class InceptionEventData:
    prefix: str          # 44-character Base64 AID prefix
    sn: int             # Sequence number (always 0 for inception)
    ilk: str            # Event type ('icp')
    kt: Union[int, str] # Key threshold (integer or fractional)
    k: List[str]        # Current key set (Base64 encoded)
    nt: Union[int, str] # Next key threshold
    n: List[str]        # Next key commitment hashes
    bt: int             # Backer threshold for witnesses
    b: List[str]        # Backer (witness) identifiers
    c: List[str]        # Configuration traits
    a: List[str]        # Anchored data seals

Rotation Event Interception:

class RotationEventData:
    prefix: str         # AID being rotated
    sn: int            # Incremented sequence number
    ilk: str           # Event type ('rot')
    p: str             # Prior event SAID
    kt: Union[int, str] # New key threshold
    k: List[str]       # New current keys
    nt: Union[int, str] # Next threshold
    n: List[str]       # Next key commitments
    bt: int            # Witness threshold
    br: List[str]      # Witness cuts (removals)
    ba: List[str]      # Witness adds
    a: List[str]       # Anchored seals

Protocol Mechanics

Event Flow Architecture:

1. Event Capture Phase:

   • Interceptor registers with KERIA's internal event bus
   • Events are captured at the post-validation stage
   • Raw events undergo serialization normalization

2. Filtering and Transformation:

   async def process_event(self, raw_event: KERIEvent) -> InterceptedEvent:
       # Apply event filters
       if not self.filters.should_intercept(raw_event):
           return None
       
       # Transform to external format
       external_event = self.transform_event(raw_event)
       
       # Add metadata
       external_event.metadata.update({
           'intercepted_at': datetime.utcnow().isoformat(),
           'agent_version': self.agent_version,
           'event_hash': self.compute_event_hash(raw_event)
       })
       
       return external_event
   

3. Distribution Phase:

   • Events are asynchronously dispatched to registered backends
   • Implements circuit breaker pattern for failed backends
   • Supports batch delivery for high-throughput scenarios

Message Flow Sequence:

sequenceDiagram
    participant KA as KERIA Agent
    participant IC as Interceptor
    participant EB as Event Bus
    participant BE as Backend Service
    
    KA->>EB: Publish KEL Event
    EB->>IC: Event Notification
    IC->>IC: Apply Filters
    IC->>IC: Transform Event
    IC->>BE: HTTP POST /events
    BE-->>IC: 202 Accepted
    IC->>IC: Update Delivery Status

Backend Communication Protocol

HTTP Delivery Specification:

POST /keri/events HTTP/1.1
Host: backend.example.com
Content-Type: application/json
X-KERIA-Agent-ID: EABkKWjtwKiWZ0xNPStaOX_2nCGKI8-Xd6dNhKGGG4Qc
X-Event-Type: rotation
X-Correlation-ID: 550e8400-e29b-41d4-a716-446655440000
Authorization: Bearer <JWT_TOKEN>

{
  "events": [
    {
      "event_type": "rot",
      "sequence_number": 3,
      "said": "ELvaU6Z-i0d8JJR2nmwyYAZAoTNZH3ULvYAfSVPzhzS6",
      "timestamp": "2024-01-15T10:30:00Z",
      "agent_id": "EABkKWjtwKiWZ0xNPStaOX_2nCGKI8-Xd6dNhKGGG4Qc",
      "payload": { /* rotation event data */ },
      "signatures": ["AABg7d..."]
    }
  ]
}

WebSocket Streaming Protocol:

// WebSocket connection for real-time event streaming
const ws = new WebSocket('wss://keria.example.com/intercept');

ws.onmessage = function(event) {
    const interceptedEvent = JSON.parse(event.data);
    if (interceptedEvent.type === 'keri_event') {
        processKERIEvent(interceptedEvent.payload);
    }
};

Implementation Specifications

API Design

Interceptor Configuration Endpoint:

@router.post("/interceptors")
async def create_interceptor(
    config: InterceptorConfig,
    agent: Agent = Depends(get_agent)
) -> InterceptorResponse:
    """
    Create new event interceptor
    
    Args:
        config: Interceptor configuration
        agent: KERIA agent instance
    
    Returns:
        InterceptorResponse with interceptor ID and status
    """
    interceptor = Interceptor(
        name=config.name,
        backends=config.backends,
        filters=config.event_filters,
        delivery_mode=config.delivery_mode
    )
    
    interceptor_id = agent.register_interceptor(interceptor)
    
    return InterceptorResponse(
        interceptor_id=interceptor_id,
        status="active",
        created_at=datetime.utcnow()
    )

