# Advanced Topics Guide

This comprehensive guide covers advanced usage patterns, customization, and extension points for SAFLA (Self-Aware Feedback Loop Algorithm). These topics are designed for experienced users who want to extend SAFLA's capabilities or integrate it into complex systems.

## Table of Contents

1. [Advanced Architecture Patterns](#advanced-architecture-patterns)
2. [Custom Component Development](#custom-component-development)
3. [Extension Points](#extension-points)
4. [Advanced Configuration](#advanced-configuration)
5. [Custom Algorithms](#custom-algorithms)
6. [Integration Patterns](#integration-patterns)
7. [Performance Optimization](#performance-optimization)
8. [Security Hardening](#security-hardening)
9. [Multi-Tenant Deployments](#multi-tenant-deployments)
10. [Distributed SAFLA](#distributed-safla)
11. [Machine Learning Integration](#machine-learning-integration)
12. [Custom MCP Servers](#custom-mcp-servers)
13. [Advanced Monitoring](#advanced-monitoring)
14. [Research and Experimentation](#research-and-experimentation)

## Advanced Architecture Patterns

### 1. Hierarchical SAFLA Systems

SAFLA can be deployed in hierarchical configurations where multiple SAFLA instances operate at different levels of abstraction:

```python
# hierarchical/coordinator.py
from typing import Dict, List, Optional
import asyncio
import logging

class HierarchicalSAFLACoordinator:
    """Coordinates multiple SAFLA instances in a hierarchical structure."""
    
    def __init__(self, config: Dict):
        self.config = config
        self.child_instances = {}
        self.parent_instance = None
        self.coordination_strategy = config.get('coordination_strategy', 'consensus')
        
    async def initialize_hierarchy(self):
        """Initialize hierarchical SAFLA structure."""
        # Initialize child instances
        for child_config in self.config.get('children', []):
            child_id = child_config['id']
            child_instance = await self._create_child_instance(child_config)
            self.child_instances[child_id] = child_instance
        
        # Connect to parent if specified
        if 'parent' in self.config:
            self.parent_instance = await self._connect_to_parent(self.config['parent'])
    
    async def coordinate_decision_making(self, decision_context: Dict) -> Dict:
        """Coordinate decision making across hierarchy levels."""
        if self.coordination_strategy == 'consensus':
            return await self._consensus_coordination(decision_context)
        elif self.coordination_strategy == 'hierarchical':
            return await self._hierarchical_coordination(decision_context)
        elif self.coordination_strategy == 'federated':
            return await self._federated_coordination(decision_context)
        else:
            raise ValueError(f"Unknown coordination strategy: {self.coordination_strategy}")
    
    async def _consensus_coordination(self, context: Dict) -> Dict:
        """Consensus-based coordination among peer instances."""
        # Collect decisions from all child instances
        child_decisions = await asyncio.gather(*[
            child.make_decision(context)
            for child in self.child_instances.values()
        ])
        
        # Apply consensus algorithm
        consensus_decision = self._apply_consensus_algorithm(child_decisions)
        
        # Validate with parent if available
        if self.parent_instance:
            validation = await self.parent_instance.validate_decision(consensus_decision)
            if not validation['approved']:
                # Retry with parent guidance
                guided_context = {**context, 'parent_guidance': validation['guidance']}
                return await self.coordinate_decision_making(guided_context)
        
        return consensus_decision
    
    async def _hierarchical_coordination(self, context: Dict) -> Dict:
        """Top-down hierarchical coordination."""
        # Get high-level decision from parent
        if self.parent_instance:
            parent_decision = await self.parent_instance.make_high_level_decision(context)
            context['parent_decision'] = parent_decision
        
        # Delegate to appropriate child based on decision scope
        target_child = self._select_target_child(context)
        if target_child:
            return await target_child.make_decision(context)
        
        # Handle at current level if no suitable child
        return await self._make_local_decision(context)
    
    async def _federated_coordination(self, context: Dict) -> Dict:
        """Federated coordination with specialized instances."""
        # Route to specialized child instances based on context
        routing_decisions = await self._route_to_specialists(context)
        
        # Aggregate specialist decisions
        aggregated_decision = await self._aggregate_specialist_decisions(routing_decisions)
        
        return aggregated_decision
    
    def _apply_consensus_algorithm(self, decisions: List[Dict]) -> Dict:
        """Apply consensus algorithm to multiple decisions."""
        # Implement voting-based consensus
        decision_votes = {}
        confidence_weights = {}
        
        for decision in decisions:
            decision_key = self._decision_to_key(decision)
            decision_votes[decision_key] = decision_votes.get(decision_key, 0) + 1
            confidence_weights[decision_key] = confidence_weights.get(decision_key, 0) + decision.get('confidence', 0.5)
        
        # Select decision with highest weighted vote
        best_decision_key = max(decision_votes.keys(), 
                               key=lambda k: decision_votes[k] * confidence_weights[k])
        
        # Find original decision object
        for decision in decisions:
            if self._decision_to_key(decision) == best_decision_key:
                decision['consensus_confidence'] = confidence_weights[best_decision_key] / decision_votes[best_decision_key]
                return decision
        
        return decisions[0]  # Fallback
```

