Claude Opus 4.8: Dynamic Workflows Drives the "Engineering Collaboration System" Paradigm Shift

class UserRequest:
    """Represents a user request in the Dynamic Workflows system"""
    
    def __init__(
        self,
        request_type: RequestType,
        natural_language: str,
        context_requirements: ContextRequirements,
        constraints: Optional[Constraints] = None
    ):
        self.request_type = request_type
        self.natural_language = natural_language
        self.context_requirements = context_requirements
        self.constraints = constraints or Constraints()
        
        # Support for three primary input modalities
        self.modalities = {
            'complex_task': ComplexTaskInput,
            'natural_language': NaturalLanguageInput,
            'long_context': LongContextInput
        }

class IntentUnderstandingLayer:
    """
    Transforms user input into structured task specifications
    Components: Intent Parser, Task Decomposer, Context Manager, 
                Cost Optimizer, Quality Guard
    """
    
    def __init__(self, config: LayerConfig):
        self.intent_parser = IntentParser()
        self.task_decomposer = TaskDecomposer()
        self.context_manager = ContextManager()
        self.cost_optimizer = CostOptimizer()
        self.quality_guard = QualityGuard()
    
    async def process_request(
        self, 
        request: UserRequest
    ) -> StructuredTaskPlan:
        # Step 1: Parse user intent
        intent = await self.intent_parser.parse(request.natural_language)
        
        # Step 2: Decompose into subtasks with dependencies
        subtasks = await self.task_decomposer.decompose(intent)
        
        # Step 3: Manage context across long sessions
        context = await self.context_manager.establish_context(
            subtasks, 
            request.context_requirements
        )
        
        # Step 4: Optimize for cost and efficiency
        execution_plan = await self.cost_optimizer.optimize(subtasks, context)
        
        # Step 5: Establish quality gates
        quality_gates = await self.quality_guard.establish_gates(execution_plan)
        
        return StructuredTaskPlan(
            subtasks=subtasks,
            context=context,
            execution_plan=execution_plan,
            quality_gates=quality_gates
        )

class IntentParser:
    """
    Parses natural language input into structured intents
    """
    
    INTENT_TYPES = {
        'code_migration': 'Cross-codebase migration operation',
        'refactoring': 'Code structure modification',
        'feature_development': 'New functionality implementation',
        'testing': 'Test suite generation or execution',
        'documentation': 'Documentation generation or update',
        'code_review': 'Automated code quality assessment',
        'debugging': 'Issue identification and resolution'
    }
    
    async def parse(self, input_text: str) -> ParsedIntent:
        """Parse natural language into structured intent"""
        
        # Use Claude's own capabilities for intent classification
        classification_prompt = f"""
        Classify the following user request into one of these intent types:
        {list(self.INTENT_TYPES.keys())}
        
        Request: {input_text}
        
        Provide:
        1. Primary intent type
        2. Secondary intent types (if multiple)
        3. Key entities mentioned
        4. Implicit requirements
        5. Success criteria
        """
        
        # In production, this would call Claude API
        return await self._classify_and_extract(classification_prompt)

from dataclasses import dataclass, field
from typing import List, Dict, Optional, Set
from enum import Enum
import asyncio

class TaskStatus(Enum):
    PENDING = "pending"
    READY = "ready"
    RUNNING = "running"
    COMPLETED = "completed"
    FAILED = "failed"
    BLOCKED = "blocked"

@dataclass
class SubTask:
    """Represents a single decomposable unit of work"""
    task_id: str
    description: str
    dependencies: Set[str] = field(default_factory=set)
    priority: int = 0
    status: TaskStatus = TaskStatus.PENDING
    assigned_agent: Optional[str] = None
    estimated_tokens: int = 0
    actual_tokens: int = 0
    result: Optional[Any] = None
    error: Optional[str] = None
    
class TaskDecomposer:
    """
    Breaks complex user requests into parallelizable subtasks
    with dependency management
    """
    
    def __init__(self, max_parallelism: int = 100):
        self.max_parallelism = max_parallelism
        self.task_graph = TaskDependencyGraph()
    
    async def decompose(
        self, 
        intent: ParsedIntent
    ) -> List[SubTask]:
        """Decompose intent into subtasks with dependencies"""
        
