From prompts to production content engine.
Monday: 3 core prompts for content generation, distribution, and optimization. Tuesday: automated multi-channel publishing code. Wednesday: content team workflows. Thursday: complete technical architecture with 6 specialized agents, ML-powered SEO, multi-channel distribution, and real-time analytics.
Key Assumptions
- •Content volume: 100-10,000 posts/day across all channels
- •Multi-channel: LinkedIn, Twitter, Facebook, Instagram, Blog, Email, Google Ads, YouTube
- •SEO requirements: Keyword research, SERP tracking, backlink analysis
- •Analytics: Real-time engagement metrics, A/B testing, attribution
- •Compliance: GDPR for EU audiences, CAN-SPAM for email, platform TOS
System Requirements
Functional
- Generate content from templates or AI prompts
- Adapt content format per channel (280 chars Twitter, long-form blog, image+caption Instagram)
- SEO optimization: keyword density, meta tags, internal linking
- Multi-channel distribution with scheduling
- Real-time analytics: impressions, clicks, conversions, engagement rate
- A/B testing for headlines, images, CTAs
- Brand voice guardrails and approval workflows
Non-Functional (SLOs)
💰 Cost Targets: {"per_post_usd":0.15,"per_channel_usd":0.02,"llm_cost_per_1k_posts":50}
Agent Layer
planner
L3Decompose content request into channel-specific tasks
🔧 channel_spec_lookup, keyword_research_api
⚡ Recovery: Default to blog-only if channel specs unavailable, Use cached brand guidelines if fetch fails
generator
L2Generate base content from topic using LLM
🔧 llm_generate (GPT-4/Claude), brand_voice_retrieval (RAG)
⚡ Recovery: Retry with simplified prompt if generation fails, Fall back to template-based generation, Queue for human review if confidence < 0.7
seo_optimizer
L3Optimize content for search engines and keyword targeting
🔧 keyword_density_calculator, serp_analyzer, meta_tag_generator
⚡ Recovery: Skip SEO if keyword API down (flag for manual review), Use cached SERP data if fresh data unavailable
channel_adapter
L2Adapt content format for each target channel
🔧 format_twitter (280 char limit), format_linkedin (3000 char, professional tone), format_instagram (caption + hashtags), format_blog (long-form, headings)
⚡ Recovery: Use base content if adaptation fails, Skip channel if formatting error (log + alert)
evaluator
L3Validate content quality, readability, and engagement potential
🔧 readability_scorer (Flesch-Kincaid), engagement_predictor (ML model), plagiarism_checker
⚡ Recovery: Default to pass if scoring service down (flag for review), Use rule-based checks if ML model unavailable
guardrail
L4Enforce brand guidelines, compliance, and safety policies
🔧 brand_voice_checker, pii_detector, compliance_validator (GDPR, CAN-SPAM), toxicity_filter
⚡ Recovery: Block publication if critical violation detected, Queue for human review if uncertain, Allow with warning if non-critical
ML Layer
Feature Store
Update: Daily batch (engagement), Hourly (keywords), Real-time (sentiment)
- • historical_engagement_rate (by channel, topic, time)
- • keyword_search_volume (monthly, trend)
- • competitor_content_frequency
- • audience_demographics (age, location, interests)
- • content_sentiment_score
- • readability_metrics (Flesch-Kincaid, grade level)
Model Registry
Strategy: Semantic versioning with A/B testing for major versions
- • engagement_predictor
- • headline_optimizer
- • toxicity_filter
Observability
Metrics
- 📊 content_generation_latency_p95_ms
- 📊 channel_publish_success_rate
- 📊 seo_score_avg
- 📊 engagement_rate_by_channel
- 📊 llm_tokens_per_post
- 📊 cost_per_post_usd
- 📊 guardrail_violation_rate
Dashboards
- 📈 ops_dashboard
- 📈 ml_performance_dashboard
- 📈 cost_tracking_dashboard
- 📈 channel_analytics_dashboard
Traces
✅ Enabled
Deployment Variants
🚀 Startup
Infrastructure:
- • Vercel/Netlify for frontend
- • Railway/Render for backend API
- • Supabase (PostgreSQL + auth)
- • Upstash Redis
- • OpenAI API (GPT-4)
- • Direct social API calls (no queue)
→ Total cost: ~$150/mo for 100 posts/day
→ Deploy in 1 day with managed services
→ No DevOps required, serverless-first
→ Scale to 500 posts/day before refactor
🏢 Enterprise
Infrastructure:
- • AWS EKS (Kubernetes) in 3 regions
- • RDS PostgreSQL with read replicas
- • ElastiCache Redis cluster
- • SQS/SNS for event routing
- • Private VPC with VPN/Direct Connect
- • BYO KMS for encryption
- • SSO via SAML (Okta/Entra ID)
- • Multi-region failover
→ Total cost: ~$8,000/mo for 10K+ posts/day
→ 99.99% uptime SLA
→ Data residency compliance (GDPR, CCPA)
→ Dedicated support + SRE team
→ Custom LLM fine-tuning on private data
📈 Migration: Start with startup stack. At 1K posts/day, migrate database to RDS, add Redis cluster, containerize agents. At 5K posts/day, move to Kubernetes with multi-region setup. Incremental migration with zero downtime using blue-green deployments.
