AutoDocs MCP Server

# Task-Graph Workflow System: Technical Architecture Documentation **Document Version**: 1.0 **Created**: 2025-08-09 **Target Audience**: Software Architects, Developers, Integration Engineers, Technical Leads **Classification**: Technical Architecture Specification --- ## Table of Contents 1. [Executive Summary](#executive-summary) 2. [System Architecture Overview](#system-architecture-overview) 3. [Detailed Component Specifications](#detailed-component-specifications) 4. [API Specifications](#api-specifications) 5. [Implementation Guidelines](#implementation-guidelines) 6. [Performance & Scalability](#performance--scalability) 7. [Deployment & Operations](#deployment--operations) 8. [Security & Compliance](#security--compliance) 9. [Future Architecture Evolution](#future-architecture-evolution) --- ## Executive Summary The Task-Graph Workflow System is an advanced multi-agent orchestration framework designed to coordinate 8 specialist agents and 2 meta-agents for complex AI-assisted workflows. Built on the proven AutoDocs MCP Server foundation, this system provides intelligent task decomposition, dynamic agent coordination, and hierarchical context management for enterprise-scale AI automation. ### Key Architectural Principles - **Intelligent Orchestration**: Task Graph Constructor analyzes requests and creates optimized execution workflows - **Dynamic Coordination**: Orchestration Manager handles parallel execution, conflict resolution, and quality assurance - **Hierarchical Context**: Multi-level context architecture (Global → Task-Specific → Agent-Local) - **Proven Foundation**: Built on production-tested AutoDocs MCP Server with 8 core tools and robust infrastructure ### Strategic Value Proposition - **10x Productivity**: Complex workflows requiring 8+ agent interactions handled seamlessly - **Enterprise Reliability**: Production-grade error handling, monitoring, and scalability - **Extensible Architecture**: Plugin-based design for custom agents and workflow patterns - **Zero Vendor Lock-in**: Open MCP protocol with standardized interfaces --- ## System Architecture Overview ### High-Level Architecture Diagram ``` ┌─────────────────────────────────────────────────────────────────────┐ │ TASK-GRAPH WORKFLOW SYSTEM │ ├─────────────────────────────────────────────────────────────────────┤ │ CLIENT INTERFACE │ │ ┌───────────────┐ ┌─────────────────┐ ┌─────────────────────────┐ │ │ │ Claude Code │ │ Cursor IDE │ │ Enterprise Systems │ │ │ │ Integration │ │ Integration │ │ (REST APIs) │ │ │ └───────────────┘ └─────────────────┘ └─────────────────────────┘ │ ├─────────────────────────────────────────────────────────────────────┤ │ META-COGNITIVE LAYER │ │ ┌───────────────────────────────────┐ ┌─────────────────────────┐ │ │ │ TASK GRAPH CONSTRUCTOR │ │ ORCHESTRATION MANAGER │ │ │ │ │ │ │ │ │ │ • Request Analysis │ │ • Workflow Execution │ │ │ │ • Task Decomposition │ │ • Agent Coordination │ │ │ │ • Dependency Mapping │ │ • Conflict Resolution │ │ │ │ • Agent Assignment │ │ • Quality Gates │ │ │ │ • Context Generation │ │ • Performance Tracking │ │ │ └───────────────────────────────────┘ └─────────────────────────┘ │ ├─────────────────────────────────────────────────────────────────────┤ │ CONTEXT MANAGEMENT │ │ ┌─────────────────┐ ┌──────────────┐ ┌──────────────────────────┐ │ │ │ Global Context │ │ Task Context │ │ Agent-Local Context │ │ │ │ │ │ │ │ │ │ │ │ • System State │ │ • Task Spec │ │ • Domain Expertise │ │ │ │ • User Session │ │ • Progress │ │ • Working Memory │ │ │ │ • Preferences │ │ • Resources │ │ • Tools & State │ │ │ └─────────────────┘ └──────────────┘ └──────────────────────────┘ │ ├─────────────────────────────────────────────────────────────────────┤ │ SPECIALIST LAYER │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────────┐ │ │ │ CORE │ │ MCP │ │ DOCS │ │ TESTING │ │ │ │ SERVICES │ │ PROTOCOL │ │ INTEGRATION │ │ SPECIALIST │ │ │ │ AGENT │ │ AGENT │ │ AGENT │ │ AGENT │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────────┘ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────────┐ │ │ │ PRODUCT │ │ PRODUCTION │ │ TECHNICAL │ │ AGENT DESIGN │ │ │ │ MANAGER │ │ OPS │ │ WRITER │ │ ARCHITECT │ │ │ │ AGENT │ │ AGENT │ │ AGENT │ │ AGENT │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────────┘ │ ├─────────────────────────────────────────────────────────────────────┤ │ INFRASTRUCTURE LAYER │ │ ┌───────────────────────────────────────────────────────────────┐ │ │ │ AUTODOCS MCP SERVER FOUNDATION │ │ │ │ │ │ │ │ • 8 Production MCP Tools • Health & Monitoring │ │ │ │ • FastMCP Protocol Handler • Version-Based Caching │ │ │ │ • Async Service Layer • Network Resilience │ │ │ │ • Graceful Shutdown • Error Handling & Recovery │ │ │ │ • Configuration Management • Performance Metrics │ │ │ │ │ │ │ └───────────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘ ``` ### Component Relationships #### Meta-Cognitive Layer - **Task Graph Constructor**: Analyzes complex requests, decomposes into task graphs, assigns agents - **Orchestration Manager**: Executes workflows, coordinates agent interactions, ensures quality #### Information Flow 1. **Request Intake**: Complex requests from clients (Claude Code, IDEs, APIs) 2. **Analysis Phase**: Task Graph Constructor analyzes and decomposes requests 3. **Orchestration Phase**: Orchestration Manager coordinates agent execution 4. **Context Flow**: Hierarchical context propagates through all layers 5. **Result Synthesis**: Orchestration Manager combines agent outputs into cohesive results #### Control Flow 1. **Sequential Execution**: Linear workflows with clear handoffs 2. **Parallel Execution**: Independent tasks executed simultaneously 3. **Conditional Branching**: Dynamic workflows based on intermediate results 4. **Error Recovery**: Automatic retry, fallback, and graceful degradation ### Integration Points #### External System Interfaces - **MCP Protocol**: Standard Model Context Protocol for AI client integration - **REST APIs**: Enterprise system integration and webhook endpoints - **WebSocket**: Real-time workflow status and live collaboration - **File System**: Local project analysis and output generation --- ## Detailed Component Specifications ### Task Graph Constructor Technical Spec #### Core Responsibilities - **Request Analysis**: Parse complex multi-faceted requests into structured requirements - **Task Decomposition**: Break complex workflows into atomic, executable tasks - **Agent Assignment**: Match tasks to optimal agents based on capability scoring - **Dependency Management**: Identify and sequence task dependencies - **Context Generation**: Create task-specific context packages for agents #### Input/Output Interfaces **Input Schema:** ```json { "request_id": "string (UUID)", "request_type": "enum [feature_development, documentation_update, system_analysis, release_management, bug_investigation]", "complexity_level": "integer (1-10)", "priority": "enum [low, medium, high, urgent]", "context": { "user_session": "object", "project_state": "object", "previous_interactions": "array" }, "requirements": { "functional": "array", "non_functional": "array", "constraints": "array" }, "preferences": { "execution_strategy": "enum [fast, thorough, balanced]", "quality_gates": "array", "notification_settings": "object" } } ``` **Output Schema (Task Graph):** ```json { "graph_id": "string (UUID)", "request_id": "string (UUID)", "created_at": "timestamp", "estimated_duration": "integer (minutes)", "tasks": [ { "task_id": "string (UUID)", "task_type": "string", "assigned_agent": "string", "priority": "integer (1-10)", "dependencies": ["string (task_ids)"], "inputs": { "required_context": "array", "input_parameters": "object", "resource_requirements": "object" }, "outputs": { "expected_artifacts": "array", "success_criteria": "array", "quality_metrics": "object" }, "validation_criteria": { "acceptance_tests": "array", "performance_thresholds": "object", "quality_gates": "array" }, "parallel_safe": "boolean", "timeout": "integer (seconds)", "retry_policy": { "max_attempts": "integer", "backoff_strategy": "string", "failure_escalation": "array" } } ], "execution_strategy": { "type": "enum [sequential, parallel, mixed]", "parallelization_groups": "array", "critical_path": "array (task_ids)", "optimization_hints": "object" }, "quality_gates": [ { "gate_id": "string", "trigger_condition": "string", "validation_tasks": "array", "failure_actions": "array" } ], "success_criteria": { "completion_percentage": "integer", "quality_threshold": "number", "performance_targets": "object" } } ``` #### Core Algorithms **Task Decomposition Algorithm:** ```python async def decompose_request(request: ComplexRequest) -> TaskGraph: """ Advanced task decomposition using domain expertise and dependency analysis. Algorithm: 1. Classify request type using ML pattern recognition 2. Apply domain-specific decomposition templates 3. Identify cross-cutting concerns and shared dependencies 4. Optimize task granularity for parallel execution 5. Validate completeness and consistency """ # Phase 1: Request Classification