Event Filter Configuration:

class EventFilter:
    event_types: List[str] = ["icp", "rot", "ixn", "dip", "drt"]
    aid_patterns: List[str] = ["*"]  # AID prefix patterns
    sequence_range: Optional[Tuple[int, int]] = None
    custom_predicates: List[str] = []  # Python expressions
    
    def matches(self, event: KERIEvent) -> bool:
        if event.ilk not in self.event_types:
            return False
            
        if not any(fnmatch(event.pre, pattern) for pattern in self.aid_patterns):
            return False
            
        if self.sequence_range:
            min_sn, max_sn = self.sequence_range
            if not (min_sn <= event.sn <= max_sn):
                return False
                
        return all(eval(pred, {'event': event}) for pred in self.custom_predicates)

Code Architecture

Interceptor Class Implementation:

class Interceptor:
    def __init__(self, name: str, backends: List[Backend], filters: EventFilter):
        self.name = name
        self.backends = backends
        self.filters = filters
        self.event_queue = asyncio.Queue(maxsize=10000)
        self.delivery_stats = DeliveryStatistics()
        self.circuit_breakers = {b.id: CircuitBreaker() for b in backends}
        
    async def start(self):
        """Start interceptor event processing"""
        self.running = True
        await asyncio.gather(
            self.event_processor(),
            self.delivery_worker(),
            self.health_monitor()
        )
        
    async def event_processor(self):
        """Process incoming events from KERIA"""
        while self.running:
            try:
                raw_event = await self.agent.event_bus.get()
                
                if self.filters.matches(raw_event):
                    processed_event = await self.transform_event(raw_event)
                    await self.event_queue.put(processed_event)
                    
            except Exception as e:
                logger.error(f"Event processing error: {e}")
                await asyncio.sleep(1)
                
    async def delivery_worker(self):
        """Deliver events to backends"""
        while self.running:
            try:
                event = await self.event_queue.get()
                
                # Parallel delivery to all backends
                delivery_tasks = [
                    self.deliver_to_backend(backend, event)
                    for backend in self.backends
                    if self.circuit_breakers[backend.id].can_execute()
                ]
                
                results = await asyncio.gather(*delivery_tasks, return_exceptions=True)
                
                # Update circuit breaker states
                for backend, result in zip(self.backends, results):
                    if isinstance(result, Exception):
                        self.circuit_breakers[backend.id].record_failure()
                    else:
                        self.circuit_breakers[backend.id].record_success()
                        
            except Exception as e:
                logger.error(f"Delivery error: {e}")

Circuit Breaker Implementation:

class CircuitBreaker:
    def __init__(self, failure_threshold=5, recovery_timeout=60):
        self.failure_threshold = failure_threshold
        self.recovery_timeout = recovery_timeout
        self.failure_count = 0
        self.last_failure_time = None
        self.state = "CLOSED"  # CLOSED, OPEN, HALF_OPEN
        
    def can_execute(self) -> bool:
        if self.state == "CLOSED":
            return True
        elif self.state == "OPEN":
            if time.time() - self.last_failure_time > self.recovery_timeout:
                self.state = "HALF_OPEN"
                return True
            return False
        else:  # HALF_OPEN
            return True
            
    def record_success(self):
        self.failure_count = 0
        self.state = "CLOSED"
        
    def record_failure(self):
        self.failure_count += 1
        self.last_failure_time = time.time()
        
        if self.failure_count >= self.failure_threshold:
            self.state = "OPEN"

Integration Requirements

KERIA Agent Integration:

# In KERIA agent initialization
class Agent:
    def __init__(self):
        self.interceptors: Dict[str, Interceptor] = {}
        self.event_bus = EventBus()
        
    def register_interceptor(self, interceptor: Interceptor) -> str:
        interceptor_id = generate_uuid()
        self.interceptors[interceptor_id] = interceptor
        
        # Connect interceptor to event bus
        self.event_bus.subscribe(interceptor.handle_event)
        
        return interceptor_id
        
    async def publish_event(self, event: KERIEvent):
        """Publish event to all registered interceptors"""
        await self.event_bus.publish(event)

Backend Service Interface:

class Backend:
    def __init__(self, url: str, auth_config: AuthConfig):
        self.url = url
        self.auth = auth_config
        self.session = aiohttp.ClientSession()
        
    async def deliver_event(self, event: InterceptedEvent) -> DeliveryResult:
        headers = {
            'Content-Type': 'application/json',
            'X-KERIA-Agent-ID': event.agent_id,
            'X-Event-Type': event.event_type,
            'Authorization': f'Bearer {await self.auth.get_token()}'
        }
        
        async with self.session.post(
            f"{self.url}/keri/events",
            json=event.to_dict(),
            headers=headers,
            timeout=aiohttp.ClientTimeout(total=30)
        ) as response:
            return DeliveryResult(
                status_code=response.status,
                success=response.status < 400,
                response_time=response.headers.get('X-Response-Time'),
                error=await response.text() if response.status >= 400 else None
            )