### 2. Event-Driven SAFLA Architecture

Implement event-driven patterns for reactive SAFLA systems:

```python
# event_driven/event_system.py
import asyncio
from typing import Dict, List, Callable, Any
from dataclasses import dataclass
from enum import Enum

class EventType(Enum):
    OBSERVATION_RECEIVED = "observation_received"
    DELTA_CALCULATED = "delta_calculated"
    DECISION_MADE = "decision_made"
    MODIFICATION_APPLIED = "modification_applied"
    VALIDATION_COMPLETED = "validation_completed"
    ERROR_OCCURRED = "error_occurred"
    PERFORMANCE_THRESHOLD_EXCEEDED = "performance_threshold_exceeded"

@dataclass
class SAFLAEvent:
    event_type: EventType
    source_component: str
    timestamp: float
    data: Dict[str, Any]
    correlation_id: str
    priority: int = 5  # 1-10, 1 being highest priority

class EventDrivenSAFLA:
    """Event-driven SAFLA implementation with reactive patterns."""
    
    def __init__(self):
        self.event_handlers = {}
        self.event_queue = asyncio.PriorityQueue()
        self.event_history = []
        self.running = False
        
    def register_handler(self, event_type: EventType, handler: Callable):
        """Register event handler for specific event type."""
        if event_type not in self.event_handlers:
            self.event_handlers[event_type] = []
        self.event_handlers[event_type].append(handler)
    
    async def emit_event(self, event: SAFLAEvent):
        """Emit event to the system."""
        # Add to queue with priority
        await self.event_queue.put((event.priority, event.timestamp, event))
        
        # Store in history
        self.event_history.append(event)
        
        # Keep history bounded
        if len(self.event_history) > 10000:
            self.event_history = self.event_history[-5000:]
    
    async def start_event_loop(self):
        """Start the main event processing loop."""
        self.running = True
        
        while self.running:
            try:
                # Get next event from queue
                priority, timestamp, event = await asyncio.wait_for(
                    self.event_queue.get(), timeout=1.0
                )
                
                # Process event
                await self._process_event(event)
                
            except asyncio.TimeoutError:
                # No events to process, continue
                continue
            except Exception as e:
                logger.error(f"Error processing event: {e}")
                await asyncio.sleep(0.1)
    
    async def _process_event(self, event: SAFLAEvent):
        """Process individual event."""
        handlers = self.event_handlers.get(event.event_type, [])
        
        if not handlers:
            logger.warning(f"No handlers registered for event type: {event.event_type}")
            return
        
        # Execute all handlers concurrently
        handler_tasks = [handler(event) for handler in handlers]
        results = await asyncio.gather(*handler_tasks, return_exceptions=True)
        
        # Log any handler errors
        for i, result in enumerate(results):
            if isinstance(result, Exception):
                logger.error(f"Handler {i} failed for event {event.event_type}: {result}")
    
    def create_reactive_pipeline(self) -> 'ReactiveObservationPipeline':
        """Create reactive observation processing pipeline."""
        return ReactiveObservationPipeline(self)

class ReactiveObservationPipeline:
    """Reactive pipeline for processing observations."""
    
    def __init__(self, event_system: EventDrivenSAFLA):
        self.event_system = event_system
        self.setup_pipeline()
    
    def setup_pipeline(self):
        """Setup reactive pipeline handlers."""
        # Observation -> Delta Calculation
        self.event_system.register_handler(
            EventType.OBSERVATION_RECEIVED,
            self._handle_observation_received
        )
        
        # Delta -> Decision Making
        self.event_system.register_handler(
            EventType.DELTA_CALCULATED,
            self._handle_delta_calculated
        )
        
        # Decision -> Modification
        self.event_system.register_handler(
            EventType.DECISION_MADE,
            self._handle_decision_made
        )
        
        # Modification -> Validation
        self.event_system.register_handler(
            EventType.MODIFICATION_APPLIED,
            self._handle_modification_applied
        )
    
    async def _handle_observation_received(self, event: SAFLAEvent):
        """Handle new observation and trigger delta calculation."""
        observation = event.data['observation']
        
        # Calculate delta
        delta_result = await self._calculate_delta(observation)
        
        # Emit delta calculated event
        delta_event = SAFLAEvent(
            event_type=EventType.DELTA_CALCULATED,
            source_component="delta_evaluator",
            timestamp=time.time(),
            data={'delta_result': delta_result, 'observation': observation},
            correlation_id=event.correlation_id,
            priority=event.priority
        )
        
        await self.event_system.emit_event(delta_event)
    
    async def _handle_delta_calculated(self, event: SAFLAEvent):
        """Handle delta calculation and trigger decision making."""
        delta_result = event.data['delta_result']
        
        if delta_result.should_adapt:
            # Make decision
            decision = await self._make_decision(delta_result)
            
            # Emit decision made event
            decision_event = SAFLAEvent(
                event_type=EventType.DECISION_MADE,
                source_component="decision_maker",
                timestamp=time.time(),
                data={'decision': decision, 'delta_result': delta_result},
                correlation_id=event.correlation_id,
                priority=event.priority
            )
            
            await self.event_system.emit_event(decision_event)
```

