@cloudkinetix/bmad-enhanced

# TaskFlow Pro Architecture Document ## Introduction This document outlines the overall project architecture for TaskFlow Pro, including backend systems, shared services, and non-UI specific concerns. Its primary goal is to serve as the guiding architectural blueprint for AI-driven development, ensuring consistency and adherence to chosen patterns and technologies. **Relationship to Frontend Architecture:** Since TaskFlow Pro includes a significant user interface, a separate Frontend Architecture Document will detail the frontend-specific design and MUST be used in conjunction with this document. Core technology stack choices documented herein (see "Tech Stack") are definitive for the entire project, including frontend components. ### Starter Template or Existing Project **N/A** - This is a greenfield project with no existing starter template or codebase foundation. ### Change Log | Date | Version | Description | Author | | :--------- | :------ | :---------------------------- | :------------------ | | 2024-01-15 | 1.0 | Initial architecture creation | Winston (Architect) | ## High Level Architecture ### Technical Summary TaskFlow Pro employs a microservices architecture with event-driven communication patterns to support AI-powered project management at scale. The system centers around an AI orchestration layer that coordinates task prioritization, resource allocation, and predictive analytics services. Core services communicate via Apache Kafka for real-time processing, while a React frontend interfaces through a Node.js API gateway. The architecture supports 100k concurrent users with <200ms response times through horizontal scaling, caching strategies, and optimized AI model serving. ### High Level Overview **Architecture Style:** Microservices with Event-Driven Architecture **Primary Patterns:** CQRS for AI operations, API Gateway for frontend integration, Circuit Breaker for external services **Scalability Approach:** Horizontal scaling with Kubernetes orchestration **Data Strategy:** Polyglot persistence (PostgreSQL, Redis, MongoDB) optimized for different data patterns **AI Integration:** Dedicated Python microservices with TensorFlow serving for ML models ### Project Diagram ```mermaid graph TB subgraph "Client Layer" WEB[React Web App] MOB[Mobile PWA] API_CLIENT[API Clients] end subgraph "API Gateway" GATEWAY[Node.js Gateway] AUTH[Auth Service] RATE_LIMIT[Rate Limiter] end subgraph "Core Services" TASK_SVC[Task Service] PROJECT_SVC[Project Service] USER_SVC[User Service] NOTIF_SVC[Notification Service] end subgraph "AI Services" AI_ORCH[AI Orchestrator] PRIORITY_AI[Task Priority AI] RESOURCE_AI[Resource Prediction AI] NLP_SVC[NLP Service] end subgraph "Integration Services" SLACK_INT[Slack Integration] TEAMS_INT[Teams Integration] GITHUB_INT[GitHub Integration] JIRA_INT[Jira Integration] end subgraph "Data Layer" POSTGRES[(PostgreSQL Primary)] REDIS[(Redis Cache)] MONGO[(MongoDB Analytics)] KAFKA[Apache Kafka] end subgraph "Infrastructure" K8S[Kubernetes] AWS[AWS Services] MONITOR[Monitoring Stack] end WEB --> GATEWAY MOB --> GATEWAY API_CLIENT --> GATEWAY GATEWAY --> AUTH GATEWAY --> RATE_LIMIT GATEWAY --> TASK_SVC GATEWAY --> PROJECT_SVC GATEWAY --> USER_SVC TASK_SVC --> AI_ORCH PROJECT_SVC --> AI_ORCH AI_ORCH --> PRIORITY_AI AI_ORCH --> RESOURCE_AI AI_ORCH --> NLP_SVC TASK_SVC --> SLACK_INT NOTIF_SVC --> TEAMS_INT PROJECT_SVC --> GITHUB_INT TASK_SVC --> JIRA_INT TASK_SVC --> POSTGRES USER_SVC --> POSTGRES AI_ORCH --> REDIS RESOURCE_AI --> MONGO AI_ORCH --> KAFKA TASK_SVC --> KAFKA PROJECT_SVC --> KAFKA ``` ### Architectural Patterns **Primary Patterns:** - **Microservices:** Bounded contexts for Task, Project, User, and AI services - **Event-Driven Architecture:** Kafka-based messaging for real-time updates - **CQRS:** Separate read/write models for AI analytics and reporting - **API Gateway:** Centralized routing, authentication, and rate limiting - **Circuit Breaker:** Resilience for external integrations and AI services **Supporting Patterns:** - **Database per Service:** Polyglot persistence optimized for each service - **Saga Pattern:** Distributed transaction management for complex workflows - **Bulkhead:** Service isolation to prevent cascade failures - **Retry with Exponential Backoff:** Resilient external API communication ## Tech Stack ### Backend Services **Primary Language:** Node.js 18+ with TypeScript **Framework:** Express.js