From prompts to production personalization platform.
Monday: 3 core prompts (customer profiling, product matching, journey orchestration). Tuesday: automation code (agents + ML pipelines). Wednesday: team workflows (data science, engineering, ops). Thursday: complete technical architecture. Agents, ML layers, scaling patterns, GDPR compliance, and startup → enterprise evolution.
Key Assumptions
System Requirements
Functional
- Ingest user events (page views, clicks, purchases) from 5+ channels in real-time
- Build unified customer profile (demographics, behavior, preferences, purchase history)
- Generate personalized recommendations (products, content, offers) in < 100ms
- Orchestrate multi-step journeys (abandoned cart, post-purchase, re-engagement)
- A/B test personalization strategies with statistical significance tracking
- Provide explainability for recommendations (why this product?)
- Support batch campaigns (email, push) and real-time triggers (web, app)
Non-Functional (SLOs)
💰 Cost Targets: {"per_user_per_month_usd":0.05,"ml_inference_per_1k_requests_usd":0.1,"feature_store_per_gb_usd":0.2}
Agent Layer
planner
L4Decomposes user request into subtasks, selects tools, coordinates execution
🔧 cache_lookup, profile_agent, recommendation_agent, guardrail_agent
⚡ Recovery: If profile_agent fails → use cached profile (stale < 1hr), If recommendation_agent fails → fallback to popular products, If guardrail_agent fails → block request (safety first)
executor
L3Executes the plan from Planner, manages state, handles retries
🔧 profile_agent.get_profile(), recommendation_agent.generate(), guardrail_agent.validate()
⚡ Recovery: Retry failed steps 3x with exponential backoff, If step fails after retries → skip step and log, If critical step fails → abort and return error
evaluator
L2Validates output quality, checks business rules, logs metrics
🔧 diversity_check, relevance_score, policy_compliance, explainability_check
⚡ Recovery: If quality_score < 0.7 → trigger re-generation, If policy violation → block recommendation, If explainability missing → log warning but allow
guardrail
L1Enforces safety, privacy, and compliance policies
🔧 pii_detector (Presidio), banned_products_filter, age_gate_check, gdpr_consent_check, diversity_enforcer
⚡ Recovery: If PII detected → redact and log incident, If banned product → remove from list, If GDPR violation → block entire request
profile
L2Builds unified customer profile from all data sources
🔧 feature_store.get_features(), event_aggregator, crm_sync, cdp_lookup
⚡ Recovery: If feature_store unavailable → compute features on-the-fly (slower), If CRM sync fails → use cached CRM data (stale < 24hr), If CDP unavailable → skip CDP enrichment
recommendation
L3Generates personalized product recommendations using ML models
🔧 model_registry.load_model(), collaborative_filter, content_filter, llm_reranker, diversity_optimizer
⚡ Recovery: If ML model fails → fallback to rule-based recommendations, If LLM reranker fails → use raw model scores, If diversity optimizer fails → return top N by score
ML Layer
Feature Store
Update: Real-time (streaming) for behavioral features, batch (daily) for demographics
- • user_demographics (age, gender, location)
- • behavioral_features (page_views_7d, purchases_30d, avg_order_value)
- • preference_features (favorite_categories, brand_affinity)
- • engagement_features (email_open_rate, app_usage_frequency)
- • product_features (category, price_tier, popularity_score)
- • contextual_features (time_of_day, device_type, channel)
Model Registry
Strategy: Semantic versioning (major.minor.patch), A/B test new versions before rollout
- • collaborative_filter
- • content_filter
- • llm_reranker
Observability Stack
Real-time monitoring, tracing & alerting
0 activeDeployment Variants
Startup Architecture
Fast to deploy, cost-efficient, scales to 100 competitors
Infrastructure
Risks & Mitigations
⚠️ LLM API costs spiral out of control
High✓ Mitigation: Set cost guardrails ($500/day max). Use cheaper models for non-critical tasks (GPT-3.5 for reranking, GPT-4 only for explainability). Cache aggressively (TTL=5min). Monitor cost per user.
⚠️ Model drift degrades recommendations
Medium✓ Mitigation: Automated drift detection (KL divergence > 0.1 triggers alert). Weekly model retraining. A/B test new models before rollout. Monitor NDCG daily.
⚠️ PII leakage to LLM
Low✓ Mitigation: Guardrail agent with Presidio PII detection. Redact before sending to LLM. Audit all LLM requests. Encrypt data at rest and in transit. Regular security audits.
⚠️ Cold-start problem (new users, no data)
High✓ Mitigation: Use content-based filtering (product attributes) instead of collaborative filtering. Show popular products. Ask for explicit preferences (quiz). Synthetic data augmentation.
⚠️ Recommendation bias (filter bubble)
Medium✓ Mitigation: Diversity optimizer (no more than 3 products from same category). Exploration bonus (10% of recommendations are random). Monitor diversity score.
⚠️ Vendor lock-in (AWS/GCP)
Medium✓ Mitigation: Use open-source tools where possible (Feast, MLflow, Kafka). Abstract cloud-specific services (S3 → object storage interface). Multi-cloud strategy for critical services.
⚠️ Team lacks ML expertise
Medium✓ Mitigation: Hire ML engineer or consultant. Use managed services (Tecton, Databricks). Invest in training. Start simple (rule-based) and iterate.
Evolution Roadmap
Progressive transformation from MVP to scale
Phase 1: MVP (0-3 months)
Phase 2: ML-Powered (3-6 months)
Phase 3: Multi-Agent + Enterprise (6-12 months)
Complete Systems Architecture
9-layer architecture from presentation to security
Presentation
4 components
API Gateway
4 components
Agent Layer
6 components
ML Layer
5 components
Integration
4 components
Data
5 components
External
4 components
Observability
5 components
Security
5 components
Sequence Diagram - Personalized Recommendation Request
Automated data flow every hour
Data Flow
User request → personalized recommendations in < 100ms
Key Integrations
E-commerce Platform (Shopify/Salesforce Commerce Cloud)
CRM (Salesforce/HubSpot)
CDP (Segment/mParticle)
Analytics (Google Analytics 4/Amplitude)
Email/SMS (SendGrid/Twilio)
Security & Compliance
Failure Modes & Fallbacks
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| LLM API down (OpenAI/Anthropic) | Use rule-based recommendations (popular products, trending items) | Degraded quality (no personalization), but system still works | 99.5% |
| Feature store unavailable | Compute features on-the-fly (slower) or use cached features (stale < 1hr) | Increased latency (50-100ms) or slightly stale recommendations | 99.9% |
| ML model inference fails | Use previous model version or rule-based recommendations | Slightly lower quality, but no downtime | 99.9% |
| Guardrail agent fails (PII detection) | Block all recommendations (safety first) | No recommendations served (better safe than sorry) | 100% |
| Database unavailable | Read from replica; write to queue for later processing | Read-only mode for profiles; writes delayed | 99.9% |
| Cache (Redis) down | Direct database queries (slower) | Increased latency (2-3x), higher database load | 99.5% |
| API Gateway rate limit exceeded | Return 429 (Too Many Requests) with retry-after header | User sees error; must retry | N/A (by design) |
RAG vs Fine-Tuning
Hallucination Detection
Evaluation Framework
Dataset Curation
Agentic RAG
Multi-Armed Bandit (MAB)
Tech Stack Summary
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