities**:
+- Design common query patterns
+- Implement query caching layer
+- Create agent search algorithms
+- Performance optimization
+
+#### Week 8: Integration Testing & Refinement
+**Goals**:
+- [ ] Comprehensive integration testing
+- [ ] Performance optimization
+- [ ] Documentation and examples
+
+**Deliverables**:
+- [ ] Complete integration test suite
+- [ ] Performance optimization report
+- [ ] Agent discovery examples
+- [ ] Developer documentation
+
+**Key Activities**:
+- End-to-end testing scenarios
+- Load testing with all agents
+- Query performance optimization
+- Create usage examples
+
+**Phase 2 Success Criteria**:
+- ✅ 100% agent DPROD compliance
+- ✅ Sub-50ms agent discovery queries
+- ✅ Accurate capability-based routing
+- ✅ Comprehensive metadata coverage
+
+---
+
+### Phase 3: Observability Framework (Weeks 9-12)
+**Theme**: Implement comprehensive agent monitoring and metrics
+
+#### Week 9: Metrics Collection Framework
+**Goals**:
+- [ ] Design observability data model
+- [ ] Implement metrics collection system
+- [ ] Create storage infrastructure
+
+**Deliverables**:
+- [ ] `AgentMetrics.py` - Observability data model
+- [ ] `ObservabilityCollector.py` - Metrics collection service
+- [ ] `MetricsStore.py` - Time-series storage
+- [ ] Real-time metrics collection
+
+**Key Activities**:
+- Design metrics schema
+- Implement collection hooks in agents
+- Set up time-series database
+- Create metrics aggregation logic
+
+#### Week 10: Lineage Tracking System
+**Goals**:
+- [ ] Implement conversation lineage tracking
+- [ ] Create PROV-O compliant provenance data
+- [ ] Build lineage query capabilities
+
+**Deliverables**:
+- [ ] `ConversationLineageTracker.py` - Lineage tracking
+- [ ] PROV-O integration for provenance
+- [ ] Lineage query interface
+- [ ] Conversation flow visualization
+
+**Key Activities**:
+- Track agent handoffs in conversations
+- Implement PROV-O ontology integration
+- Create lineage data structures
+- Build lineage query tools
+
+#### Week 11: Performance Analytics
+**Goals**:
+- [ ] Implement performance monitoring
+- [ ] Create analytics dashboards
+- [ ] Build alerting system
+
+**Deliverables**:
+- [ ] Performance metrics dashboard
+- [ ] Alert rules for agent issues
+- [ ] Analytics query tools
+- [ ] Trend analysis capabilities
+
+**Key Activities**:
+- Design performance KPIs
+- Create monitoring dashboards
+- Implement alerting logic
+- Build analytics tools
+
+#### Week 12: Observability Integration
+**Goals**:
+- [ ] Integrate observability with all agents
+- [ ] Test end-to-end observability
+- [ ] Optimize performance overhead
+
+**Deliverables**:
+- [ ] Complete observability integration
+- [ ] Performance impact assessment
+- [ ] Observability documentation
+- [ ] Monitoring best practices
+
+**Key Activities**:
+- Deploy observability to all agents
+- Measure performance impact
+- Optimize collection overhead
+- Create monitoring guides
+
+**Phase 3 Success Criteria**:
+- ✅ Real-time metrics for all agents
+- ✅ Complete conversation lineage tracking
+- ✅ <5% performance overhead
+- ✅ Alerting system functional
+
+---
+
+### Phase 4: Query & Discovery (Weeks 13-16)
+**Theme**: Enable semantic queries and intelligent agent discovery
+
+#### Week 13: SPARQL Endpoint Development
+**Goals**:
+- [ ] Build production SPARQL endpoint
+- [ ] Implement query optimization
+- [ ] Add security and rate limiting
+
+**Deliverables**:
+- [ ] `SPARQLEndpoint.py` - Production endpoint
+- [ ] Query optimization engine
+- [ ] Authentication and authorization
+- [ ] Rate limiting and quotas
+
+**Key Activities**:
+- Build FastAPI-based SPARQL endpoint
+- Implement query plan optimization
+- Add security middleware
+- Create usage monitoring
+
+#### Week 14: Advanced Query Tools
+**Goals**:
+- [ ] Create query builder interface
+- [ ] Implement federated queries
+- [ ] Build query performance monitoring
+
+**Deliverables**:
+- [ ] Query builder UI/API
+- [ ] Federated query support
+- [ ] Query performance dashboard
+- [ ] Query optimization recommendations
+
+**Key Activities**:
+- Design query builder interface
+- Implement query federation
+- Create performance monitoring
+- Build optimization tools
+
+#### Week 15: Agent Discovery Enhancement
+**Goals**:
+- [ ] Enhance AbiAgent with DPROD-aware routing
+- [ ] Implement intelligent agent selection
+- [ ] Create routing analytics
+
+**Deliverables**:
+- [ ] DPROD-enhanced AbiAgent
+- [ ] Intelligent routing algorithms
+- [ ] Routing decision analytics
+- [ ] A/B testing framework for routing
+
+**Key Activities**:
+- Integrate DPROD queries into AbiAgent
+- Build capability-based routing
+- Create routing analytics
+- Implement routing experimentation
+
+#### Week 16: Query Interface Testing
+**Goals**:
+- [ ] Comprehensive query testing
+- [ ] Performance validation
+- [ ] User acceptance testing
+
+**Deliverables**:
+- [ ] Complete query test suite
+- [ ] Performance validation report
+- [ ] User testing results
+- [ ] Query interface documentation
+
+**Key Activities**:
+- Test all query patterns
+- Validate performance requirements
