10 Best Practices for Reliable AI Agent Systems

class ErrorHandler:
    def __init__(self):
        self.error_types = {
            'MODEL_ERROR': {
                'severity': 'HIGH',
                'retry': True,
                'fallback': True,
                'user_message': 'AI service temporarily unavailable'
            },
            'VALIDATION_ERROR': {
                'severity': 'MEDIUM',
                'retry': False,
                'fallback': False,
                'user_message': 'Invalid input provided'
            },
            'RATE_LIMIT_ERROR': {
                'severity': 'MEDIUM',
                'retry': True,
                'fallback': True,
                'user_message': 'Service busy, please try again'
            },
            'TIMEOUT_ERROR': {
                'severity': 'HIGH',
                'retry': True,
                'fallback': True,
                'user_message': 'Request timeout, please try again'
            },
            'AUTHENTICATION_ERROR': {
                'severity': 'HIGH',
                'retry': False,
                'fallback': False,
                'user_message': 'Authentication required'
            }
        }
    
    async def handle_error(self, error, context):
        """Handle errors with appropriate strategies"""
        error_type = self.classify_error(error)
        error_config = self.error_types.get(error_type, {})
        
        # Log error with full context
        await self.log_error(error, context, error_type)
        
        # Determine handling strategy
        if error_config.get('retry', False):
            return await self.retry_with_backoff(error, context)
        elif error_config.get('fallback', False):
            return await self.execute_fallback(context)
        else:
            return await self.return_error_response(error_config['user_message'])
    
    def classify_error(self, error):
        """Classify error type based on error characteristics"""
        error_str = str(error).lower()
        
        if 'model' in error_str or 'llm' in error_str:
            return 'MODEL_ERROR'
        elif 'validation' in error_str or 'invalid' in error_str:
            return 'VALIDATION_ERROR'
        elif 'rate limit' in error_str or 'quota' in error_str:
            return 'RATE_LIMIT_ERROR'
        elif 'timeout' in error_str or 'timed out' in error_str:
            return 'TIMEOUT_ERROR'
        elif 'auth' in error_str or 'unauthorized' in error_str:
            return 'AUTHENTICATION_ERROR'
        else:
            return 'UNKNOWN_ERROR'

class RetryManager:
    def __init__(self, max_retries=3, base_delay=1, max_delay=60):
        self.max_retries = max_retries
        self.base_delay = base_delay
        self.max_delay = max_delay
        self.retry_counts = {}
    
    async def retry_with_backoff(self, func, *args, **kwargs):
        """Execute function with exponential backoff retry"""
        retry_key = f"{func.__name__}_{hash(str(args))}"
        retry_count = self.retry_counts.get(retry_key, 0)
        
        try:
            result = await func(*args, **kwargs)
            # Reset retry count on success
            self.retry_counts[retry_key] = 0
            return result
        except Exception as e:
            if retry_count >= self.max_retries:
                # Max retries exceeded
                self.retry_counts[retry_key] = 0
                raise e
            
            # Calculate delay with exponential backoff
            delay = min(
                self.base_delay * (2 ** retry_count),
                self.max_delay
            )
            
            # Add jitter to prevent thundering herd
            jitter = random.uniform(0.1, 0.3) * delay
            total_delay = delay + jitter
            
            # Increment retry count
            self.retry_counts[retry_key] = retry_count + 1
            
            # Wait before retry
            await asyncio.sleep(total_delay)
            
            # Recursive retry
            return await self.retry_with_backoff(func, *args, **kwargs)

class CircuitBreaker:
    def __init__(self, failure_threshold=5, timeout=60, retry_timeout=30):
        self.failure_threshold = failure_threshold
        self.timeout = timeout
        self.retry_timeout = retry_timeout
        self.failure_count = 0
        self.last_failure_time = None
        self.state = 'CLOSED'  # CLOSED, OPEN, HALF_OPEN
        self.breaker_name = None
    
    async def call(self, func, *args, **kwargs):
        """Execute function with circuit breaker protection"""
        if self.state == 'OPEN':
            if self.should_attempt_reset():
                self.state = 'HALF_OPEN'
            else:
                raise CircuitBreakerOpen(f"Circuit breaker {self.breaker_name} is open")
        
        try:
            result = await func(*args, **kwargs)
            self.on_success()
            return result
        except Exception as e:
            self.on_failure()
            raise e
    
    def on_success(self):
        """Handle successful call"""
        self.failure_count = 0
        self.state = 'CLOSED'
    
    def on_failure(self):
        """Handle failed call"""
        self.failure_count += 1
        self.last_failure_time = datetime.now()
        
        if self.failure_count >= self.failure_threshold:
            self.state = 'OPEN'
            self.last_failure_time = datetime.now()
    
    def should_attempt_reset(self):
        """Check if circuit breaker should attempt reset"""
        if self.last_failure_time is None:
            return True
        
        return (datetime.now() - self.last_failure_time).seconds >= self.retry_timeout

class MonitoringSystem:
    def __init__(self, metrics_backend, logging_backend, alerting_backend):
        self.metrics = MetricsCollector(metrics_backend)
        self.logger = StructuredLogger(logging_backend)
        self.alerter = AlertingSystem(alerting_backend)
        self.health_checks = {}
    
    async def monitor_agent_performance(self, agent_id, request_data, response_data, processing_time):
        """Monitor agent performance metrics"""
        # Record response time
        await self.metrics.record_histogram(
            'agent_response_time',
            processing_time,
            tags={'agent_id': agent_id}
        )
        
