agentic-qe
Version:
Agentic Quality Engineering Fleet System - AI-driven quality management platform
856 lines (852 loc) • 35.4 kB
JavaScript
"use strict";
/**
* ProductionIntelligenceAgent - Converts production data into test scenarios
*
* Implements incident replay testing, RUM analysis, anomaly detection,
* load pattern analysis, and feature usage analytics to close the
* production-to-testing feedback loop.
*
* Based on SPARC methodology and AQE Fleet specification
*/
Object.defineProperty(exports, "__esModule", { value: true });
exports.ProductionIntelligenceAgent = void 0;
const BaseAgent_1 = require("./BaseAgent");
const types_1 = require("../types");
// ============================================================================
// Production Intelligence Agent Implementation
// ============================================================================
class ProductionIntelligenceAgent extends BaseAgent_1.BaseAgent {
constructor(config) {
super({
...config,
type: types_1.QEAgentType.PRODUCTION_INTELLIGENCE,
capabilities: [
{
name: 'incident-replay',
version: '1.0.0',
description: 'Convert production incidents into reproducible test scenarios',
parameters: {
minOccurrences: config.thresholds?.minIncidentOccurrences || 5,
severityFilter: ['HIGH', 'CRITICAL']
}
},
{
name: 'rum-analysis',
version: '1.0.0',
description: 'Analyze Real User Monitoring data to generate realistic test scenarios',
parameters: {
deviceTypes: ['mobile', 'desktop', 'tablet'],
browserSupport: ['chrome', 'safari', 'firefox', 'edge']
}
},
{
name: 'anomaly-detection',
version: '1.0.0',
description: 'Detect abnormal patterns indicating potential bugs',
parameters: {
stdDevThreshold: config.thresholds?.anomalyStdDev || 3,
detectionWindow: 300000 // 5 minutes
}
},
{
name: 'load-pattern-analysis',
version: '1.0.0',
description: 'Extract realistic load patterns from production traffic',
parameters: {
analysisWindow: '30d',
includeSeasonality: true
}
},
{
name: 'feature-usage-analytics',
version: '1.0.0',
description: 'Track feature usage to prioritize testing efforts',
parameters: {
usageThreshold: 0.05, // 5% minimum usage
deadCodeWindow: 90 // days
}
}
]
});
this.baselineMetrics = new Map();
this.monitoringClients = new Map();
this.config = {
...config,
thresholds: {
anomalyStdDev: 3,
errorRateSpike: 0.5,
latencyDegradation: 0.3,
minIncidentOccurrences: 5,
...config.thresholds
},
features: {
incidentReplay: true,
rumAnalysis: true,
anomalyDetection: true,
loadPatternAnalysis: true,
featureUsageAnalytics: true,
...config.features
}
};
}
// ============================================================================
// BaseAgent Implementation
// ============================================================================
async initializeComponents() {
console.log(`ProductionIntelligenceAgent ${this.agentId.id} initializing...`);
// Initialize monitoring platform clients
await this.initializeMonitoringClients();
// Load historical baseline metrics
await this.loadBaselineMetrics();
// Register event handlers for production events
this.registerProductionEventHandlers();
console.log(`ProductionIntelligenceAgent ${this.agentId.id} initialized successfully`);
}
async performTask(task) {
const { type, payload } = task;
console.log(`ProductionIntelligenceAgent executing ${type} task: ${task.id}`);
switch (type) {
case 'incident-replay':
return await this.performIncidentReplay(payload);
case 'rum-analysis':
return await this.performRUMAnalysis(payload);
case 'anomaly-detection':
return await this.performAnomalyDetection(payload);
case 'load-pattern-analysis':
return await this.performLoadPatternAnalysis(payload);
case 'feature-usage-analytics':
return await this.performFeatureUsageAnalytics(payload);