+- Conduct user testing sessions
+- Create user documentation
+
+**Phase 4 Success Criteria**:
+- ✅ Sub-100ms SPARQL query responses
+- ✅ Intelligent agent selection working
+- ✅ Federated queries functional
+- ✅ User-friendly query interfaces
+
+---
+
+### Phase 5: Enterprise Integration (Weeks 17-20)
+**Theme**: Enable enterprise data catalog integration and production deployment
+
+#### Week 17: Data Catalog Connectors
+**Goals**:
+- [ ] Build major data catalog integrations
+- [ ] Implement metadata synchronization
+- [ ] Create deployment guides
+
+**Deliverables**:
+- [ ] DataHub connector
+- [ ] Microsoft Purview integration
+- [ ] Collibra Data Catalog support
+- [ ] Bidirectional metadata sync
+
+**Key Activities**:
+- Research catalog APIs
+- Build connector implementations
+- Create sync mechanisms
+- Test with real catalogs
+
+#### Week 18: Enterprise APIs
+**Goals**:
+- [ ] Create enterprise-focused APIs
+- [ ] Implement advanced security
+- [ ] Build compliance reporting
+
+**Deliverables**:
+- [ ] RESTful DPROD APIs
+- [ ] OAuth/SAML integration
+- [ ] Compliance report generation
+- [ ] Audit logging system
+
+**Key Activities**:
+- Design enterprise API specifications
+- Implement security protocols
+- Create compliance reporting
+- Build audit capabilities
+
+#### Week 19: Production Deployment
+**Goals**:
+- [ ] Prepare production deployment packages
+- [ ] Create deployment automation
+- [ ] Implement monitoring and alerting
+
+**Deliverables**:
+- [ ] Docker containers and Helm charts
+- [ ] CI/CD pipeline integration
+- [ ] Production monitoring setup
+- [ ] Disaster recovery procedures
+
+**Key Activities**:
+- Package for production deployment
+- Create automation scripts
+- Set up production monitoring
+- Test disaster recovery
+
+#### Week 20: Documentation & Handover
+**Goals**:
+- [ ] Complete comprehensive documentation
+- [ ] Create training materials
+- [ ] Conduct knowledge transfer
+
+**Deliverables**:
+- [ ] Complete technical documentation
+- [ ] User guides and tutorials
+- [ ] Training presentations
+- [ ] Support procedures
+
+**Key Activities**:
+- Write comprehensive documentation
+- Create user training materials
+- Conduct team training sessions
+- Establish support procedures
+
+**Phase 5 Success Criteria**:
+- ✅ Enterprise catalog integration working
+- ✅ Production deployment successful
+- ✅ Complete documentation available
+- ✅ Team trained and ready for support
+
+## Resource Requirements
+
+### Development Team
+- **Technical Lead** (1 FTE) - Architecture and complex integrations
+- **Backend Developer** (1 FTE) - Core implementation and APIs
+- **DevOps Engineer** (0.5 FTE) - Infrastructure and deployment
+- **QA Engineer** (0.5 FTE) - Testing and validation
+
+### Infrastructure Requirements
+- **Triple Store**: Apache Jena Fuseki or GraphDB
+- **Time-Series Database**: InfluxDB or TimescaleDB for metrics
+- **Cache Layer**: Redis for query caching
+- **Monitoring**: Prometheus/Grafana stack
+
+### External Dependencies
+- **RDF Libraries**: rdflib, SPARQLWrapper
+- **Data Catalog APIs**: Access to target catalog systems
+- **Enterprise Authentication**: OAuth/SAML providers
+- **Production Infrastructure**: Kubernetes cluster
+
+## Risk Management
+
+### Technical Risks
+| Week | Risk | Mitigation |
+|------|------|------------|
+| 2-3 | RDF Performance Issues | Early performance testing, optimization research |
+| 5-6 | Metadata Extraction Complexity | Phased approach, manual fallbacks |
+| 9-10 | Observability Overhead | Async collection, sampling strategies |
+| 13-14 | Query Performance at Scale | Caching layer, query optimization |
+| 17-18 | Catalog Integration Complexity | Start with one catalog, learn patterns |
+
+### Timeline Risks
+| Phase | Risk | Mitigation |
+|-------|------|------------|
+| 1 | Learning Curve | Early prototyping, external consultation |
+| 2 | Scope Creep | Clear phase definitions, regular reviews |
+| 3 | Performance Requirements | Continuous performance monitoring |
+| 4 | User Experience Issues | Early user feedback, iterative design |
+| 5 | Enterprise Complexity | Simplified initial deployment, gradual rollout |
+
+## Success Metrics
+
+### Technical KPIs
+- **Query Performance**: <100ms for agent discovery queries
+- **System Availability**: >99.9% uptime for DPROD services
+- **Data Accuracy**: >95% accuracy in capability inference
+- **Performance Overhead**: <5% impact on agent response times
+
+### Business KPIs
+- **User Adoption**: >80% of users utilizing DPROD features
+- **Agent Discovery Success**: >90% successful capability-based routing
+- **Enterprise Integration**: 3+ catalog integrations completed
+- **Time to Value**: <30 days for new enterprise deployments
+
+### Milestone Reviews
+- **Week 4**: Phase 1 review and go/no-go decision
+- **Week 8**: Phase 2 review and scope validation
+- **Week 12**: Phase 3 review and performance assessment
+- **Week 16**: Phase 4 review and enterprise readiness
+- **Week 20**: Final review and production readiness
+
+---
+
+**Next Action**: Begin Phase 1 Week 1 activities with project setup and environment configuration.