        # Record success/failure
        success = response_data.get('success', False)
        await self.metrics.increment_counter(
            'agent_requests_total',
            tags={'agent_id': agent_id, 'status': 'success' if success else 'failure'}
        )
        
        # Record token usage
        if 'token_usage' in response_data:
            await self.metrics.record_histogram(
                'agent_token_usage',
                response_data['token_usage'],
                tags={'agent_id': agent_id}
            )
        
        # Check for performance anomalies
        await self.check_performance_anomalies(agent_id, processing_time, success)
    
    async def check_performance_anomalies(self, agent_id, processing_time, success):
        """Check for performance anomalies and alert"""
        # Get historical performance data
        historical_data = await self.metrics.get_historical_data(
            'agent_response_time',
            agent_id,
            hours=24
        )
        
        if historical_data:
            # Calculate performance baseline
            baseline_mean = sum(historical_data) / len(historical_data)
            baseline_std = (sum((x - baseline_mean) ** 2 for x in historical_data) / len(historical_data)) ** 0.5
            
            # Check for anomalies
            if processing_time > baseline_mean + 3 * baseline_std:
                await self.alerter.send_alert(
                    'PERFORMANCE_ANOMALY',
                    f"Agent {agent_id} response time {processing_time}s exceeds baseline {baseline_mean}s by 3+ standard deviations"
                )
            
            if not success and len(historical_data) > 10:
                # Check failure rate
                recent_failures = await self.metrics.get_recent_failures(agent_id, hours=1)
                if recent_failures > 5:  # More than 5 failures in last hour
                    await self.alerter.send_alert(
                        'HIGH_FAILURE_RATE',
                        f"Agent {agent_id} has {recent_failures} failures in the last hour"
                    )

class HealthCheckSystem:
    def __init__(self, monitoring_system):
        self.monitoring = monitoring_system
        self.health_checks = {
            'database': self.check_database_health,
            'vector_store': self.check_vector_store_health,
            'llm_api': self.check_llm_api_health,
            'memory': self.check_memory_health,
            'disk': self.check_disk_health
        }
    
    async def run_health_checks(self):
        """Run all health checks and return status"""
        health_status = {
            'overall_status': 'healthy',
            'checks': {},
            'timestamp': datetime.now()
        }
        
        for check_name, check_func in self.health_checks.items():
            try:
                check_result = await check_func()
                health_status['checks'][check_name] = check_result
                
                if check_result['status'] != 'healthy':
                    health_status['overall_status'] = 'degraded'
            except Exception as e:
                health_status['checks'][check_name] = {
                    'status': 'unhealthy',
                    'error': str(e)
                }
                health_status['overall_status'] = 'unhealthy'
        
        return health_status
    
    async def check_database_health(self):
        """Check database connectivity and performance"""
        try:
            start_time = time.time()
            # Test database connection
            result = await self.database.execute_query("SELECT 1")
            response_time = time.time() - start_time
            
            return {
                'status': 'healthy' if response_time < 1.0 else 'degraded',
                'response_time': response_time,
                'message': 'Database connection successful'
            }
        except Exception as e:
            return {
                'status': 'unhealthy',
                'error': str(e),
                'message': 'Database connection failed'
            }
    
    async def check_llm_api_health(self):
        """Check LLM API connectivity and performance"""
        try:
            start_time = time.time()
            # Test LLM API with simple request
            response = await self.llm_client.generate("Test", max_tokens=1)
            response_time = time.time() - start_time
            
            return {
                'status': 'healthy' if response_time < 5.0 else 'degraded',
                'response_time': response_time,
                'message': 'LLM API connection successful'
            }
        except Exception as e:
            return {
                'status': 'unhealthy',
                'error': str(e),
                'message': 'LLM API connection failed'
            }

class InputValidator:
    def __init__(self):
        self.validation_rules = {
            'text_input': {
                'max_length': 10000,
                'min_length': 1,
                'allowed_chars': r'^[a-zA-Z0-9\s\.,!?\-_()]+$',
                'required': True
            },
            'user_id': {
                'pattern': r'^[a-zA-Z0-9_-]{3,50}$',
                'required': True
            },
            'session_id': {
                'pattern': r'^[a-f0-9-]{36}$',  # UUID format
                'required': True
            },
            'file_upload': {
                'max_size': 10 * 1024 * 1024,  # 10MB
                'allowed_types': ['text/plain', 'application/pdf', 'image/jpeg'],
                'required': False
            }
        }
    
    async def validate_input(self, input_data, input_type):
        """Validate input data against rules"""
        if input_type not in self.validation_rules:
            raise ValidationError(f"Unknown input type: {input_type}")
        
        rules = self.validation_rules[input_type]
        errors = []
        
        # Check required fields
        if rules.get('required', False) and not input_data:
            errors.append(f"{input_type} is required")
            return {'valid': False, 'errors': errors}
        
        # Check length constraints
        if 'max_length' in rules and len(input_data) > rules['max_length']:
            errors.append(f"{input_type} exceeds maximum length of {rules['max_length']}")
        
        if 'min_length' in rules and len(input_data) < rules['min_length']:
            errors.append(f"{input_type} below minimum length of {rules['min_length']}")
        
        # Check pattern matching
        if 'pattern' in rules:
            import re
            if not re.match(rules['pattern'], input_data):
                errors.append(f"{input_type} does not match required pattern")
        