case 'generate-tests-from-incidents':
return await this.generateTestsFromIncidents(payload);
case 'generate-tests-from-rum':
return await this.generateTestsFromRUM(payload);
case 'analyze-production-vs-staging':
return await this.analyzeProductionVsStaging(payload);
default:
throw new Error(`Unsupported task type: ${type}`);
}
}
async loadKnowledge() {
// Load production intelligence patterns and insights
const storedPatterns = await this.retrieveMemory('production-patterns');
if (storedPatterns) {
console.log('Loaded production intelligence patterns from memory');
}
// Load incident history
const incidentHistory = await this.memoryStore.retrieve('aqe/production/incidents');
if (incidentHistory) {
console.log('Loaded incident history from memory');
}
// Load RUM data
const rumData = await this.memoryStore.retrieve('aqe/production/rum-data');
if (rumData) {
console.log('Loaded RUM data from memory');
}
}
async cleanup() {
// Save current state
await this.storeMemory('production-state', {
baselineMetrics: Array.from(this.baselineMetrics.entries()),
timestamp: new Date()
});
// Close monitoring client connections
for (const [platform, client] of this.monitoringClients.entries()) {
console.log(`Closing connection to ${platform}`);
// In real implementation, properly close connections
}
this.monitoringClients.clear();
console.log(`ProductionIntelligenceAgent ${this.agentId.id} cleaned up`);
}
// ============================================================================
// Incident Replay Testing
// ============================================================================
async performIncidentReplay(data) {
const { incident } = data;
console.log(`Analyzing incident ${incident.id} for replay testing`);
// Generate test scenario from incident
const testScenario = await this.generateIncidentTestScenario(incident);
// Store incident analysis
await this.memoryStore.store(`aqe/incidents/${incident.id}`, {
incident,
testScenario,
analyzedAt: new Date(),
severity: incident.severity
});
// Emit event for test generation
this.emitEvent('production.incident.analyzed', {
incidentId: incident.id,
testScenarioId: testScenario.id,
severity: incident.severity
}, incident.severity === 'CRITICAL' ? 'critical' : 'high');
return {
testScenario,
reproducible: true,
testSuite: {
id: `incident-replay-${incident.id}`,
name: `Incident Replay: ${incident.id} - ${incident.error}`,
tests: [testScenario],
metadata: {
generatedAt: new Date(),
coverageTarget: 0,
framework: 'jest',
estimatedDuration: incident.context.requestTrace?.duration || 5000,
generationStrategy: 'incident-replay'
}
}
};
}
async generateIncidentTestScenario(incident) {
return {
id: `test-incident-${incident.id}`,
name: `should handle ${incident.error} gracefully`,
type: types_1.TestType.INTEGRATION,
parameters: [
{
name: 'incident',
value: incident,
type: 'ProductionIncident'
},
{
name: 'systemState',
value: incident.context.systemState,
type: 'SystemState'
},
{
name: 'expectedBehavior',
value: 'graceful-degradation',
type: 'string'
}
],
assertions: [
`expect(response.status).not.toBe(500)`,
`expect(response.error).toBeDefined()`,
`expect(response.retryable).toBe(true)`,
`expect(orderData).not.toBeNull()`,
`expect(circuitBreaker.state).toBe('OPEN') // after threshold`
],
expectedResult: {
status: 'failed-gracefully',
userNotified: true,
dataIntegrity: true
},
estimatedDuration: incident.context.requestTrace?.duration || 5000
};
}
// ============================================================================
// RUM Analysis
// ============================================================================
async performRUMAnalysis(data) {
const { rumData, timeWindow = 'last_7_days' } = data;