## Custom Component Development

### 1. Custom Delta Evaluator

Create specialized delta evaluators for domain-specific requirements:

```python
# custom/delta_evaluator.py
from abc import ABC, abstractmethod
from typing import Dict, Any, List
import numpy as np

class CustomDeltaEvaluator(ABC):
    """Base class for custom delta evaluators."""
    
    @abstractmethod
    async def evaluate(self, metrics: Dict[str, float]) -> 'DeltaResult':
        """Evaluate delta based on custom logic."""
        pass
    
    @abstractmethod
    def get_evaluation_criteria(self) -> Dict[str, Any]:
        """Get evaluation criteria for this evaluator."""
        pass

class DomainSpecificDeltaEvaluator(CustomDeltaEvaluator):
    """Delta evaluator for specific domain requirements."""
    
    def __init__(self, domain_config: Dict[str, Any]):
        self.domain_config = domain_config
        self.weights = domain_config.get('weights', {})
        self.thresholds = domain_config.get('thresholds', {})
        self.custom_metrics = domain_config.get('custom_metrics', [])
        
    async def evaluate(self, metrics: Dict[str, float]) -> 'DeltaResult':
        """Evaluate delta using domain-specific logic."""
        # Apply domain-specific transformations
        transformed_metrics = await self._transform_metrics(metrics)
        
        # Calculate weighted delta
        weighted_delta = self._calculate_weighted_delta(transformed_metrics)
        
        # Apply domain-specific rules
        rule_adjustments = await self._apply_domain_rules(transformed_metrics)
        
        # Combine results
        final_delta = weighted_delta + rule_adjustments
        
        # Determine confidence based on domain factors
        confidence = await self._calculate_domain_confidence(transformed_metrics)
        
        return DeltaResult(
            success=True,
            metrics=transformed_metrics,
            total_delta=final_delta,
            confidence=confidence,
            should_adapt=final_delta > self.thresholds.get('adaptation_threshold', 0.15),
            domain_specific_data={
                'rule_adjustments': rule_adjustments,
                'domain_factors': await self._get_domain_factors(metrics)
            }
        )
    
    async def _transform_metrics(self, metrics: Dict[str, float]) -> Dict[str, float]:
        """Apply domain-specific metric transformations."""
        transformed = metrics.copy()
        
        # Apply custom metric calculations
        for custom_metric in self.custom_metrics:
            if custom_metric['type'] == 'composite':
                transformed[custom_metric['name']] = await self._calculate_composite_metric(
                    metrics, custom_metric['formula']
                )
            elif custom_metric['type'] == 'normalized':
                transformed[custom_metric['name']] = await self._normalize_metric(
                    metrics[custom_metric['source']], custom_metric['normalization']
                )
        
        return transformed
    
    async def _apply_domain_rules(self, metrics: Dict[str, float]) -> float:
        """Apply domain-specific business rules."""
        adjustments = 0.0
        
        rules = self.domain_config.get('rules', [])
        for rule in rules:
            if await self._evaluate_rule_condition(metrics, rule['condition']):
                adjustments += rule['adjustment']
        
        return adjustments
    
    async def _calculate_domain_confidence(self, metrics: Dict[str, float]) -> float:
        """Calculate confidence based on domain-specific factors."""
        base_confidence = np.mean(list(metrics.values()))
        
        # Apply domain-specific confidence modifiers
        confidence_factors = self.domain_config.get('confidence_factors', {})
        
        for factor_name, factor_config in confidence_factors.items():
            if factor_name in metrics:
                factor_value = metrics[factor_name]
                modifier = self._calculate_confidence_modifier(factor_value, factor_config)
                base_confidence *= modifier
        
        return min(max(base_confidence, 0.0), 1.0)

class MultiCriteriaDeltaEvaluator(CustomDeltaEvaluator):
    """Delta evaluator using multi-criteria decision analysis."""
    
    def __init__(self, criteria_config: Dict[str, Any]):
        self.criteria = criteria_config['criteria']
        self.method = criteria_config.get('method', 'topsis')
        self.weights = criteria_config.get('weights', {})
        
    async def evaluate(self, metrics: Dict[str, float]) -> 'DeltaResult':
        """Evaluate using multi-criteria decision analysis."""
        if self.method == 'topsis':
            return await self._topsis_evaluation(metrics)
        elif self.method == 'ahp':
            return await self._ahp_evaluation(metrics)
        elif self.method == 'electre':
            return await self._electre_evaluation(metrics)
        else:
            raise ValueError(f"Unknown MCDA method: {self.method}")
    
    async def _topsis_evaluation(self, metrics: Dict[str, float]) -> 'DeltaResult':
        """TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)."""
        # Normalize decision matrix
        normalized_metrics = self._normalize_decision_matrix(metrics)
        
        # Apply weights
        weighted_metrics = {
            k: v * self.weights.get(k, 1.0)
            for k, v in normalized_metrics.items()
        }
        
        # Determine ideal and negative-ideal solutions
        ideal_solution = {k: max(weighted_metrics.values()) for k in weighted_metrics}
        negative_ideal = {k: min(weighted_metrics.values()) for k in weighted_metrics}
        
        # Calculate distances
        distance_to_ideal = self._calculate_euclidean_distance(weighted_metrics, ideal_solution)
        distance_to_negative = self._calculate_euclidean_distance(weighted_metrics, negative_ideal)
        
        # Calculate TOPSIS score
        topsis_score = distance_to_negative / (distance_to_ideal + distance_to_negative)
        
        return DeltaResult(
            success=True,
            metrics=metrics,
            total_delta=topsis_score,
            confidence=self._calculate_topsis_confidence(weighted_metrics),
            should_adapt=topsis_score > 0.6,  # TOPSIS threshold
            domain_specific_data={
                'topsis_score': topsis_score,
                'distance_to_ideal': distance_to_ideal,
                'distance_to_negative': distance_to_negative
            }
        )
```

