BrightAgent Architecture

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System Overview

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Core Components

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🎯 Main Agent & Orchestration

• Supervisor Agent: Central orchestrator that receives user queries and routes them to appropriate specialized agents
• BrightAgent API: Primary interface for user interactions and system communication
• Query Processing: Natural language understanding and intent classification

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🤖 Specialized Agent Ecosystem

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Data Processing Agents

• Retrieval Agent: Implements RAG (Retrieval Augmented Generation) for intelligent data fetching
• Engineering Agent: Handles data transformation, dbt code generation, and data pipeline management
• Analytics Agent: Performs statistical analysis and generates Jupyter notebooks with insights
• Visualization Agent: Creates interactive charts, dashboards, and visual representations

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Intelligence & Governance

• Governance Agent: Ensures data quality, compliance, and security across all operations
• TEI Agent: Text Embedding Interface for semantic understanding and vector operations
• Context Management: Maintains session state and cross-agent communication

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🗄️ Data Infrastructure

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Knowledge Systems

• Knowledge Graph: Structured representation of relationships and metadata
• Data Catalog: Comprehensive inventory of available data assets and their properties
• Vector Store: Semantic search and embedding storage for intelligent retrieval

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External Integrations

• Data Sources: Databases, APIs, file systems, and streaming platforms
• Warehouse Integration: Connection to modern data warehouses (Snowflake, BigQuery, etc.)
• Third-party Tools: Integration with existing data stack components

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Data Flow Architecture

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1. Query Initiation

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2. Agent Coordination

• Parallel Processing: Multiple agents can work simultaneously on different aspects
• Context Sharing: Agents share relevant context and intermediate results
• Quality Assurance: Built-in validation and review processes

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3. Response Generation

• Multimodal Output: Jupyter notebooks, visualizations, code, and text responses
• Interactive Elements: Dashboards with real-time data connections
• Actionable Insights: Recommendations and next steps for users

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Key Architectural Principles

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🔄 Event-Driven Design

• Asynchronous agent communication
• Real-time data processing capabilities
• Scalable message handling

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🛡️ Security & Governance

• Role-based access control across all agents
• Data lineage tracking throughout the pipeline
• Automated compliance monitoring

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⚡ Performance Optimization

• Intelligent caching strategies
• Resource pooling and load balancing
• Optimized query execution paths

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🔌 Extensibility

• Plugin architecture for custom agents
• API-first design for external integrations
• Configurable workflows and business rules

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Agent Interaction Patterns

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Sequential Processing

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Parallel Processing

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Feedback Loops

• Quality feedback from Governance Agent to other agents
• Performance metrics influencing future query routing
• User feedback refining agent responses

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Deployment Architecture

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Cloud-Native Design

• Containerized agent deployment
• Auto-scaling based on workload
• Multi-zone availability

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Edge Computing

• Local processing for sensitive data
• Reduced latency for real-time analytics
• Hybrid cloud-edge orchestration