Phase 4: Scale & Polish

Timeline: Weeks 13-16 Status: Planned Prerequisites: Phase 3 complete with all agents operational

Overview

Phase 4 prepares the system for production scale. Focus shifts from building features to optimization, observability, and operational excellence. The goal is supporting 10K users with <500ms latency and <$500/month cost.

Goal: Production-ready system handling 10K users, 10K QPS, with 99.9% uptime.

Deliverables

Week 13: Performance Optimization

Focus: Reduce latency and cost through intelligent caching and batching

Edge Caching Strategy

Multi-Tier Cache Architecture:

// Cache hierarchy
const CACHE_LAYERS = {
  // L1: Cloudflare edge (ultra-fast, limited size)
  edge: {
    userEmbeddings: '15min',
    feedResults: '5min',
    thompsonParams: '30min'
  },

  // L2: Cloudflare KV (fast, larger capacity)
  kv: {
    contentEmbeddings: '7days',
    qualityScores: '24hours',
    agentScores: '1hour'
  },

  // L3: Qdrant (source of truth)
  qdrant: {
    fullEmbeddings: 'permanent',
    vectorSearch: 'no-cache'  // Always fresh
  }
};

Cache Invalidation Strategy:

class SmartCacheInvalidator {
  async onUserInteraction(userId: string, action: string) {
    if (SIGNIFICANT_ACTIONS.includes(action)) {
      // Invalidate user embedding (preferences changed)
      await this.invalidate(`user:${userId}:embedding`);

      // Keep feed cache for 5min (balance freshness/cost)
      // Will auto-refresh on next request after expiry
    }
  }

  async onContentUpdate(contentId: string) {
    // Invalidate quality scores
    await this.invalidate(`content:${contentId}:quality`);

    // Invalidate any feeds containing this content
    const affectedUsers = await this.getUsersWithContent(contentId);
    await Promise.all(
      affectedUsers.map(u => this.invalidate(`feed:${u}`))
    );
  }
}

Database Query Optimization

Qdrant Optimization:

// Before: Slow filtered search
const results = await qdrant.search({
  vector: userEmbedding,
  filter: {
    must: [
      { key: 'region', match: { value: 'AU' } },
      { key: 'category', match: { value: 'photographer' } },
      { key: 'quality', range: { gte: 0.8 } }
    ]
  },
  limit: 100
});

// After: Pre-filtered collections + vector search
const results = await qdrant.search({
  collection: `au_photographers_quality`,  // Pre-filtered
  vector: userEmbedding,
  limit: 100
});
// 10x faster!

Supabase Connection Pooling:

// Use Supavisor (Supabase's connection pooler)
const supabase = createClient(SUPABASE_URL, SUPABASE_KEY, {
  db: {
    pool: {
      min: 2,
      max: 10,
      idleTimeoutMillis: 30000
    }
  }
});

Batch Processing

// Batch agent scoring (10 at a time)
class BatchedAgentScoring {
  private queue: ScoringRequest[] = [];

  async score(content: Content): Promise<number> {
    return new Promise((resolve) => {
      this.queue.push({ content, resolve });

      if (this.queue.length >= 10 || this.timeoutReached()) {
        this.processBatch();
      }
    });
  }

  private async processBatch() {
    const batch = this.queue.splice(0, 10);

    // Single LLM call for 10 evaluations
    const scores = await this.batchScore(
      batch.map(r => r.content)
    );

    // Resolve all promises
    batch.forEach((req, i) => req.resolve(scores[i]));
  }
}

Tasks: 1. Implement edge caching hierarchy 2. Optimize Qdrant queries with pre-filtering 3. Set up Supabase connection pooling 4. Add batching for LLM calls 5. Measure latency improvements

Success Criteria: - P99 latency < 500ms (down from ~1s) - Cache hit rate > 80% - Database connections stable - Cost reduction > 40%

Week 14: Monitoring & Observability

Focus: Comprehensive system visibility

Metrics Dashboard

System Health: - Request rate, latency (p50, p99, p99.9) - Error rate, success rate - Cache hit rates - Database query performance - Worker/Durable Object utilization

AI Performance: - Agent scoring distribution - Thompson Sampling convergence - MAGRPO stability (policy variance) - Embedding quality (clustering metrics) - Quality scores over time

Business Metrics: - Active users (DAU, MAU) - Session duration, retention - Content save rate, engagement - Vendor discovery rate - NPS, user satisfaction

Implementation:

// Metrics collection
class MetricsCollector {
  async recordFeedRequest(userId: string, latency: number) {
    await this.increment('feed_requests_total');
    await this.histogram('feed_request_latency', latency);

    // Track by user segment
    const segment = await this.getUserSegment(userId);
    await this.increment(`feed_requests_${segment}`);
  }

  async recordAgentDecision(
    agent: string,
    score: number,
    latency: number
  ) {
    await this.gauge(`agent_${agent}_score`, score);
    await this.histogram(`agent_${agent}_latency`, latency);
  }
}

Alerting Rules:

const ALERTS = [
  {
    name: 'high_error_rate',
    condition: 'error_rate > 0.01',
    severity: 'critical',
    channel: 'pagerduty'
  },
  {
    name: 'slow_feed_generation',
    condition: 'feed_latency_p99 > 1000',
    severity: 'warning',
    channel: 'slack'
  },
  {
    name: 'quality_drift',
    condition: 'avg_quality_score < 0.7',
    severity: 'warning',
    channel: 'email'
  }
];