        # Check allowed characters
        if 'allowed_chars' in rules:
            import re
            if not re.match(rules['allowed_chars'], input_data):
                errors.append(f"{input_type} contains invalid characters")
        
        return {
            'valid': len(errors) == 0,
            'errors': errors
        }
    
    async def sanitize_input(self, input_data, input_type):
        """Sanitize input data to prevent security issues"""
        if not input_data:
            return input_data
        
        # Remove potentially dangerous characters
        sanitized = input_data
        
        # Remove script tags and HTML
        import re
        sanitized = re.sub(r'<script.*?</script>', '', sanitized, flags=re.DOTALL | re.IGNORECASE)
        sanitized = re.sub(r'<[^>]+>', '', sanitized)
        
        # Remove SQL injection patterns
        sql_patterns = [
            r'union\s+select',
            r'drop\s+table',
            r'delete\s+from',
            r'insert\s+into',
            r'update\s+set'
        ]
        for pattern in sql_patterns:
            sanitized = re.sub(pattern, '', sanitized, flags=re.IGNORECASE)
        
        # Truncate to maximum length
        if 'max_length' in self.validation_rules.get(input_type, {}):
            max_length = self.validation_rules[input_type]['max_length']
            sanitized = sanitized[:max_length]
        
        return sanitized

class OutputSanitizer:
    def __init__(self):
        self.sanitization_rules = {
            'remove_personal_info': True,
            'remove_sensitive_data': True,
            'validate_json': True,
            'escape_html': True,
            'limit_length': 50000
        }
    
    async def sanitize_output(self, output_data, output_type='text'):
        """Sanitize AI output to prevent security issues"""
        if not output_data:
            return output_data
        
        sanitized = output_data
        
        # Remove personal information
        if self.sanitization_rules['remove_personal_info']:
            sanitized = await self.remove_personal_information(sanitized)
        
        # Remove sensitive data
        if self.sanitization_rules['remove_sensitive_data']:
            sanitized = await self.remove_sensitive_data(sanitized)
        
        # Validate JSON if applicable
        if self.sanitization_rules['validate_json'] and output_type == 'json':
            sanitized = await self.validate_and_sanitize_json(sanitized)
        
        # Escape HTML
        if self.sanitization_rules['escape_html']:
            sanitized = await self.escape_html(sanitized)
        
        # Limit length
        if self.sanitization_rules['limit_length']:
            max_length = self.sanitization_rules['limit_length']
            if len(sanitized) > max_length:
                sanitized = sanitized[:max_length] + "... [truncated]"
        
        return sanitized
    
    async def remove_personal_information(self, text):
        """Remove personal information from text"""
        import re
        
        # Remove email addresses
        text = re.sub(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', '[EMAIL]', text)
        
        # Remove phone numbers
        text = re.sub(r'\b\d{3}-\d{3}-\d{4}\b', '[PHONE]', text)
        
        # Remove social security numbers
        text = re.sub(r'\b\d{3}-\d{2}-\d{4}\b', '[SSN]', text)
        
        # Remove credit card numbers
        text = re.sub(r'\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b', '[CARD]', text)
        
        return text
    
    async def validate_and_sanitize_json(self, json_data):
        """Validate and sanitize JSON output"""
        try:
            import json
            parsed = json.loads(json_data)
            
            # Recursively sanitize JSON values
            sanitized = await self.sanitize_json_recursive(parsed)
            
            return json.dumps(sanitized)
        except json.JSONDecodeError:
            return '{"error": "Invalid JSON format"}'
    
    async def sanitize_json_recursive(self, obj):
        """Recursively sanitize JSON object"""
        if isinstance(obj, dict):
            return {k: await self.sanitize_json_recursive(v) for k, v in obj.items()}
        elif isinstance(obj, list):
            return [await self.sanitize_json_recursive(item) for item in obj]
        elif isinstance(obj, str):
            return await self.remove_personal_information(obj)
        else:
            return obj

class TestingFramework:
    def __init__(self):
        self.test_suites = {
            'unit_tests': UnitTestSuite(),
            'integration_tests': IntegrationTestSuite(),
            'end_to_end_tests': E2ETestSuite(),
            'performance_tests': PerformanceTestSuite(),
            'security_tests': SecurityTestSuite()
        }
    
    async def run_all_tests(self):
        """Run all test suites"""
        results = {}
        
        for suite_name, suite in self.test_suites.items():
            print(f"Running {suite_name}...")
            results[suite_name] = await suite.run_tests()
        
        return results
    
    async def run_continuous_testing(self):
        """Run continuous testing in production"""
        while True:
            # Run health checks
            health_status = await self.run_health_checks()
            
            # Run smoke tests
            smoke_results = await self.run_smoke_tests()
            
            # Run performance tests
            perf_results = await self.run_performance_tests()
            
            # Report results
            await self.report_test_results(health_status, smoke_results, perf_results)
            
            # Wait before next test cycle
            await asyncio.sleep(300)  # 5 minutes

class AIAgentUnitTests:
    def __init__(self, agent):
        self.agent = agent
        self.test_cases = []
    
    async def test_agent_response_generation(self):
        """Test agent response generation"""
        test_cases = [
            {
                'input': 'Hello, how are you?',
                'expected_keywords': ['hello', 'good', 'fine'],
                'max_response_time': 2.0
            },
            {
                'input': 'What is the weather like?',
                'expected_keywords': ['weather', 'temperature', 'forecast'],
                'max_response_time': 3.0
            },
            {
                'input': 'Help me with my task',
                'expected_keywords': ['help', 'task', 'assist'],
                'max_response_time': 2.5
            }
        ]
        
        results = []
        for test_case in test_cases:
            start_time = time.time()
            response = await self.agent.generate_response(test_case['input'])
            response_time = time.time() - start_time
            