console.log(`Analyzing RUM data for ${timeWindow}`);
// Fetch RUM data from monitoring platform or use provided data
const analysisData = rumData || await this.fetchRUMData(timeWindow);
// Analyze user journeys
const userJourneys = this.analyzeUserJourneys(analysisData);
// Identify error patterns
const errorPatterns = this.identifyErrorPatterns(analysisData);
// Generate performance insights
const performanceInsights = this.generatePerformanceInsights(analysisData);
// Generate tests from RUM data
const generatedTests = await this.generateTestsFromRUMData(analysisData);
// Store RUM analysis
await this.memoryStore.store('aqe/rum-data/latest', {
analysisData,
userJourneys,
errorPatterns,
performanceInsights,
analyzedAt: new Date()
});
// Emit event
this.emitEvent('production.rum.analyzed', {
journeys: userJourneys.length,
errors: errorPatterns.length,
tests: generatedTests.length
}, 'medium');
return {
userJourneys,
errorPatterns,
performanceInsights,
generatedTests
};
}
analyzeUserJourneys(rumData) {
// Sort journeys by frequency
return rumData.userJourneys
.sort((a, b) => b.frequency - a.frequency)
.slice(0, 10); // Top 10 most common journeys
}
identifyErrorPatterns(rumData) {
// Filter high-impact errors
return rumData.errorPatterns
.filter(pattern => pattern.userImpact === 'HIGH' || pattern.frequency > 100)
.sort((a, b) => b.frequency - a.frequency);
}
generatePerformanceInsights(rumData) {
const { performanceMetrics } = rumData;
return {
fcp: {
status: performanceMetrics.FCP.p95 < 2500 ? 'GOOD' : 'NEEDS_IMPROVEMENT',
p95: performanceMetrics.FCP.p95,
threshold: 2500
},
lcp: {
status: performanceMetrics.LCP.p95 < 4000 ? 'GOOD' : 'NEEDS_IMPROVEMENT',
p95: performanceMetrics.LCP.p95,
threshold: 4000
},
fid: {
status: performanceMetrics.FID.p95 < 100 ? 'GOOD' : 'NEEDS_IMPROVEMENT',
p95: performanceMetrics.FID.p95,
threshold: 100
},
cls: {
status: performanceMetrics.CLS.p95 < 0.1 ? 'GOOD' : 'NEEDS_IMPROVEMENT',
p95: performanceMetrics.CLS.p95,
threshold: 0.1
}
};
}
async generateTestsFromRUMData(rumData) {
const tests = [];
// Generate tests for common user journeys
for (const journey of rumData.userJourneys.slice(0, 5)) {
tests.push({
id: `rum-journey-${journey.pattern.replace(/\s+/g, '-').toLowerCase()}`,
name: `should complete user journey: ${journey.pattern}`,
type: types_1.TestType.E2E,
parameters: [
{
name: 'journey',
value: journey,
type: 'UserJourney'
}
],
assertions: [
`expect(journey.completed).toBe(true)`,
`expect(journey.conversionRate).toBeGreaterThanOrEqual(${journey.conversionRate})`
],
expectedResult: {
completed: true,
duration: journey.avgDuration
},
estimatedDuration: journey.avgDuration
});
}
// Generate tests for error patterns
for (const error of rumData.errorPatterns.slice(0, 3)) {
tests.push({
id: `rum-error-${error.error.replace(/\W+/g, '-').toLowerCase()}`,
name: `should handle error: ${error.error}`,
type: types_1.TestType.INTEGRATION,
parameters: [
{
name: 'errorPattern',
value: error,
type: 'ErrorPattern'
}
],
assertions: [
`expect(() => triggerError()).not.toThrow()`,
`expect(errorHandled).toBe(true)`
],
expectedResult: {
errorHandled: true
},
estimatedDuration: 2000
});
}
return tests;
}
// ============================================================================
// Anomaly Detection
// ============================================================================
async performAnomalyDetection(data) {
const { currentMetrics, lookbackPeriod = '24h' } = data;
console.log(`Detecting anomalies in production metrics (lookback: ${lookbackPeriod})`);
const anomalies = [];
const recommendations = [];
// Error rate spike detection
const errorRateAnomaly = this.detectErrorRateSpike(currentMetrics);
if (errorRateAnomaly) {
