From manual chaos to autonomous orchestration.
Monday: 3 core prompts (task decomposition, dependency tracking, notification generation). Tuesday: automated orchestration code. Wednesday: cross-functional team workflows. Thursday: complete production architecture with multi-agent coordination, ML evaluation, and enterprise scaling patterns.
Key Assumptions
- •Launch complexity: 50-300 tasks per launch with 3-7 cross-functional teams
- •Volume: 10-100 concurrent launches (startup to enterprise)
- •Integration: Notion/Linear (tasks), Slack (notifications), Google Calendar/Outlook (scheduling)
- •Compliance: SOC2 Type II for enterprise customers, basic audit trail for startups
- •Data residency: US/EU options for enterprise, global for startup tier
System Requirements
Functional
- Decompose launch brief into 50-300 actionable tasks with dependencies
- Track task status across Notion, Linear, Jira, Asana in real-time
- Generate context-aware notifications (Slack, email, in-app) based on role and urgency
- Detect dependency conflicts and suggest resolution paths
- Provide launch health dashboard with risk scoring and bottleneck identification
- Support multi-launch orchestration with resource conflict detection
- Enable post-launch analysis with task completion metrics and team velocity
Non-Functional (SLOs)
💰 Cost Targets: {"per_launch_usd":15,"per_active_user_month_usd":25,"llm_cost_per_task_usd":0.02}
Agent Layer
planner
L3Decompose launch brief into 50-300 actionable tasks with estimates and dependencies
🔧 Claude API (task generation), Vector DB (retrieve similar launches), Rule engine (apply org-specific templates)
⚡ Recovery: If LLM fails: fallback to template-based generation, If partial output: request human review for gaps, If hallucinated tasks: cross-check against product taxonomy
executor
L2Orchestrate task creation, updates, and sync across Notion/Linear/Jira
🔧 Notion API (bulk create/update), Linear API (GraphQL mutations), Jira REST API, Redis (idempotency cache)
⚡ Recovery: If API timeout: retry with exponential backoff (3 attempts), If rate limit: queue for next sync window, If conflict: flag for human resolution, continue with others
evaluator
L3Validate task quality, detect missing dependencies, score launch health
🔧 GPT-4 (dependency inference), Rule engine (policy checks), TimescaleDB (historical velocity data)
⚡ Recovery: If inference fails: use heuristic rules only, If low confidence (<0.7): flag for PM review, If policy violation: block launch creation, suggest fixes
guardrail
L4Enforce org policies, redact PII, prevent unsafe task assignments
🔧 PII detection service (AWS Comprehend / custom NER), RBAC engine (check user permissions), Policy store (versioned rules)
⚡ Recovery: If PII detection fails: block task creation (fail-safe), If RBAC check fails: deny access, log incident, If policy ambiguous: escalate to admin
dependency
L3Detect cross-task dependencies, identify critical path, flag conflicts
🔧 Sentence transformer (semantic similarity), Graph algorithm (Tarjan's for cycles), Vector DB (retrieve similar task pairs)
⚡ Recovery: If cycle detected: break at lowest-priority edge, suggest manual review, If semantic model fails: use keyword matching fallback, If no historical data: use conservative heuristics
notification
L2Generate context-aware notifications based on role, urgency, and user preferences
🔧 Slack API (post message), Email service (SendGrid/SES), Push notification service (FCM), Template engine (Jinja2)
⚡ Recovery: If Slack fails: fallback to email, If all channels fail: queue for retry, log incident, If user unreachable: escalate to manager
ML Layer
Feature Store
Update: Real-time for velocity, daily for historical aggregates
- • task_completion_velocity (tasks/day per team)
- • dependency_density (avg deps per task)
