🤖 Agentic AI Data Platform Architecture

Domain-Driven Data Collection & Self-Service Analytics

🔍
Domain Discovery Agent
Organization Analysis
Business Process Mining
NLP Document Parser
Domain Boundary ML
Domain Ontology Builder
Org Structure Analysis
→
Domain Identification
→
Boundary Definition
→
Validation
class DomainDiscoveryAgent: def __init__(self): self.nlp_processor = spaCy_enterprise() self.graph_analyzer = NetworkX_analyzer() self.ml_classifier = DomainBoundaryML() def discover_domains(self, org_data): # Analyze organizational structure org_graph = self.build_org_graph(org_data) # Extract business processes processes = self.extract_processes(org_data) # Identify domain boundaries boundaries = self.ml_classifier.predict_boundaries( org_graph, processes ) return self.validate_domains(boundaries)
NLP: spaCy, Transformers, BERT
Graph Analysis: NetworkX, Neo4j
ML: scikit-learn, TensorFlow
Process Mining: PM4Py, Celonis API
Ontology: OWL, RDF, SPARQL
Orchestration: Apache Airflow
🎯 Automated domain boundary detection using Conway's Law principles
📊 Business process mining and workflow analysis
🔗 Cross-domain relationship mapping
🧠 Continuous learning from organizational changes
📋 Domain governance and compliance checking
🔄 Real-time domain evolution monitoring
🔄
Data Collection Orchestration Agent
Source Discovery
Schema Registry
Pipeline Builder
SLA Monitor
Data Contract Manager
Quality Validator
Source Detection
→
Schema Analysis
→
Pipeline Generation
→
Quality Validation
class DataCollectionOrchestrator: def __init__(self): self.source_discovery = SourceDiscoveryEngine() self.pipeline_builder = AutoPipelineBuilder() self.contract_manager = DataContractManager() self.quality_engine = DataQualityEngine() async def orchestrate_collection(self, domain): # Discover data sources sources = await self.source_discovery.scan_domain(domain) # Build collection pipelines pipelines = [] for source in sources: contract = self.contract_manager.negotiate(source) pipeline = self.pipeline_builder.create(source, contract) pipelines.append(pipeline) # Deploy and monitor return await self.deploy_pipelines(pipelines)
Streaming: Apache Kafka, Pulsar
Batch: Apache Spark, Flink
Connectors: Debezium, Airbyte
Schema: Confluent Schema Registry
Workflow: Apache Airflow, Prefect
Monitoring: Prometheus, Grafana
99.9%
Pipeline Uptime
< 5min
Average Recovery Time
10TB/day
Data Processing Capacity
🛡️
Data Quality Governance Agent
Quality Rules Engine
Anomaly Detection
Lineage Tracker
Remediation Engine
Compliance Monitor
Audit Trail
class DataQualityGovernanceAgent: def __init__(self): self.rules_engine = QualityRulesEngine() self.anomaly_detector = AnomalyDetector() self.lineage_tracker = LineageTracker() self.remediation_engine = RemediationEngine() def monitor_quality(self, data_stream): # Apply quality rules quality_score = self.rules_engine.evaluate(data_stream) # Detect anomalies anomalies = self.anomaly_detector.detect(data_stream) # Track lineage lineage = self.lineage_tracker.trace(data_stream) # Auto-remediate if needed if quality_score < threshold: self.remediation_engine.remediate(data_stream, anomalies) return QualityReport(quality_score, anomalies, lineage)
📊 Completeness: Null value detection and handling
🎯 Accuracy: Cross-reference validation
🔄 Consistency: Format and standard compliance
⏰ Timeliness: Freshness and latency monitoring
🔍 Validity: Business rule validation
🌐 Uniqueness: Duplicate detection and resolution
Issue Detection
→
Root Cause Analysis
→
Auto Remediation
→
Validation
💬
Natural Language Query Agent
NLU Parser
Intent Recognition
Query Optimizer
Result Interpreter
Context Manager
Learning Engine
class NaturalLanguageQueryAgent: def __init__(self): self.nlu_parser = NLUParser() self.intent_recognizer = IntentRecognizer() self.query_optimizer = QueryOptimizer() self.context_manager = ContextManager() self.result_interpreter = ResultInterpreter() async def process_query(self, natural_query, user_context): # Parse natural language parsed = self.nlu_parser.parse(natural_query) # Recognize intent intent = self.intent_recognizer.classify(parsed) # Build optimized query sql_query = self.query_optimizer.build( intent, user_context, parsed ) # Execute and interpret results = await self.execute_query(sql_query) return self.result_interpreter.explain(results, intent)
Example Queries:
• "Show me sales trends for the last quarter"
• "Which products have declining customer satisfaction?"
• "Compare revenue across regions this year vs last year"
• "Find customers who haven't purchased in 6 months"
• "What's the average order value by customer segment?"
NLU: spaCy, NLTK, Transformers
Intent: Rasa, Dialogflow
Query Gen: SQLAlchemy, Text-to-SQL
Optimization: Apache Calcite
ML: Hugging Face, OpenAI API
Context: Redis, Elasticsearch
🔍
Data Discovery Agent
Semantic Search
Data Catalog
Recommendation Engine
Quality Indicators
Metadata Manager
Usage Analytics
class DataDiscoveryAgent: def __init__(self): self.semantic_search = SemanticSearchEngine() self.catalog = DataCatalog() self.recommender = RecommendationEngine() self.metadata_manager = MetadataManager() async def discover_data(self, search_query, user_profile): # Semantic search semantic_results = await self.semantic_search.search( search_query, user_profile.domain ) # Get recommendations recommendations = self.recommender.recommend( user_profile, semantic_results ) # Enrich with metadata enriched_results = [] for result in semantic_results: metadata = self.metadata_manager.get_metadata(result) quality_score = self.get_quality_score(result) enriched_results.append({ 'dataset': result, 'metadata': metadata, 'quality_score': quality_score, 'usage_stats': self.get_usage_stats(result) }) return { 'results': enriched_results, 'recommendations': recommendations }
🔍 Intelligent semantic search across all domains
📊 Real-time data quality and freshness indicators
🤖 ML-powered dataset recommendations
📈 Usage analytics and popularity metrics
🏷️ Automated tagging and categorization
🔗 Relationship mapping between datasets
UI Components:
• Interactive search interface with filters
• Visual data lineage graphs
• Quality score dashboards
• Collaborative tagging system
• Bookmark and favorite functionality
📊
Visualization & Insight Agent
Chart Selector
Insight Generator
Dashboard Builder
Narrative Engine
Interactive Components
Export Engine
class VisualizationInsightAgent: def __init__(self): self.chart_selector = SmartChartSelector() self.insight_generator = InsightGenerator() self.dashboard_builder = DashboardBuilder() self.narrative_engine = NarrativeEngine() self.export_engine = ExportEngine() async def create_visualization(self, data, user_intent): # Analyze data characteristics data_profile = self.analyze_data_profile(data) # Select optimal chart type chart_config = self.chart_selector.select_chart( data_profile, user_intent ) # Generate insights insights = self.insight_generator.analyze(data) # Create narrative narrative = self.narrative_engine.generate_story( data, insights, chart_config ) # Build interactive dashboard dashboard = self.dashboard_builder.create( chart_config, insights, narrative ) return { 'visualization': dashboard, 'insights': insights, 'narrative': narrative, 'recommendations': self.get_recommendations(data) }
Charting: D3.js, Chart.js, Plotly
Dashboards: Apache Superset, Grafana
Interactive: React, Vue.js, Observable
Statistical: R, Python (matplotlib, seaborn)
ML Insights: scikit-learn, TensorFlow
NLG: GPT-4, T5, Custom transformers
Export: Puppeteer, Canvas API
Real-time: WebSockets, Server-Sent Events
Smart Chart Selection Examples:

Time Series Data: Auto-selects line charts with trend analysis
Categorical Comparisons: Bar charts with significance testing
Correlations: Scatter plots with regression lines
Distributions: Histograms with statistical overlays
Hierarchical Data: Treemaps with drill-down capability
Geographic Data: Interactive maps with clustering
Network Data: Force-directed graphs with community detection
# Example: Auto-generated dashboard code dashboard_config = { "title": "Sales Performance Analysis", "charts": [ { "type": "line_chart", "data_source": "sales_trends", "x_axis": "date", "y_axis": "revenue", "insights": ["25% growth in Q3", "Seasonal peak in December"] }, { "type": "heatmap", "data_source": "regional_performance", "insights": ["West Coast outperforming by 15%"] } ], "narrative": "Revenue shows strong upward trend..." }
🧠 Automatic anomaly detection and highlighting
📈 Trend analysis with statistical significance
🔍 Correlation discovery between variables
📊 Comparative analysis across segments
⚡ Real-time insight generation
📝 Natural language explanation of findings
🎯 Actionable recommendations
🔄 Automated dashboard updates
🌐
Cross-Domain Integration Architecture
Sales Domain
Marketing Domain
Finance Domain
Operations Domain
Data Contract Manager
Event Bus
Schema Registry
ETL Orchestrator
Real-time Processor
ML Pipeline
Quality Monitor
Domain Data Lake
Unified Data Warehouse
Feature Store
class CrossDomainIntegrationAgent: def __init__(self): self.contract_manager = DataContractManager() self.event_bus = EventBus() self.schema_registry = SchemaRegistry() self.transformation_engine = TransformationEngine() async def integrate_domains(self, source_domain, target_domain, integration_type): # Establish data contract contract = await self.contract_manager.negotiate_contract( source_domain, target_domain, integration_type ) # Register schemas source_schema = self.schema_registry.get_schema(source_domain) target_schema = self.schema_registry.get_schema(target_domain) # Create transformation mappings mappings = self.transformation_engine.create_mappings( source_schema, target_schema, contract ) # Setup event streams if integration_type == 'real_time': stream = self.event_bus.create_stream( source_domain, target_domain, mappings ) return await self.deploy_stream(stream) else: pipeline = self.create_batch_pipeline( source_domain, target_domain, mappings ) return await self.deploy_pipeline(pipeline)
Event-Driven
→
API Gateway
→
Message Queue
→
Data Mesh
🚀 Event-driven architecture with Apache Kafka
🔗 API-first integration with GraphQL federation
📦 Microservices with domain boundaries
🕸️ Data mesh implementation
🔄 CQRS and Event Sourcing patterns
🛡️ Circuit breaker and retry mechanisms
Contracts: OpenAPI, AsyncAPI, Protobuf
Versioning: Semantic versioning with compatibility checks
Security: OAuth 2.0, JWT, mTLS
Monitoring: Distributed tracing with Jaeger
Testing: Contract testing with Pact
Documentation: Auto-generated API docs
🗺️
Implementation Roadmap & Metrics
Phase 1: Foundation
(Months 1-3)
→
Phase 2: Automation
(Months 4-8)
→
Phase 3: Intelligence
(Months 9-12)
→
Phase 4: Optimization
(Months 13+)
Phase 1 - Foundation (Months 1-3):
• Deploy Domain Discovery Agent
• Establish initial data governance framework
• Set up core infrastructure (Kafka, Airflow, etc.)
• Create first 2-3 domain boundaries
• Implement basic data quality monitoring