            # Check response time
            time_valid = response_time <= test_case['max_response_time']
            
            # Check content
            content_valid = all(
                keyword.lower() in response.lower() 
                for keyword in test_case['expected_keywords']
            )
            
            results.append({
                'test_case': test_case['input'],
                'response_time': response_time,
                'time_valid': time_valid,
                'content_valid': content_valid,
                'overall_valid': time_valid and content_valid
            })
        
        return results
    
    async def test_error_handling(self):
        """Test agent error handling capabilities"""
        error_test_cases = [
            {
                'input': None,
                'expected_error': 'ValidationError'
            },
            {
                'input': 'x' * 10000,  # Very long input
                'expected_error': 'ValidationError'
            },
            {
                'input': '<script>alert("xss")</script>',
                'expected_error': 'SanitizationError'
            }
        ]
        
        results = []
        for test_case in error_test_cases:
            try:
                response = await self.agent.generate_response(test_case['input'])
                results.append({
                    'test_case': test_case['input'],
                    'expected_error': test_case['expected_error'],
                    'actual_result': 'No error raised',
                    'test_passed': False
                })
            except Exception as e:
                error_type = type(e).__name__
                results.append({
                    'test_case': test_case['input'],
                    'expected_error': test_case['expected_error'],
                    'actual_error': error_type,
                    'test_passed': error_type == test_case['expected_error']
                })
        
        return results

class IntegrationTestSuite:
    def __init__(self, test_environment):
        self.test_env = test_environment
        self.test_data = self.load_test_data()
    
    async def test_database_integration(self):
        """Test database integration"""
        results = []
        
        # Test database connection
        try:
            connection = await self.test_env.database.connect()
            results.append({'test': 'database_connection', 'passed': True})
        except Exception as e:
            results.append({'test': 'database_connection', 'passed': False, 'error': str(e)})
        
        # Test data storage
        try:
            test_data = {'test': 'integration_test', 'timestamp': datetime.now()}
            stored_id = await self.test_env.database.store(test_data)
            retrieved_data = await self.test_env.database.retrieve(stored_id)
            results.append({'test': 'data_storage', 'passed': retrieved_data == test_data})
        except Exception as e:
            results.append({'test': 'data_storage', 'passed': False, 'error': str(e)})
        
        return results
    
    async def test_external_api_integration(self):
        """Test external API integration"""
        results = []
        
        # Test LLM API
        try:
            response = await self.test_env.llm_client.generate("Test message", max_tokens=10)
            results.append({'test': 'llm_api', 'passed': bool(response)})
        except Exception as e:
            results.append({'test': 'llm_api', 'passed': False, 'error': str(e)})
        
        # Test vector database
        try:
            test_vector = [0.1] * 768  # Example embedding
            result = await self.test_env.vector_db.similarity_search(test_vector, limit=1)
            results.append({'test': 'vector_db', 'passed': True})
        except Exception as e:
            results.append({'test': 'vector_db', 'passed': False, 'error': str(e)})
        
        return results

class SecurityFramework:
    def __init__(self):
        self.security_policies = {
            'authentication': {
                'required': True,
                'methods': ['jwt', 'oauth2'],
                'session_timeout': 3600
            },
            'authorization': {
                'required': True,
                'rbac_enabled': True,
                'permission_checks': True
            },
            'data_encryption': {
                'at_rest': True,
                'in_transit': True,
                'algorithm': 'AES-256'
            },
            'audit_logging': {
                'enabled': True,
                'retention_days': 365,
                'sensitive_data_masking': True
            }
        }
    
    async def authenticate_request(self, request):
        """Authenticate incoming request"""
        auth_header = request.headers.get('Authorization')
        if not auth_header:
            raise AuthenticationError("No authorization header")
        
        # Extract token
        if auth_header.startswith('Bearer '):
            token = auth_header[7:]
        else:
            raise AuthenticationError("Invalid authorization format")
        
        # Validate token
        try:
            payload = await self.validate_jwt_token(token)
            return payload
        except Exception as e:
            raise AuthenticationError(f"Token validation failed: {str(e)}")
    
    async def authorize_request(self, user, resource, action):
        """Authorize user action on resource"""
        # Check user permissions
        user_permissions = await self.get_user_permissions(user['user_id'])
        
        # Check resource access
        resource_permissions = await self.get_resource_permissions(resource)
        
        # Check action authorization
        required_permission = f"{resource}:{action}"
        if required_permission not in user_permissions:
            raise AuthorizationError(f"User lacks permission: {required_permission}")
        
        return True
    
    async def audit_log(self, user, action, resource, details=None):
        """Log security-relevant events"""
        audit_entry = {
            'timestamp': datetime.now(),
            'user_id': user.get('user_id'),
            'action': action,
            'resource': resource,
            'details': details,
            'ip_address': user.get('ip_address'),
            'user_agent': user.get('user_agent')
        }
        
        # Mask sensitive data
        if self.security_policies['audit_logging']['sensitive_data_masking']:
            audit_entry = await self.mask_sensitive_data(audit_entry)
        
        # Store audit log
        await self.store_audit_log(audit_entry)

class PrivacyProtection:
    def __init__(self):
        self.privacy_rules = {
            'data_minimization': True,
            'purpose_limitation': True,
            'storage_limitation': True,
            'consent_management': True,
            'right_to_erasure': True
        }
    
    async def anonymize_user_data(self, user_data):
        """Anonymize user data for processing"""
        anonymized = user_data.copy()
        