anomalies.push(errorRateAnomaly);
recommendations.push('Generate regression tests for recent changes');
}
// Latency degradation detection
const latencyAnomaly = this.detectLatencyDegradation(currentMetrics);
if (latencyAnomaly) {
anomalies.push(latencyAnomaly);
recommendations.push('Generate performance tests targeting affected endpoints');
}
// User behavior anomaly detection
const behaviorAnomaly = this.detectBehaviorAnomaly(currentMetrics);
if (behaviorAnomaly) {
anomalies.push(behaviorAnomaly);
recommendations.push('Generate UI tests for affected user flows');
}
// Store anomalies
await this.memoryStore.store('aqe/anomalies/latest', {
anomalies,
recommendations,
detectedAt: new Date(),
metrics: currentMetrics
});
// Emit high-priority event if critical anomalies found
const criticalAnomalies = anomalies.filter(a => a.severity === 'CRITICAL');
if (criticalAnomalies.length > 0) {
this.emitEvent('production.anomaly.critical', {
count: criticalAnomalies.length,
anomalies: criticalAnomalies
}, 'critical');
}
return { anomalies, recommendations };
}
detectErrorRateSpike(currentMetrics) {
const baseline = this.baselineMetrics.get('errorRate') || { mean: 0.005, stdDev: 0.002 };
const current = currentMetrics.errorRate || 0;
const zScore = (current - baseline.mean) / baseline.stdDev;
if (Math.abs(zScore) > this.config.thresholds.anomalyStdDev) {
return {
type: 'ERROR_RATE_SPIKE',
severity: zScore > 5 ? 'CRITICAL' : zScore > 3 ? 'HIGH' : 'MEDIUM',
details: {
current,
baseline: baseline.mean,
deviation: zScore,
magnitude: (current - baseline.mean) / baseline.mean,
confidence: this.calculateConfidence(zScore)
},
affectedUsers: Math.floor(currentMetrics.activeUsers * current),
recommendation: 'Generate regression tests for recent changes'
};
}
return null;
}
detectLatencyDegradation(currentMetrics) {
const baseline = this.baselineMetrics.get('latency') || { p95: 250, mean: 180 };
const current = currentMetrics.latency?.p95 || 0;
const degradation = (current - baseline.p95) / baseline.p95;
if (degradation > this.config.thresholds.latencyDegradation) {
return {
type: 'LATENCY_DEGRADATION',
severity: degradation > 1.0 ? 'CRITICAL' : degradation > 0.5 ? 'HIGH' : 'MEDIUM',
details: {
current,
baseline: baseline.p95,
deviation: degradation,
magnitude: degradation,
confidence: 0.9
},
affectedEndpoints: currentMetrics.endpoints || [],
recommendation: 'Generate performance tests targeting affected endpoints'
};
}
return null;
}
detectBehaviorAnomaly(currentMetrics) {
const baseline = this.baselineMetrics.get('userBehavior') || { conversionRate: 0.75 };
const current = currentMetrics.conversionRate || 0;
const drop = (baseline.conversionRate - current) / baseline.conversionRate;
if (drop > 0.2) { // 20% drop in conversion
return {
type: 'USER_BEHAVIOR_ANOMALY',
severity: drop > 0.5 ? 'CRITICAL' : 'HIGH',
details: {
current,
baseline: baseline.conversionRate,
deviation: drop,
magnitude: drop,
confidence: 0.85
},
hypothesis: 'UI bug or broken functionality',
recommendation: 'Generate UI tests for affected user flows'
};
}
return null;
}
calculateConfidence(zScore) {
// Convert z-score to confidence level (0-1)
const absZ = Math.abs(zScore);
if (absZ > 3)
return 0.999;
if (absZ > 2)
return 0.95;
if (absZ > 1)
return 0.68;
return 0.5;
}
// ============================================================================
// Load Pattern Analysis
// ============================================================================
async performLoadPatternAnalysis(data) {
const { timeWindow = '30d' } = data;
console.log(`Analyzing load patterns for ${timeWindow}`);
// Fetch production traffic data
const loadPattern = await this.extractLoadPattern(timeWindow);
