A conceptual architecture for domain-isolated AI workspaces with intelligent orchestration.
This repository contains technical design documents exploring a hierarchical AI workspace system that provides complete domain isolation with intelligent orchestration. Think of it as "Chrome Profiles meets AI Agents" - each workspace maintains its own identity, tools, and sub-agents, coordinated by a centralized master instance.
This is a design and research document, not an implementation. The purpose is to:
- Explore the architecture of multi-workspace AI systems
- Analyze current technological readiness and gaps
- Document design patterns for workspace isolation
- Evaluate hardware and software requirements
- Identify open questions and technical challenges
- MASTER_AI_WORKSPACE_VISION.md - High-level vision, user experience model, technical analysis, and open questions
- SOFTWARE_ARCHITECTURE.md - Detailed software architecture, component design, and technology stack
- HARDWARE_ARCHITECTURE.md - Physical infrastructure, network topology, and hardware specifications
Each workspace operates as an isolated environment with:
- Separate MCP tool configurations
- Isolated browser sessions and accounts
- Dedicated file systems
- Independent credential stores
- Specialized sub-agent teams
A centralized routing layer that:
- Analyzes user intent and routes to appropriate workspaces
- Coordinates multi-workspace queries
- Maintains conversation context across workspace switches
- Synthesizes results from multiple domains
- Enforces isolation boundaries (no direct tool access)
- School: Canvas, GitLab, Teams integration with school accounts
- Work: Slack, Jira, corporate tools with work credentials
- Personal: Personal projects, productivity tools, finance APIs
- Project-Specific: Dedicated environments for major projects
The design builds on existing and emerging technologies:
- MCP (Model Context Protocol) - Standardized AI tool interfaces
- LangGraph - Multi-agent orchestration framework
- Docker - Workspace containerization and isolation
- Local LLMs - Privacy-focused inference (Llama, DeepSeek, etc.)
- Agentic Browsers - AI-driven web automation (emerging)
Pre-implementation / Conceptual Phase
While the architecture is technically sound, several dependencies are still maturing:
- Agentic browser technology (6-12 months)
- Cost-effective local inference hardware
- Robust multi-agent frameworks
- Workspace isolation patterns for AI systems
Approximately 80% of this architecture could be implemented with current technology, with limitations around browser automation and account management.
This repository represents design thinking and architectural exploration. Pull requests will not be accepted as this is a personal research document rather than a collaborative implementation project.
However, discussion is welcome:
- Open an issue to discuss architectural decisions
- Share related research or implementations
- Suggest alternative approaches or technologies
Based on current technology trajectories:
- Phase 0 (Current): MCP tools and basic agent frameworks ready
- Phase 1 (3-6 months): Container-based MVP with manual account switching
- Phase 2 (6-12 months): Agentic browser integration maturing
- Phase 3 (12-18 months): Production-ready system possible
- Anthropic MCP - Model Context Protocol specification
- LangGraph - Multi-agent orchestration
- Browser-Use - Agentic browser automation
- Ollama - Local LLM deployment
This work is shared for educational and research purposes. See LICENSE for details.
Note: This is speculative architecture. No implementation currently exists. The documents represent design exploration and technical analysis of what such a system might look like.