with Helmet security middleware **API Style:** REST + GraphQL (GraphQL for complex queries) **Authentication:** OAuth 2.0 with JWT tokens **Validation:** Joi for request validation **Documentation:** OpenAPI 3.0 with Swagger UI ### AI/ML Services **Primary Language:** Python 3.9+ **ML Framework:** TensorFlow 2.12+ with TensorFlow Serving **NLP Library:** Hugging Face Transformers **Data Processing:** Pandas, NumPy, scikit-learn **Model Serving:** TensorFlow Serving with REST API **Training Pipeline:** Kubeflow for ML workflows ### Databases **Primary Database:** PostgreSQL 14+ (ACID compliance, complex queries) **Cache Layer:** Redis 7+ (session storage, real-time data) **Analytics Store:** MongoDB 6+ (ML training data, event logs) **Search Engine:** Elasticsearch 8+ (full-text search, analytics) ### Message Queue **Primary Queue:** Apache Kafka 3.0+ (event streaming, real-time processing) **Dead Letter Queue:** Kafka with retry topics **Schema Registry:** Confluent Schema Registry for event versioning ### Infrastructure **Container Platform:** Docker with Kubernetes 1.25+ **Cloud Provider:** AWS (EKS, RDS, ElastiCache, S3) **Service Mesh:** Istio for service-to-service communication **Monitoring:** Datadog for APM, logging, and metrics **CI/CD:** GitHub Actions with ArgoCD for GitOps ## Service Architecture ### Core Services #### Task Service **Responsibilities:** - Task CRUD operations and lifecycle management - Task priority calculation coordination with AI services - Task dependency management and validation - Integration with external systems (GitHub, Jira) **Technology:** Node.js/TypeScript, Express, PostgreSQL **Scaling:** Horizontal with database read replicas **Dependencies:** AI Orchestrator, Integration Services, Kafka #### Project Service **Responsibilities:** - Project lifecycle management and health tracking - Resource allocation coordination - Project analytics and reporting - Timeline prediction integration **Technology:** Node.js/TypeScript, Express, PostgreSQL **Scaling:** Horizontal with service mesh load balancing **Dependencies:** Task Service, AI Orchestrator, User Service #### User Service **Responsibilities:** - User authentication and authorization - Role-based access control (RBAC) - User profile and preference management - Team and organization management **Technology:** Node.js/TypeScript, Express, PostgreSQL **Scaling:** Horizontal with Redis session clustering **Dependencies:** Auth Service, Notification Service #### AI Orchestrator **Responsibilities:** - Coordinate AI service requests and responses - Model version management and A/B testing - AI service health monitoring and failover - Feature flag management for AI capabilities **Technology:** Python, FastAPI, Redis, TensorFlow Serving **Scaling:** Horizontal with model caching **Dependencies:** Priority AI, Resource AI, NLP Service ### AI Services #### Task Priority AI Service **Model Type:** Gradient Boosting (XGBoost) with neural network ensemble **Features:** Deadline proximity, dependency complexity, business impact, team capacity **Training:** Continuous learning from user feedback and task completion patterns **Serving:** TensorFlow Serving with <100ms inference time **Accuracy Target:** >85% priority prediction accuracy #### Resource Prediction AI Service **Model Type:** Time series forecasting (LSTM) with capacity analysis **Features:** Historical utilization, project complexity, team skills, seasonal patterns **Training:** Weekly retraining with 12 months historical data **Serving:** Batch prediction with Redis caching **Accuracy Target:** >80% resource allocation prediction accuracy #### Natural Language Processing Service **Model Type:** Fine-tuned BERT for project management domain **Capabilities:** Task extraction, sentiment analysis, duplicate detection **Languages:** English (95%+ accuracy), Spanish (future) **Serving:** Hugging Face Transformers with GPU acceleration **Response Time:** <3 seconds for text processing ## Data Architecture ### Database Design #### PostgreSQL (Primary) **Schema Design:** - **Users:** User profiles, authentication, RBAC - **Projects:** Project metadata, settings, team assignments - **Tasks:** Task details, dependencies, history - **Integrations:** External system configurations and mappings **Optimization:** - Partitioning by organization_id for multi-tenancy - Indexing on frequently queried fields (user_id, project_id, status) - Read replicas for