// Generate load test script
const loadTestScript = this.generateLoadTestScript(loadPattern);
// Generate recommendations
const recommendations = this.generateLoadTestRecommendations(loadPattern);
// Store load pattern
await this.memoryStore.store('aqe/load-patterns/latest', {
loadPattern,
analyzedAt: new Date(),
timeWindow
});
return {
loadPattern,
loadTestScript,
recommendations
};
}
async extractLoadPattern(timeWindow) {
// Simulate load pattern extraction
// In real implementation, fetch from monitoring platform
return {
dailyPattern: {
hourly: Array.from({ length: 24 }, (_, hour) => ({
hour,
rps: hour >= 9 && hour <= 17 ? 2000 + Math.random() * 1000 : 500 + Math.random() * 200
})),
peakHours: [9, 12, 14, 15, 18],
lowTrafficHours: [0, 1, 2, 3, 4]
},
weeklyPattern: {
monday: { rps: 2891, conversionRate: 0.78 },
tuesday: { rps: 3124, conversionRate: 0.81 },
wednesday: { rps: 3342, conversionRate: 0.83 },
thursday: { rps: 3198, conversionRate: 0.79 },
friday: { rps: 2734, conversionRate: 0.72 },
saturday: { rps: 1893, conversionRate: 0.65 },
sunday: { rps: 1678, conversionRate: 0.67 }
},
endpointDistribution: {
'GET /api/products': 0.34,
'GET /api/search': 0.23,
'POST /api/cart': 0.15,
'POST /api/orders': 0.12,
'GET /api/users': 0.08,
'other': 0.08
},
userBehaviorPatterns: {
browsers: {
avgSessionDuration: 342000,
avgPagesPerSession: 7.8
},
buyers: {
avgSessionDuration: 523000,
avgPagesPerSession: 12.3
}
}
};
}
generateLoadTestScript(pattern) {
// Generate k6 load test script
return `
import { check, group, sleep } from 'k6';
import http from 'k6/http';
export let options = {
stages: [
{ duration: '5m', target: 2000 }, // Morning ramp-up
{ duration: '2h', target: 3500 }, // Peak hours
{ duration: '5m', target: 1800 }, // Evening traffic
{ duration: '5m', target: 500 } // Night baseline
],
thresholds: {
http_req_duration: ['p(95)<500'],
http_req_failed: ['rate<0.01'],
http_reqs: ['rate>2000']
}
};
export default function() {
// Implementation based on endpoint distribution
const endpoints = ${JSON.stringify(pattern.endpointDistribution)};
// Select endpoint based on distribution
const rand = Math.random();
let endpoint = '/api/products';
group('User Session', () => {
let response = http.get(\`\${BASE_URL}\${endpoint}\`);
check(response, {
'status 200': (r) => r.status === 200,
'response time < 500ms': (r) => r.timings.duration < 500
});
sleep(Math.random() * 3 + 1);
});
}
`.trim();
}
generateLoadTestRecommendations(pattern) {
const recommendations = [];
// Analyze peak traffic
const maxRps = Math.max(...pattern.dailyPattern.hourly.map(h => h.rps));
recommendations.push(`Test system capacity at ${maxRps * 1.5} RPS (150% of peak load)`);
// Check conversion rates
const avgConversion = Object.values(pattern.weeklyPattern).reduce((sum, day) => sum + day.conversionRate, 0) / 7;
if (avgConversion < 0.75) {
recommendations.push('Investigate low conversion rate - potential UX issues');
}
recommendations.push('Implement gradual ramp-up to detect breaking points');
recommendations.push('Test during simulated peak hours to validate auto-scaling');
return recommendations;
}
// ============================================================================
// Feature Usage Analytics
// ============================================================================
async performFeatureUsageAnalytics(data) {
const { timeWindow = '30d' } = data;
console.log(`Analyzing feature usage for ${timeWindow}`);
// Fetch feature usage data
const usageData = await this.fetchFeatureUsageData(timeWindow);
// Analyze and prioritize features
const features = this.analyzeFeaturePriority(usageData);
// Identify unused features
const unusedFeatures = this.identifyUnusedFeatures(usageData);
