ai-agent/events/docs/architecture.md

Event-Driven Framework Architecture

Overview

The event-driven framework provides high-performance event processing with:

• Throughput of 100+ events per second
• Thread-safe operation
• AES-256 encryption compliance
• Tight integration with scheduler system

Core Components

classDiagram
    class EventSystem {
        +publish(event, priority)
        +subscribe(event_type, handler)
    }
    
    class EventDispatcher {
        +register_handler(event_type, handler)
        +dispatch(event)
        +start()
        +stop()
    }
    
    class EventQueue {
        +push(priority, event)
        +pop() event
    }
    
    EventSystem --> EventDispatcher
    EventDispatcher --> EventQueue
    EventDispatcher --> Scheduler

EventQueue

• Priority-based processing (min-heap)
• Thread-safe operations using RLock
• Efficient wakeup signaling with Event objects
• FIFO ordering for same-priority events

EventDispatcher

• Maintains handler registry
• Routes events to appropriate handlers
• Manages worker thread lifecycle
• Integrates with scheduler for delayed events

EventSystem

• Public API for publishing/subscribing
• Handles encryption/decryption
• Wraps dispatcher functionality

Performance Characteristics

Metric	Value	Test Case
Throughput	≥100 events/sec	test_event_throughput
Concurrent Publishers	10 threads	test_concurrent_publishers
Latency	<10ms per event	test_scheduled_events

Security Implementation

• All events encrypted with AES-256 in transit
• Encryption can be disabled for debugging
• Thread-safe operations prevent race conditions
• Error handling prevents crashes from bad events

Scheduler Integration

The event system integrates with the scheduler through:

1. Delayed event execution via schedule_event
2. Shared thread pool resources
3. Common encryption implementation

sequenceDiagram
    participant Publisher
    participant EventSystem
    participant Scheduler
    participant Handler
    
    Publisher->>EventSystem: publish(event)
    EventSystem->>Scheduler: schedule_event(delayed)
    Scheduler->>EventSystem: execute delayed
    EventSystem->>Handler: dispatch(event)

Scaling Considerations

• Queue size monitoring recommended
• Handler execution time critical for throughput
• Consider dedicated thread pools for slow handlers
• Horizontal scaling possible with distributed queue

NLP Processing Module

Security Architecture

• Encryption: All model data encrypted at rest using AES-256
• Access Control: RBAC enforced via @requires_permission decorators
• Audit Trail: All operations logged via security/audit.py

Integration Points

1. Security Subsystem:

   • Uses RBAC engine for permission checks
   • Writes audit logs for all NLP operations

2. Event Processing:

   • Intent analysis available as a service
   • Secure decorator for custom NLP operations

Implementation Notes

• Base class: nlp/intent.IntentRecognizer
• Tests: nlp/tests/test_intent.py
• Follows security requirements from symphony-core.md