request_type = await classify_request_type(request.requirements) complexity_score = calculate_complexity(request) # Phase 2: Template-Based Decomposition base_tasks = apply_decomposition_template(request_type, request.requirements) # Phase 3: Dependency Analysis dependency_graph = analyze_task_dependencies(base_tasks) optimized_graph = optimize_for_parallelization(dependency_graph) # Phase 4: Agent Assignment agent_assignments = await assign_optimal_agents(optimized_graph.tasks) # Phase 5: Context Generation task_contexts = generate_task_contexts(optimized_graph, request.context) return TaskGraph( tasks=optimized_graph.tasks, dependencies=optimized_graph.dependencies, agent_assignments=agent_assignments, contexts=task_contexts ) ``` **Agent Assignment Engine:** ```python async def assign_optimal_agent(task: Task) -> AgentAssignment: """ Multi-criteria agent selection using capability scoring. Scoring Factors: - Domain expertise match (40%) - Current workload capacity (25%) - Historical performance on similar tasks (20%) - Context compatibility (15%) """ candidates = get_available_agents(task.required_capabilities) scored_candidates = [] for agent in candidates: score = await calculate_agent_score(agent, task) scored_candidates.append((agent, score)) # Select highest scoring agent with availability optimal_agent = max(scored_candidates, key=lambda x: x[1])[0] return AgentAssignment( agent=optimal_agent, confidence_score=score, backup_agents=get_backup_candidates(scored_candidates) ) ``` ### Orchestration Manager Technical Spec #### Core Responsibilities - **Workflow Execution**: Execute task graphs with optimal parallelization - **State Management**: Track workflow state and task progress - **Agent Coordination**: Manage agent communication and handoffs - **Conflict Resolution**: Resolve conflicts between agent outputs - **Quality Assurance**: Implement quality gates and validation checkpoints - **Performance Monitoring**: Track execution metrics and optimization opportunities #### State Management System **Workflow State Schema:** ```json { "workflow_id": "string (UUID)", "graph_id": "string (UUID)", "current_state": "enum [pending, running, paused, completed, failed]", "started_at": "timestamp", "estimated_completion": "timestamp", "progress": { "completed_tasks": "integer", "total_tasks": "integer", "completion_percentage": "number", "current_phase": "string" }, "task_states": [ { "task_id": "string", "state": "enum [pending, assigned, running, validating, completed, failed]", "assigned_agent": "string", "started_at": "timestamp", "completed_at": "timestamp", "attempts": "integer", "outputs": "object", "validation_results": "object", "performance_metrics": "object" } ], "active_agents": ["string"], "resource_utilization": "object", "quality_metrics": "object" } ``` #### Execution Engine **Parallel Task Scheduler:** ```python class ParallelTaskScheduler: """ Advanced task scheduler with dependency management and resource optimization. """ def __init__(self, max_concurrent_tasks: int = 8): self.max_concurrent = max_concurrent_tasks self.active_tasks = {} self.completed_tasks = set() self.failed_tasks = set() self.agent_pool = AgentPool() async def execute_workflow(self, workflow: WorkflowState) -> WorkflowResult: """ Execute workflow with optimal parallelization and error recovery. """ while not self.is_workflow_complete(workflow): # Get ready tasks (dependencies satisfied) ready_tasks = self.get_ready_tasks(workflow) # Schedule tasks respecting concurrency limits await self.schedule_ready_tasks(ready_tasks) # Wait for task completions and handle results completed = await self.wait_for_task_completion() # Process completed tasks and update workflow state for task_result in completed: await self.process_task_completion(task_result, workflow) # Handle any failed tasks with recovery strategies await self.handle_task_failures(workflow) return self.generate_workflow_result(workflow) async def schedule_ready_tasks(self, ready_tasks: List[Task]) -> None: """Schedule tasks with optimal agent assignment and resource management.""" available_slots = self.max_concurrent - len(self.active_tasks) tasks_to_schedule = ready_tasks[:available_slots] for task in tasks_to_schedule: agent = await self.agent_pool.assign_optimal_agent(task) # Create task execution context execution_context = await self.create_execution_context(task) # Start task execution task_future = asyncio.create_task( agent.execute_task(task, execution_context) ) self.active_tasks[task.task_id] = { 'task': task, 'agent': agent, 'future': task_future, 'started_at': time.time() } ``` #### Conflict Resolution Engine **Multi-Agent Decision Synthesis:** ```python class ConflictResolver: """ Advanced conflict resolution for multi-agent decisions. """ async def resolve_conflicts( self, task: Task, agent_outputs: List[AgentOutput] ) -> ResolvedOutput: """ Resolve conflicts between multiple agent outputs using various strategies. """ if len(agent_outputs) == 1: return agent_outputs[0] # No conflict # Detect conflict types conflicts = self.detect_conflicts(agent_outputs) if not conflicts: # Outputs are compatible - merge them return await self.merge_compatible_outputs(agent_outputs) # Apply resolution strategy based on conflict type resolution_strategy = self.select_resolution_strategy(conflicts, task) return await resolution_strategy.resolve(agent_outputs, task) def detect_conflicts(self, outputs: List[AgentOutput]) -> List[Conflict]: """Detect and classify conflicts between agent outputs.""" conflicts = [] # Check for direct contradictions contradictions = self.find_contradictions(outputs) conflicts.extend(contradictions) # Check for incompatible recommendations incompatible = self.find_incompatible_recommendations(outputs) conflicts.extend(incompatible) # Check for quality disagreements quality_conflicts = self.find_quality_disagreements(outputs) conflicts.extend(quality_conflicts) return conflicts ``` ### Context Management Architecture #### Hierarchical Context Model **Context Layer Structure:** ``` Global Context (System-wide) ├── User Session Context │ ├── Authentication & Permissions │ ├── User Preferences & Settings │ └── Session History & State ├── System State Context │ ├── Available Agents & Capabilities │ ├── Resource Utilization Metrics │ └── Configuration & Environment └── Project Context ├── Codebase Structure & Metadata ├── Dependencies & Versions └── Development History & Patterns Task-Specific Context (Per Task Graph) ├── Task Graph Definition │ ├── Task Dependencies & Flow │ ├── Success Criteria & Quality Gates │ └── Resource Requirements ├── Execution State │ ├── Progress & Milestones │ ├── Intermediate Results │ └── Performance Metrics └── Shared Working Memory ├── Cross-Task Data Dependencies ├── Shared Resources & Locks └── Communication History Agent-Local Context (Per Agent Instance) ├── Domain Expertise │ ├── Specialized Knowledge Base │ ├── Best Practices & Patterns │ └── Tool Configurations ├── Working Memory │ ├── Current Task State │ ├── Intermediate Calculations │ └── Error Context & Recovery └── Performance History ├── Task Execution Metrics ├── Success/Failure Patterns └── Learning & Adaptation Data ``` #### Context Lifecycle Management **Context Creation and Enrichment:** ```python class HierarchicalContextManager: """ Manages multi-level context with efficient loading and synchronization. """ def __init__(self): self.global_context = GlobalContext() self.task_contexts = {} # task_id -> TaskContext self.agent_contexts = {} # agent_id -> AgentContext async def create_task_context( self, task_graph: TaskGraph, request: ComplexRequest ) -> TaskContext: """Create rich task context from global context and request.""" task_context = TaskContext( task_graph=task_graph, request_context=request.context, created_at=datetime.utcnow() ) # Enrich with relevant global context task_context.project_info = await self.extract_project_context( request.context.get('project_path') ) task_context.user_preferences = self.global_context.get_user_preferences( request.context.get('user_id') ) # Create shared working memory task_context.shared_memory = SharedWorkingMemory( capacity=self.calculate_memory_requirements(task_graph) ) self.task_contexts[task_graph.graph_id] = task_context return task_context async def get_agent_context( self, agent_id: str, task: Task, task_context: TaskContext ) -> AgentContext: """Create agent-specific context with task and global context.""" if agent_id not in self.agent_contexts: self.agent_contexts[agent_id] = AgentContext(agent_id=agent_id) agent_context = self.agent_contexts[agent_id] # Load relevant portions of higher-level contexts agent_context.load_global_context( self.global_context, relevance_filter=task.required_capabilities ) agent_context.load_task_context( task_context, task_specific_filter=task.task_id ) # Add task-specific working memory agent_context.working_memory.load_task_state(task) return agent_context ``` #### Context Access Control **Access Pattern Implementation:** ```python class ContextAccessManager: """ Manages secure and efficient context access across