        # Remove direct identifiers
        direct_identifiers = ['email', 'phone', 'ssn', 'address']
        for identifier in direct_identifiers:
            if identifier in anonymized:
                anonymized[identifier] = f"[ANONYMIZED_{identifier.upper()}]"
        
        # Hash indirect identifiers
        indirect_identifiers = ['user_id', 'session_id']
        for identifier in indirect_identifiers:
            if identifier in anonymized:
                anonymized[identifier] = hashlib.sha256(
                    anonymized[identifier].encode()
                ).hexdigest()[:16]
        
        return anonymized
    
    async def check_data_retention_policy(self, data_type, creation_date):
        """Check if data should be retained based on policy"""
        retention_policies = {
            'conversation_logs': 90,  # days
            'user_preferences': 365,  # days
            'analytics_data': 180,    # days
            'audit_logs': 365        # days
        }
        
        retention_days = retention_policies.get(data_type, 30)
        cutoff_date = datetime.now() - timedelta(days=retention_days)
        
        return creation_date > cutoff_date
    
    async def handle_data_deletion_request(self, user_id):
        """Handle user data deletion request (GDPR right to erasure)"""
        deletion_results = {}
        
        # Delete from all data stores
        data_stores = ['conversation_logs', 'user_preferences', 'analytics_data']
        
        for store in data_stores:
            try:
                deleted_count = await self.delete_user_data(store, user_id)
                deletion_results[store] = {
                    'success': True,
                    'deleted_records': deleted_count
                }
            except Exception as e:
                deletion_results[store] = {
                    'success': False,
                    'error': str(e)
                }
        
        return deletion_results

class CachingStrategy:
    def __init__(self, redis_client, local_cache):
        self.redis = redis_client
        self.local_cache = local_cache
        self.cache_policies = {
            'response_cache': {'ttl': 3600, 'max_size': 1000},
            'model_cache': {'ttl': 7200, 'max_size': 100},
            'context_cache': {'ttl': 1800, 'max_size': 500}
        }
    
    async def get_cached_response(self, request_hash):
        """Get cached response if available"""
        # Check local cache first
        local_result = await self.local_cache.get(request_hash)
        if local_result:
            return local_result
        
        # Check Redis cache
        redis_result = await self.redis.get(f"response:{request_hash}")
        if redis_result:
            # Store in local cache for faster access
            await self.local_cache.set(request_hash, redis_result)
            return redis_result
        
        return None
    
    async def cache_response(self, request_hash, response, cache_type='response_cache'):
        """Cache response with appropriate TTL"""
        policy = self.cache_policies.get(cache_type, {})
        ttl = policy.get('ttl', 3600)
        
        # Store in both caches
        await self.local_cache.set(request_hash, response, ttl=ttl)
        await self.redis.setex(f"response:{request_hash}", ttl, response)
    
    async def invalidate_cache(self, pattern):
        """Invalidate cache entries matching pattern"""
        # Clear local cache
        await self.local_cache.clear_pattern(pattern)
        
        # Clear Redis cache
        keys = await self.redis.keys(f"*{pattern}*")
        if keys:
                await self.redis.delete(*keys)

class ResourceManager:
    def __init__(self):
        self.resource_limits = {
            'memory_mb': 1024,
            'cpu_percent': 80,
            'disk_mb': 5120,
            'network_mbps': 100
        }
        self.current_usage = {}
        self.optimization_strategies = {}
    
    async def monitor_resource_usage(self):
        """Monitor current resource usage"""
        import psutil
        
        self.current_usage = {
            'memory_mb': psutil.virtual_memory().used / 1024 / 1024,
            'cpu_percent': psutil.cpu_percent(),
            'disk_mb': psutil.disk_usage('/').used / 1024 / 1024,
            'network_mbps': self.get_network_usage()
        }
        
        return self.current_usage
    
    async def optimize_resources(self):
        """Optimize resource usage based on current load"""
        optimizations = []
        
        # Memory optimization
        if self.current_usage['memory_mb'] > self.resource_limits['memory_mb']:
            optimizations.append(await self.optimize_memory_usage())
        
        # CPU optimization
        if self.current_usage['cpu_percent'] > self.resource_limits['cpu_percent']:
            optimizations.append(await self.optimize_cpu_usage())
        
        # Disk optimization
        if self.current_usage['disk_mb'] > self.resource_limits['disk_mb']:
            optimizations.append(await self.optimize_disk_usage())
        
        return optimizations
    
    async def optimize_memory_usage(self):
        """Optimize memory usage"""
        # Clear unused caches
        await self.clear_unused_caches()
        
