From manual lead follow-up to autonomous nurture at scale.
Monday showed 3 core prompts for lead scoring, personalization, and engagement. Tuesday automated them into working code. Wednesday mapped team workflows. Today: the complete production architecture. Multi-agent orchestration, ML scoring pipeline, CRM integration, GDPR compliance, and scaling from 1K to 100K leads daily.
Key Assumptions
System Requirements
Functional
- Ingest leads from forms, APIs, webhooks, CSV imports
- Score leads in real-time using ML models + rule engine
- Generate personalized content (emails, messages) per lead segment
- Route high-value leads to sales, nurture low-engagement leads
- Track engagement (opens, clicks, replies) and update scores
- Integrate with CRM for bi-directional sync
- A/B test messaging variants and optimize automatically
Non-Functional (SLOs)
💰 Cost Targets: {"per_lead_scored_usd":0.002,"per_email_sent_usd":0.0005,"per_1k_leads_monthly_usd":50}
Agent Layer
planner
L4Decomposes lead nurture task into subtasks and orchestrates agents
🔧 task_decomposer, agent_selector, dependency_resolver
⚡ Recovery: Retry with exponential backoff (3 attempts), Fallback to rule-based routing if LLM fails, Alert human if critical path blocked
executor
L3Runs the primary workflow: score → personalize → send
🔧 scoring_service, personalization_service, email_service, crm_adapter
⚡ Recovery: Checkpoint progress at each step, Resume from last checkpoint on failure, Timeout individual steps (30s max), Fallback to default template if personalization fails
evaluator
L3Validates output quality, checks business rules, flags anomalies
🔧 content_quality_checker, business_rule_validator, anomaly_detector
⚡ Recovery: Flag for human review if quality < 0.7, Auto-reject if critical rule violated, Log all validation failures for retraining
guardrail
L4Enforces compliance (GDPR, CAN-SPAM), redacts PII, filters unsafe content
🔧 pii_detector, consent_checker, content_filter, jurisdiction_validator
⚡ Recovery: Block message if any violation detected, Alert compliance team immediately, Log violation for audit trail
scoring
L2Runs ML model to score lead quality and predict conversion probability
🔧 feature_extractor, model_inference_service, segment_classifier
⚡ Recovery: Fallback to rule-based scoring if model unavailable, Use cached score if inference times out (>500ms), Alert ML team if confidence < 0.5
personalization
L3Generates personalized email content using LLM + templates
🔧 llm_api (GPT-4/Claude), template_renderer, tone_adjuster
⚡ Recovery: Fallback to pre-approved template if LLM fails, Retry with different prompt if output invalid, Human review if personalization confidence < 0.6
engagement
L3Selects optimal channel and timing for outreach
🔧 channel_optimizer, timing_predictor, frequency_cap_checker
⚡ Recovery: Default to email if prediction fails, Respect frequency caps even if model recommends send, Skip send if lead opted out
ML Layer
Feature Store
Update: Real-time for behavioral, hourly for firmographics
- • lead_firmographics (company size, industry, revenue)
- • behavioral_signals (page views, downloads, time on site)
- • engagement_metrics (email opens, clicks, replies)
- • temporal_features (days_since_signup, time_of_day)
- • derived_features (engagement_velocity, content_affinity)
Model Registry
Strategy: Shadow mode → A/B test → full rollout
- • lead_scoring_v3
- • conversion_predictor_v2
- • segment_classifier_v1
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 rate limits during traffic spike
Medium✓ Mitigation: Multi-provider setup (OpenAI + Anthropic + Azure). Rate limit per provider tracked. Auto-failover. Queue backlog with retry. Alert if queue >5000.
⚠️ Model drift degrades scoring accuracy over time
High✓ Mitigation: Continuous monitoring (PSI, KL divergence). Weekly accuracy checks. Auto-alert if drift >0.1. Monthly retraining pipeline. A/B test new models before rollout.
⚠️ PII leakage to LLM provider
Low✓ Mitigation: Presidio PII detection before LLM. Redact all detected PII. Audit logs of LLM requests. DPA with LLM providers. Option for on-prem LLM deployment (enterprise).
⚠️ Email deliverability issues (spam, blacklist)
Medium✓ Mitigation: DKIM/SPF/DMARC setup. Dedicated IP warming. Monitor sender reputation. Bounce/complaint rate alerts. Backup email provider. List hygiene (remove bounces).