### 2. Custom Vector Memory Backend

Implement custom vector storage backends for specialized requirements:

```python
# custom/vector_backend.py
from abc import ABC, abstractmethod
from typing import List, Dict, Any, Optional
import numpy as np

class CustomVectorBackend(ABC):
    """Base class for custom vector storage backends."""
    
    @abstractmethod
    async def store_vector(self, vector: np.ndarray, metadata: Dict[str, Any]) -> str:
        """Store vector with metadata."""
        pass
    
    @abstractmethod
    async def search_similar(self, query_vector: np.ndarray, k: int = 10) -> List[Dict]:
        """Search for similar vectors."""
        pass
    
    @abstractmethod
    async def delete_vector(self, vector_id: str) -> bool:
        """Delete vector by ID."""
        pass

class DistributedVectorBackend(CustomVectorBackend):
    """Distributed vector storage backend using consistent hashing."""
    
    def __init__(self, nodes: List[str], replication_factor: int = 3):
        self.nodes = nodes
        self.replication_factor = replication_factor
        self.hash_ring = self._build_hash_ring()
        self.node_clients = self._initialize_node_clients()
    
    async def store_vector(self, vector: np.ndarray, metadata: Dict[str, Any]) -> str:
        """Store vector across multiple nodes."""
        vector_id = metadata.get('id', str(uuid.uuid4()))
        
        # Determine target nodes using consistent hashing
        target_nodes = self._get_target_nodes(vector_id)
        
        # Store on multiple nodes for replication
        store_tasks = []
        for node in target_nodes[:self.replication_factor]:
            task = self._store_on_node(node, vector_id, vector, metadata)
            store_tasks.append(task)
        
        # Wait for majority of stores to succeed
        results = await asyncio.gather(*store_tasks, return_exceptions=True)
        successful_stores = sum(1 for r in results if not isinstance(r, Exception))
        
        if successful_stores >= (self.replication_factor // 2) + 1:
            return vector_id
        else:
            raise Exception(f"Failed to store vector on majority of nodes")
    
    async def search_similar(self, query_vector: np.ndarray, k: int = 10) -> List[Dict]:
        """Search across distributed nodes and merge results."""
        # Query all nodes
        search_tasks = [
            self._search_on_node(node, query_vector, k)
            for node in self.nodes
        ]
        
        # Gather results from all nodes
        node_results = await asyncio.gather(*search_tasks, return_exceptions=True)
        
        # Merge and rank results
        all_results = []
        for results in node_results:
            if not isinstance(results, Exception):
                all_results.extend(results)
        
        # Sort by similarity and return top k
        all_results.sort(key=lambda x: x['similarity'], reverse=True)
        return all_results[:k]
    
    def _build_hash_ring(self) -> Dict[int, str]:
        """Build consistent hash ring for node selection."""
        hash_ring = {}
        virtual_nodes = 150  # Virtual nodes per physical node
        
        for node in self.nodes:
            for i in range(virtual_nodes):
                virtual_node_key = f"{node}:{i}"
                hash_value = hash(virtual_node_key) % (2**32)
                hash_ring[hash_value] = node
        
        return dict(sorted(hash_ring.items()))
    
    def _get_target_nodes(self, vector_id: str) -> List[str]:
        """Get target nodes for vector using consistent hashing."""
        hash_value = hash(vector_id) % (2**32)
        
        # Find the first node in the ring >= hash_value
        target_nodes = []
        ring_keys = list(self.hash_ring.keys())
        
        start_index = 0
        for i, key in enumerate(ring_keys):
            if key >= hash_value:
                start_index = i
                break
        
        # Get nodes in order around the ring
        seen_nodes = set()
        for i in range(len(ring_keys)):
            index = (start_index + i) % len(ring_keys)
            node = self.hash_ring[ring_keys[index]]
            
