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Core System Documentation

This section covers the core systems and infrastructure components that power EVOSEAL's evolution pipeline.

Overview

The core system provides the foundational infrastructure for EVOSEAL's self-improving AI capabilities: - Event System: Component communication and observability - Error Handling & Resilience: Robust error recovery and system resilience - Workflow Orchestration: End-to-end workflow management - Version Control & Experiment Tracking: Comprehensive tracking and versioning - Agentic Systems: AI agent implementations - Knowledge Management: Knowledge base and prompt systems

Core Components

Communication & Events

Reliability & Resilience

Tracking & Management

AI & Knowledge Systems

Key Features

🔄 Event-Driven Architecture

  • Comprehensive Event Types: 40+ event types covering all pipeline aspects
  • Structured Event Data: Specialized classes with automatic data synchronization
  • Advanced Filtering: Multi-criteria event filtering with custom functions
  • Performance Monitoring: Built-in metrics collection and threshold alerting

🛡️ Resilience & Recovery

  • Circuit Breaker Pattern: Failure isolation with configurable thresholds
  • Health Monitoring: Real-time component health tracking
  • Recovery Strategies: Multiple recovery actions with automatic fallback
  • Background Monitoring: Continuous system health assessment

🎯 Workflow Orchestration

  • Complete Lifecycle Management: From initialization to completion
  • State Persistence: Automatic state recovery across system restarts
  • Resource Monitoring: Real-time resource usage tracking
  • Checkpoint System: Comprehensive checkpointing with recovery

📊 Experiment Tracking

  • Complete Experiment Lifecycle: From creation to analysis
  • Version Integration: Full Git integration with automatic tagging
  • Metrics Collection: Real-time metrics and performance tracking
  • Multi-experiment Comparison: Advanced comparison and analysis tools

Architecture Integration

Component Communication

graph TD
    A[Evolution Pipeline] -->|Events| B[Event Bus]
    B -->|Distribute| C[Safety Components]
    B -->|Notify| D[Workflow Orchestrator]
    B -->|Log| E[Error Handler]
    C -->|Status| B
    D -->|Progress| B
    E -->|Alerts| B

Data Flow

  1. Event Generation: Components publish events for state changes
  2. Event Processing: Event bus distributes to subscribers
  3. Workflow Coordination: Orchestrator manages execution flow
  4. Error Handling: Resilience manager handles failures
  5. State Persistence: Version control tracks all changes

Getting Started

Basic Setup

  1. Event System: Configure event publishing and subscription
  2. Error Handling: Set up resilience mechanisms
  3. Workflow: Define workflow steps and dependencies
  4. Tracking: Initialize version control and experiment tracking

Integration Example

from evoseal.core.events import EventBus, create_component_event
from evoseal.core.resilience import ResilienceManager
from evoseal.core.orchestration import WorkflowOrchestrator

# Initialize core systems
event_bus = EventBus()
resilience = ResilienceManager(event_bus=event_bus)
orchestrator = WorkflowOrchestrator(
    event_bus=event_bus,
    resilience_manager=resilience
)

# Publish events
event = create_component_event(
    component_type="evolution_pipeline",
    component_id="main",
    operation="started"
)
event_bus.publish(event)

Configuration

Event System Configuration

events:
  max_history: 1000
  enable_metrics: true
  log_level: INFO

Resilience Configuration

resilience:
  circuit_breaker:
    failure_threshold: 5
    timeout: 30
  health_monitoring:
    interval: 10
    enabled: true

Orchestration Configuration

orchestration:
  execution_strategy: "adaptive"
  checkpoint_interval: 100
  resource_monitoring: true

Performance Considerations

Event System

  • Efficient Publishing: Asynchronous event processing
  • Memory Management: Configurable event history limits
  • Filtering Performance: Optimized event filtering algorithms

Resilience

  • Circuit Breaker: Prevents cascade failures
  • Health Checks: Minimal overhead monitoring
  • Recovery Speed: Fast automatic recovery mechanisms

Orchestration

  • Parallel Execution: Concurrent workflow step execution
  • Resource Optimization: Adaptive resource allocation
  • State Efficiency: Optimized state serialization

Monitoring & Observability

Metrics Collection

  • Event processing rates and latencies
  • Component health and availability
  • Workflow execution progress and timing
  • Error rates and recovery success

Logging Integration

  • Structured logging with correlation IDs
  • Event-driven log aggregation
  • Performance metrics logging
  • Error and exception tracking

Best Practices

Event System

  • Use appropriate event types for different scenarios
  • Implement proper error handling in event handlers
  • Monitor event processing performance
  • Use event filtering to reduce noise

Resilience

  • Configure appropriate circuit breaker thresholds
  • Implement graceful degradation strategies
  • Monitor health check performance
  • Test recovery procedures regularly

Orchestration

  • Design workflows with proper dependencies
  • Use checkpointing for long-running processes
  • Monitor resource usage patterns
  • Implement proper cleanup procedures

Troubleshooting

Common Issues

  • Event Processing Delays: Check event handler performance
  • Circuit Breaker Activation: Review failure patterns and thresholds
  • Workflow Hangs: Verify step dependencies and resource availability
  • Memory Issues: Monitor event history and state persistence

Debugging Tools

  • Event system metrics and history
  • Resilience manager status reports
  • Workflow orchestrator state inspection
  • Version control and experiment tracking logs

The core system provides a robust foundation for building scalable, reliable, and observable AI evolution pipelines.

Last update: 2025-07-20
Created: 2025-07-20