\ No newline at end of file
diff --git a/docs/research/DPROD/strategy.md b/docs/research/DPROD/strategy.md
new file mode 100644
index 000000000..8d1666d3c
--- /dev/null
+++ b/docs/research/DPROD/strategy.md
@@ -0,0 +1,179 @@
+# DPROD Implementation Strategy
+
+## Executive Summary
+
+The integration of Data Product Ontology (DPROD) into ABI represents a strategic opportunity to position our AI agent system as the **first DPROD-compliant multi-agent platform**, providing significant competitive advantages in enterprise markets requiring data governance, observability, and lineage tracking.
+
+## Strategic Rationale
+
+### Business Drivers
+
+**1. Enterprise Market Differentiation**
+- Enterprise customers increasingly require data governance compliance
+- DPROD compliance enables integration with existing data catalog systems
+- Positions ABI as a mature, enterprise-ready AI platform
+
+**2. Regulatory Alignment**
+- EU AI Act and similar regulations emphasize AI system observability
+- DPROD provides standardized metadata for regulatory compliance
+- Proactive compliance reduces future regulatory risk
+
+**3. Ecosystem Interoperability**
+- W3C standards ensure long-term compatibility
+- Enables integration with data mesh architectures
+- Facilitates vendor-neutral AI agent management
+
+### Technical Advantages
+
+**1. Agent Discoverability**
+- Semantic queries to find agents by capability
+- Automated agent selection based on requirements
+- Improved user experience through intelligent routing
+
+**2. Observability & Analytics**
+- Standardized metrics collection across all agents
+- Performance comparison and optimization insights
+- Real-time monitoring of agent health and usage
+
+**3. Conversation Lineage**
+- Track multi-agent conversation flows
+- Debug complex agent interactions
+- Audit trails for compliance and optimization
+
+## Core Strategy: "AI Agents as Data Products"
+
+### Conceptual Framework
+
+```
+Traditional View: DPROD View:
+AI Agent = Service → AI Agent = Data Product
+Prompt = API Call → Prompt = Data Input
+Response = Result → Response = Data Output
+Logs = Side Effect → Logs = Observability Data
+```
+
+### Key Principles
+
+**1. Semantic Standardization**
+- Every AI agent becomes a DPROD-compliant data product
+- Consistent metadata schema across all agent types
+- Machine-readable capability descriptions
+
+**2. Data-Driven Operations**
+- Agent performance metrics as structured data
+- SPARQL queries for operational insights
+- Evidence-based agent optimization
+
+**3. Lineage-First Design**
+- Track conversation flows as data lineage
+- Understand agent handoff patterns
+- Enable conversation replay and analysis
+
+## Implementation Philosophy
+
+### Evolutionary Enhancement
+- **Build on existing architecture** - enhance, don't replace
+- **Leverage current ontology module** - extend with DPROD patterns
+- **Maintain backward compatibility** - transparent to current users
+
+### Standards-First Approach
+- **W3C compliance** - full DPROD specification adherence
+- **Enterprise-ready** - integrate with existing data governance tools
+- **Future-proof** - align with emerging AI governance standards
+
+### User-Centric Benefits
+- **Enhanced transparency** - users understand which models they're using
+- **Improved agent selection** - intelligent routing based on capabilities
+- **Performance visibility** - real-time insights into agent effectiveness
+
+## Strategic Positioning
+
+### Market Differentiation
+
+**Unique Value Proposition**:
+*"The only AI agent platform with built-in data governance, observability, and lineage tracking through W3C standards compliance."*
+
+**Key Differentiators**:
+- Native DPROD compliance
+- Enterprise data governance integration
+- Transparent multi-agent orchestration
+- Standards-based interoperability
+
+### Competitive Advantages
+
+**1. Enterprise Readiness**
+- Immediate integration with existing data catalogs
+- Built-in compliance with data governance requirements
+- Standardized observability and monitoring
+
+**2. Technical Leadership**
+- First-mover advantage in DPROD adoption
+- Thought leadership in AI governance standards
+- Reference implementation for industry best practices
+
+**3. Ecosystem Integration**
+- Compatible with data mesh architectures
+- Works with existing enterprise tooling
+- Vendor-neutral approach reduces lock-in concerns
+
+## Success Metrics
+
+### Technical KPIs
+- **Agent Discoverability**: Time to find appropriate agent for task
+- **Observability Coverage**: Percentage of agent interactions with metrics
+- **Lineage Completeness**: Conversation flows fully tracked
+- **Query Performance**: SPARQL query response times
+
+### Business KPIs
+- **Enterprise Adoption**: Organizations using DPROD features