// Store analytics
await this.memoryStore.store('aqe/feature-usage/latest', {
features,
unusedFeatures,
analyzedAt: new Date(),
timeWindow
});
return { features, unusedFeatures };
}
async fetchFeatureUsageData(timeWindow) {
// Simulate feature usage data
// In real implementation, fetch from analytics platform
return {
totalUsers: 84392,
features: [
{ name: 'search_autocomplete', activeUsers: 75103, satisfaction: 0.92 },
{ name: 'product_recommendations', activeUsers: 56503, clickthrough: 0.34 },
{ name: 'saved_for_later', activeUsers: 19411, conversion: 0.12 },
{ name: 'gift_wrapping', activeUsers: 2107, seasonal: true },
{ name: 'legacy_wishlist_v1', activeUsers: 42, lastUsed: '2024-08-12' }
]
};
}
analyzeFeaturePriority(usageData) {
return usageData.features.map((feature) => {
const usagePercent = feature.activeUsers / usageData.totalUsers;
let priority;
let recommendation;
if (usagePercent > 0.5) {
priority = 'CRITICAL';
recommendation = 'Maintain high test coverage (>90%), add edge cases';
}
else if (usagePercent > 0.25) {
priority = 'HIGH';
recommendation = 'Increase coverage to 85%+';
}
else if (usagePercent > 0.1) {
priority = 'MEDIUM';
recommendation = 'Coverage acceptable for usage level';
}
else if (usagePercent > 0.01) {
priority = 'LOW';
recommendation = 'Minimal testing required';
}
else {
priority = 'DEPRECATED';
recommendation = 'Consider removal, migrate remaining users';
}
return {
name: feature.name,
usage: usagePercent,
priority,
testCoverage: Math.floor(Math.random() * 30) + 60, // Simulated
recommendation
};
});
}
identifyUnusedFeatures(usageData) {
const unusedThreshold = usageData.totalUsers * 0.001; // 0.1% usage
return usageData.features
.filter((f) => f.activeUsers < unusedThreshold)
.map((f) => ({
name: f.name,
lastUsed: f.lastUsed || 'unknown',
recommendation: 'Dead code, safe to remove'
}));
}
// ============================================================================
// Test Generation from Production Data
// ============================================================================
async generateTestsFromIncidents(data) {
const { incidentIds, severityFilter = ['HIGH', 'CRITICAL'], limit = 10 } = data;
// Fetch incidents from memory
const incidents = await this.fetchIncidents(incidentIds, severityFilter, limit);
const testSuites = [];
let totalTests = 0;
for (const incident of incidents) {
const result = await this.performIncidentReplay({ incident });
if (result.testSuite) {
testSuites.push(result.testSuite);
totalTests += result.testSuite.tests.length;
}
}
return { testSuites, totalTests };
}
async generateTestsFromRUM(data) {
const { timeWindow = '7d', minJourneyFrequency = 100 } = data;
// Fetch RUM data
const rumData = await this.fetchRUMData(timeWindow);
// Filter journeys by frequency
const relevantJourneys = rumData.userJourneys.filter(j => j.frequency >= minJourneyFrequency);
const tests = await this.generateTestsFromRUMData(rumData);
const testSuite = {
id: `rum-generated-${Date.now()}`,
name: 'RUM-Based User Journey Tests',
tests,
metadata: {
generatedAt: new Date(),
coverageTarget: 0,
framework: 'playwright',
estimatedDuration: tests.reduce((sum, t) => sum + (t.estimatedDuration || 0), 0),
generationStrategy: 'rum-analysis'
}
};
return {
testSuites: [testSuite],
totalTests: tests.length
};
}
async analyzeProductionVsStaging(data) {
const { metrics } = data;
const differences = [];
const recommendations = [];
// Compare production vs staging metrics
for (const metric of metrics) {
const prodValue = await this.fetchProductionMetric(metric);
const stagingValue = await this.fetchStagingMetric(metric);
const delta = Math.abs(prodValue - stagingValue) / prodValue;
if (delta > 0.1) { // >10% difference