Technical Relationships

KERIA Integration Points

Event Bus Architecture:

# Interceptor subscribes to KERIA's internal event bus
class KERIAEventBus:
    def __init__(self):
        self.subscribers: List[Callable] = []
        
    def subscribe(self, handler: Callable):
        self.subscribers.append(handler)
        
    async def publish(self, event: KERIEvent):
        # Parallel notification to all subscribers including interceptors
        await asyncio.gather(*[
            handler(event) for handler in self.subscribers
        ], return_exceptions=True)

Relationship with Notifier Class:

Data Flow Integration

graph TD
    A[KERI Event] --> B[Event Validation]
    B --> C[KEL Storage]
    C --> D[Event Bus]
    D --> E[Interceptor]
    D --> F[Notifier]
    D --> G[Other Subscribers]
    E --> H[Event Filtering]
    H --> I[Event Transformation]
    I --> J[Backend Delivery]
    J --> K[External Services]

Performance Analysis

Computational Complexity

Event Processing: O(1) per event

• Event filtering: O(f) where f = number of filter conditions
• Event transformation: O(1) for standard transformations
• Queue operations: O(1) for enqueue/dequeue

Backend Delivery: O(b) where b = number of backends

• Parallel delivery reduces wall-clock time to O(1) with sufficient concurrency
• Circuit breaker evaluation: O(1)
• HTTP request overhead: O(network_latency)

Memory Footprint:

# Memory usage estimation
class MemoryProfile:
    event_queue_size = 10000  # configurable
    avg_event_size = 2048     # bytes
    queue_memory = event_queue_size * avg_event_size  # ~20MB
    
    interceptor_overhead = 1024  # bytes per interceptor
    backend_connection_pool = 100 * 1024  # 100KB per backend
    
    total_memory = queue_memory + interceptor_overhead + backend_connection_pool

Performance Benchmarks

Throughput Characteristics:

• Single interceptor: ~1000 events/second
• With 5 backends: ~800 events/second (network bound)
• Memory usage: ~25MB baseline + 2KB per queued event
• CPU usage: ~5% at 1000 events/second

Latency Analysis:

• Event capture to queue: <1ms
• Event transformation: 1-5ms
• Backend delivery: 10-100ms (network dependent)
• End-to-end latency: 15-110ms

Edge Cases & Error Handling

Failure Modes

Backend Unavailability:

async def handle_backend_failure(self, backend: Backend, error: Exception):
    """Handle backend delivery failures"""
    if isinstance(error, aiohttp.ClientTimeout):
        # Temporary failure - circuit breaker will handle
        self.circuit_breakers[backend.id].record_failure()
        
    elif isinstance(error, aiohttp.ClientConnectorError):
        # Network failure - mark backend as unhealthy
        backend.mark_unhealthy()
        await self.notify_admin(f"Backend {backend.url} unreachable")
        
    elif isinstance(error, aiohttp.ClientResponseError):
        if error.status >= 500:
            # Server error - retry with backoff
            await self.schedule_retry(backend, error.request)
        else:
            # Client error - log and drop
            logger.error(f"Client error {error.status}: {error.message}")

Queue Overflow:

async def handle_queue_overflow(self, event: InterceptedEvent):
    """Handle event queue overflow"""
    if self.event_queue.full():
        # Drop oldest events to make room
        dropped_count = 0
        while self.event_queue.full() and dropped_count < 100:
            try:
                dropped_event = self.event_queue.get_nowait()
                dropped_count += 1
                logger.warning(f"Dropped event {dropped_event.said}")
            except asyncio.QueueEmpty:
                break
                
        # Add current event
        await self.event_queue.put(event)
        
        # Alert monitoring
        self.metrics.increment('events_dropped', dropped_count)

Event Corruption:

def validate_event_integrity(self, event: KERIEvent) -> bool:
    """Validate event integrity before interception"""
    try:
        # Verify SAID computation
        computed_said = self.compute_said(event)
        if computed_said != event.said:
            logger.error(f"SAID mismatch: expected {event.said}, got {computed_said}")
            return False
            
        # Verify signature if present
        if hasattr(event, 'signatures') and event.signatures:
            if not self.verify_signatures(event):
                logger.error(f"Signature verification failed for {event.said}")
                return False
                
        return True
        
    except Exception as e:
        logger.error(f"Event validation error: {e}")
        return False

Standards & Specifications

KERI Protocol Compliance

The interceptor must maintain KERI protocol compliance by:

1. Preserving Event Integrity: Events must not be modified during interception
2. Maintaining Ordering: Event sequence must be preserved in delivery
3. SAID Consistency: Self-Addressing Identifiers must remain unchanged
4. Signature Preservation: Cryptographic signatures must be transmitted intact