+- **Integration Success**: Successful data catalog integrations
+- **Compliance Value**: Reduction in governance overhead
+- **User Satisfaction**: Improved agent selection accuracy
+
+### Ecosystem KPIs
+- **Standards Adoption**: Industry adoption of our DPROD patterns
+- **Community Engagement**: Contributions to DPROD specification
+- **Partnership Opportunities**: Integrations with data governance vendors
+
+## Risk Mitigation
+
+### Technical Risks
+- **Complexity**: Phased implementation to manage scope
+- **Performance**: Efficient RDF storage and query optimization
+- **Compatibility**: Extensive testing with existing systems
+
+### Adoption Risks
+- **Learning Curve**: Comprehensive documentation and examples
+- **Migration**: Backward compatibility and gradual feature rollout
+- **Enterprise Sales**: Clear ROI demonstration and case studies
+
+### Specification Risks
+- **Standard Evolution**: Active participation in W3C working groups
+- **Implementation Gaps**: Pragmatic interpretation with community feedback
+- **Vendor Lock-in**: Open source approach and standard compliance
+
+## Next Steps
+
+### Immediate Actions (Next 30 Days)
+1. **Technical Architecture Definition** - Detailed implementation patterns
+2. **Proof of Concept** - Single agent DPROD compliance
+3. **Stakeholder Alignment** - Internal team consensus on approach
+
+### Short Term (Next 90 Days)
+1. **Core Infrastructure** - Ontology module enhancement
+2. **Agent Registration** - DPROD metadata for all agents
+3. **Basic Observability** - Metrics collection framework
+
+### Medium Term (Next 6 Months)
+1. **Full Implementation** - All agents DPROD-compliant
+2. **Query Interface** - SPARQL endpoint for agent discovery
+3. **Enterprise Integration** - Data catalog connectors
+
+### Long Term (12+ Months)
+1. **Advanced Analytics** - AI-driven agent optimization
+2. **Standards Leadership** - Contribute to DPROD evolution
+3. **Ecosystem Partnerships** - Integrations with major platforms
+
+---
+
+**Strategic Outcome**: Position ABI as the definitive enterprise AI agent platform through standards compliance, transparency, and data governance excellence.
\ No newline at end of file
diff --git a/docs/research/DPROD/technical-architecture.md b/docs/research/DPROD/technical-architecture.md
new file mode 100644
index 000000000..a6f132d5e
--- /dev/null
+++ b/docs/research/DPROD/technical-architecture.md
@@ -0,0 +1,663 @@
+# DPROD Technical Architecture
+
+## Architecture Overview
+
+The DPROD implementation in ABI follows a **layered architecture** that integrates seamlessly with existing components while adding semantic data management capabilities. The design emphasizes modularity, performance, and standards compliance.
+
+## System Architecture Diagram
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│ ABI Core System │
+├─────────────────────────────────────────────────────────────────┤
+│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │
+│ │ AbiAgent │ │ Local Agents │ │ Cloud Agents │ │
+│ │ (Enhanced) │ │ (Qwen,DeepSeek, │ │ (ChatGPT,Claude,│ │
+│ │ │ │ Gemma) │ │ Grok, etc.) │ │
+│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │
+├─────────────────────────────────────────────────────────────────┤
+│ DPROD Integration Layer │
+├─────────────────────────────────────────────────────────────────┤
+│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │
+│ │ Agent Registry │ │ Lineage Tracker │ │ Observability │ │
+│ │ Service │ │ │ │ Framework │ │
+│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │
+├─────────────────────────────────────────────────────────────────┤
+│ Data & Query Layer │
+├─────────────────────────────────────────────────────────────────┤
+│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │
+│ │ RDF Triple │ │ SPARQL Query │ │ Metrics Store │ │
+│ │ Store │ │ Engine │ │ │ │
+│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │
+├─────────────────────────────────────────────────────────────────┤
+│ External Integrations │
+├─────────────────────────────────────────────────────────────────┤
+│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │
+│ │ Data Catalogs │ │ Enterprise APIs │ │ Monitoring │ │
+│ │ (DataHub, etc.) │ │ │ │ Systems │ │
+│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │
+└─────────────────────────────────────────────────────────────────┘
+```
+
+## Core Components
+
+### 1. DPROD Ontology Extension
+
+**Location**: `src/core/modules/ontology/dprod/`
+
+```python
+# src/core/modules/ontology/dprod/models/DPRODDataProduct.py
+from dataclasses import dataclass
+from typing import List, Optional, Dict, Any
+from datetime import datetime
+
+@dataclass
+class DPRODDataProduct:
+ """DPROD-compliant data product representation of an AI agent."""