- • risk_score_historical (past launch outcomes)
- • team_capacity_utilization (hours booked / hours available)
- • task_complexity_score (description length, subtasks, dependencies)
- • notification_engagement_rate (opens, clicks, actions)
Model Registry
Strategy: Semantic versioning, A/B test for 2 weeks before full rollout
- • task_decomposer_v3
- • dependency_classifier_v2
- • risk_predictor_v1
- • notification_ranker_v1
Observability
Metrics
- 📊 launch_creation_latency_p95_ms
- 📊 task_generation_count
- 📊 task_sync_success_rate
- 📊 dependency_detection_accuracy
- 📊 notification_delivery_latency_p95_sec
- 📊 llm_api_latency_p95_ms
- 📊 llm_cost_per_launch_usd
- 📊 agent_error_rate
- 📊 user_active_sessions
Dashboards
- 📈 ops_dashboard
- 📈 ml_dashboard
- 📈 business_metrics
- 📈 agent_performance
Traces
✅ Enabled
Deployment Variants
🚀 Startup
Infrastructure:
- • Vercel (Next.js app)
- • Supabase (PostgreSQL + Auth)
- • Upstash (Redis)
- • Anthropic API (Claude)
- • Notion/Linear/Slack APIs
- • Sentry (error tracking)
- • Simple Analytics (privacy-friendly)
→ Single-tenant per customer (no multi-tenancy complexity)
→ Managed services for everything (no Kubernetes)
→ Pay-as-you-go LLM costs
→ Basic RBAC (3 roles: Admin, PM, Member)
→ Email support, community Slack
→ Time to deploy: 2-4 weeks
🏢 Enterprise
Infrastructure:
- • Kubernetes (EKS/GKE/AKS)
- • PostgreSQL (Aurora/AlloyDB with multi-region replication)
- • Redis Cluster (geo-distributed)
- • Multi-LLM (Claude + GPT + Gemini with failover)
- • Private VPC + VPN/PrivateLink
- • BYO KMS/HSM for encryption keys
- • SSO/SAML (Okta/Auth0/Azure AD)
- • Dedicated compliance dashboard
- • Custom SLAs (99.9% uptime)
- • 24/7 support + dedicated Slack channel
→ Multi-tenant with org-level isolation (separate schemas or databases)
→ Data residency options (US, EU, custom regions)
→ Advanced RBAC (custom roles, per-resource permissions)
→ Audit logs with 7-year retention
→ SOC2 Type II certified
→ White-label UI options
→ Time to deploy: 8-12 weeks
📈 Migration: Startup → Enterprise: (1) Migrate DB to Aurora/AlloyDB with replication. (2) Deploy K8s cluster, containerize app. (3) Add multi-tenancy (schema-per-org). (4) Integrate SSO/SAML. (5) Enable data residency. (6) Pass SOC2 audit. (7) Offer white-label. Estimated timeline: 3-6 months.
Risks & Mitigations
⚠️ LLM hallucination creates invalid tasks (fake dependencies, wrong assignees)
Medium (1-2% of tasks)✓ Mitigation: 5-layer hallucination detection (confidence scores, cross-reference team roster, timeline checks, dependency validation, human review queue). Target: <1% hallucination rate reaching production.
⚠️ Integration API downtime (Notion/Linear/Slack unavailable)
Low (99.9% uptime SLA from providers)✓ Mitigation: Multi-provider redundancy (if Notion down, use Linear). Queue tasks for retry. Fallback to in-app notifications if Slack down. SLA: 99.5% system uptime despite 3rd-party failures.
⚠️ Cost explosion (LLM API costs scale faster than revenue)
Medium (if not optimized)✓ Mitigation: Cost optimization: prompt caching (40% savings), smaller models for simple tasks (60% savings for notifications), batch processing (20% savings), cost alerts (>$500/day). Target: <$10/launch at scale.
⚠️ Data privacy violation (PII leaked to LLM, non-compliant data storage)
Low (with proper controls)✓ Mitigation: Guardrail Agent redacts PII before sending to LLM. Encrypted storage (AES-256). Audit logs (7-year retention). SOC2 Type II certification. Data residency options (US/EU). Zero-tolerance policy: block task creation if PII detected.
⚠️ Agent orchestration deadlock (circular dependencies in agent calls)
Low (edge case)✓ Mitigation: Max iteration limit (10 loops). Cycle detection (graph algorithms). Timeout after 30 sec. Rollback to last valid state. Alert ops team. Improve cycle detection logic with each incident.