        # Garbage collect
        import gc
        gc.collect()
        
        # Reduce model memory footprint
        await self.optimize_model_memory()
        
        return "Memory optimization completed"

class FallbackSystem:
    def __init__(self):
        self.fallback_strategies = {
            'llm_unavailable': self.fallback_to_rule_based,
            'database_unavailable': self.fallback_to_cache,
            'vector_db_unavailable': self.fallback_to_keyword_search,
            'external_api_unavailable': self.fallback_to_cached_responses
        }
        self.fallback_performance = {}
    
    async def execute_with_fallback(self, primary_function, context):
        """Execute primary function with fallback options"""
        try:
            # Attempt primary function
            result = await primary_function(context)
            self.record_success('primary', result)
            return result
        except Exception as e:
            # Determine fallback strategy
            fallback_strategy = self.determine_fallback_strategy(e, context)
            
            if fallback_strategy:
                try:
                    result = await fallback_strategy(context)
                    self.record_success('fallback', result)
                    return result
                except Exception as fallback_error:
                    # All strategies failed
                    return await self.handle_complete_failure(context, e, fallback_error)
            else:
                return await self.handle_complete_failure(context, e, None)
    
    async def fallback_to_rule_based(self, context):
        """Fallback to rule-based responses when LLM is unavailable"""
        # Use predefined responses based on keywords
        user_input = context.get('user_input', '').lower()
        
        if 'hello' in user_input or 'hi' in user_input:
            return {'response': 'Hello! I\'m currently experiencing technical difficulties, but I\'m here to help with basic questions.'}
        elif 'help' in user_input:
            return {'response': 'I can help you with common questions. Please try rephrasing your question.'}
        elif 'weather' in user_input:
            return {'response': 'I\'m unable to check the weather right now. Please try a weather app or website.'}
        else:
            return {'response': 'I\'m experiencing technical difficulties. Please try again later or contact support.'}
    
    async def fallback_to_cache(self, context):
        """Fallback to cached responses when database is unavailable"""
        # Search cache for similar queries
        query_hash = hashlib.md5(context.get('user_input', '').encode()).hexdigest()
        cached_response = await self.cache.get(f"fallback:{query_hash}")
        
        if cached_response:
            return cached_response
        
        # Return generic cached response
        return await self.cache.get("fallback:generic_response")

class SLOManager:
    def __init__(self):
        self.slos = {
            'availability': 0.999,  # 99.9% uptime
            'response_time_p95': 2.0,  # 95% of requests under 2 seconds
            'error_rate': 0.01,  # Less than 1% error rate
            'throughput': 1000  # 1000 requests per minute
        }
        self.slo_violations = []
        self.remediation_actions = {}
    
    async def check_slo_compliance(self, metrics):
        """Check compliance with SLOs"""
        violations = []
        
        # Check availability
        if metrics.get('availability', 1.0) < self.slos['availability']:
            violations.append({
                'slo': 'availability',
                'target': self.slos['availability'],
                'actual': metrics.get('availability'),
                'severity': 'critical'
            })
        
        # Check response time
        if metrics.get('response_time_p95', 0) > self.slos['response_time_p95']:
            violations.append({
                'slo': 'response_time_p95',
                'target': self.slos['response_time_p95'],
                'actual': metrics.get('response_time_p95'),
                'severity': 'high'
            })
        
        # Check error rate
        if metrics.get('error_rate', 0) > self.slos['error_rate']:
            violations.append({
                'slo': 'error_rate',
                'target': self.slos['error_rate'],
                'actual': metrics.get('error_rate'),
                'severity': 'high'
            })
        
        # Check throughput
        if metrics.get('throughput', 0) < self.slos['throughput']:
            violations.append({
                'slo': 'throughput',
                'target': self.slos['throughput'],
                'actual': metrics.get('throughput'),
                'severity': 'medium'
            })
        
        return violations
    
    async def trigger_remediation(self, violations):
        """Trigger remediation actions for SLO violations"""
        for violation in violations:
            if violation['severity'] in ['critical', 'high']:
                await self.execute_remediation(violation)
    
    async def execute_remediation(self, violation):
        """Execute remediation action for SLO violation"""
        slo = violation['slo']
        
        if slo == 'availability':
            await self.scale_up_instances()
        elif slo == 'response_time_p95':
            await self.optimize_performance()
        elif slo == 'error_rate':
            await self.investigate_errors()
        elif slo == 'throughput':
            await self.increase_capacity()

class MonitoringSystem:
    def __init__(self, alerting_system, metrics_backend):
        self.alerting = alerting_system
        self.metrics = metrics_backend
        self.alert_rules = self.load_alert_rules()
        self.monitoring_dashboard = {}
    
    async def monitor_agent_health(self, agent_id):
        """Monitor agent health in real-time"""
        health_metrics = await self.collect_health_metrics(agent_id)
        
        # Check alert conditions
        alerts = await self.check_alert_conditions(agent_id, health_metrics)
        
        # Update dashboard
        await self.update_dashboard(agent_id, health_metrics)
        
        # Send alerts if needed
        for alert in alerts:
            await self.alerting.send_alert(alert)
        
        return health_metrics
    
    async def collect_health_metrics(self, agent_id):
        """Collect comprehensive health metrics"""
        metrics = {
            'response_time': await self.get_avg_response_time(agent_id),
            'error_rate': await self.get_error_rate(agent_id),
            'throughput': await self.get_throughput(agent_id),
            'memory_usage': await self.get_memory_usage(agent_id),
            'cpu_usage': await self.get_cpu_usage(agent_id),
            'active_connections': await self.get_active_connections(agent_id)
        }
        
        return metrics
    
    async def check_alert_conditions(self, agent_id, metrics):
        """Check if alert conditions are met"""
        alerts = []
        
        # Response time alerts
        if metrics['response_time'] > 5.0:  # 5 seconds
            alerts.append({
                'type': 'PERFORMANCE',
                'severity': 'HIGH',
                'message': f"Agent {agent_id} response time {metrics['response_time']}s exceeds threshold",
                'agent_id': agent_id,
                'metric': 'response_time',
                'value': metrics['response_time']
            })
        