analytics and reporting queries #### Redis (Cache) **Usage Patterns:** - Session storage with 24-hour TTL - AI model predictions with 1-hour TTL - Real-time notifications with pub/sub - Rate limiting counters with sliding windows #### MongoDB (Analytics) **Collections:** - **Events:** User actions, system events, AI decisions - **Training Data:** ML model training datasets - **Analytics:** Aggregated metrics and insights - **Logs:** Application and AI service logs ### Data Flow ```mermaid graph LR USER[User Action] --> API[API Gateway] API --> SERVICE[Core Service] SERVICE --> POSTGRES[(PostgreSQL)] SERVICE --> KAFKA[Kafka Event] KAFKA --> AI[AI Service] AI --> REDIS[(Redis Cache)] KAFKA --> MONGO[(MongoDB)] AI --> ML_MODEL[ML Model] ML_MODEL --> PREDICTION[Prediction] PREDICTION --> REDIS ``` ## Integration Architecture ### External System Integration #### Slack Integration **Authentication:** OAuth 2.0 with workspace-level permissions **Capabilities:** Message posting, slash commands, interactive components **Resilience:** Circuit breaker with 3-retry policy **Rate Limiting:** Slack API limits (50 requests/minute per workspace) #### Microsoft Teams Integration **Authentication:** Microsoft Graph API with delegated permissions **Capabilities:** Adaptive cards, bot framework, activity feed **Resilience:** Exponential backoff with jitter **Rate Limiting:** Graph API throttling (10,000 requests/10 minutes) #### GitHub Integration **Authentication:** GitHub App with fine-grained permissions **Capabilities:** Repository webhooks, issue synchronization, PR tracking **Resilience:** Webhook retry with exponential backoff **Rate Limiting:** GitHub API limits (5,000 requests/hour per user) #### Jira Integration **Authentication:** OAuth 2.0 with project-level access **Capabilities:** Issue synchronization, workflow mapping, field mapping **Resilience:** Atlassian Connect framework with retry logic **Rate Limiting:** Jira Cloud API limits (10 requests/second per app) ### API Design #### REST API Standards - **HTTP Methods:** Proper verb usage (GET, POST, PUT, DELETE) - **Status Codes:** Consistent HTTP status code usage - **Versioning:** URL versioning (/api/v1/) with backward compatibility - **Pagination:** Cursor-based pagination for large datasets - **Filtering:** Query parameter filtering with validation #### GraphQL API - **Schema:** Type-safe schema with automatic documentation - **Resolvers:** Efficient N+1 query prevention with DataLoader - **Subscriptions:** Real-time updates for task and project changes - **Caching:** Query result caching with Redis ## Security Architecture ### Authentication & Authorization **Authentication Flow:** 1. OAuth 2.0 with PKCE for web/mobile clients 2. JWT tokens with 15-minute access token expiry 3. Refresh tokens with 30-day expiry and rotation 4. Multi-factor authentication (TOTP/SMS) for admin users **Authorization Model:** - **Role-Based Access Control (RBAC):** Project Manager, Team Member, Executive - **Resource-Based Permissions:** Project-level and organization-level access - **API Key Authentication:** For service-to-service communication - **Rate Limiting:** Per-user and per-organization limits ### Data Security **Encryption:** - **At Rest:** AES-256 encryption for databases and file storage - **In Transit:** TLS 1.3 for all API communications - **Key Management:** AWS KMS for encryption key rotation - **Secrets:** HashiCorp Vault for service credentials **Compliance:** - **GDPR:** Data portability, right to deletion, consent management - **CCPA:** Data transparency and opt-out mechanisms - **SOC2 Type II:** Annual compliance audits and controls - **OWASP:** Top 10 security vulnerability prevention ### Network Security **Network Architecture:** - **Zero Trust:** Service-to-service mTLS authentication - **API Gateway:** Centralized security policy enforcement - **WAF:** AWS WAF for application-layer protection - **DDoS Protection:** CloudFlare for DDoS mitigation ## Infrastructure and Deployment ### Container Architecture **Containerization:** - **Base Images:** Distroless containers for security - **Multi-stage Builds:** Optimized image sizes - **Health Checks:** Kubernetes liveness and readiness probes - **Resource Limits:** CPU and memory limits per service **Kubernetes Configuration:** - **Namespaces:** Environment separation (dev, staging, prod) - **Service Mesh:** Istio for traffic management and security - **Ingress:** NGINX Ingress