        # Phase 1: Initial task generation
        initial_tasks = await self._generate_initial_tasks(intent)
        
        # Phase 2: Dependency analysis
        dependencies = await self._analyze_dependencies(initial_tasks)
        
        # Phase 3: Task refinement
        refined_tasks = await self._refine_tasks(
            initial_tasks, 
            dependencies
        )
        
        # Phase 4: Parallelism optimization
        optimized_tasks = self._optimize_parallelism(refined_tasks)
        
        # Build task graph
        self.task_graph.build(optimized_tasks)
        
        return optimized_tasks
    
    async def _generate_initial_tasks(
        self, 
        intent: ParsedIntent
    ) -> List[SubTask]:
        """Generate initial task list based on intent type"""
        
        task_templates = {
            'code_migration': [
                SubTask(task_id="scan_source", 
                        description="Scan source codebase structure"),
                SubTask(task_id="analyze_dependencies",
                        description="Analyze external dependencies"),
                SubTask(task_id="map_functions",
                        description="Map function signatures across versions"),
                SubTask(task_id="generate_stubs",
                        description="Generate API stubs for new versions"),
                SubTask(task_id="migrate_files",
                        description="Migrate files with semantic preservation"),
                SubTask(task_id="fix_imports",
                        description="Fix import statements and references"),
                SubTask(task_id="run_tests",
                        description="Execute test suite"),
                SubTask(task_id="validate_output",
                        description="Validate migration completeness"),
            ],
            'refactoring': [
                SubTask(task_id="identify_patterns",
                        description="Identify code patterns to refactor"),
                SubTask(task_id="analyze_impact",
                        description="Analyze refactoring impact surface"),
                SubTask(task_id="create_backups",
                        description="Create backup snapshots"),
                SubTask(task_id="apply_refactoring",
                        description="Apply refactoring changes"),
                SubTask(task_id="update_tests",
                        description="Update corresponding tests"),
                SubTask(task_id="verify_correctness",
                        description="Verify functional equivalence"),
            ]
        }
        
        return task_templates.get(intent.type, [])
    
    def _optimize_parallelism(
        self, 
        tasks: List[SubTask]
    ) -> List[SubTask]:
        """Optimize task ordering for maximum parallelism"""
        
        # Assign priorities based on dependencies
        for task in tasks:
            task.priority = len(task.dependencies)
        
        # Sort by priority (fewer dependencies = higher priority)
        return sorted(tasks, key=lambda t: t.priority, reverse=True)

import json
import hashlib
from datetime import datetime
from typing import Dict, Any, Optional, List
from dataclasses import dataclass, asdict

@dataclass
class SessionContext:
    """Maintains state across long-running sessions"""
    session_id: str
    created_at: datetime
    last_updated: datetime
    task_states: Dict[str, TaskStatus]
    shared_memory: Dict[str, Any]
    checkpoint_history: List[Dict]
    token_budget_remaining: int
    
class ContextManager:
    """
    Manages context across long sessions
    - Multi-session state
    - Cross-session memory
    - Checkpointing and recovery
    """
    
    def __init__(
        self,
        max_tokens: int = 1_000_000,
        checkpoint_interval: int = 50
    ):
        self.max_tokens = max_tokens
        self.checkpoint_interval = checkpoint_interval
        self.sessions: Dict[str, SessionContext] = {}
        self.global_memory: Dict[str, Any] = {}
        
    async def establish_context(
        self,
        tasks: List[SubTask],
        requirements: ContextRequirements
    ) -> SessionContext:
        """Establish context for a new workflow session"""
        
        session_id = self._generate_session_id(tasks)
        