+
+ # Core DPROD properties
+ id: str # URI identifier
+ type: str = "DataProduct" # DPROD type
+ label: str # Human-readable name
+ description: str # Agent description
+
+ # Ports (DPROD core concept)
+ input_ports: List['DPRODDataService'] # User prompts, context
+ output_ports: List['DPRODDataService'] # Agent responses
+ observability_port: Optional['DPRODDataService'] = None
+
+ # Agent-specific metadata
+ capabilities: List[str] # ["coding", "reasoning", "multilingual"]
+ model_info: Dict[str, Any] # Model type, parameters, etc.
+ performance_tier: str # "high", "medium", "low"
+ privacy_level: str # "local", "cloud", "hybrid"
+
+ # DPROD compliance
+ conforms_to: str # Schema URI
+ created_at: datetime
+ modified_at: datetime
+ version: str = "1.0"
+
+ def to_jsonld(self) -> Dict[str, Any]:
+ """Convert to JSON-LD format for RDF storage."""
+ return {
+ "@context": "https://ekgf.github.io/dprod/dprod.jsonld",
+ "id": self.id,
+ "type": self.type,
+ "label": self.label,
+ "description": self.description,
+ "inputPort": [port.to_jsonld() for port in self.input_ports],
+ "outputPort": [port.to_jsonld() for port in self.output_ports],
+ "observabilityPort": self.observability_port.to_jsonld() if self.observability_port else None,
+ "abi:capabilities": self.capabilities,
+ "abi:modelInfo": self.model_info,
+ "abi:performanceTier": self.performance_tier,
+ "abi:privacyLevel": self.privacy_level,
+ "dcat:conformsTo": self.conforms_to,
+ "dcterms:created": self.created_at.isoformat(),
+ "dcterms:modified": self.modified_at.isoformat(),
+ "owl:versionInfo": self.version
+ }
+
+@dataclass
+class DPRODDataService:
+ """DPROD data service representing agent input/output ports."""
+
+ id: str
+ type: str = "DataService"
+ label: str
+ endpoint_url: Optional[str] = None
+
+ # Distribution information
+ access_service_of: 'DPRODDistribution'
+
+ def to_jsonld(self) -> Dict[str, Any]:
+ return {
+ "id": self.id,
+ "type": self.type,
+ "label": self.label,
+ "endpointURL": self.endpoint_url,
+ "isAccessServiceOf": self.access_service_of.to_jsonld()
+ }
+
+@dataclass
+class DPRODDistribution:
+ """DPROD distribution representing data format and schema."""
+
+ type: str = "Distribution"
+ format: str # MIME type
+ is_distribution_of: 'DPRODDataset'
+
+ def to_jsonld(self) -> Dict[str, Any]:
+ return {
+ "type": self.type,
+ "format": self.format,
+ "isDistributionOf": self.is_distribution_of.to_jsonld()
+ }
+
+@dataclass
+class DPRODDataset:
+ """DPROD dataset representing the actual data."""
+
+ id: str
+ type: str = "Dataset"
+ label: Optional[str] = None
+ conforms_to: str # Schema URI
+
+ def to_jsonld(self) -> Dict[str, Any]:
+ return {
+ "id": self.id,
+ "type": self.type,
+ "label": self.label,
+ "conformsTo": self.conforms_to
+ }
+```
+
+### 2. Agent Registration System
+
+**Location**: `src/core/modules/dprod/services/`
+
+```python
+# src/core/modules/dprod/services/AgentRegistryService.py
+from typing import Dict, List, Optional
+from ..models.DPRODDataProduct import DPRODDataProduct, DPRODDataService, DPRODDistribution, DPRODDataset
+from ..storage.DPRODTripleStore import DPRODTripleStore
+
+class AgentRegistryService:
+ """Service for registering agents as DPROD-compliant data products."""
+
+ def __init__(self, triple_store: DPRODTripleStore):
+ self.triple_store = triple_store
+ self.base_uri = "https://abi.naas.ai"
+
+ def register_agent(self, agent_name: str, agent_config: Dict) -> DPRODDataProduct:
+ """Register an AI agent as a DPROD data product."""