Controller with SSL termination - **Storage:** Persistent volumes for databases ### Deployment Strategy **CI/CD Pipeline:** 1. **Source:** GitHub with branch protection rules 2. **Build:** GitHub Actions with parallel test execution 3. **Security:** SAST/DAST scanning with Snyk and OWASP ZAP 4. **Deploy:** ArgoCD for GitOps-based deployment 5. **Monitoring:** Automatic rollback on health check failures **Environment Strategy:** - **Development:** Feature branch deployments with preview URLs - **Staging:** Production-like environment for integration testing - **Production:** Blue-green deployment with canary releases ### Environments - **Development:** Single-node Kubernetes with local databases - **Staging:** Multi-node cluster with production data subset - **Production:** Multi-AZ deployment with auto-scaling and disaster recovery ### Environment Promotion Flow ```text Feature Branch → Development → Staging → Production ↓ ↓ ↓ ↓ Unit Tests Integration E2E Tests Monitoring Tests + AI + Load + Alerting Model Tests Testing ``` ### Rollback Strategy - **Primary Method:** Blue-green deployment with instant traffic switching - **Trigger Conditions:** Health check failures, error rate >1%, response time >500ms - **Recovery Time Objective:** <5 minutes for critical services ## Error Handling Strategy ### General Approach - **Error Model:** Structured error responses with error codes and user messages - **Exception Hierarchy:** Custom exceptions for business logic, technical errors - **Error Propagation:** Fail-fast with graceful degradation for non-critical features ### Logging Standards - **Library:** Winston (Node.js), structlog (Python) - **Format:** JSON structured logging with correlation IDs - **Levels:** ERROR, WARN, INFO, DEBUG with environment-based filtering - **Required Context:** - Correlation ID: UUID v4 for request tracing - Service Context: Service name, version, instance ID - User Context: User ID (when authenticated), organization ID ### Error Handling Patterns #### External API Errors - **Retry Policy:** Exponential backoff with jitter (1s, 2s, 4s, 8s) - **Circuit Breaker:** 5 failures trigger open state, 30s timeout - **Timeout Configuration:** 5s for synchronous calls, 30s for batch operations - **Error Translation:** Map external errors to internal error codes #### Business Logic Errors - **Custom Exceptions:** TaskNotFound, InsufficientPermissions, ValidationError - **User-Facing Errors:** Localized error messages with actionable guidance - **Error Codes:** Hierarchical error codes (TASK_001, AUTH_002, etc.) #### Data Consistency - **Transaction Strategy:** Database transactions with rollback on failure - **Eventual Consistency:** Saga pattern for distributed transactions - **Conflict Resolution:** Last-write-wins with user notification ## Performance and Monitoring ### Performance Targets - **API Response Time:** <200ms for 95th percentile - **AI Inference Time:** <100ms for task prioritization - **Database Query Time:** <50ms for simple queries, <500ms for complex analytics - **Concurrent Users:** 100,000 with horizontal scaling - **Throughput:** 10,000 requests/second at peak load ### Monitoring Strategy **Application Performance Monitoring:** - **APM Tool:** Datadog with distributed tracing - **Metrics:** Request rate, error rate, response time (RED method) - **Alerts:** SLA violation alerts with PagerDuty integration - **Dashboards:** Real-time operational dashboards **Infrastructure Monitoring:** - **System Metrics:** CPU, memory, disk, network utilization - **Kubernetes Metrics:** Pod health, resource usage, cluster capacity - **Database Monitoring:** Query performance, connection pools, replication lag - **AI Model Monitoring:** Inference time, accuracy drift, model versioning ### Scaling Strategy **Horizontal Scaling:** - **Auto-scaling:** Kubernetes HPA based on CPU and custom metrics - **Load Balancing:** Round-robin with health checks - **Database Scaling:** Read replicas with connection pooling - **Cache Scaling:** Redis cluster with consistent hashing ## Coding Standards ### General Standards **Code Quality:** - **Linting:** ESLint (Node.js), Black + Flake8 (Python) - **Type Safety:** TypeScript strict mode, Python type hints - **Code Coverage:** >90% unit test coverage - **Documentation:** JSDoc for public APIs, docstrings for Python **Version Control:** - **Branching:** GitFlow with feature branches - **Commit Messages:** Conventional commits format - **Code Reviews:** Required for all changes, automated checks - **Branch Protection:** No direct commits to main/develop ### Language-Specific Standards #### Node.js/TypeScript - **Style Guide:** Airbnb TypeScript style guide - **Async Patterns:** Async/await over Promises, avoid callback hell - **Error Handling:** Structured error objects with stack traces - **Module System:** ES6 imports/exports - **Testing:** Jest with supertest for API testing #### Python - **Style Guide:** PEP 8 with Black formatting - **Type Hints:** Full type annotation for public APIs - **Error Handling:** Specific exception types, logging with context - **Package Management:** Poetry for dependency management - **Testing:** pytest with fixtures and parametrized tests ## Test Strategy and Standards ### Testing Philosophy - **Approach:** Test-driven development (TDD) for critical business logic - **Coverage Goals:** >90% unit test coverage, >80% integration test coverage - **Test Pyramid:** 70% unit tests, 20% integration tests, 10% E2E tests ### Test Types and Organization #### Unit Tests - **Framework:** Jest (Node.js), pytest (Python) - **File Convention:** `*.test.ts` for TypeScript, `test_*.py` for Python - **Location:** `__tests__` directory adjacent to source files - **Mocking Library:** Jest mocks (Node.js), pytest-mock (Python) - **Coverage Requirement:** >90% for business logic services **AI Agent Requirements:** - Generate tests for all public methods - Cover edge cases and error conditions - Follow AAA pattern (Arrange, Act, Assert) - Mock all external dependencies #### Integration Tests - **Scope:** Service-to-service communication, database operations - **Location:** `tests/integration` directory - **Test Infrastructure:** - **Database:** Testcontainers PostgreSQL for integration tests - **Message Queue:** Embedded Kafka for tests - **External APIs:** WireMock for stubbing - **AI Services:** Mock models with deterministic responses #### End-to-End Tests - **Framework:** Playwright for web UI testing - **Scope:** Critical user journeys across frontend and backend - **Environment:** Dedicated E2E environment with test data - **Test Data:** Synthetic data generation with realistic patterns ### Test Data Management - **Strategy:** Factory pattern for test data generation - **Database:** Isolated test database per test suite - **Cleanup:** Automatic cleanup after each test - **Fixtures:** Shared test data for common scenarios ## Security Standards ### Secure Development - **SAST:** SonarQube for static analysis - **Dependency Scanning:** Snyk for vulnerability detection - **Secrets Management:** No hardcoded secrets, environment variables only - **Input Validation:** All user inputs validated and sanitized ### Operational Security - **Access Control:** Principle of least privilege - **Audit Logging:** All authentication and authorization events - **Incident Response:** 24/7 monitoring with automated alerting - **Backup Strategy:** Daily encrypted backups with 30-day retention ### Dependency Management - **Node.js:** npm audit with automated security updates - **Python:** Safety for vulnerability scanning - **Base Images:** Regularly updated distroless containers - **Approval Process:** Security review for new dependencies ### Security Testing - **SAST Tool:** SonarQube integrated into CI/CD pipeline - **DAST Tool:** OWASP ZAP for runtime security testing - **Penetration Testing:** Quarterly external security assessments ## Checklist Results Report \*execute-checklist architect-checklist ## Next Steps ### Design Architect Prompt "Please review the TaskFlow Pro Architecture Document and create a comprehensive Frontend Architecture Document focusing on the React-based user interface. Pay special attention to the AI-powered dashboard components, natural language interface implementation, and real-time update mechanisms. Ensure the frontend architecture aligns with the microservices backend and supports the <200ms response time requirements." ### Developer Handoff "Please review the TaskFlow Pro Architecture Document and coding standards before beginning implementation. Start with Epic 1 - Foundation & AI Infrastructure, specifically Story 1.1 - Project Setup and Infrastructure. Follow the monorepo structure, containerization approach, and CI/CD pipeline specifications outlined in the architecture. All development must adhere to the TypeScript and Python coding standards defined in this document."