        # Error rate alerts
        if metrics['error_rate'] > 0.05:  # 5% error rate
            alerts.append({
                'type': 'RELIABILITY',
                'severity': 'CRITICAL',
                'message': f"Agent {agent_id} error rate {metrics['error_rate']:.2%} exceeds threshold",
                'agent_id': agent_id,
                'metric': 'error_rate',
                'value': metrics['error_rate']
            })
        
        # Memory usage alerts
        if metrics['memory_usage'] > 0.9:  # 90% memory usage
            alerts.append({
                'type': 'RESOURCE',
                'severity': 'HIGH',
                'message': f"Agent {agent_id} memory usage {metrics['memory_usage']:.1%} exceeds threshold",
                'agent_id': agent_id,
                'metric': 'memory_usage',
                'value': metrics['memory_usage']
            })
        
        return alerts

class IntelligentAlerting:
    def __init__(self):
        self.alert_history = {}
        self.alert_cooldowns = {}
        self.alert_escalation = {}
        self.false_positive_detection = {}
    
    async def process_alert(self, alert):
        """Process alert with intelligent filtering"""
        # Check for false positives
        if await self.is_false_positive(alert):
            return False
        
        # Check cooldown period
        if await self.is_in_cooldown(alert):
            return False
        
        # Check alert escalation
        escalation_level = await self.determine_escalation_level(alert)
        
        # Send alert
        await self.send_alert(alert, escalation_level)
        
        # Update alert history
        await self.update_alert_history(alert)
        
        return True
    
    async def is_false_positive(self, alert):
        """Detect false positive alerts"""
        alert_key = f"{alert['type']}_{alert['agent_id']}_{alert['metric']}"
        
        # Check recent alert history
        recent_alerts = self.alert_history.get(alert_key, [])
        if len(recent_alerts) > 5:  # Too many recent alerts
            return True
        
        # Check for transient issues
        if alert['metric'] == 'response_time' and alert['value'] < 10:
            # Short-lived response time spike
            return True
        
        return False
    
    async def is_in_cooldown(self, alert):
        """Check if alert is in cooldown period"""
        alert_key = f"{alert['type']}_{alert['agent_id']}"
        last_alert_time = self.alert_cooldowns.get(alert_key)
        
        if last_alert_time:
            cooldown_period = 300  # 5 minutes
            if datetime.now() - last_alert_time < timedelta(seconds=cooldown_period):
                return True
        
        return False
    
    async def determine_escalation_level(self, alert):
        """Determine alert escalation level"""
        severity = alert.get('severity', 'MEDIUM')
        agent_id = alert.get('agent_id')
        
        # Check escalation history
        escalation_count = self.alert_escalation.get(agent_id, 0)
        
        if severity == 'CRITICAL' or escalation_count > 3:
            return 'IMMEDIATE'
        elif severity == 'HIGH':
            return 'URGENT'
        else:
            return 'NORMAL'

class DocumentationSystem:
    def __init__(self):
        self.documentation_types = {
            'api_documentation': self.generate_api_docs,
            'architecture_docs': self.generate_architecture_docs,
            'deployment_docs': self.generate_deployment_docs,
            'troubleshooting_docs': self.generate_troubleshooting_docs,
            'user_guides': self.generate_user_guides
        }
    
    async def generate_comprehensive_docs(self, agent_system):
        """Generate comprehensive documentation for agent system"""
        docs = {}
        
        for doc_type, generator in self.documentation_types.items():
            docs[doc_type] = await generator(agent_system)
        
        return docs
    
    async def generate_api_docs(self, agent_system):
        """Generate API documentation"""
        api_docs = {
            'endpoints': [],
            'authentication': {},
            'rate_limits': {},
            'error_codes': {},
            'examples': []
        }
        
        # Document all endpoints
        for endpoint in agent_system.endpoints:
            api_docs['endpoints'].append({
                'path': endpoint.path,
                'method': endpoint.method,
                'description': endpoint.description,
                'parameters': endpoint.parameters,
                'responses': endpoint.responses,
                'examples': endpoint.examples
            })
        
        return api_docs
    
    async def generate_troubleshooting_docs(self, agent_system):
        """Generate troubleshooting documentation"""
        troubleshooting = {
            'common_issues': [],
            'error_messages': {},
            'diagnostic_tools': [],
            'escalation_procedures': {}
        }
        
        # Document common issues
        common_issues = [
            {
                'issue': 'High response times',
                'symptoms': ['Response time > 5 seconds', 'User complaints'],
                'causes': ['High load', 'Resource constraints', 'Network issues'],
                'solutions': ['Scale up instances', 'Optimize queries', 'Check network']
            },
            {
                'issue': 'Authentication failures',
                'symptoms': ['401 errors', 'Token validation failures'],
                'causes': ['Expired tokens', 'Invalid credentials', 'Service issues'],
                'solutions': ['Refresh tokens', 'Check credentials', 'Restart auth service']
            }
        ]
        
        troubleshooting['common_issues'] = common_issues
        
        return troubleshooting

class MaintenanceSystem:
    def __init__(self):
        self.maintenance_tasks = {
            'daily': self.daily_maintenance,
            'weekly': self.weekly_maintenance,
            'monthly': self.monthly_maintenance,
            'quarterly': self.quarterly_maintenance
        }
        self.maintenance_log = []
    
    async def run_maintenance(self, frequency):
        """Run maintenance tasks for specified frequency"""
        if frequency not in self.maintenance_tasks:
            raise ValueError(f"Unknown maintenance frequency: {frequency}")
        
        maintenance_func = self.maintenance_tasks[frequency]
        results = await maintenance_func()
        