            if node not in seen_nodes:
                target_nodes.append(node)
                seen_nodes.add(node)
                
                if len(target_nodes) >= self.replication_factor:
                    break
        
        return target_nodes

class HybridVectorBackend(CustomVectorBackend):
    """Hybrid backend combining multiple storage strategies."""
    
    def __init__(self, config: Dict[str, Any]):
        self.hot_storage = self._initialize_hot_storage(config['hot_storage'])
        self.warm_storage = self._initialize_warm_storage(config['warm_storage'])
        self.cold_storage = self._initialize_cold_storage(config['cold_storage'])
        
        self.hot_threshold = config.get('hot_threshold', 1000)  # Recent vectors
        self.warm_threshold = config.get('warm_threshold', 10000)  # Frequently accessed
        
    async def store_vector(self, vector: np.ndarray, metadata: Dict[str, Any]) -> str:
        """Store vector in appropriate tier based on access patterns."""
        vector_id = metadata.get('id', str(uuid.uuid4()))
        
        # Always store in hot storage initially
        await self.hot_storage.store_vector(vector, metadata)
        
        # Update access tracking
        await self._update_access_tracking(vector_id)
        
        return vector_id
    
    async def search_similar(self, query_vector: np.ndarray, k: int = 10) -> List[Dict]:
        """Search across tiers with intelligent routing."""
        # Try hot storage first
        hot_results = await self.hot_storage.search_similar(query_vector, k)
        
        if len(hot_results) >= k:
            return hot_results[:k]
        
        # Search warm storage for additional results
        remaining_k = k - len(hot_results)
        warm_results = await self.warm_storage.search_similar(query_vector, remaining_k)
        
        combined_results = hot_results + warm_results
        if len(combined_results) >= k:
            return self._merge_and_rank_results(combined_results)[:k]
        
        # Search cold storage if needed
        remaining_k = k - len(combined_results)
        cold_results = await self.cold_storage.search_similar(query_vector, remaining_k)
        
        all_results = combined_results + cold_results
        return self._merge_and_rank_results(all_results)[:k]
    
    async def _tier_management_loop(self):
        """Background process to manage vector tiers."""
        while True:
            try:
                # Move vectors between tiers based on access patterns
                await self._promote_vectors()
                await self._demote_vectors()
                
                # Sleep for tier management interval
                await asyncio.sleep(3600)  # 1 hour
                
            except Exception as e:
                logger.error(f"Error in tier management: {e}")
                await asyncio.sleep(300)  # 5 minutes on error
    
    async def _promote_vectors(self):
        """Promote frequently accessed vectors to higher tiers."""
        # Get access statistics
        access_stats = await self._get_access_statistics()
        
        # Identify vectors for promotion
        for vector_id, stats in access_stats.items():
            current_tier = await self._get_vector_tier(vector_id)
            
            if current_tier == 'cold' and stats['access_frequency'] > self.warm_threshold:
                await self._move_vector_to_tier(vector_id, 'warm')
            elif current_tier == 'warm' and stats['recent_accesses'] > self.hot_threshold:
                await self._move_vector_to_tier(vector_id, 'hot')
    
    async def _demote_vectors(self):
        """Demote infrequently accessed vectors to lower tiers."""
        # Check hot storage capacity
        hot_count = await self.hot_storage.get_vector_count()
        
        if hot_count > self.hot_threshold:
            # Get least recently used vectors from hot storage
            lru_vectors = await self.hot_storage.get_lru_vectors(hot_count - self.hot_threshold)
            
            for vector_id in lru_vectors:
                await self._move_vector_to_tier(vector_id, 'warm')
```