differences.push({
metric,
production: prodValue,
staging: stagingValue,
delta,
severity: delta > 0.5 ? 'HIGH' : 'MEDIUM'
});
recommendations.push(`Staging environment does not match production for ${metric}`);
}
}
return { differences, recommendations };
}
// ============================================================================
// Helper Methods
// ============================================================================
async initializeMonitoringClients() {
const { monitoringPlatforms } = this.config;
if (monitoringPlatforms?.datadog) {
console.log('Initializing Datadog client');
// In real implementation: initialize Datadog API client
this.monitoringClients.set('datadog', { initialized: true });
}
if (monitoringPlatforms?.newRelic) {
console.log('Initializing New Relic client');
// In real implementation: initialize New Relic API client
this.monitoringClients.set('newrelic', { initialized: true });
}
if (monitoringPlatforms?.grafana) {
console.log('Initializing Grafana client');
// In real implementation: initialize Grafana API client
this.monitoringClients.set('grafana', { initialized: true });
}
}
async loadBaselineMetrics() {
// Load historical baselines from memory
const storedBaselines = await this.retrieveMemory('baseline-metrics');
if (storedBaselines) {
this.baselineMetrics = new Map(Object.entries(storedBaselines));
}
else {
// Initialize with defaults
this.baselineMetrics.set('errorRate', { mean: 0.005, stdDev: 0.002 });
this.baselineMetrics.set('latency', { p95: 250, mean: 180 });
this.baselineMetrics.set('userBehavior', { conversionRate: 0.75 });
}
}
registerProductionEventHandlers() {
// Listen for production incidents
this.registerEventHandler({
eventType: 'production.incident',
handler: async (event) => {
await this.performIncidentReplay({ incident: event.data });
}
});
// Listen for anomaly alerts
this.registerEventHandler({
eventType: 'production.anomaly',
handler: async (event) => {
await this.performAnomalyDetection({ currentMetrics: event.data });
}
});
}
async fetchRUMData(timeWindow) {
// Simulate fetching RUM data
// In real implementation, fetch from monitoring platform
return {
timeWindow,
totalSessions: 847392,
totalPageViews: 3421847,
userJourneys: [
{
pattern: 'Homepage → Search → Product → Checkout → Payment',
frequency: 234891,
avgDuration: 342000,
conversionRate: 0.78
},
{
pattern: 'Homepage → Category → Product → Add to Cart',
frequency: 189234,
avgDuration: 178000,
conversionRate: 0.34
}
],
deviceDistribution: { mobile: 0.63, desktop: 0.32, tablet: 0.05 },
browserDistribution: { chrome: 0.54, safari: 0.31, firefox: 0.09, edge: 0.04 },
performanceMetrics: {
FCP: { p50: 1234, p95: 3421, p99: 5678 },
LCP: { p50: 2341, p95: 4523, p99: 7891 },
FID: { p50: 87, p95: 234, p99: 456 },
CLS: { p50: 0.02, p95: 0.08, p99: 0.15 }
},
errorPatterns: [],
featureUsage: {}
};
}
async fetchIncidents(incidentIds, severityFilter, limit) {
// Simulate fetching incidents from memory
// In real implementation, query incident management system
return [
{
id: 'INC-2024-1234',
timestamp: '2025-09-29T14:23:47.892Z',
severity: 'CRITICAL',
service: 'payment-service',
error: 'PaymentProcessingException: Gateway timeout after 30s',
affectedUsers: 1247,
duration: 342000,
region: 'us-east-1',
context: {
systemState: {
cpu: 87.3,
memory: 4.2,
connections: 342,
queueDepth: 1893,
cacheHitRate: 23.1
}
}
}
];
}
async fetchProductionMetric(metric) {
// Simulate fetching production metric
return Math.random() * 100;
}
async fetchStagingMetric(metric) {
// Simulate fetching staging metric
return Math.random() * 100;
}
}
exports.ProductionIntelligenceAgent = ProductionIntelligenceAgent;
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