        # Log maintenance results
        await self.log_maintenance(frequency, results)
        
        return results
    
    async def daily_maintenance(self):
        """Daily maintenance tasks"""
        tasks = []
        
        # Clean up old logs
        cleanup_result = await self.cleanup_old_logs(days=7)
        tasks.append(('cleanup_logs', cleanup_result))
        
        # Update health checks
        health_result = await self.update_health_checks()
        tasks.append(('health_checks', health_result))
        
        # Optimize databases
        db_result = await self.optimize_databases()
        tasks.append(('database_optimization', db_result))
        
        return tasks
    
    async def weekly_maintenance(self):
        """Weekly maintenance tasks"""
        tasks = []
        
        # Update dependencies
        deps_result = await self.update_dependencies()
        tasks.append(('dependency_update', deps_result))
        
        # Security scans
        security_result = await self.run_security_scans()
        tasks.append(('security_scan', security_result))
        
        # Performance analysis
        perf_result = await self.analyze_performance()
        tasks.append(('performance_analysis', perf_result))
        
        return tasks
    
    async def monthly_maintenance(self):
        """Monthly maintenance tasks"""
        tasks = []
        
        # Full system backup
        backup_result = await self.create_full_backup()
        tasks.append(('full_backup', backup_result))
        
        # Capacity planning
        capacity_result = await self.analyze_capacity()
        tasks.append(('capacity_analysis', capacity_result))
        
        # Documentation update
        docs_result = await self.update_documentation()
        tasks.append(('documentation_update', docs_result))
        
        return tasks

class TestingFramework:
    def __init__(self):
        self.test_levels = {
            'unit': UnitTestSuite(),
            'integration': IntegrationTestSuite(),
            'system': SystemTestSuite(),
            'acceptance': AcceptanceTestSuite(),
            'performance': PerformanceTestSuite(),
            'security': SecurityTestSuite()
        }
        self.test_results = {}
        self.continuous_testing = False
    
    async def run_comprehensive_tests(self):
        """Run all test levels"""
        results = {}
        
        for level, test_suite in self.test_levels.items():
            print(f"Running {level} tests...")
            level_results = await test_suite.run_all_tests()
            results[level] = level_results
            
            # Stop if critical tests fail
            if level in ['unit', 'integration'] and not level_results['passed']:
                print(f"Critical {level} tests failed. Stopping test execution.")
                break
        
        self.test_results = results
        return results
    
    async def run_continuous_testing(self):
        """Run continuous testing in production"""
        self.continuous_testing = True
        
        while self.continuous_testing:
            # Run smoke tests
            smoke_results = await self.run_smoke_tests()
            
            # Run health checks
            health_results = await self.run_health_checks()
            
            # Run performance tests
            perf_results = await self.run_performance_tests()
            
            # Report results
            await self.report_test_results(smoke_results, health_results, perf_results)
            
            # Wait before next test cycle
            await asyncio.sleep(60)  # 1 minute
    
    async def run_smoke_tests(self):
        """Run critical smoke tests"""
        smoke_tests = [
            self.test_basic_functionality,
            self.test_authentication,
            self.test_database_connection,
            self.test_external_apis
        ]
        
        results = []
        for test in smoke_tests:
            try:
                result = await test()
                results.append({'test': test.__name__, 'passed': result})
            except Exception as e:
                results.append({'test': test.__name__, 'passed': False, 'error': str(e)})
        
        return results

class PerformanceTestSuite:
    def __init__(self):
        self.load_test_scenarios = {
            'normal_load': {'users': 100, 'duration': 300},
            'peak_load': {'users': 500, 'duration': 600},
            'stress_test': {'users': 1000, 'duration': 300},
            'spike_test': {'users': 2000, 'duration': 60}
        }
        self.performance_metrics = {}
    
    async def run_load_tests(self):
        """Run comprehensive load tests"""
        results = {}
        
        for scenario_name, scenario_config in self.load_test_scenarios.items():
            print(f"Running {scenario_name} test...")
            scenario_results = await self.run_load_scenario(scenario_name, scenario_config)
            results[scenario_name] = scenario_results
        
        return results
    
    async def run_load_scenario(self, scenario_name, config):
        """Run specific load test scenario"""
        users = config['users']
        duration = config['duration']
        
        # Simulate concurrent users
        tasks = []
        for i in range(users):
            task = asyncio.create_task(self.simulate_user_behavior(duration))
            tasks.append(task)
        
        # Wait for all tasks to complete
        results = await asyncio.gather(*tasks, return_exceptions=True)
        
        # Analyze results
        analysis = await self.analyze_load_test_results(results)
        
        return {
            'scenario': scenario_name,
            'users': users,
            'duration': duration,
            'results': analysis
        }
    
    async def simulate_user_behavior(self, duration):
        """Simulate realistic user behavior"""
        start_time = time.time()
        requests_made = 0
        response_times = []
        errors = 0
        
        while time.time() - start_time < duration:
            try:
                # Simulate user request
                request_start = time.time()
                response = await self.make_test_request()
                request_time = time.time() - request_start
                
                response_times.append(request_time)
                requests_made += 1
                
                # Simulate user think time
                await asyncio.sleep(random.uniform(1, 5))
                
            except Exception as e:
                errors += 1
        
        return {
            'requests_made': requests_made,
            'response_times': response_times,
            'errors': errors,
            'avg_response_time': sum(response_times) / len(response_times) if response_times else 0
        }