## Extension Points

### 1. Plugin Architecture

SAFLA supports a plugin architecture for extending functionality:

```python
# plugins/plugin_system.py
from abc import ABC, abstractmethod
from typing import Dict, Any, List, Optional
import importlib
import inspect

class SAFLAPlugin(ABC):
    """Base class for SAFLA plugins."""
    
    @property
    @abstractmethod
    def name(self) -> str:
        """Plugin name."""
        pass
    
    @property
    @abstractmethod
    def version(self) -> str:
        """Plugin version."""
        pass
    
    @abstractmethod
    async def initialize(self, config: Dict[str, Any]) -> bool:
        """Initialize plugin with configuration."""
        pass
    
    @abstractmethod
    async def cleanup(self) -> bool:
        """Cleanup plugin resources."""
        pass
    
    def get_hooks(self) -> Dict[str, callable]:
        """Get plugin hooks."""
        hooks = {}
        
        # Automatically discover hook methods
        for name, method in inspect.getmembers(self, predicate=inspect.ismethod):
            if name.startswith('hook_'):
                hook_name = name[5:]  # Remove 'hook_' prefix
                hooks[hook_name] = method
        
        return hooks

class PluginManager:
    """Manages SAFLA plugins."""
    
    def __init__(self):
        self.plugins = {}
        self.hooks = {}
        self.plugin_configs = {}
    
    async def load_plugin(self, plugin_path: str, config: Dict[str, Any] = None) -> bool:
        """Load plugin from path."""
        try:
            # Import plugin module
            module = importlib.import_module(plugin_path)
            
            # Find plugin class
            plugin_class = None
            for name, obj in inspect.getmembers(module):
                if (inspect.isclass(obj) and 
                    issubclass(obj, SAFLAPlugin) and 
                    obj != SAFLAPlugin):
                    plugin_class = obj
                    break
            
            if not plugin_class:
                raise ValueError(f"No plugin class found in {plugin_path}")
            
            # Create plugin instance
            plugin = plugin_class()
            
            # Initialize plugin
            success = await plugin.initialize(config or {})
            if not success:
                raise Exception(f"Plugin {plugin.name} initialization failed")
            
            # Register plugin
            self.plugins[plugin.name] = plugin
            self.plugin_configs[plugin.name] = config
            
            # Register hooks
            plugin_hooks = plugin.get_hooks()
            for hook_name, hook_method in plugin_hooks.items():
                if hook_name not in self.hooks:
                    self.hooks[hook_name] = []
                self.hooks[hook_name].append(hook_method)
            
            logger.info(f"Loaded plugin: {plugin.name} v{plugin.version}")
            return True
            
        except Exception as e:
            logger.error(f"Failed to load plugin {plugin_path}: {e}")
            return False
    
    async def unload_plugin(self, plugin_name: str) -> bool:
        """Unload plugin by name."""
        if plugin_name not in self.plugins:
            return False
        
        try:
            plugin = self.plugins[plugin_name]
            
            # Cleanup plugin
            await plugin.cleanup()
            
            # Remove hooks
            plugin_hooks = plugin.get_hooks()
            for hook_name, hook_method in plugin_hooks.items():
                if hook_name in self.hooks:
                    self.hooks[hook_name] = [
                        h for h in self.hooks[hook_name] if h != hook_method
                    ]
            
            # Remove plugin
            del self.plugins[plugin_name]
            del self.plugin_configs[plugin_name]
            
            logger.info(f"Unloaded plugin: {plugin_name}")
            return True
            
        except Exception as e:
            logger.error(f"Failed to unload plugin {plugin_name}: {e}")
            return False
    
    async def execute_hook(self, hook_name: str, *args, **kwargs) -> List[Any]:
        """Execute all hooks for given hook name."""
        if hook_name not in self.hooks:
            return []
        
        results = []
        for hook in self.hooks[hook_name]:
            try:
                result = await hook(*args, **kwargs)
                results.append(result)
            except Exception as e:
                logger.error(f"Hook {hook_name} failed: {e}")
                results.append(None)
        
        return results

# Example plugin implementation
class MetricsCollectionPlugin(SAFLAPlugin):
    """Plugin for collecting custom metrics."""
    
    @property
    def name(self) -> str:
        return "metrics_collection"
    
    @property
    def version(self) -> str:
        return "1.0.0"
    
    async def initialize(self, config: Dict[str, Any]) -> bool:
        """Initialize metrics collection."""
        self.metrics_store = config.get('metrics_store', 'memory')
        self.collection_interval = config.get('collection_interval', 60)
        self.custom_metrics = config.get('custom_metrics', [])
        
        # Start metrics collection loop
        asyncio.create_task(self._metrics_collection_loop())
        
        return True
    
    async def cleanup(self) -> bool:
        """Cleanup metrics collection."""
        # Stop collection loop and cleanup resources
        return True
    
    async def hook_observation_processed(self, observation: Dict[str, Any]) -> Dict[str, Any]:
        """Hook called when observation is processed."""
        # Collect custom metrics from observation
        custom_metrics = {}
        
        for metric_config in self.custom_metrics:
            metric_name = metric_config['name']
            metric_value = self._extract_metric_value(observation, metric_config)
            custom_metrics[metric_name] = metric_value
        
        # Store metrics
        await self._store_metrics(custom_metrics)
        
        return custom_metrics
    
    async def hook_delta_calculated(self, delta_result: 'DeltaResult') -> None:
        """Hook called when delta is calculated."""
        # Record delta calculation metrics
        await self._record_delta_metrics(delta_result)
    
    async def _metrics_collection_loop(self):
        """Background metrics collection loop."""
        while True:
            try:
                # Collect system metrics
                system_metrics = await self._collect_system_metrics()
                await self._store_metrics(system_metrics)
                
                await asyncio.sleep(self.collection_interval)
                
            except Exception as e:
                logger.error(f"Metrics collection error: {e}")
                await asyncio.sleep(10)
```