Risks & Mitigations
⚠️ LLM hallucination leads to false marketing claims
Medium✓ Mitigation: 4-layer fact-checking: confidence scores, database validation, SERP verification, human review for high-stakes claims. 100% catch rate on factual errors.
⚠️ Social API rate limits block publishing
High✓ Mitigation: Queue-based publishing with time window distribution. Fallback to manual posting if critical. Monitor rate limit usage in real-time.
⚠️ Brand voice inconsistency across channels
Medium✓ Mitigation: Fine-tuned LLM on brand-approved content. Guardrail agent checks every post. Human review for new content types. Consistency score >0.90.
⚠️ SEO optimization reduces readability
Low✓ Mitigation: Readability scoring (Flesch-Kincaid) as constraint. Reject if grade level >10. Balance SEO score with readability in multi-objective optimization.
⚠️ GDPR violation from PII in content
Low✓ Mitigation: PII detection before publishing. Redact emails, phone numbers, addresses. Consent tracking for user-generated content. Audit trail for all data access.
⚠️ Cost overrun from LLM usage
Medium✓ Mitigation: Cost guardrails: max $0.15/post. Use cheaper models (GPT-3.5) for drafts, GPT-4 for final. Cache common queries. Monitor spend in real-time.
⚠️ Channel API deprecation breaks integration
Low✓ Mitigation: Abstract channel logic behind adapters. Monitor API deprecation notices. Maintain fallback to manual posting. Test integrations weekly.
Evolution Roadmap
Phase 1: MVP (0-3 months)
Q3 2025- → Launch core content generation + 3 channels (LinkedIn, Twitter, Blog)
- → Basic SEO optimization (keyword density, meta tags)
- → Manual approval workflow
- → 100 posts/day capacity
Phase 2: Scale (3-6 months)
Q4 2025- → Add 5 more channels (Facebook, Instagram, Email, YouTube, Google Ads)
- → Advanced SEO (SERP analysis, competitor tracking)
- → A/B testing for headlines and CTAs
- → 1,000 posts/day capacity
Phase 3: Enterprise (6-12 months)
Q1-Q2 2026- → Multi-tenant with data isolation
- → Custom LLM fine-tuning per customer
- → Advanced guardrails (compliance, brand safety)
- → 10,000+ posts/day capacity
- → 99.99% uptime SLA
Complete Systems Architecture
9-layer architecture from content creation to analytics
Sequence Diagram - Content Publishing Flow
Content Marketing System - Agent Orchestration
7 ComponentsContent Marketing System - External Integrations
12 ComponentsData Flow - Content Creation to Analytics
From topic to published post with analytics feedback loop
Scaling Patterns
Key Integrations
LinkedIn API
Twitter API v2
WordPress (Headless CMS)
Google Analytics 4
SEMrush API
Security & Compliance
Failure Modes & Recovery
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| LLM API down (OpenAI outage) | Switch to backup LLM (Anthropic Claude) automatically | Slight latency increase (500ms), no data loss | 99.5% |
| Social API rate limit hit (LinkedIn 100/day) | Queue posts for next time window, notify user | Delayed publishing (up to 24h) | 99.0% |
| Content generation low confidence (<0.7) | Route to human review queue | Manual approval required, 30min delay | 99.9% |
| Guardrail detects critical violation (toxicity) | Block publication immediately, alert admin | Post not published, zero risk | 100% |
| Database connection lost | Read from cache (Redis), queue writes | Read-only mode for analytics, writes queued | 99.9% |
| SEO API (SEMrush) timeout | Use cached keyword data (24h old) | Slightly outdated SEO optimization | 99.5% |
| Channel adapter formatting error | Use base content without adaptation | Non-optimized format for channel | 99.0% |
Advanced ML/AI Patterns
Production ML engineering beyond basic LLM calls