Phase 2 - Automation (Months 4-8):
• Deploy Data Collection Orchestration Agent
• Automate pipeline creation for identified domains
• Implement cross-domain integration patterns
• Deploy Quality Governance Agent
• Create self-healing data pipelines

Phase 3 - Intelligence (Months 9-12):
• Deploy NL Query Agent
• Implement Data Discovery Agent
• Launch self-service analytics platform
• Deploy Visualization & Insight Agent
• Add ML-powered recommendations

Phase 4 - Optimization (Months 13+):
• Advanced ML model deployment
• Predictive analytics capabilities
• Advanced automation and self-optimization
• Real-time decision support systems
95%
Data Pipeline Reliability
80%
Reduction in Manual Data Tasks
50%
Faster Time to Insights
90%
User Satisfaction Score
Operational: Pipeline uptime, data freshness, processing latency
Quality: Data accuracy, completeness, consistency scores
Business: Time to insight, user adoption, query success rate
Technical: System performance, cost optimization, scalability metrics
Technical Risks:
• Agent complexity and interdependencies
• Scalability challenges with large data volumes
• Integration complexity with legacy systems

Mitigation Strategies:
• Incremental deployment with rollback capabilities
• Comprehensive testing and monitoring
• Change management and user training programs
• Vendor risk assessment and contingency planning