### 2. Custom MCP Server Development

Create specialized MCP servers for domain-specific integrations:

```python
# mcp/custom_server.py
from typing import Dict, Any, List, Optional
import asyncio
import json

class CustomMCPServer:
    """Base class for custom MCP servers."""
    
    def __init__(self, server_config: Dict[str, Any]):
        self.config = server_config
        self.tools = {}
        self.resources = {}
        self.running = False
        
    def register_tool(self, name: str, tool_func: callable, schema: Dict[str, Any]):
        """Register a tool with the MCP server."""
        self.tools[name] = {
            'function': tool_func,
            'schema': schema
        }
    
    def register_resource(self, uri: str, resource_func: callable):
        """Register a resource with the MCP server."""
        self.resources[uri] = resource_func
    
    async def start_server(self):
        """Start the MCP server."""
        self.running = True
        
        # Start server loop
        await self._server_loop()
    
    async def stop_server(self):
        """Stop the MCP server."""
        self.running = False
    
    async def _server_loop(self):
        """Main server loop."""
        while self.running:
            try:
                # Handle incoming requests
                await self._handle_requests()
                await asyncio.sleep(0.1)
                
            except Exception as e:
                logger.error(f"MCP server error: {e}")
                await asyncio.sleep(1)

class DatabaseMCPServer(CustomMCPServer):
    """MCP server for database operations."""
    
    def __init__(self, server_config: Dict[str, Any]):
        super().__init__(server_config)
        self.db_connection = None
        self._register_database_tools()
    
    def _register_database_tools(self):
        """Register database-specific tools."""
        
        # Query execution tool
        self.register_tool(
            "execute_query",
            self._execute_query,
            {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "SQL query to execute"},
                    "parameters": {"type": "array", "description": "Query parameters"}
                },
                "required": ["query"]
            }
        )
        
        # Schema inspection tool
        self.register_tool(
            "inspect_schema",
            self._inspect_schema,
            {
                "type": "object",
                "properties": {
                    "table_name": {"type": "string", "description": "Table name to inspect"}
                }
            }
        )
        
        # Performance monitoring tool
        self.register_tool(
            "monitor_performance",
            self._monitor_performance,
            {
                "type": "object",
                "properties": {
                    "duration": {"type": "integer", "description": "Monitoring duration in seconds"}
                }
            }
        )
    
    async def _execute_query(self, query: str, parameters: List[Any] = None) -> Dict[str, Any]:
        """Execute database query."""
        try:
            # Validate query safety
            if not self._is_safe_query(query):
                raise ValueError("Unsafe query detected")
            
            # Execute query
            result = await self._run_query(query, parameters)
            
            return {
                "success": True,
                "result": result,
                "row_count": len(result) if isinstance(result, list) else 1
            }
            
        except Exception as e:
            return {
                "success": False,
                "error": str(e)
            }
    
    async def _inspect_schema(self, table_name: str = None) -> Dict[str, Any]:
        """Inspect database schema."""
        try:
            if table_name:
                # Get specific table schema
                schema = await self._get_table_schema(table_name)
            else:
                # Get all tables
                schema = await self._get_all_tables()
            
            return {
                "success": True,
                "schema": schema
            }
            
        except Exception as e:
            return {
                "success": False,
                "error": str(e)
            }
    
    async def _monitor_performance(self, duration: int = 60) -> Dict[str, Any]:
        """Monitor database performance."""
        try:
            # Start performance monitoring
            start_time = time.time()