+
+ # Generate URIs
+ agent_uri = f"{self.base_uri}/data-product/{agent_name.lower()}"
+ input_port_uri = f"{agent_uri}/input"
+ output_port_uri = f"{agent_uri}/output"
+ observability_port_uri = f"{agent_uri}/observability"
+
+ # Create input port (user prompts)
+ input_port = DPRODDataService(
+ id=input_port_uri,
+ label="User Prompt Service",
+ endpoint_url=f"{self.base_uri}/api/agents/{agent_name}/chat",
+ access_service_of=DPRODDistribution(
+ format="application/json",
+ is_distribution_of=DPRODDataset(
+ id=f"{agent_uri}/dataset/input",
+ label="User Prompts",
+ conforms_to=f"{self.base_uri}/schema/UserPrompt"
+ )
+ )
+ )
+
+ # Create output port (agent responses)
+ output_port = DPRODDataService(
+ id=output_port_uri,
+ label="AI Response Service",
+ access_service_of=DPRODDistribution(
+ format="application/json",
+ is_distribution_of=DPRODDataset(
+ id=f"{agent_uri}/dataset/output",
+ label="AI Responses",
+ conforms_to=f"{self.base_uri}/schema/{agent_name}Response"
+ )
+ )
+ )
+
+ # Create observability port (metrics)
+ observability_port = DPRODDataService(
+ id=observability_port_uri,
+ label="Observability Port",
+ endpoint_url=f"{self.base_uri}/api/observability/{agent_name}",
+ access_service_of=DPRODDistribution(
+ format="application/json",
+ is_distribution_of=DPRODDataset(
+ id=f"{agent_uri}/dataset/observability",
+ label="Agent Metrics",
+ conforms_to=f"{self.base_uri}/schema/AgentMetrics"
+ )
+ )
+ )
+
+ # Extract capabilities from agent config
+ capabilities = self._extract_capabilities(agent_config)
+ model_info = self._extract_model_info(agent_config)
+
+ # Create DPROD data product
+ data_product = DPRODDataProduct(
+ id=agent_uri,
+ label=agent_config.get("name", agent_name),
+ description=agent_config.get("description", ""),
+ input_ports=[input_port],
+ output_ports=[output_port],
+ observability_port=observability_port,
+ capabilities=capabilities,
+ model_info=model_info,
+ performance_tier=self._assess_performance_tier(agent_config),
+ privacy_level=self._determine_privacy_level(agent_config),
+ conforms_to=f"{self.base_uri}/schema/AIAgent",
+ created_at=datetime.now(),
+ modified_at=datetime.now()
+ )
+
+ # Store in triple store
+ self.triple_store.store_data_product(data_product)
+
+ return data_product
+
+ def _extract_capabilities(self, agent_config: Dict) -> List[str]:
+ """Extract agent capabilities from configuration."""
+ capabilities = []
+
+ # Analyze agent name and description for capabilities
+ name = agent_config.get("name", "").lower()
+ description = agent_config.get("description", "").lower()
+
+ capability_keywords = {
+ "coding": ["code", "programming", "software", "development"],
+ "reasoning": ["reasoning", "analysis", "logic", "problem-solving"],
+ "multilingual": ["multilingual", "chinese", "french", "language"],
+ "creative": ["creative", "writing", "content", "brainstorm"],
+ "mathematical": ["math", "calculation", "equation", "proof"],
+ "research": ["research", "search", "information", "current"],
+ "local": ["local", "privacy", "offline", "ollama"],
+ "fast": ["fast", "quick", "lightweight", "efficient"]
+ }
+
+ for capability, keywords in capability_keywords.items():
+ if any(keyword in name or keyword in description for keyword in keywords):
+ capabilities.append(capability)
+
+ return capabilities
+
+ def _extract_model_info(self, agent_config: Dict) -> Dict[str, Any]:
+ """Extract model information from agent configuration."""
+ model_info = {}
+
+ # Try to get model from chat_model attribute
+ if "chat_model" in agent_config:
+ chat_model = agent_config["chat_model"]
+ model_info["model_class"] = chat_model.__class__.__name__
+
+ # Extract model name/ID
+ if hasattr(chat_model, "model_name"):
+ model_info["model_name"] = chat_model.model_name
+ elif hasattr(chat_model, "model"):
+ model_info["model_name"] = chat_model.model
+
+ # Extract temperature
+ if hasattr(chat_model, "temperature"):
+ model_info["temperature"] = chat_model.temperature
+
+ return model_info
+
+ def _assess_performance_tier(self, agent_config: Dict) -> str:
+ """Assess agent performance tier based on model characteristics."""
+ model_name = self._extract_model_info(agent_config).get("model_name", "").lower()
+
+ if any(model in model_name for model in ["gpt-4", "claude-3.5", "grok"]):
+ return "high"
+ elif any(model in model_name for model in ["gpt-3.5", "gemini", "mistral"]):
+ return "medium"
+ else:
+ return "standard"
+
+ def _determine_privacy_level(self, agent_config: Dict) -> str:
+ """Determine privacy level based on deployment type."""
+ model_name = self._extract_model_info(agent_config).get("model_name", "").lower()
+
+ if any(local_indicator in model_name for local_indicator in ["ollama", "qwen", "deepseek", "gemma"]):
+ return "local"
+ else:
+ return "cloud"
+```
+
+### 3. Observability Framework
+
+**Location**: `src/core/modules/dprod/observability/`
+
+```python
+# src/core/modules/dprod/observability/ObservabilityCollector.py
+from dataclasses import dataclass
+from datetime import datetime
+from typing import Dict, Any, Optional
+import time
+
+@dataclass
+class AgentMetrics:
+ """DPROD-compliant agent observability metrics."""
+
+ agent_name: str
+ timestamp: datetime
+ conversation_id: Optional[str] = None
+
+ # Performance metrics
+ response_time_ms: float
+ token_count_input: int
+ token_count_output: int
+
+ # Quality metrics
+ success: bool
+ error_type: Optional[str] = None
+ error_message: Optional[str] = None
+
+ # Resource metrics
+ memory_usage_mb: Optional[float] = None
+ cpu_usage_percent: Optional[float] = None
+
+ def to_dprod_observability(self) -> Dict[str, Any]:
+ """Convert to DPROD observability format."""
