AutoDocs MCP Server

# Intention-Driven Programming: An Experiment in Agent-Based Task Orchestration **Status**: Conceptual Design & Initial Exploration **Created**: 2025-08-10 **Context**: Learning from Claude sub-agent capabilities and factory line principles ## The Core Hypothesis Programming could fundamentally shift from **language-based implementation** to **intention-based orchestration** by leveraging specialist agents that understand how to decompose complex tasks into coordinated, executable work streams. This represents the next evolution of meta-programming: instead of writing code that generates code, we create agent systems that understand intentions and generate the right code through coordinated specialist work. ## The Factory Line Analogy The modern industrial revolution succeeded by breaking complex manufacturing into: 1. **Idempotent tasks** - each step produces predictable, repeatable results 2. **Dependency graphs** - understanding what must complete before the next step 3. **Specialist workers** - each person optimized for specific types of work 4. **Orchestration management** - coordination of the entire production line Programming today resembles pre-industrial craftsmanship: a single developer handling all aspects of implementation. But what if we could apply factory line principles to software development? ## The Technical Vision ### Core Components #### 1. Task Decomposition Engine - Analyzes high-level programming intentions - Breaks them down into discrete, idempotent tasks - Maps dependencies between tasks based on data flow requirements - Creates executable task graphs #### 2. Specialist Agent Ecosystem **Foundation Agents** (every project needs): - Project definition and scope analysis - Architecture and system design - Quality assurance and validation - Documentation and communication **Domain Specialist Agents** (project-specific): - Core business logic implementation - Testing strategies and execution - API design and protocol implementation - Database design and migration - Frontend/UI development - DevOps and deployment - Security analysis and hardening **Meta Agents** (system management): - Agent discovery (what specialists do I need for this project?) - Agent creation (build new specialists for unique requirements) - Orchestration management (task graph execution) - Context coordination (managing data flow between tasks) #### 3. Orchestration Framework - **Input/Output Cascading**: Ensuring task outputs become proper inputs for dependent tasks - **Prerequisite Validation**: Checking all requirements are met before task execution - **Parallel Execution**: Running independent tasks simultaneously - **Error Recovery**: Handling failures and retry strategies - **Progress Tracking**: Real-time visibility into task graph execution ### Current Implementation Context This experiment builds on the AutoDocs MCP Server project, which already demonstrates sophisticated agent specialization: - 8 specialist agents (core-services, mcp-protocol, testing-specialist, docs-integration, etc.) - Complex multi-agent coordination for documentation and development tasks - File-based state management and context sharing - Claude Code integration with hooks and session management ## The Experiment Design ### Phase 1: Proof of Concept **Objective**: Can current Claude sub-agents effectively decompose and execute a moderately complex programming task? **Test Case**: "Build a REST API endpoint that fetches GitHub repository information, caches it locally, and provides search functionality" **Expected Decomposition**: 1. API design and specification 2. GitHub API client implementation 3. Local caching layer (Redis/file-based) 4. Search indexing and query handling 5. Error handling and rate limiting 6. Unit and integration testing 7. API documentation 8. Deployment configuration **Success Criteria**: - Task breakdown is logical and dependency-aware - Each task can be executed independently by appropriate specialists - Final integration produces working, tested, documented code - Time to completion is competitive with traditional development ### Phase 2: Orchestration Intelligence **Objective**: Build meta-agents that can intelligently manage task graphs and agent coordination **Components to Build**: - Task decomposition agent that understands programming patterns - Dependency mapping agent that identifies data flow requirements - Agent assignment optimizer that matches tasks to specialist capabilities - Execution coordinator that manages parallel processing and error recovery ### Phase 3: Agent Ecosystem Development **Objective**: Create a self-improving system that can discover and build new specialist agents as needed **Components to Build**: - Agent discovery system (what specialists do I need for this type of work?) - Agent creation framework (build new specialists for unique requirements) - Agent capability assessment (what can each agent actually do well?) - Agent performance optimization (improve specialist effectiveness over time) ## Technical Challenges & Questions ### Context Management - How do we ensure task outputs are properly formatted as inputs for dependent tasks? - How do we handle context that spans multiple tasks (database schemas, API contracts, etc.)