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# Task Graph System - Project Index **Project Type**: Research & Development **Status**: 🔬 Research Phase - Architecture & Feasibility **Priority**: Low - Experimental Initiative **Owner**: Research Team ## Quick Navigation ### Core Documents - 📖 [Project Overview](README.md) - High-level system description - 🎯 [Product Specification](CLAUDE_AGENTS_PRODUCT_SPECIFICATION.md) - Comprehensive product vision - 🧪 [Intention-Driven Experiment](INTENTION_DRIVEN_PROGRAMMING_EXPERIMENT.md) - Research approach ### Technical Architecture - 🏗️ [System Architecture](architecture/technical_architecture.md) - Core system design - 🤖 [Agent Specifications](agent_specifications/) - Individual agent designs - [Orchestration Manager](agent_specifications/orchestration_manager.md) - [Task Graph Constructor](agent_specifications/task_graph_constructor.md) - 📡 [Communication Protocols](protocols/agent_communication.md) - Inter-agent messaging ### Implementation & Documentation - 📋 [Implementation Plans](implementation/) - Development roadmaps - [Master Plan](implementation/master_implementation_plan.md) - [Revised Plan](implementation/revised_master_implementation_plan.md) - 📚 [User Guide](documentation/user_guide.md) - End-user documentation ## Project Overview ### Purpose Task Graph System is an experimental AI agent orchestration framework designed to enable complex, multi-step task execution through intelligent agent coordination and dynamic task graph construction. ### Vision Create a system where AI agents can collaborate autonomously to break down complex problems into manageable sub-tasks, execute them in parallel where possible, and synthesize results into comprehensive solutions. ### Scope - **Agent Orchestration**: Multi-agent coordination and task distribution - **Dynamic Planning**: Adaptive task graph construction based on problem analysis - **Parallel Execution**: Concurrent task processing with dependency management - **Intent Recognition**: Natural language to executable task graph translation - **Result Synthesis**: Intelligent combination of partial results ### Key Research Questions - [ ] Can AI agents effectively decompose complex problems autonomously? - [ ] What protocols enable reliable inter-agent communication? - [ ] How can task dependencies be managed dynamically? - [ ] What user interfaces best support intention-driven programming? - [ ] How can system reliability be ensured with autonomous agents? ## Current Status ### Phase: Architecture Research & Feasibility Study **Start Date**: Research phase (ongoing) **Focus**: System design, protocol definition, feasibility assessment ### Research Objectives - Define agent architecture and communication protocols - Prototype task graph construction algorithms - Evaluate AI model capabilities for task decomposition - Design user interaction patterns - Assess technical feasibility and resource requirements ### Recent Research Activities - ✅ Initial product specification completed - ✅ Agent role definitions established - ✅ Communication protocol draft created - 🔄 Technical architecture under development - 📋 Implementation planning in progress ### Next Research Milestones - [ ] **Prototype Development** - Build minimal viable system - Target: Q1 2026 - [ ] **Feasibility Assessment** - Evaluate core assumptions - Target: Q2 2026 - [ ] **Go/No-Go Decision** - Determine project viability - Target: Q2 2026 ## Technical Architecture ### Core Components - **Orchestration Manager**: Central coordinator for agent activities - **Task Graph Constructor**: Dynamic problem decomposition engine - **Agent Communication Layer**: Message routing and protocol enforcement - **Execution Engine**: Parallel task processing with dependency management - **Intent Parser**: Natural language to task graph translation ### Key Technical Challenges - **Agent Reliability**: Ensuring consistent behavior from AI agents - **Task Dependencies**: Managing complex dependency graphs dynamically - **Error Handling**: Graceful degradation when agents fail - **Performance**: Balancing thoroughness with execution speed - **User Interface**: Making complex capabilities accessible ### Technology Considerations - **AI Models**: Large language models for task understanding and execution - **Message Queues**: Asynchronous communication infrastructure - **Graph Databases**: Dynamic task graph storage and manipulation - **Container Orchestration**: Scalable agent deployment and management ## Research Methodology ### Experimental Approach 1. **Literature Review**: Study existing multi-agent systems and task planning 2. **Prototype Development**: Build minimal components to test core concepts 3. **Controlled Testing**: Evaluate performance on well-defined problem sets 4. **User Studies**: Assess usability of intention-driven interfaces 5. **Performance Analysis**: Measure efficiency vs traditional approaches ### Success Criteria - **Task Decomposition Quality**: Agents break down problems effectively - **Coordination Efficiency**: Minimal overhead from agent communication - **User Experience**: Natural interaction with complex capabilities - **System Reliability**: Predictable behavior under various conditions - **Performance Gains**: Measurable improvement over single-agent approaches ## Risk Assessment ### Technical Risks | Risk | Probability | Impact | Mitigation Strategy | |------|-------------|--------|-------------------| | AI Model Limitations | High | High | Extensive testing, fallback strategies | | Coordination Complexity | Medium | High | Simplified protocols, gradual complexity | | Performance Overhead | Medium | Medium | Benchmarking, optimization focus | | System Reliability | Medium | High | Comprehensive error handling | ### Research Risks - **Feasibility**: Core concepts may not be practically implementable - **Resource Requirements**: System may require prohibitive computational resources - **User Adoption**: Interface complexity may limit practical usage - **Technical Debt**: Experimental code may not scale to production systems ## Resource Requirements ### Research Phase - **Personnel**: 1 researcher, part-time engagement - **Infrastructure**: Development environment, cloud compute for testing - **Timeline**: 6-12 months for feasibility assessment - **Budget**: Minimal - primarily time investment ### Potential Development Phase - **Team Size**: 2-3 developers for prototype development - **Infrastructure**: Distributed system infrastructure, AI model hosting - **Timeline**: 12-18 months for working prototype - **Budget**: Significant - full development resources ## Future Vision ### Short-term Goals (6 months) - Complete architecture specification - Build proof-of-concept components - Validate core technical assumptions - Assess resource requirements for full development ### Medium-term Goals (12-18 months) - Develop working prototype system - Conduct user testing and feedback collection - Performance benchmarking against existing solutions - Go/no-go decision for full product development ### Long-term Vision (2+ years) - Production-ready multi-agent orchestration platform - Integration with existing AI development workflows - Community ecosystem of specialized agents - Commercial viability and market adoption ## Relationship to Other Projects ### Synergies with AutoDocs MCP - **MCP Protocol**: Potential agent communication mechanism - **Documentation Context**: Agents could leverage AutoDocs for technical context - **Development Experience**: Lessons from MCP server development ### Integration Opportunities - **Claude Code**: Natural integration point for intention-driven programming - **Development Tools**: Could enhance existing AI development workflows - **Enterprise Solutions**: Potential for complex business process automation --- ## Getting Started ### For Researchers 1. **Background Reading**: Review [Product Specification](CLAUDE_AGENTS_PRODUCT_SPECIFICATION.md) 2. **Technical Deep Dive**: Study [Architecture](architecture/technical_architecture.md) 3. **Current Work**: Check [Implementation Plans](implementation/) for next steps ### For Contributors 1. **Research Phase**: System is not yet ready for code contributions 2. **Concept Feedback**: Reviews of architecture and approach welcome 3. **Domain Expertise**: Insights on multi-agent systems and task planning valuable ### For Stakeholders 1. **Project Status**: Currently in research and feasibility assessment phase 2. **Timeline**: 6-12 months for initial feasibility determination 3. **Investment**: Low current commitment, potential for significant future investment --- *Project initiated: 2025* *Research phase: Ongoing* *Status last updated: August 11, 2025*