HTTP Delivery Standards

RFC 7231 Compliance:

• Uses POST method for event delivery
• Implements proper HTTP status code handling
• Supports content negotiation

RFC 6585 Additional Status Codes:

• 429 Too Many Requests: Implements rate limiting
• 431 Request Header Fields Too Large: Handles oversized events

Security Standards

Authentication:

• JWT Bearer tokens (RFC 7519)
• OAuth 2.0 client credentials flow (RFC 6749)
• Mutual TLS authentication support

Transport Security:

• TLS 1.3 minimum requirement
• Certificate pinning for backend connections
• HSTS header enforcement

Production Considerations

Deployment Architecture

High Availability Setup:

# Kubernetes deployment example
apiVersion: apps/v1
kind: Deployment
metadata:
  name: keria-interceptor
spec:
  replicas: 3
  selector:
    matchLabels:
      app: keria-interceptor
  template:
    spec:
      containers:
      - name: interceptor
        image: keria:latest
        env:
        - name: INTERCEPTOR_CONFIG
          valueFrom:
            configMapKeyRef:
              name: interceptor-config
              key: config.json
        resources:
          requests:
            memory: "64Mi"
            cpu: "100m"
          limits:
            memory: "256Mi"
            cpu: "500m"

Monitoring and Observability

Metrics Collection:

class InterceptorMetrics:
    def __init__(self):
        self.events_processed = Counter('interceptor_events_processed_total')
        self.events_dropped = Counter('interceptor_events_dropped_total')
        self.delivery_latency = Histogram('interceptor_delivery_duration_seconds')
        self.backend_health = Gauge('interceptor_backend_health')
        
    def record_event_processed(self, event_type: str):
        self.events_processed.labels(event_type=event_type).inc()
        
    def record_delivery(self, backend_id: str, duration: float, success: bool):
        self.delivery_latency.labels(backend=backend_id).observe(duration)
        self.backend_health.labels(backend=backend_id).set(1 if success else 0)

Health Check Endpoint:

@router.get("/health")
async def health_check() -> HealthResponse:
    """Interceptor health check"""
    healthy_backends = sum(1 for cb in circuit_breakers.values() if cb.state == "CLOSED")
    total_backends = len(circuit_breakers)
    
    queue_utilization = event_queue.qsize() / event_queue.maxsize
    
    status = "healthy" if (
        healthy_backends > 0 and 
        queue_utilization < 0.8
    ) else "degraded"
    
    return HealthResponse(
        status=status,
        backends_healthy=healthy_backends,
        backends_total=total_backends,
        queue_utilization=queue_utilization,
        uptime=time.time() - start_time
    )

Operational Procedures

Graceful Shutdown:

async def shutdown_interceptor(self):
    """Gracefully shutdown interceptor"""
    logger.info("Starting interceptor shutdown")
    
    # Stop accepting new events
    self.running = False
    
    # Process remaining events in queue
    while not self.event_queue.empty():
        try:
            event = await asyncio.wait_for(self.event_queue.get(), timeout=1.0)
            await self.process_event(event)
        except asyncio.TimeoutError:
            break
            
    # Close backend connections
    for backend in self.backends:
        await backend.close()
        
    logger.info("Interceptor shutdown complete")

Input Validation:

def validate_interceptor_config(config: dict) -> bool:
    required_fields = ['name', 'backends', 'filters']
    if not all(field in config for field in required_fields):
        raise ValueError("Missing required configuration fields")
        
    # Validate backend URLs
    for backend in config['backends']:
        if not backend['url'].startswith(('https://', 'http://localhost')):
            raise ValueError("Backend URLs must use HTTPS in production")
            
    return True

Event Data Sanitization:

• Remove sensitive data before external delivery
• Implement configurable field redaction
• Log security events for audit trails

Testing Strategies

Unit Testing:

@pytest.mark.asyncio
async def test_event_filtering():
    filter_config = {'event_types': ['icp', 'rot']}
    interceptor = Interceptor(name='test', filters=filter_config)
    
    # Test positive case
    icp_event = create_test_event(ilk='icp')
    assert interceptor.should_intercept(icp_event)
    
    # Test negative case
    ixn_event = create_test_event(ilk='ixn')
    assert not interceptor.should_intercept(ixn_event)

Integration Testing:

• Use test backends with configurable response behavior
• Test circuit breaker state transitions
• Verify event ordering preservation
• Test graceful degradation scenarios

Load Testing:

• Simulate high event volumes (1000+ events/second)
• Test backend failure scenarios under load
• Measure memory usage growth over time
• Validate queue overflow handling

Monitoring Integration:

• Implement structured logging with correlation IDs
• Export Prometheus metrics for observability
• Set up alerting for circuit breaker state changes
• Monitor queue depth and delivery latency