Design a Frontend Architecture for a Cross-Platform Desktop Conversational AI App

Context

You are designing the frontend architecture for a cross-platform desktop conversational AI application. The app should run on macOS, Windows, and Linux using a web technology stack (e.g., Electron, Tauri, or a native WebView shell). The application must support token-streaming chat, attachments, multiple concurrent chats/workspaces, and multi-window usage while emphasizing security, performance, and extensibility.

Requirements

1. Core modules
   • Chat view with token streaming
   • File attachments and previews
   • Multi-chat workspaces and tabbed navigation
   • Multi-window support
2. State management and persistence
   • Session history and drafts
   • Offline-first data model
   • Encryption (end-to-end or at-rest)
   • Cloud sync with conflict resolution
3. Performance
   • Virtualized rendering for long conversations
   • Incremental rendering of streaming tokens
   • Use of Web Workers and OffscreenCanvas where helpful
   • Memory constraints and backpressure
   • Avoiding layout thrash
4. Security
   • IPC boundaries and sandboxing
   • Filesystem/network permission model
   • Secret handling and storage
   • Content sanitization
   • Update integrity and code signing
5. Observability
   • Telemetry and privacy-preserving analytics
   • Crash reporting and performance tracing
6. Build and deployment
   • Packaging across OSes
   • Auto-updates and differential updates
   • Differential loading and lazy loading
   • Feature flags and rollback strategy
7. Testing
   • Unit, integration, and E2E
   • Contract tests for IPC boundaries
8. Accessibility and i18n
9. Extensibility
   • Plugin system (capabilities/permissions)
   • Theming
10. Trade-offs
    • Compare Electron, Tauri, and native WebView stacks
11. Deliverables
    • A high-level component diagram
    • A high-level state/data flow diagram
    • Discussion of key design decisions and trade-offs