Logging Strategy

// Structured logging for debugging
logger.info('feed_generated', {
  userId,
  feedSize: 20,
  latency: 450,
  agents: {
    discovery: { score: 0.8, latency: 120 },
    quality: { score: 0.9, latency: 80 },
    archivist: { score: 0.7, latency: 100 },
    serendipity: { score: 0.6, latency: 150 }
  },
  thompsonSampling: {
    exploration: 0.3,
    exploitation: 0.7
  },
  cacheHits: 12,
  cacheMisses: 8
});

Tasks: 1. Set up metrics collection (Prometheus/Grafana) 2. Build real-time dashboards 3. Configure alerting rules 4. Implement structured logging 5. Create runbooks for common issues

Success Criteria: - All key metrics visible in real-time - Alerts fire before users notice issues - Mean time to detection (MTTD) < 5 minutes - Mean time to resolution (MTTR) < 30 minutes

Week 15: A/B Testing Framework

Focus: Systematic experimentation and optimization

Experiment Framework

class ExperimentFramework {
  async assignVariant(userId: string, experiment: string): Promise<string> {
    // Deterministic assignment based on user ID
    const hash = this.hashUserId(userId, experiment);

    if (hash < 0.5) return 'control';
    return 'treatment';
  }

  async trackExperiment(
    userId: string,
    experiment: string,
    metric: string,
    value: number
  ) {
    const variant = await this.assignVariant(userId, experiment);

    await this.record({
      experiment,
      variant,
      metric,
      value,
      userId,
      timestamp: Date.now()
    });
  }

  async analyzeExperiment(experiment: string): Promise<Results> {
    const control = await this.getMetrics(experiment, 'control');
    const treatment = await this.getMetrics(experiment, 'treatment');

    return {
      lift: (treatment.mean - control.mean) / control.mean,
      pValue: this.tTest(control, treatment),
      significant: pValue < 0.05,
      sampleSize: { control: control.n, treatment: treatment.n }
    };
  }
}

Planned Experiments

Experiment 1: Foundation Model - Control: Sentence-BERT + simple aggregation - Treatment: Custom trained foundation model - Metric: Engagement, quality, retention - Duration: 2 weeks, 1000 users

Experiment 2: Thompson Sampling - Control: Beta-Bernoulli Thompson Sampling - Treatment: ENR-based Thompson Sampling - Metric: Regret, sample efficiency - Duration: 2 weeks, 1000 users

Experiment 3: Agent Count - Control: 4 agents (Discovery, Quality, Archivist, Serendipity) - Treatment: 6 agents (+ Engagement Forecaster, custom agent) - Metric: Quality, diversity, cost - Duration: 2 weeks, 500 users per variant

Tasks: 1. Build experiment framework 2. Set up statistical analysis 3. Create experiment dashboard 4. Run 3 key experiments 5. Document learnings

Success Criteria: - Framework supports concurrent experiments - Statistical significance detection - Learnings documented and actioned - At least 1 significant improvement shipped

Week 16: Production Hardening

Focus: Reliability, security, and operational readiness

Reliability Improvements

Graceful Degradation:

class ResilientOrchestrator {
  async rankFeed(userId: string): Promise<Content[]> {
    try {
      // Try full multi-agent ranking
      return await this.multiAgentRank(userId);
    } catch (error) {
      logger.warn('multi_agent_failed', { userId, error });

      try {
        // Fallback: Simple ranking
        return await this.simpleRank(userId);
      } catch (error) {
        logger.error('simple_rank_failed', { userId, error });

        // Last resort: Trending content
        return await this.getTrendingContent();
      }
    }
  }
}

Circuit Breaker:

class CircuitBreaker {
  private failureCount = 0;
  private lastFailure = 0;
  private state = 'closed';

  async call<T>(fn: () => Promise<T>): Promise<T> {
    if (this.state === 'open') {
      if (Date.now() - this.lastFailure > RESET_TIMEOUT) {
        this.state = 'half-open';
      } else {
        throw new Error('Circuit breaker open');
      }
    }

    try {
      const result = await fn();
      this.onSuccess();
      return result;
    } catch (error) {
      this.onFailure();
      throw error;
    }
  }

  private onFailure() {
    this.failureCount++;
    this.lastFailure = Date.now();

    if (this.failureCount > FAILURE_THRESHOLD) {
      this.state = 'open';
    }
  }
}

Security Hardening

Rate limiting per user (1000 req/hour)
API key rotation
Input validation and sanitization
SQL injection prevention
CORS configuration
Secrets management audit

Documentation

API documentation (OpenAPI spec)
Runbooks for common operations
Incident response playbooks
Architecture decision records
Deployment guides

Tasks: 1. Add graceful degradation 2. Implement circuit breakers 3. Security audit and fixes 4. Complete documentation 5. Disaster recovery testing

Success Criteria: - System survives agent failures - Zero security vulnerabilities - All operations documented - Disaster recovery tested

Final Metrics

Scale: - 10K users supported - 10K QPS capacity - 99.9% uptime

Performance: - P99 latency < 500ms - Cache hit rate > 80% - Error rate < 0.1%

Cost: - Total: < $500/month - Per user: < $0.05/month

Quality: - NPS > 50 - 30-day retention > 70% - Feed relevance > 0.85

Production Deployment Checklist

Post-Launch

Week 17+: Continuous Improvement 1. Monitor metrics daily 2. Run weekly experiments 3. Monthly architecture reviews 4. Quarterly planning 5. Scale based on growth

Resources

Monitoring: - Grafana Dashboards - Cloudflare Analytics - Sentry Error Tracking

Security: - OWASP Top 10 - Cloudflare WAF - Secrets Management

Operations: - Deployment Guide - Testing Guide - Runbooks

Congratulations! The system is production-ready. Time to scale and delight users.