+ return {
+ "@context": "https://ekgf.github.io/dprod/dprod.jsonld",
+ "observabilityLog": {
+ "agent": self.agent_name,
+ "timestamp": self.timestamp.isoformat(),
+ "conversationId": self.conversation_id,
+ "metrics": {
+ "responseTimeMs": self.response_time_ms,
+ "tokenUsage": {
+ "input": self.token_count_input,
+ "output": self.token_count_output,
+ "total": self.token_count_input + self.token_count_output
+ },
+ "success": self.success,
+ "errorType": self.error_type,
+ "errorMessage": self.error_message,
+ "resourceUsage": {
+ "memoryMb": self.memory_usage_mb,
+ "cpuPercent": self.cpu_usage_percent
+ }
+ },
+ "conformsTo": "https://abi.naas.ai/schema/AgentObservability"
+ }
+ }
+
+class ObservabilityCollector:
+ """Collect observability metrics for AI agents."""
+
+ def __init__(self, metrics_store: 'MetricsStore'):
+ self.metrics_store = metrics_store
+ self.active_requests: Dict[str, float] = {} # Track request start times
+
+ def start_request(self, agent_name: str, conversation_id: str) -> str:
+ """Start tracking a request."""
+ request_id = f"{agent_name}_{conversation_id}_{time.time()}"
+ self.active_requests[request_id] = time.time()
+ return request_id
+
+ def end_request(self, request_id: str, agent_name: str, conversation_id: str,
+ success: bool, token_input: int, token_output: int,
+ error_type: Optional[str] = None, error_message: Optional[str] = None) -> AgentMetrics:
+ """End tracking and collect metrics."""
+
+ start_time = self.active_requests.pop(request_id, time.time())
+ response_time = (time.time() - start_time) * 1000 # Convert to milliseconds
+
+ metrics = AgentMetrics(
+ agent_name=agent_name,
+ timestamp=datetime.now(),
+ conversation_id=conversation_id,
+ response_time_ms=response_time,
+ token_count_input=token_input,
+ token_count_output=token_output,
+ success=success,
+ error_type=error_type,
+ error_message=error_message
+ )
+
+ # Store metrics
+ self.metrics_store.store_metrics(metrics)
+
+ return metrics
+```
+
+### 4. Enhanced AbiAgent Integration
+
+**Location**: `src/core/modules/abi/agents/AbiAgent.py` (enhancement)
+
+```python
+# Enhancement to existing AbiAgent
+class AbiAgent(IntentAgent):
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ # DPROD components
+ self.dprod_registry = AgentRegistryService(DPRODTripleStore())
+ self.lineage_tracker = ConversationLineageTracker()
+ self.observability_collector = ObservabilityCollector(MetricsStore())
+
+ # Register all agents as DPROD data products on startup
+ self._register_agents_as_data_products()
+
+ def _register_agents_as_data_products(self):
+ """Register all loaded agents as DPROD data products."""
+ for agent in self.agents:
+ agent_config = {
+ "name": agent.name,
+ "description": agent.description,
+ "chat_model": agent._chat_model
+ }
+ self.dprod_registry.register_agent(agent.name, agent_config)
+
+ def find_best_agent_by_capability(self, required_capabilities: List[str]) -> Optional[str]:
+ """Use DPROD metadata to find agent with required capabilities."""
+
+ # Build SPARQL query
+ capabilities_filter = ' '.join([f'"{cap}"' for cap in required_capabilities])
+
+ query = f"""
+ PREFIX dprod:
+ PREFIX abi:
+ PREFIX rdfs:
+
+ SELECT ?agent ?label ?score
+ WHERE {{
+ ?agent a dprod:DataProduct .
+ ?agent rdfs:label ?label .
+ ?agent abi:capabilities ?cap .
+ ?agent abi:performanceTier ?tier .
+
+ FILTER(?cap IN ({capabilities_filter}))
+
+ BIND(
+ IF(?tier = "high", 3,
+ IF(?tier = "medium", 2, 1)) AS ?score
+ )
+ }}
+ ORDER BY DESC(?score)
+ LIMIT 1
+ """
+
+ try:
+ results = self.dprod_registry.triple_store.query(query)
+ if results:
+ # Extract agent name from URI
+ agent_uri = results[0]['agent']
+ agent_name = agent_uri.split('/')[-1] # Get last part of URI
+ return agent_name
+ except Exception as e:
+ logger.warning(f"DPROD query failed, falling back to default routing: {e}")
+
+ return None
+
+ def invoke_with_observability(self, input_data: str, conversation_id: str = None) -> Any:
+ """Enhanced invoke with DPROD observability tracking."""