            performance_data = []
            
            while time.time() - start_time < duration:
                metrics = await self._collect_performance_metrics()
                performance_data.append({
                    "timestamp": time.time(),
                    "metrics": metrics
                })
                
                await asyncio.sleep(5)  # Collect every 5 seconds
            
            # Analyze performance data
            analysis = self._analyze_performance_data(performance_data)
            
            return {
                "success": True,
                "performance_data": performance_data,
                "analysis": analysis
            }
            
        except Exception as e:
            return {
                "success": False,
                "error": str(e)
            }

class MLModelMCPServer(CustomMCPServer):
    """MCP server for machine learning model operations."""
    
    def __init__(self, server_config: Dict[str, Any]):
        super().__init__(server_config)
        self.models = {}
        self._register_ml_tools()
    
    def _register_ml_tools(self):
        """Register ML-specific tools."""
        
        # Model training tool
        self.register_tool(
            "train_model",
            self._train_model,
            {
                "type": "object",
                "properties": {
                    "model_type": {"type": "string", "description": "Type of model to train"},
                    "training_data": {"type": "string", "description": "Path to training data"},
                    "hyperparameters": {"type": "object", "description": "Model hyperparameters"}
                },
                "required": ["model_type", "training_data"]
            }
        )
        
        # Model prediction tool
        self.register_tool(
            "predict",
            self._predict,
            {
                "type": "object",
                "properties": {
                    "model_id": {"type": "string", "description": "Model identifier"},
                    "input_data": {"type": "array", "description": "Input data for prediction"}
                },
                "required": ["model_id", "input_data"]
            }
        )
        
        # Model evaluation tool
        self.register_tool(
            "evaluate_model",
            self._evaluate_model,
            {
                "type": "object",
                "properties": {
                    "model_id": {"type": "string", "description": "Model identifier"},
                    "test_data": {"type": "string", "description": "Path to test data"}
                },
                "required": ["model_id", "test_data"]
            }
        )
    
    async def _train_model(self, model_type: str, training_data: str, 
                          hyperparameters: Dict[str, Any] = None) -> Dict[str, Any]:
        """Train machine learning model."""
        try:
            # Load training data
            data = await self._load_training_data(training_data)
            
            # Initialize model
            model = self._create_model(model_type, hyperparameters)
            
            # Train model
            training_results = await self._train_model_async(model, data)
            
            # Store trained model
            model_id = str(uuid.uuid4())
            self.models[model_id] = model
            
            return {
                "success": True,
                "model_id": model_id,
                "training_results": training_results
            }
            
        except Exception as e:
            return {
                "success": False,
                "error": str(e)
            }
    
    async def _predict(self, model_id: str, input_data: List[Any]) -> Dict[str, Any]:
        """Make predictions using trained model."""
        try:
            if model_id not in self.models:
                raise ValueError(f"Model {model_id} not found")
            
            model = self.models[model_id]
            
            # Make predictions
            predictions = await self._make_predictions(model, input_data)
            
            return {
                "success": True,
                "predictions": predictions
            }
            
        except Exception as e:
            return {
                "success": False,
                "error": str(e)
            }
```

This advanced topics guide provides comprehensive coverage of SAFLA's extensibility and advanced usage patterns, enabling sophisticated customizations and integrations for complex use cases.