agents. """ def __init__(self): self.access_policies = self.load_access_policies() self.context_cache = LRUCache(maxsize=1000) async def get_context_for_agent( self, agent_id: str, task_id: str, context_requirements: List[str] ) -> FilteredContext: """Get filtered context appropriate for agent and task.""" # Check access permissions permitted_contexts = self.check_access_permissions( agent_id, context_requirements ) # Load only permitted context elements context = FilteredContext() for context_type in permitted_contexts: context_data = await self.load_context_data( context_type, task_id, agent_id ) context.add_context(context_type, context_data) # Cache for efficiency cache_key = f"{agent_id}:{task_id}:{hash(tuple(permitted_contexts))}" self.context_cache[cache_key] = context return context ``` --- ## API Specifications ### MCP Tool Extensions #### Task Graph MCP Tools **1. submit_complex_request** ```python @mcp.tool async def submit_complex_request( request_type: str, requirements: dict, complexity_level: int = 5, execution_strategy: str = "balanced", context: dict = None ) -> dict: """ Submit a complex multi-agent request for task graph processing. Args: request_type: Type of request (feature_development, system_analysis, etc.) requirements: Structured requirements with functional/non-functional specs complexity_level: Estimated complexity from 1-10 execution_strategy: "fast", "thorough", or "balanced" context: Additional context (project_path, user_preferences, etc.) Returns: Task graph with execution plan and workflow ID """ ``` **2. get_workflow_status** ```python @mcp.tool async def get_workflow_status(workflow_id: str) -> dict: """ Get real-time status of a running workflow. Args: workflow_id: Unique workflow identifier Returns: Current workflow state with progress, active tasks, and metrics """ ``` **3. agent_collaboration_request** ```python @mcp.tool async def agent_collaboration_request( requesting_agent: str, target_agents: List[str], collaboration_type: str, context: dict, urgency: str = "normal" ) -> dict: """ Enable direct agent-to-agent collaboration requests. Args: requesting_agent: ID of agent requesting collaboration target_agents: List of target agent IDs collaboration_type: "consultation", "review", "handoff", "merge" context: Collaboration context and requirements urgency: Priority level for collaboration request Returns: Collaboration session details and communication channels """ ``` ### Agent Communication Protocols #### Task Initiation Protocol **Agent Task Assignment Message:** ```json { "message_type": "task_assignment", "message_id": "string (UUID)", "timestamp": "ISO 8601", "workflow_id": "string (UUID)", "task_id": "string (UUID)", "assigned_agent": "string", "task_details": { "task_type": "string", "priority": "integer (1-10)", "estimated_duration": "integer (minutes)", "timeout": "integer (seconds)", "retry_policy": "object" }, "context": { "global_context": "object", "task_context": "object", "agent_context": "object" }, "inputs": { "parameters": "object", "dependencies": "array (task_results)", "resources": "object" }, "success_criteria": { "acceptance_tests": "array", "quality_metrics": "object", "output_requirements": "object" }, "communication_channels": { "status_updates": "string (endpoint)", "collaboration_requests": "string (endpoint)", "error_reporting": "string (endpoint)" } } ``` #### Progress Reporting Protocol **Agent Progress Update Message:** ```json { "message_type": "progress_update", "message_id": "string (UUID)", "timestamp": "ISO 8601", "workflow_id": "string (UUID)", "task_id": "string (UUID)", "agent_id": "string", "status": "enum [started, in_progress, blocked, completed, failed]", "progress": { "completion_percentage": "number (0-100)", "current_phase": "string", "completed_milestones": "array", "next_milestone": "string" }, "intermediate_results": "object (optional)", "performance_metrics": { "execution_time": "number (seconds)", "resource_usage": "object", "quality_indicators": "object" }, "issues": [ { "issue_type": "enum [warning, error, blocker]", "description": "string", "suggested_resolution": "string", "escalation_required": "boolean" } ], "collaboration_requests": "array (optional)" } ``` ### External Integration APIs #### Workflow Submission API **REST Endpoint: POST /api/v1/workflows** ```json { "request": { "type": "string", "requirements": "object", "complexity": "integer", "strategy": "string" }, "context": "object", "preferences": "object", "callback_url": "string (optional)", "webhook_events": "array (optional)" } Response: { "workflow_id": "string (UUID)", "estimated_duration": "integer (minutes)", "task_count": "integer", "assigned_agents": "array", "status_endpoint": "string (URL)", "websocket_endpoint": "string (URL)" } ``` #### Status Monitoring API **WebSocket: /ws/workflows/{workflow_id}** ```json { "event_type": "enum [status_change, task_completed, agent_communication, error, completion]", "timestamp": "ISO 8601", "workflow_id": "string", "data": { "current_status": "string", "progress": "object", "active_agents": "array", "recent_updates": "array" } } ``` --- ## Implementation Guidelines ### Development Environment Setup #### Dependencies and Requirements **Core Dependencies:** ```toml # pyproject.toml additions for Task Graph System [tool.task-graph-system] name = "autodocs-task-graph" version = "1.0.0" [tool.task-graph-system.dependencies] # Meta-cognitive layer pydantic = "^2.5.0" # Data models and validation fastapi = "^0.104.0" # REST API endpoints websockets = "^12.0" # Real-time communication networkx = "^3.2" # Graph algorithms and analysis redis = "^5.0.0" # Distributed state management # AI and ML capabilities sentence-transformers = "^2.2.0" # Semantic similarity scikit-learn = "^1.3.0" # ML algorithms for agent selection transformers = "^4.35.0" # Text processing and analysis # Workflow orchestration celery = "^5.3.0" # Distributed task execution kombu = "^5.3.0" # Message passing croniter = "^2.0.0" # Scheduling and timing # Enhanced monitoring prometheus-client = "^0.19.0" # Metrics collection opentelemetry-api = "^1.21.0" # Distributed tracing structlog = "^23.2.0" # Structured logging (already present) # Development and testing pytest-asyncio = "^0.21.0" # Already present pytest-mock = "^3.12.0" # Already present factory-boy = "^3.3.0" # Test data generation ``` **Configuration Management:** ```python # src/autodocs_mcp/task_graph/config.py from typing import Dict, List, Optional from pydantic import BaseModel, Field from pathlib import Path class TaskGraphConfig(BaseModel): """Configuration for Task Graph Workflow System.""" # Orchestration settings max_concurrent_workflows: int = Field(default=10, ge=1, le=100) max_concurrent_tasks_per_workflow: int = Field(default=8, ge=1, le=20) task_timeout_default: int = Field(default=1800, ge=60, le=7200) # 30 minutes workflow_timeout_default: int = Field(default=14400, ge=300, le=86400) # 4 hours # Agent pool configuration agent_pool_size: int = Field(default=16, ge=8, le=50) agent_idle_timeout: int = Field(default=300, ge=60, le=3600) # 5 minutes agent_health_check_interval: int = Field(default=30, ge=10, le=300) # Context management context_cache_size: int = Field(default=1000, ge=100, le=10000) context_ttl: int = Field(default=3600, ge=300, le=86400) # 1 hour max_context_memory_mb: int = Field(default=512, ge=64, le=2048) # Quality and performance quality_gate_timeout: int = Field(default=300, ge=30, le=1800) # 5 minutes performance_monitoring_enabled: bool = Field(default=True) distributed_tracing_enabled: bool = Field(default=False) # State persistence redis_url: Optional[str] = Field(default=None) state_persistence_enabled: bool = Field(default=False) state_backup_interval: int = Field(default=300, ge=60, le=3600) # Integration endpoints webhook_endpoints: Dict[str, str] = Field(default_factory=dict) notification_channels: List[str] = Field(default_factory=list) @property def redis_enabled(self) -> bool: return self.redis_url is not None and self.state_persistence_enabled ``` #### Testing Framework Configuration **Test Infrastructure:** ```python # tests/task_graph/conftest.py import pytest import asyncio from unittest.mock import AsyncMock from typing import Dict, Any from autodocs_mcp.task_graph.orchestrator import OrchestrationManager from autodocs_mcp.task_graph.constructor import TaskGraphConstructor from autodocs_mcp.task_graph.context import HierarchicalContextManager from autodocs_mcp.task_graph.agents import AgentPool @pytest.fixture async def mock_agent_pool(): """Mock agent pool with all 8 specialist agents.""" pool = AsyncMock(spec=AgentPool) # Mock agent assignments for each specialist agents = { 'core-services': AsyncMock(), 'mcp-protocol': AsyncMock(), 'docs-integration': AsyncMock(), 'testing-specialist': AsyncMock(), 'product-manager': AsyncMock(), 'production-ops': AsyncMock(), 'technical-writer': AsyncMock(), 'agent-design-architect': AsyncMock() } for agent_id, agent_mock in agents.items(): agent_mock.agent_id = agent_id agent_mock.execute_task = AsyncMock(return_value={'success': True}) agent_mock.get_capabilities = AsyncMock(return_value=['default']) pool.get_agent.side_effect = lambda agent_id: agents.get(agent_id) pool.assign_optimal_agent = AsyncMock(return_value=agents['core-services']) return pool @pytest.fixture async def mock_context_manager(): """Mock hierarchical context manager.""" manager = AsyncMock(spec=HierarchicalContextManager) manager.create_task_context = AsyncMock(return_value={'task_context': 'mock'}) manager.get_agent_context = AsyncMock(return_value={'agent_context': 'mock'}) return manager @pytest.fixture async def task_graph_constructor(mock_agent_pool, mock_context_manager): """Task Graph Constructor with mocked dependencies.""" constructor = TaskGraphConstructor( agent_pool=mock_agent_pool, context_manager=mock_context_manager ) return constructor @pytest.fixture async def orchestration_manager(mock_agent_pool, mock_context_manager): """Orchestration Manager with mocked dependencies.""" manager = OrchestrationManager( agent_pool=mock_agent_pool, context_manager=mock_context_manager ) return manager @pytest.fixture def sample_complex_request(): """Sample complex request for testing.""" return { "request_id": "test-request-123", "request_type": "feature_development", "complexity_level": 7, "requirements": { "functional": [ "Add new MCP tool for semantic search", "Implement caching for search results", "Add configuration options" ], "non_functional": [ "Response time < 500ms", "95% cache hit rate", "Comprehensive test coverage" ] }, "context": { "project_path": "/test/project", "user_preferences": {"quality": "high"} } } ``` ### Agent Integration Patterns #### Making Existing Agents Task-Graph Compatible **Agent Interface Adaptation:** ```python # src/autodocs_mcp/task_graph/agents/base.py from abc import ABC, abstractmethod from typing import Any, Dict, List, Optional from pydantic import BaseModel class TaskContext(BaseModel): """Context information for task execution.""" task_id: str task_type: str global_context: Dict[str, Any] task_context: Dict[str, Any] agent_context: Dict[str, Any] class TaskResult(BaseModel): """Standard task execution result.""" success: bool outputs: Dict[str, Any] performance_metrics: Dict[str, Any] intermediate_artifacts: List[Any] = [] error_context: Optional[Dict[str, Any]] = None class TaskGraphAgent(ABC): """Base class for task-graph compatible agents.""" def __init__(self, agent_id: str): self.agent_id = agent_id self.capabilities = self.get_capabilities() self.current_tasks = {} @abstractmethod def get_capabilities(self) -> List[str]: """Return list of agent capabilities.""" pass @abstractmethod async def execute_task( self, task: Task, context: TaskContext ) -> TaskResult: """Execute a task with given context.""" pass async def validate_task_compatibility(self, task: Task) -> bool: """Check if agent can handle the given task.""" required_caps = task.required_capabilities return all(cap in self.capabilities for cap in required_caps) async def estimate_task_duration(self, task: Task) -> int: """Estimate task duration in seconds.""" # Default implementation - agents can override complexity_multiplier = { 1: 60, # 1 minute for simple tasks 5: 300, # 5 minutes for medium tasks 10: 1800 # 30 minutes for complex tasks } return complexity_multiplier.get(task.complexity_level, 300) ``` **Core Services Agent Integration:** ```python # src/autodocs_mcp/task_graph/agents/core_services_agent.py from .base import TaskGraphAgent, TaskResult, TaskContext from ..core.dependency_parser import PyProjectParser from ..core.cache_manager import FileCacheManager class CoreServicesTaskGraphAgent(TaskGraphAgent): """Core Services agent adapted for task graph execution.""" def __init__(self): super().__init__(agent_id="core-services") self.parser = None self.cache_manager = None def get_capabilities(self) -> List[str]: """Core services agent capabilities.""" return [ "dependency_parsing", "cache_management", "version_resolution", "documentation_fetching", "performance_optimization", "concurrent_processing" ] async def execute_task( self, task: Task, context: TaskContext ) -> TaskResult: """Execute core services task.""" try: # Initialize services if needed await self._ensure_services_initialized() # Route to appropriate handler based on task type if task.task_type == "dependency_analysis": result = await self._handle_dependency_analysis(task, context) elif task.task_type == "cache_optimization": result = await self._handle_cache_optimization(task, context) elif task.task_type == "performance_analysis": result = await self._handle_performance_analysis(task, context) else: raise ValueError(f"Unknown task type: {task.task_type}") return TaskResult( success=True, outputs=result, performance_metrics=self._collect_performance_metrics() ) except Exception as e: return TaskResult( success=False, outputs={}, performance_metrics=self._collect_performance_metrics(), error_context={ "error_type": type(e).__name__, "error_message": str(e), "task_id": task.task_id } ) async def _handle_dependency_analysis( self, task: Task, context: TaskContext ) -> Dict[str, Any]: """Handle dependency parsing and analysis tasks.""" project_path = task.inputs.get("project_path") if not project_path: raise ValueError("project_path required for dependency analysis") # Parse project dependencies parsing_result = await self.parser.parse_project(Path(project_path)) # Analyze dependency health and conflicts analysis = await self._analyze_dependency_health(parsing_result) return { "parsing_result": parsing_result.model_dump(), "health_analysis": analysis, "recommendations": self._generate_dependency_recommendations(analysis) } ``` #### Context Handling Patterns **Context Consumption and Contribution:** ```python class ContextAwareAgent(TaskGraphAgent): """Agent with advanced context handling capabilities.""" async def load_context(self, context: TaskContext) -> None: """Load and process relevant context for task execution.""" # Load global context relevant to agent domain self.global_state = self._filter_global_context( context.global_context, self.capabilities ) # Process task-specific context self.task_state = context.task_context # Initialize agent-local context self.working_memory = context.agent_context.get("working_memory", {}) self.domain_knowledge = self._load_domain_knowledge() async def contribute_context(self, result: TaskResult) -> Dict[str, Any]: """Contribute context for downstream tasks.""" context_contribution = { "agent_id": self.agent_id, "execution_timestamp": time.time(), "outputs_summary": self._summarize_outputs(result.outputs), "learned_patterns": self._extract_learned_patterns(), "recommendations": self._generate_recommendations(), "resource_usage": result.performance_metrics } return context_contribution def _filter_global_context( self, global_context: Dict[str, Any], capabilities: List[str] ) -> Dict[str, Any]: """Filter global context to relevant information.""" filtered_context = {} # Include context relevant to agent capabilities for capability in capabilities: if capability in global_context: filtered_context[capability] = global_context[capability] # Always include system state and user preferences filtered_context.update({ "system_state": global_context.get("system_state", {}), "user_preferences": global_context.get("user_preferences", {}), "project_metadata": global_context.get("project_metadata", {}) }) return filtered_context ``` ### Quality Assurance Framework #### Validation Strategies **Multi-Level Validation System:** ```python # src/autodocs_mcp/task_graph/validation.py from enum import Enum from typing import Dict, List, Any, Optional from pydantic import BaseModel class ValidationLevel(Enum): INPUT = "input" PROCESS = "process" OUTPUT = "output" INTEGRATION = "integration" class ValidationResult(BaseModel): success: bool level: ValidationLevel validator_id: str checks_passed: int checks_failed: int issues: List[Dict[str, Any]] recommendations: List[str] class TaskValidator: """Comprehensive task validation system.""" def __init__(self): self.validators = { ValidationLevel.INPUT: [ self._validate_task_inputs, self._validate_context_completeness, self._validate_resource_availability ], ValidationLevel.PROCESS: [ self._validate_execution_progress, self._validate_agent_behavior, self._validate_performance_metrics ], ValidationLevel.OUTPUT: [ self._validate_output_quality, self._validate_success_criteria, self._validate_deliverable_completeness ], ValidationLevel.INTEGRATION: [ self._validate_downstream_compatibility, self._validate_context_contribution, self._validate_workflow_continuity ] } async def validate_task( self, task: Task, context: TaskContext, result: Optional[TaskResult] = None, level: ValidationLevel = ValidationLevel.INPUT ) -> ValidationResult: """Run comprehensive validation at specified level.""" validators = self.validators[level] issues = [] checks_passed = 0 checks_failed = 0 for validator in validators: try: validator_result = await validator(task, context, result) if validator_result["success"]: checks_passed += 1 else: checks_failed += 1 issues.extend(validator_result["issues"]) except Exception as e: checks_failed += 1 issues.append({ "type": "validation_error", "message": f"Validator {validator.