+
+ # Start observability tracking
+ request_id = self.observability_collector.start_request(
+ agent_name="Abi",
+ conversation_id=conversation_id or str(uuid.uuid4())
+ )
+
+ try:
+ # Count input tokens (approximate)
+ input_tokens = len(input_data.split())
+
+ # Execute normal invoke
+ result = super().invoke(input_data)
+
+ # Count output tokens (approximate)
+ output_tokens = len(str(result).split()) if result else 0
+
+ # End observability tracking (success)
+ metrics = self.observability_collector.end_request(
+ request_id=request_id,
+ agent_name="Abi",
+ conversation_id=conversation_id,
+ success=True,
+ token_input=input_tokens,
+ token_output=output_tokens
+ )
+
+ return result
+
+ except Exception as e:
+ # End observability tracking (failure)
+ metrics = self.observability_collector.end_request(
+ request_id=request_id,
+ agent_name="Abi",
+ conversation_id=conversation_id,
+ success=False,
+ token_input=len(input_data.split()),
+ token_output=0,
+ error_type=type(e).__name__,
+ error_message=str(e)
+ )
+ raise
+```
+
+### 5. SPARQL Query Interface
+
+**Location**: `src/core/modules/dprod/query/`
+
+```python
+# src/core/modules/dprod/query/SPARQLEndpoint.py
+from fastapi import FastAPI, HTTPException
+from typing import Dict, List, Any
+from ..storage.DPRODTripleStore import DPRODTripleStore
+
+class SPARQLEndpoint:
+ """SPARQL query endpoint for DPROD data."""
+
+ def __init__(self, triple_store: DPRODTripleStore):
+ self.triple_store = triple_store
+ self.app = FastAPI()
+ self._setup_routes()
+
+ def _setup_routes(self):
+ """Setup FastAPI routes for SPARQL queries."""
+
+ @self.app.post("/sparql")
+ async def execute_sparql(query: str) -> Dict[str, Any]:
+ """Execute SPARQL query against DPROD data."""
+ try:
+ results = self.triple_store.query(query)
+ return {
+ "status": "success",
+ "results": results,
+ "count": len(results)
+ }
+ except Exception as e:
+ raise HTTPException(status_code=400, detail=str(e))
+
+ @self.app.get("/agents")
+ async def list_agents() -> List[Dict[str, Any]]:
+ """List all agents as DPROD data products."""
+ query = """
+ PREFIX dprod:
+ PREFIX rdfs:
+ PREFIX abi:
+
+ SELECT ?agent ?label ?description ?capabilities ?performanceTier ?privacyLevel
+ WHERE {
+ ?agent a dprod:DataProduct .
+ ?agent rdfs:label ?label .
+ ?agent rdfs:comment ?description .
+ ?agent abi:capabilities ?capabilities .
+ ?agent abi:performanceTier ?performanceTier .
+ ?agent abi:privacyLevel ?privacyLevel .
+ }
+ """
+ return self.triple_store.query(query)
+
+ @self.app.get("/agents/by-capability/{capability}")
+ async def find_agents_by_capability(capability: str) -> List[Dict[str, Any]]:
+ """Find agents with specific capability."""
+ query = f"""
+ PREFIX dprod:
+ PREFIX rdfs:
+ PREFIX abi:
+
+ SELECT ?agent ?label ?performanceTier
+ WHERE {{
+ ?agent a dprod:DataProduct .
+ ?agent rdfs:label ?label .
+ ?agent abi:capabilities "{capability}" .
+ ?agent abi:performanceTier ?performanceTier .
+ }}
+ ORDER BY DESC(?performanceTier)
+ """
+ return self.triple_store.query(query)
+
+ @self.app.get("/lineage/{conversation_id}")
+ async def get_conversation_lineage(conversation_id: str) -> List[Dict[str, Any]]:
+ """Get conversation lineage for a specific conversation."""
+ query = f"""
+ PREFIX prov:
+ PREFIX abi:
+
+ SELECT ?step ?from_agent ?to_agent ?timestamp ?activity
+ WHERE {{
+ ?conversation abi:conversationId "{conversation_id}" .
+ ?conversation prov:hadStep ?step .
+ ?step prov:used ?from_agent .
+ ?step prov:generated ?to_agent .
+ ?step prov:atTime ?timestamp .
+ ?step prov:wasAssociatedWith ?activity .
+ }}
+ ORDER BY ?timestamp
+ """
+ return self.triple_store.query(query)
+```
+
+## Data Flow Architecture
+
+### Agent Registration Flow
+```
+Agent Startup → Extract Metadata → Create DPROD Model → Store in Triple Store → Expose via SPARQL
+```
+
+### Conversation Flow with Observability
+```
+User Input → Start Metrics → Route to Agent → Execute → Collect Metrics → Store Observability → Track Lineage
+```
+
+### Query Flow
+```
+SPARQL Query → Triple Store → Result Processing → JSON Response → Client Integration
+```
+
+## Performance Considerations
+
+### RDF Storage Optimization
+- **Indexing Strategy**: Index frequently queried properties (capabilities, performance tier)
+- **Caching Layer**: Redis cache for common SPARQL queries
+- **Query Optimization**: Pre-compiled queries for agent discovery
+
+### Observability Overhead
+- **Async Collection**: Non-blocking metrics collection
+- **Batch Processing**: Batch metrics storage to reduce I/O
+- **Sampling**: Sample high-frequency operations for large deployments
+
+### Scalability Patterns
+- **Horizontal Scaling**: Distribute triple store across nodes
+- **Query Federation**: Federate queries across multiple stores
+- **Caching Strategy**: Multi-level caching for metadata and metrics
+
+---
+
+This technical architecture provides the foundation for implementing DPROD compliance in ABI while maintaining performance and scalability requirements.
\ No newline at end of file