⚠️ Model drift (task generation quality degrades over time)
Medium (as product/org evolves)✓ Mitigation: Continuous evaluation (weekly metrics). Drift detection (KL divergence on task descriptions). Retrain quarterly on new data. A/B test new models. Rollback policy if accuracy drops >5%.
⚠️ Scalability bottleneck (database/LLM API can't handle load)
Medium (at 100+ concurrent launches)✓ Mitigation: Horizontal scaling (read replicas, connection pooling). Multi-LLM load balancing. Queue-based processing (decouple API from LLM calls). Load testing (simulate 500 concurrent launches). Auto-scaling (K8s HPA).
Evolution Roadmap
Phase 1: MVP (0-3 months)
Months 1-3- → Launch with 3 core agents (Planner, Executor, Evaluator)
- → Integrate Notion + Slack
- → Support 10 launches/year (startup tier)
- → Basic RBAC (3 roles)
- → Deploy on Vercel + Supabase
Phase 2: Growth (3-6 months)
Months 4-6- → Add Dependency + Notification + Guardrail agents (6 total)
- → Integrate Linear, Jira, Asana
- → Support 50 launches/year (growth tier)
- → ML evaluation framework (offline + online metrics)
- → Migrate to microservices + queue (SQS/RabbitMQ)
Phase 3: Enterprise (6-12 months)
Months 7-12- → Multi-tenancy (org-level isolation)
- → SSO/SAML integration (Okta/Auth0)
- → Multi-region deployment (US + EU)
- → Advanced RBAC (custom roles, per-resource permissions)
- → White-label UI options
- → Support 200+ launches/year (enterprise tier)
Complete Systems Architecture
9-layer architecture from presentation to security
Sequence Diagram - Launch Creation Flow
Product Launch System - Agent Orchestration
7 ComponentsProduct Launch System - External Integrations
10 ComponentsData Flow - Launch Creation to Execution
PM creates launch → tasks synced → team notified in 10 seconds
Scaling Patterns
Key Integrations
Notion
Linear
Slack
Google Calendar / Outlook
Jira / Asana
Security & Compliance
Failure Modes & Fallbacks
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| LLM API down (Anthropic/OpenAI) | Switch to secondary LLM (GPT if Claude down, vice versa) → If both down: use template-based task generation → Queue for retry when API recovers | Degraded quality (template tasks less detailed), 10-20% slower | 99.5% (multi-LLM redundancy) |
| Notion/Linear API timeout or rate limit | Queue tasks for next sync window (5 min) → If persistent: create tasks in system DB only, manual sync later | Delayed sync (5-15 min), no data loss | 99.0% (eventual consistency acceptable) |
| Dependency detection low confidence (<0.7) | Flag dependencies as 'suggested' (not auto-applied) → Prompt PM to review → Use conservative heuristics (block tasks with shared keywords) | Reduced automation, PM must manually confirm | 95% accuracy target (5% flagged for review) |
| PostgreSQL primary down | Promote read replica to primary (auto-failover in 30-60 sec) → If multi-region: route to secondary region | 30-60 sec read-only mode, no data loss (WAL replication) | 99.9% (RDS/Aurora auto-failover) |
| Guardrail agent PII detection fails | Block task creation (fail-safe) → Alert admin → Manual review required | Launch creation blocked until manual approval | 100% safety (no PII leakage tolerated) |
| Notification delivery fails (Slack + email both down) | Queue notifications → Retry every 5 min for 1 hour → If still failing: in-app notification only + log incident | Delayed notifications (up to 1 hour), users must check dashboard | 99.0% (notifications are important but not critical) |
| Agent orchestration deadlock (circular dependency in agent calls) | Detect cycle (max 10 iterations) → Break loop → Flag launch for manual review → Rollback to last valid state | Launch creation fails, PM must re-submit with clarifications | 99.5% (rare edge case) |
Advanced ML/AI Patterns
Production ML engineering beyond basic API calls