__name__} failed: {str(e)}", "severity": "error" }) return ValidationResult( success=checks_failed == 0, level=level, validator_id=f"task_validator_{level.value}", checks_passed=checks_passed, checks_failed=checks_failed, issues=issues, recommendations=self._generate_recommendations(issues) ) ``` #### Testing Approaches **Integration Test Patterns:** ```python # tests/task_graph/integration/test_workflow_execution.py import pytest import asyncio from unittest.mock import AsyncMock class TestWorkflowExecution: """Integration tests for complete workflow execution.""" @pytest.mark.asyncio async def test_simple_sequential_workflow( self, task_graph_constructor, orchestration_manager, sample_complex_request ): """Test basic sequential workflow execution.""" # Phase 1: Task Graph Construction task_graph = await task_graph_constructor.construct_graph( sample_complex_request ) assert task_graph.tasks is not None assert len(task_graph.tasks) > 0 assert task_graph.execution_strategy is not None # Phase 2: Workflow Execution workflow_result = await orchestration_manager.execute_workflow( task_graph ) assert workflow_result.success is True assert workflow_result.completion_percentage == 100 assert len(workflow_result.completed_tasks) == len(task_graph.tasks) @pytest.mark.asyncio async def test_parallel_workflow_execution( self, task_graph_constructor, orchestration_manager ): """Test parallel task execution with dependency management.""" complex_request = { "request_type": "system_analysis", "requirements": { "functional": [ "Analyze code quality", "Check test coverage", "Review documentation", "Assess performance" ] }, "execution_strategy": "parallel" } task_graph = await task_graph_constructor.construct_graph(complex_request) # Verify parallel execution plan assert task_graph.execution_strategy.type == "parallel" # Execute workflow start_time = asyncio.get_event_loop().time() result = await orchestration_manager.execute_workflow(task_graph) execution_time = asyncio.get_event_loop().time() - start_time # Parallel execution should be faster than sequential assert result.success is True assert execution_time < (len(task_graph.tasks) * 30) # Less than 30s per task @pytest.mark.asyncio async def test_error_recovery_workflow( self, task_graph_constructor, orchestration_manager, mock_agent_pool ): """Test workflow resilience with task failures.""" # Configure one agent to fail failing_agent = mock_agent_pool.get_agent('core-services') failing_agent.execute_task = AsyncMock( side_effect=Exception("Simulated task failure") ) complex_request = { "request_type": "feature_development", "requirements": {"functional": ["Add new feature"]} } task_graph = await task_graph_constructor.construct_graph(complex_request) result = await orchestration_manager.execute_workflow(task_graph) # Should handle failure gracefully assert "error_recovery" in result.metadata assert result.partial_success is True assert len(result.failed_tasks) > 0 ``` --- ## Performance & Scalability ### Performance Characteristics #### Throughput Expectations **Request Processing Capacity:** - **Simple Requests** (1-3 tasks): 50-100 requests/minute - **Medium Requests** (4-8 tasks): 20-30 requests/minute - **Complex Requests** (8+ tasks): 5-10 requests/minute - **Concurrent Workflows**: 10 active workflows maximum (configurable) **Task Execution Performance:** - **Task Startup Overhead**: < 200ms per task - **Agent Assignment Latency**: < 50ms average - **Context Loading Time**: < 100ms for standard contexts - **Inter-Agent Communication**: < 10ms for local communication #### Latency Profiles **Workflow Completion Times (90th Percentile):** ``` Simple Documentation Update: 2-5 minutes Feature Development Request: 15-45 minutes System Analysis & Review: 30-90 minutes Complex Multi-Phase Release: 2-6 hours ``` **Real-Time Operation Latencies:** ``` Workflow Status Query: < 50ms Agent Status Update: < 20ms Context Access: < 100ms Quality Gate Validation: < 500ms ``` ### Resource Utilization Patterns #### Memory Usage **Context Management:** ```python class PerformanceMonitor: """Monitor system performance and resource utilization.""" def calculate_memory_requirements(self, workflow_count: int) -> Dict[str, int]: """Calculate memory requirements for active workflows.""" base_memory_mb = 64 # Base system overhead # Memory per workflow component memory_per_workflow = { "task_graph": 2, # Task graph structure "workflow_state": 4, # Execution state tracking "global_context": 8, # Shared global context "agent_contexts": 16, # Agent-specific contexts (8 agents * 2MB) "working_memory": 32, # Task execution working memory "communication": 4, # Inter-agent communication buffers } workflow_memory = sum(memory_per_workflow.values()) total_memory_mb = base_memory_mb + (workflow_count * workflow_memory) return { "base_memory_mb": base_memory_mb, "memory_per_workflow_mb": workflow_memory, "total_memory_mb": total_memory_mb, "recommended_limit_mb": int(total_memory_mb * 1.5) # 50% buffer } ``` #### CPU Usage Optimization **Parallel Execution Strategies:** ```python class OptimizedTaskScheduler: """CPU-optimized task scheduling with load balancing.""" def __init__(self, max_cpu_cores: int = 8): self.max_cpu_cores = max_cpu_cores self.cpu_usage_tracker = CPUUsageTracker() async def optimize_task_distribution( self, ready_tasks: List[Task] ) -> List[List[Task]]: """Distribute tasks optimally across available CPU cores.""" # Classify tasks by CPU intensity cpu_intensive = [] io_intensive = [] mixed_workload = [] for task in ready_tasks: classification = await self._classify_task_workload(task) if classification == "cpu_intensive": cpu_intensive.append(task) elif classification == "io_intensive": io_intensive.append(task) else: mixed_workload.append(task) # Create optimal distribution task_groups = [] # CPU intensive tasks: limit to available cores if cpu_intensive: cores_for_cpu = min(len(cpu_intensive), self.max_cpu_cores) task_groups.extend(self._distribute_evenly(cpu_intensive, cores_for_cpu)) # I/O intensive tasks: can over-subscribe cores if io_intensive: cores_for_io = self.max_cpu_cores * 2 # Over-subscribe for I/O task_groups.extend(self._distribute_evenly(io_intensive, cores_for_io)) return task_groups ``` ### Optimization Strategies #### Context Optimization **Efficient Context Management:** ```python class ContextOptimizer: """Optimize context loading and caching for performance.""" def __init__(self): self.context_cache = LRUCache(maxsize=1000) self.preload_patterns = self._analyze_preload_patterns() async def optimize_context_loading( self, workflow: WorkflowState ) -> ContextLoadingPlan: """Create optimal context loading plan for workflow.""" # Analyze context dependencies across tasks context_graph = self._build_context_dependency_graph(workflow.tasks) # Identify shared context that can be loaded once shared_contexts = self._find_shared_contexts(context_graph) # Plan preloading for predictable access patterns preload_contexts = self._plan_context_preloading(workflow.tasks) return ContextLoadingPlan( shared_contexts=shared_contexts, preload_contexts=preload_contexts, estimated_memory_savings=self._calculate_memory_savings( shared_contexts, preload_contexts ) ) async def implement_context_pruning( self, agent_context: AgentContext, task: Task ) -> PrunedContext: """Remove irrelevant context to optimize memory usage.""" # Analyze task requirements vs. available context required_context = self._analyze_context_requirements(task) available_context = agent_context.get_all_context() # Keep only relevant context pruned_context = {} relevance_threshold = 0.7 for context_key, context_data in available_context.items(): relevance_score = self._calculate_relevance( context_key, context_data, required_context ) if relevance_score >= relevance_threshold: pruned_context[context_key] = context_data return PrunedContext( context_data=pruned_context, original_size_kb=self._calculate_size(available_context), pruned_size_kb=self._calculate_size(pruned_context), memory_savings_percentage=( (1 - len(pruned_context) / len(available_context)) * 100 ) ) ``` ### Monitoring & Observability #### Key Performance Metrics **System Health Indicators:** ```python class TaskGraphMetrics: """Comprehensive metrics collection for task graph system.""" def __init__(self): self.metrics = { # Throughput metrics "workflows_per_minute": RateMetric(), "tasks_per_minute": RateMetric(), "successful_completions_per_minute": RateMetric(), # Latency metrics "workflow_completion_time": HistogramMetric(), "task_execution_time": HistogramMetric(), "agent_assignment_time": HistogramMetric(), "context_loading_time": HistogramMetric(), # Resource utilization "memory_usage_mb": GaugeMetric(), "cpu_utilization_percent": GaugeMetric(), "active_agent_count": GaugeMetric(), "context_cache_hit_rate": RatioMetric(), # Quality metrics "task_success_rate": RatioMetric(), "quality_gate_pass_rate": RatioMetric(), "error_rate_per_agent": CounterMetric(), "retry_rate": RatioMetric(), } async def collect_workflow_metrics( self, workflow: WorkflowState ) -> Dict[str, Any]: """Collect comprehensive metrics for completed workflow.""" completion_time = workflow.completed_at - workflow.started_at task_count = len(workflow.tasks) successful_tasks = len([t for t in workflow.tasks if t.status == "completed"]) return { "workflow_id": workflow.workflow_id, "completion_time_seconds": completion_time.total_seconds(), "task_count": task_count, "successful_tasks": successful_tasks, "success_rate": successful_tasks / task_count, "average_task_time": completion_time.total_seconds() / task_count, "agents_utilized": len(set(t.assigned_agent for t in workflow.tasks)), "context_cache_hits": workflow.performance_metrics.get("cache_hits", 0), "quality_gates_passed": workflow.quality_metrics.get("gates_passed", 0), "resource_usage": { "peak_memory_mb": workflow.performance_metrics.get("peak_memory", 0), "cpu_time_seconds": workflow.performance_metrics.get("cpu_time", 0), "network_requests": workflow.performance_metrics.get("network_requests", 0) } } ``` --- ## Deployment & Operations ### Deployment Architecture #### Infrastructure Requirements **Compute Resources:** ```yaml # kubernetes/task-graph-deployment.yaml apiVersion: apps/v1 kind: Deployment metadata: name: autodocs-task-graph labels: app: autodocs-task-graph spec: replicas: 3 selector: matchLabels: app: autodocs-task-graph template: metadata: labels: app: autodocs-task-graph spec: containers: - name: task-graph-system image: autodocs/task-graph:latest ports: - containerPort: 8000 - containerPort: 8080 # WebSocket port resources: requests: memory: "512Mi" cpu: "500m" limits: memory: "2Gi" cpu: "2000m" env: - name: TASK_GRAPH_MAX_CONCURRENT_WORKFLOWS value: "10" - name: TASK_GRAPH_REDIS_URL valueFrom: secretKeyRef: name: redis-credentials key: url - name: TASK_GRAPH_LOG_LEVEL value: "INFO" livenessProbe: httpGet: path: /health port: 8000 initialDelaySeconds: 30 periodSeconds: 10 readinessProbe: httpGet: path: /ready port: 8000 initialDelaySeconds: 5 periodSeconds: 5 - name: redis image: redis:7-alpine ports: - containerPort: 6379 resources: requests: memory: "128Mi" cpu: "100m" limits: memory: "256Mi" cpu: "200m" ``` **Service Dependencies:** ```yaml # kubernetes/services.yaml apiVersion: v1 kind: Service metadata: name: autodocs-task-graph spec: selector: app: autodocs-task-graph ports: - name: http port: 80 targetPort: 8000 - name: websocket port: 8080 targetPort: 8080 type: LoadBalancer --- apiVersion: v1 kind: Service metadata: name: redis-service spec: selector: app: autodocs-task-graph ports: - port: 6379 targetPort: 6379 type: ClusterIP ``` #### Configuration Management **Environment-Specific Settings:** ```python # src/autodocs_mcp/task_graph/deployment/config.py from enum import Enum from typing import Dict, Any import os class DeploymentEnvironment(Enum): DEVELOPMENT = "development" STAGING = "staging" PRODUCTION = "production" class DeploymentConfig: """Environment-specific configuration for task graph system.""" def __init__(self, environment: DeploymentEnvironment): self.environment = environment self.config = self._load_environment_config() def _load_environment_config(self) -> Dict[str, Any]: """Load configuration based on deployment environment.""" base_config = { "max_concurrent_workflows": 10, "max_concurrent_tasks": 8, "task_timeout": 1800, "enable_distributed_tracing": False, "log_level": "INFO" } if self.environment == DeploymentEnvironment.DEVELOPMENT: return { **base_config, "max_concurrent_workflows": 3, "enable_debug_logging": True, "log_level": "DEBUG", "mock_external_services": True } elif self.environment == DeploymentEnvironment.STAGING: return { **base_config, "max_concurrent_workflows": 5, "enable_distributed_tracing": True, "performance_monitoring": True, "enable_chaos_testing": True } elif self.environment == DeploymentEnvironment.PRODUCTION: return { **base_config, "max_concurrent_workflows": 20, "enable_distributed_tracing": True, "performance_monitoring": True, "high_availability": True, "backup_enabled": True, "security_hardening": True } ``` ### Operational Procedures #### Health Checks **Comprehensive System Health Monitoring:** ```python # src/autodocs_mcp/task_graph/health/health_checker.py from typing import Dict, List, Any from datetime import datetime, timedelta import asyncio class TaskGraphHealthChecker: """Advanced health checking for task graph system.""" async def get_system_health(self) -> Dict[str, Any]: """Get comprehensive system health status.""" health_checks = await asyncio.gather( self._check_orchestrator_health(), self._check_agent_pool_health(), self._check_context_manager_health(), self._check_redis_health(), self._check_performance_health(), return_exceptions=True ) overall_health = all( isinstance(check, dict) and check.get("status") == "healthy" for check in health_checks ) return { "overall_status": "healthy" if overall_health else "degraded", "timestamp": datetime.utcnow().isoformat(), "components": { "orchestrator": health_checks[0], "agent_pool": health_checks[1], "context_manager": health_checks[2], "redis": health_checks[3], "performance": health_checks[4] }, "system_metrics": await self._collect_system_metrics() } async def _check_orchestrator_health(self) -> Dict[str, Any]: """Check orchestration manager health.""" try: # Check if orchestrator can accept new workflows can_accept_workflows = await self.orchestrator.can_accept_workflow() # Check active workflow count active_workflows = await self.orchestrator.get_active_workflow_count() max_workflows = self.orchestrator.config.max_concurrent_workflows # Check recent error rate error_rate = await self._calculate_recent_error_rate("orchestrator") status = "healthy" if not can_accept_workflows or error_rate > 0.1: status = "degraded" if active_workflows >= max_workflows or error_rate > 0.3: status = "unhealthy" return { "status": status, "active_workflows": active_workflows, "max_workflows": max_workflows, "can_accept_new": can_accept_workflows, "error_rate": error_rate, "last_check": datetime.utcnow().isoformat() } except Exception as e: return { "status": "unhealthy", "error": str(e), "last_check": datetime.utcnow().isoformat() } ``` #### Backup & Recovery **State Persistence and Recovery:** ```python class TaskGraphBackupManager: """Manage backup and recovery of task graph system state.""" def __init__(self, backup_config: Dict[str, Any]): self.backup_interval = backup_config.get("interval", 300) # 5 minutes self.retention_days = backup_config.get("retention_days", 7) self.backup_storage = backup_config.get("storage", "local") async def create_system_backup(self) -> Dict[str, Any]: """Create comprehensive system state backup.""" backup_id = f"backup_{int(time.time())}" backup_data = { "backup_id": backup_id, "timestamp": datetime.utcnow().isoformat(), "system_version": self._get_system_version(), "active_workflows": await self._backup_active_workflows(), "agent_states": await self._backup_agent_states(), "context_cache": await self._backup_context_cache(), "configuration": await self._backup_configuration(), "performance_metrics": await self._backup_metrics() } # Store backup based on configuration if self.backup_storage == "redis": await self._store_redis_backup(backup_id, backup_data) elif self.backup_storage == "s3": await self._store_s3_backup(backup_id, backup_data) else: await self._store_local_backup(backup_id, backup_data) # Cleanup old backups await self._cleanup_old_backups() return { "backup_id": backup_id, "size_bytes": len(json.dumps(backup_data)), "components_backed_up": len(backup_data) - 3, # Exclude metadata "storage_location": self.backup_storage } async def restore_from_backup(self, backup_id: str) -> Dict[str, Any]: """Restore system state from backup.""" # Load backup data backup_data = await self._load_backup(backup_id) if not backup_data: raise ValueError(f"Backup {backup_id} not found") restoration_log = [] try: # Restore active workflows if "active_workflows" in backup_data: await self._restore_workflows(backup_data["active_workflows"]) restoration_log.append("workflows_restored") # Restore agent states if "agent_states" in backup_data: await self._restore_agent_states(backup_data["agent_states"]) restoration_log.append("agent_states_restored") # Restore context cache if "context_cache" in backup_data: await self._restore_context_cache(backup_data["context_cache"]) restoration_log.append("context_cache_restored") return { "success": True, "backup_id": backup_id, "restored_components": restoration_log, "restoration_time": datetime.utcnow().isoformat() } except Exception as e: return { "success": False, "backup_id": backup_id, "error": str(e), "partially_restored": restoration_log } ``` #### Scaling Operations **Horizontal Scaling Procedures:** ```python class TaskGraphScaler: """Manage horizontal scaling of task graph system.""" async def scale_up(self, target_instances: int) -> Dict[str, Any]: """Scale up system to handle increased load.""" current_instances = await self._get_current_instance_count() if target_instances <= current_instances: return {"message": "No scaling needed", "current": current_instances} # Calculate