? - How do we manage state that persists across task boundaries? ### Quality Assurance - How do we validate that the task decomposition is correct and complete? - How do we ensure individual task outputs meet quality standards before passing to dependent tasks? - How do we handle integration testing when tasks are executed by different agents? ### Error Handling & Recovery - What happens when a task fails halfway through execution? - How do we handle cascading failures where one task failure invalidates dependent work? - How do we implement rollback and retry strategies for complex task graphs? ### Performance & Efficiency - Is agent coordination overhead acceptable compared to traditional development? - How do we optimize for parallel execution while respecting dependencies? - How do we minimize redundant work when multiple tasks need similar context? ## Current Implementation Strategy ### Building on AutoDocs Infrastructure The AutoDocs project provides an existing foundation: - **Claude Code Integration**: Native sub-agent system with hooks and session management - **Specialist Agent Library**: Proven agents for common development tasks - **File-Based Coordination**: Working patterns for cross-agent context sharing - **Quality Standards**: Established patterns for testing, documentation, and deployment ### Constraint-Driven Design **Claude Agents Only**: No external orchestration services - leverage existing Claude sub-agent capabilities **Hook-Based Coordination**: Use Claude Code hooks for task transitions and validation **File-Based State**: Manage task state and context through file system (`.claude/dev/`) **Session-Aware**: Work within Claude Code session lifecycle limitations ### Implementation Approach 1. **Enhance Existing Agents**: Add orchestration capabilities to current AutoDocs agents 2. **Create Meta-Agents**: Build task decomposition and coordination specialists 3. **Prototype Simple Workflows**: Test with 2-3 task sequences before scaling complexity 4. **Measure & Iterate**: Compare intention-driven vs traditional development approaches ## Success Metrics ### Programming Efficiency - **Time to Implementation**: How much faster is intention → working code? - **Context Switching Overhead**: Reduced mental load from task coordination - **Quality Consistency**: More consistent results across different types of tasks ### System Intelligence - **Decomposition Accuracy**: How well does the system break down complex intentions? - **Dependency Recognition**: Does it correctly identify task prerequisites and data flow? - **Agent Assignment**: How effectively does it match tasks to specialist capabilities? ### Developer Experience - **Intention Clarity**: How precisely can developers express what they want? - **Progress Visibility**: Can developers understand what the system is doing? - **Control & Override**: Can developers intervene when the system makes mistakes? ## Next Steps & Open Questions ### Immediate Exploration 1. **Test Current Capabilities**: How well can existing AutoDocs agents handle task decomposition? 2. **Map Dependency Patterns**: What are common data flow patterns in programming tasks? 3. **Prototype Simple Orchestration**: Build a basic task → agent → execution flow ### Research Questions 1. **Granularity Balance**: What's the optimal size for individual tasks? 2. **Agent Specialization**: How specific should specialist agents be? 3. **Context Boundaries**: Where do task boundaries naturally occur in programming work? 4. **Error Recovery**: What recovery strategies work best for programming task graphs? ### Technical Development 1. **Build Task Decomposition Agent**: Meta-agent that understands programming patterns 2. **Create Agent Discovery System**: Identify what specialists are needed for each project type 3. **Design Context Coordination**: Manage data flow and state across task boundaries 4. **Implement Quality Gates**: Validation points between tasks ## Why This Matters If successful, intention-driven programming could fundamentally change how we approach software development: - **Faster Development**: Complex features implemented through coordinated specialist work - **Higher Quality**: Each task handled by agents optimized for that type of work - **Better Architecture**: System-level thinking built into the decomposition process - **Knowledge Leverage**: Specialist agents accumulate and apply domain expertise - **Reduced Cognitive Load**: Developers focus on intentions rather than implementation details This isn't about replacing programmers - it's about amplifying their capability by providing an intelligent orchestration layer that understands how to coordinate specialist work toward programming goals. The factory line didn't eliminate skilled workers - it made them more effective by organizing their work intelligently. Intention-driven programming could do the same for software development. ## Document Status **Current Phase**: Conceptual design and initial capability assessment **Next Phase**: Prototype task decomposition with existing AutoDocs agents **Timeline**: Experimental exploration over next 2-4 weeks **Success Criteria**: Working demonstration of intention → coordinated execution → functional code --- *This document captures the complete vision and context for the intention-driven programming experiment. It serves as the foundation for future blog posts, README documentation, and technical implementation planning.*