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System Architecture

Data Agents are modular pipelines that transform raw public data into structured, real-time context. This section describes the technical architecture, component interactions, and system complexity.

Data Flow Pipeline​

Data processing follows four sequential stages:

Ingestion → Processing → Structuring → Delivery

Ingestion​

Data Agents connect to external sources through:

  • REST APIs with authentication and rate limiting
  • Web scraping with retry logic and error handling
  • Database connections for structured data sources
  • WebSocket streams for real-time data feeds

Each connector handles source-specific protocols, authentication mechanisms, and rate limit management. Failed requests are retried with exponential backoff.

Processing​

Raw ingested data undergoes transformation through:

  • Filtering: Removal of irrelevant or duplicate entries based on configurable rules
  • Enrichment: Addition of metadata, cross-references, and computed fields
  • Analysis: Application of domain-specific algorithms and AI models
  • Validation: Schema validation and data quality checks

Processing logic is defined per agent and can include custom Python functions, ML model inference, and rule-based transformations. The processing stage determines what data is retained and how it's transformed before structuring.

Structuring​

Processed data is normalized into consistent formats:

  • JSON schemas with defined field types and constraints
  • Event streams with timestamps and metadata
  • Tabular formats (CSV) for batch processing
  • Custom schemas for domain-specific requirements

The structuring stage ensures output consistency regardless of source format variations. Schema validation occurs here to guarantee downstream compatibility.

Delivery​

Structured outputs are exposed through:

  • REST API endpoints with query parameters and filtering
  • MCP (Model Context Protocol) endpoints for AI agent consumption
  • Webhook delivery for event-driven architectures
  • Message queues for high-throughput scenarios

Delivery mechanisms handle request queuing, rate limiting, and connection management. Multiple consumers can subscribe to the same agent output.

Execution Environment​

Data Agents execute on Heisenberg's decentralized node network. This architecture provides:

Parallel Execution: Multiple agents and agent instances run concurrently across nodes, enabling independent scaling of different data sources.

Fault Tolerance: Node failures don't interrupt agent execution. Agents automatically migrate to healthy nodes, and state is preserved through distributed storage.

Latency Characteristics: Processing latency depends on source response times, processing complexity, and network conditions. Typical end-to-end latency ranges from seconds to minutes depending on agent configuration.

Scalability Constraints: Throughput is limited by source API rate limits, processing compute requirements, and network bandwidth. Each agent can be scaled independently based on demand.

System Complexity​

State Management: Agents maintain state for incremental processing, deduplication, and checkpointing. State is stored in distributed key-value stores.

Error Handling: Transient failures (network issues, rate limits) trigger automatic retries. Permanent failures (invalid credentials, schema changes) require manual intervention and are logged for monitoring.

Resource Requirements: Each agent consumes CPU, memory, and network resources proportional to data volume and processing complexity. Resource allocation is managed by the node network scheduler.

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