resource requirements additional_instances = target_instances - current_instances resource_requirements = self._calculate_scaling_resources(additional_instances) # Check resource availability available_resources = await self._check_resource_availability() if not self._can_scale(resource_requirements, available_resources): return { "success": False, "reason": "Insufficient resources", "required": resource_requirements, "available": available_resources } # Perform scaling scaling_results = [] for i in range(additional_instances): instance_result = await self._create_new_instance( instance_id=f"task-graph-{current_instances + i + 1}" ) scaling_results.append(instance_result) return { "success": True, "scaled_from": current_instances, "scaled_to": target_instances, "new_instances": scaling_results } ``` --- ## Security & Compliance ### Security Considerations #### Authentication & Authorization **Multi-Level Security Framework:** ```python # src/autodocs_mcp/task_graph/security/auth.py from typing import Dict, List, Optional from enum import Enum import jwt from datetime import datetime, timedelta class SecurityLevel(Enum): PUBLIC = "public" AUTHENTICATED = "authenticated" PRIVILEGED = "privileged" ADMINISTRATIVE = "administrative" class TaskGraphAuthManager: """Advanced authentication and authorization for task graph system.""" def __init__(self, security_config: Dict[str, Any]): self.jwt_secret = security_config["jwt_secret"] self.token_expiry = security_config.get("token_expiry_minutes", 60) self.rbac_enabled = security_config.get("rbac_enabled", True) async def authenticate_request( self, request_token: str, required_level: SecurityLevel = SecurityLevel.AUTHENTICATED ) -> Dict[str, Any]: """Authenticate and authorize request.""" try: # Decode JWT token payload = jwt.decode( request_token, self.jwt_secret, algorithms=["HS256"] ) # Extract user information user_id = payload.get("user_id") user_roles = payload.get("roles", []) security_clearance = payload.get("security_level", "public") # Check authorization level if not self._has_required_clearance(security_clearance, required_level): return { "authenticated": False, "reason": "insufficient_privileges" } return { "authenticated": True, "user_id": user_id, "roles": user_roles, "security_level": security_clearance, "expires_at": payload.get("exp") } except jwt.ExpiredSignatureError: return {"authenticated": False, "reason": "token_expired"} except jwt.InvalidTokenError: return {"authenticated": False, "reason": "invalid_token"} async def authorize_workflow_operation( self, user_context: Dict[str, Any], operation: str, resource: str ) -> bool: """Authorize specific workflow operations.""" if not self.rbac_enabled: return True user_roles = user_context.get("roles", []) # Define operation permissions operation_permissions = { "submit_workflow": ["user", "developer", "admin"], "cancel_workflow": ["developer", "admin"], "view_workflow": ["user", "developer", "admin"], "modify_system": ["admin"], "access_sensitive_data": ["privileged_user", "admin"] } required_roles = operation_permissions.get(operation, ["admin"]) return any(role in user_roles for role in required_roles) ``` #### Data Protection **Context Isolation and Security:** ```python class SecureContextManager: """Security-hardened context manager with data isolation.""" def __init__(self): self.encryption_key = self._load_encryption_key() self.access_audit_log = SecurityAuditLog() async def create_secure_context( self, context_data: Dict[str, Any], security_classification: str, access_permissions: List[str] ) -> SecureContext: """Create security-hardened context with encryption and access control.""" # Classify and encrypt sensitive data encrypted_context = {} for key, value in context_data.items(): if self._is_sensitive_data(key, value): encrypted_context[key] = self._encrypt_data(value) else: encrypted_context[key] = value # Create secure context wrapper secure_context = SecureContext( context_data=encrypted_context, classification=security_classification, access_permissions=access_permissions, created_at=datetime.utcnow(), encryption_enabled=True ) # Log context creation await self.access_audit_log.log_context_access( operation="create", context_id=secure_context.context_id, classification=security_classification ) return secure_context async def access_secure_context( self, context_id: str, requesting_agent: str, operation: str ) -> Optional[Dict[str, Any]]: """Access secure context with authorization and auditing.""" # Load secure context secure_context = await self._load_secure_context(context_id) if not secure_context: return None # Check access permissions if not self._check_access_permission( requesting_agent, secure_context.access_permissions, operation ): await self.access_audit_log.log_access_denied( agent=requesting_agent, context_id=context_id, operation=operation ) return None # Decrypt sensitive data for authorized access decrypted_context = {} for key, value in secure_context.context_data.items(): if self._is_encrypted(value): decrypted_context[key] = self._decrypt_data(value) else: decrypted_context[key] = value # Log successful access await self.access_audit_log.log_context_access( operation="access", agent=requesting_agent, context_id=context_id ) return decrypted_context ``` #### Audit Logging **Comprehensive Security Audit Trail:** ```python class SecurityAuditLog: """Comprehensive security audit logging for compliance.""" def __init__(self, config: Dict[str, Any]): self.log_retention_days = config.get("retention_days", 90) self.encrypt_logs = config.get("encrypt_audit_logs", True) self.compliance_mode = config.get("compliance_mode", "standard") async def log_workflow_security_event( self, event_type: str, workflow_id: str, user_id: str, details: Dict[str, Any] ) -> None: """Log security-relevant workflow events.""" audit_entry = { "timestamp": datetime.utcnow().isoformat(), "event_type": event_type, "workflow_id": workflow_id, "user_id": user_id, "ip_address": details.get("ip_address"), "user_agent": details.get("user_agent"), "security_level": details.get("security_level"), "operation": details.get("operation"), "resource": details.get("resource"), "success": details.get("success", True), "risk_score": self._calculate_risk_score(event_type, details) } # Add compliance-specific fields if self.compliance_mode == "hipaa": audit_entry.update(self._add_hipaa_fields(details)) elif self.compliance_mode == "sox": audit_entry.update(self._add_sox_fields(details)) # Store audit entry await self._store_audit_entry(audit_entry) # Trigger alerts for high-risk events if audit_entry["risk_score"] >= 8.0: await self._trigger_security_alert(audit_entry) ``` ### Integration Security #### External API Security **Secure Communication with External Systems:** ```python class SecureExternalIntegration: """Security-hardened external system integration.""" def __init__(self, security_config: Dict[str, Any]): self.api_keys = security_config["api_keys"] self.rate_limits = security_config.get("rate_limits", {}) self.allowed_domains = security_config.get("allowed_domains", []) async def make_secure_api_call( self, endpoint: str, method: str, data: Dict[str, Any], user_context: Dict[str, Any] ) -> Dict[str, Any]: """Make secure API call with rate limiting and validation.""" # Validate endpoint domain if not self._is_allowed_domain(endpoint): raise SecurityError(f"Domain not allowed: {endpoint}") # Check rate limits if not await self._check_rate_limit(user_context["user_id"], endpoint): raise RateLimitError("API rate limit exceeded") # Sanitize outgoing data sanitized_data = self._sanitize_outgoing_data(data) # Add authentication headers = self._add_authentication_headers(endpoint) # Make API call with timeout and retries response = await self._make_http_request( endpoint=endpoint, method=method, data=sanitized_data, headers=headers, timeout=30 ) # Validate and sanitize response validated_response = self._validate_api_response(response) # Log API call for audit await self._log_api_call( endpoint=endpoint, user_id=user_context["user_id"], success=response.status_code < 400 ) return validated_response ``` --- ## Future Architecture Evolution ### Extensibility Framework #### New Agent Integration **Plugin Architecture for Custom Agents:** ```python # src/autodocs_mcp/task_graph/plugins/agent_plugin.py from abc import ABC, abstractmethod from typing import Dict, List, Any, Optional class AgentPlugin(ABC): """Base class for custom agent plugins.""" @abstractmethod def get_plugin_metadata(self) -> Dict[str, Any]: """Return plugin metadata including capabilities and requirements.""" pass @abstractmethod async def initialize_plugin(self, config: Dict[str, Any]) -> None: """Initialize plugin with configuration.""" pass @abstractmethod async def register_capabilities(self) -> List[str]: """Register agent capabilities with the system.""" pass @abstractmethod async def execute_plugin_task( self, task: Task, context: TaskContext ) -> TaskResult: """Execute a task assigned to this plugin.""" pass class CustomDomainAgent(AgentPlugin): """Example custom domain agent plugin.""" def __init__(self, domain_expertise: str): self.domain = domain_expertise self.capabilities = [] def get_plugin_metadata(self) -> Dict[str, Any]: return { "plugin_name": f"{self.domain}_agent", "plugin_version": "1.0.0", "author": "Custom Developer", "domain_expertise": self.domain, "required_dependencies": ["domain-specific-lib>=1.0.0"], "supported_task_types": [f"{self.domain}_analysis", f"{self.domain}_optimization"] } async def initialize_plugin(self, config: Dict[str, Any]) -> None: """Initialize custom domain agent.""" # Load domain-specific knowledge self.knowledge_base = await self._load_domain_knowledge( config.get("knowledge_base_path") ) # Initialize domain-specific tools self.domain_tools = await self._initialize_domain_tools( config.get("tools_config", {}) ) # Register capabilities based on available tools and knowledge self.capabilities = await self._discover_capabilities() async def register_capabilities(self) -> List[str]: return self.capabilities async def execute_plugin_task( self, task: Task, context: TaskContext ) -> TaskResult: """Execute domain-specific task.""" if task.task_type == f"{self.domain}_analysis": return await self._perform_domain_analysis(task, context) elif task.task_type == f"{self.domain}_optimization": return await self._perform_domain_optimization(task, context) else: raise ValueError(f"Unsupported task type: {task.task_type}") ``` #### Custom Workflow Patterns **Extensible Workflow Pattern System:** ```python class WorkflowPatternRegistry: """Registry for custom workflow execution patterns.""" def __init__(self): self.patterns = {} self._register_builtin_patterns() def register_pattern( self, pattern_name: str, pattern_class: WorkflowPattern ) -> None: """Register a custom workflow pattern.""" # Validate pattern implementation if not self._validate_pattern(pattern_class): raise ValueError(f"Invalid pattern implementation: {pattern_name}") self.patterns[pattern_name] = pattern_class async def execute_pattern( self, pattern_name: str, task_graph: TaskGraph, context: Dict[str, Any] ) -> WorkflowResult: """Execute workflow using specified pattern.""" if pattern_name not in self.patterns: raise ValueError(f"Unknown workflow pattern: {pattern_name}") pattern_instance = self.patterns[pattern_name]() return await pattern_instance.execute(task_graph, context) class MapReduceWorkflowPattern(WorkflowPattern): """Map-Reduce pattern for parallel data processing workflows.""" async def execute( self, task_graph: TaskGraph, context: Dict[str, Any] ) -> WorkflowResult: """Execute map-reduce workflow pattern.""" # Phase 1: Map - Distribute data processing tasks map_tasks = self._identify_map_tasks(task_graph) map_results = await self._execute_parallel_tasks(map_tasks) # Phase 2: Shuffle - Organize intermediate results shuffled_data = await self._shuffle_intermediate_results(map_results) # Phase 3: Reduce - Combine results reduce_tasks = self._create_reduce_tasks(shuffled_data) final_results = await self._execute_reduce_tasks(reduce_tasks) return WorkflowResult( success=True, pattern="map_reduce", results=final_results, performance_metrics=self._collect_performance_metrics() ) ``` ### Performance Enhancement Roadmap #### Machine Learning Integration **Predictive Optimization with ML:** ```python class MLOptimizationEngine: """Machine learning-powered optimization for task graph execution.""" def __init__(self): self.task_duration_model = None self.agent_performance_model = None self.workload_prediction_model = None async def initialize_ml_models(self) -> None: """Initialize and train ML models from historical data.""" # Load historical execution data historical_data = await self._load_execution_history() # Train task duration prediction model self.task_duration_model = await self._train_duration_model( historical_data["task_executions"] ) # Train agent performance prediction model self.agent_performance_model = await self._train_performance_model( historical_data["agent_metrics"] ) # Train workload prediction model self.workload_prediction_model = await self._train_workload_model( historical_data["system_metrics"] ) async def predict_optimal_execution_plan( self, task_graph: TaskGraph ) -> OptimizedExecutionPlan: """Use ML to predict optimal execution plan.""" # Predict task durations predicted_durations = {} for task in task_graph.tasks: duration = await self.task_duration_model.predict( task_features=self._extract_task_features(task) ) predicted_durations[task.task_id] = duration # Predict optimal agent assignments optimal_assignments = {} for task in task_graph.tasks: agent_scores = await self.agent_performance_model.predict_scores( task_features=self._extract_task_features(task), available_agents=self._get_available_agents() ) optimal_assignments[task.task_id] = max(agent_scores, key=agent_scores.get) # Optimize execution sequence optimized_sequence = await self._optimize_execution_sequence( task_graph, predicted_durations, optimal_assignments ) return OptimizedExecutionPlan( task_assignments=optimal_assignments, execution_sequence=optimized_sequence, predicted_completion_time=sum(predicted_durations.values()), confidence_score=self._calculate_prediction_confidence() ) ``` ### Technology Evolution Plans #### Distributed System Enhancement **Multi-Node Task Graph Execution:** ```python class DistributedTaskGraphSystem: """Distributed execution system for large-scale task graphs.""" def __init__(self, cluster_config: Dict[str, Any]): self.cluster_nodes = cluster_config["nodes"] self.load_balancer = DistributedLoadBalancer() self.consensus_manager = ConsensusManager() async def distribute_workflow( self, workflow: WorkflowState ) -> DistributedWorkflowPlan: """Distribute workflow across multiple nodes.""" # Analyze task graph for optimal distribution distribution_analysis = await self._analyze_distribution_opportunities( workflow.task_graph ) # Assign tasks to optimal nodes node_assignments = await self._assign_tasks_to_nodes( workflow.tasks, distribution_analysis ) # Create distributed execution plan execution_plan = DistributedWorkflowPlan( workflow_id=workflow.workflow_id, node_assignments=node_assignments, coordination_protocol="raft", fault_tolerance_level="high" ) # Replicate critical state across nodes await self._replicate_workflow_state(workflow, execution_plan) return execution_plan async def execute_distributed_workflow( self, execution_plan: DistributedWorkflowPlan ) -> DistributedWorkflowResult: """Execute workflow across distributed cluster.""" # Start coordination protocol coordination_session = await self.consensus_manager.start_session( execution_plan.workflow_id ) # Execute tasks on assigned nodes node_futures = [] for node_id, task_group in execution_plan.node_assignments.items(): node_future = self._execute_tasks_on_node(node_id, task_group) node_futures.append(node_future) # Wait for completion with fault tolerance results = await self._wait_for_distributed_completion( node_futures, fault_tolerance_level=execution_plan.fault_tolerance_level ) # Aggregate results final_result = await self._aggregate_distributed_results(results) return final_result ``` --- ## Conclusion The Task-Graph Workflow System represents a significant architectural evolution of the AutoDocs MCP Server, transforming it from a documentation intelligence tool into a comprehensive multi-agent orchestration platform. This system provides: ### Key Architectural Strengths 1. **Proven Foundation**: Built on the robust, production-tested AutoDocs MCP Server with 8 core tools 2. **Intelligent Orchestration**: Advanced task decomposition and agent coordination capabilities 3. **Hierarchical Context Management**: Multi-level context architecture optimized for AI workflows 4. **Enterprise Scalability**: Production-grade monitoring, security, and operational features ### Implementation Readiness - **Immediate Development**: Clear implementation guidelines and code templates - **Incremental Adoption**: Can be deployed alongside existing AutoDocs functionality - **Extensible Design**: Plugin architecture supports custom agents and workflow patterns - **Production Operations**: Comprehensive deployment, monitoring, and security frameworks ### Strategic Value This architecture enables organizations to: - **Automate Complex Workflows**: Handle multi-step processes requiring diverse expertise - **Scale AI Capabilities**: Coordinate multiple AI agents for enterprise-scale automation - **Maintain Quality**: Built-in validation, conflict resolution, and quality assurance - **Ensure Reliability**: Production-grade error handling, monitoring, and recovery The Task-Graph Workflow System positions the AutoDocs project at the forefront of AI-powered workflow automation, providing a foundation for sophisticated multi-agent applications while maintaining the reliability and performance standards established by the current MCP server implementation. --- **Document Classification**: Technical Architecture Specification **Review Status**: Ready for Technical Review **Implementation Priority**: Strategic Enhancement **Expected Development Timeline**: 12-16 weeks for MVP, 6 months for full feature set