⚠️ CRM sync failures cause data inconsistency
Medium✓ Mitigation: Idempotency keys for all CRM writes. Retry logic (3x with backoff). Async queue for failed syncs. Daily reconciliation job. Alert if sync lag >1 hour.
⚠️ Cost overruns from LLM token usage
Medium✓ Mitigation: Per-lead cost tracking. Monthly budget alerts. Token usage optimization (prompt engineering). Fallback to cheaper models for low-value leads. Auto-throttle if budget exceeded.
⚠️ Agent orchestration deadlock or infinite loop
Low✓ Mitigation: Max iteration limit (5 loops). Timeout per agent (30s). Circuit breaker on repeated failures. Dead letter queue for stuck tasks. Monitoring + alerts.
Evolution Roadmap
Progressive transformation from MVP to scale
Phase 1: MVP (0-3 months)
Phase 2: Scale (3-6 months)
Phase 3: Enterprise (6-12 months)
Complete Systems Architecture
9-layer architecture from ingestion to delivery
Presentation
4 components
API Gateway
4 components
Agent Layer
7 components
ML Layer
5 components
Integration
5 components
Data
4 components
External
4 components
Observability
5 components
Security
5 components
Request Flow - Lead Scoring & Nurture
Automated data flow every hour
End-to-End Data Flow
From lead capture to email delivery in <2 seconds
Key Integrations
Salesforce CRM
HubSpot CRM
SendGrid Email
Clearbit Enrichment
Segment Analytics
Security & Compliance
Failure Modes & Recovery
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| LLM API down (OpenAI/Anthropic) | Switch to backup LLM provider (Azure OpenAI) → Fallback to pre-approved templates | Degraded personalization, not broken | 99.5% |
| Scoring model returns low confidence (<0.5) | Use rule-based scoring → Flag for human review | Lower accuracy, quality maintained | 99.9% |
| CRM API timeout (Salesforce/HubSpot) | Retry 3x with backoff → Queue for async sync → Alert if queue >1000 | Delayed sync, eventual consistency | 99.0% |
| Email service rate limit exceeded | Queue messages → Spread sends over time → Use secondary provider | Delayed delivery (minutes to hours) | 99.5% |
| Database connection pool exhausted | Queue requests → Scale read replicas → Shed non-critical load | Increased latency, some requests rejected | 99.9% |
| Guardrail agent detects PII in output | Block message immediately → Alert compliance team → Log violation | Message not sent (correct behavior) | 100% |
| Feature store unavailable | Use cached features (up to 1h old) → Use default features → Degrade to rule-based | Slightly stale data, reduced accuracy | 99.5% |
┌─────────────┐
│ Planner │ ← Orchestrates all agents
│ Agent │
└──────┬──────┘
│
┌────────────────┼────────────────┐
│ │ │
┌────▼────┐ ┌────▼────┐ ┌────▼────┐
│ Executor│ │Evaluator│ │Guardrail│
│ Agent │ │ Agent │ │ Agent │
└────┬────┘ └────┬────┘ └────┬────┘
│ │ │
┌────┼────────────────┼────────────────┤
│ │ │ │
┌──▼──┐ ┌▼──────┐ ┌─────▼──┐ ┌─────▼────┐
│Score│ │Personal│ │Engage │ │Compliance│
│Agent│ │ Agent │ │ Agent │ │ Check │
└──┬──┘ └───┬────┘ └────┬───┘ └──────────┘
│ │ │
└────────┴───────────┘
│
┌──────▼──────┐
│ CRM/Email │
│ Delivery │
└─────────────┘🔄Agent Collaboration Flow
🎭Agent Types
Reactive Agent
LowScoring Agent - Responds to input (features) → Returns output (score)
Reflexive Agent
MediumGuardrail Agent - Uses rules + context (consent status, jurisdiction)
Deliberative Agent
HighPersonalization Agent - Plans content based on segment, retrieves context, generates
Orchestrator Agent
HighestPlanner Agent - Makes routing decisions, handles loops, coordinates all agents
📈Levels of Autonomy
RAG vs Fine-Tuning Decision
Hallucination Detection
Evaluation Framework
Dataset Curation
Agentic RAG
Multi-Model Ensemble
Technology Stack
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