        context = SessionContext(
            session_id=session_id,
            created_at=datetime.now(),
            last_updated=datetime.now(),
            task_states={task.task_id: TaskStatus.PENDING for task in tasks},
            shared_memory={},
            checkpoint_history=[],
            token_budget_remaining=self.max_tokens
        )
        
        self.sessions[session_id] = context
        
        # Initialize shared memory with task metadata
        await self._initialize_shared_memory(context, tasks)
        
        return context
    
    async def checkpoint(
        self, 
        session: SessionContext,
        task_id: str,
        result: Any
    ) -> None:
        """Create a checkpoint for recovery purposes"""
        
        checkpoint = {
            'timestamp': datetime.now().isoformat(),
            'task_id': task_id,
            'result_hash': self._hash_result(result),
            'session_state': asdict(session)
        }
        
        session.checkpoint_history.append(checkpoint)
        session.last_updated = datetime.now()
        
        # Store result in shared memory
        session.shared_memory[f'result_{task_id}'] = result
        
        # Update token budget
        result_size = len(json.dumps(result))
        session.token_budget_remaining -= result_size
        
    async def restore_from_checkpoint(
        self,
        session: SessionContext,
        checkpoint_index: int
    ) -> Dict[str, Any]:
        """Restore session state from checkpoint"""
        
        if checkpoint_index >= len(session.checkpoint_history):
            raise ValueError(f"Invalid checkpoint index: {checkpoint_index}")
        
        checkpoint = session.checkpoint_history[checkpoint_index]
        return checkpoint['session_state']
    
    async def get_context_summary(
        self,
        session: SessionContext,
        max_memory_tokens: int = 50_000
    ) -> str:
        """Generate a context summary optimized for token budget"""
        
        # Summarize completed tasks
        completed = [
            task_id for task_id, status in session.task_states.items()
            if status == TaskStatus.COMPLETED
        ]
        
        # Summarize failed tasks
        failed = [
            task_id for task_id, status in session.task_states.items()
            if status == TaskStatus.FAILED
        ]
        
        summary = f"""
Session: {session.session_id}
Created: {session.created_at}
Last Updated: {session.last_updated}
Token Budget Remaining: {session.token_budget_remaining:,} / {self.max_tokens:,}

Progress:
- Total Tasks: {len(session.task_states)}
- Completed: {len(completed)}
- Failed: {len(failed)}
- Pending: {len(session.task_states) - len(completed) - len(failed)}

Completed Task IDs: {', '.join(completed) if completed else 'None'}
Failed Task IDs: {', '.join(failed) if failed else 'None'}
"""
        
        return summary
    
    def _generate_session_id(self, tasks: List[SubTask]) -> str:
        """Generate unique session ID based on task combination"""
        task_ids = ''.join(sorted([t.task_id for t in tasks]))
        timestamp = datetime.now().isoformat()
        hash_input = f"{task_ids}_{timestamp}"
        return hashlib.sha256(hash_input.encode()).hexdigest()[:16]
    
    def _hash_result(self, result: Any) -> str:
        """Generate hash for result deduplication"""
        return hashlib.sha256(
            json.dumps(result, sort_keys=True).encode()
        ).hexdigest()[:16]

import asyncio
from typing import Dict, List, Callable, Any, Optional
from dataclasses import dataclass, field
from enum import Enum
import logging
from collections import defaultdict

class WorkflowState(Enum):
    INITIALIZING = "initializing"
    RUNNING = "running"
    PAUSED = "paused"
    COMPLETED = "completed"
    FAILED = "failed"

@dataclass
class WorkflowExecution:
    """Represents a single workflow execution"""
    workflow_id: str
    state: WorkflowState
    active_tasks: Dict[str, asyncio.Task]
    completed_results: Dict[str, Any]
    failed_tasks: Dict[str, Exception]
    started_at: Optional[datetime] = None
    completed_at: Optional[datetime] = None

class WorkflowStateMachine:
    """
    Manages workflow state transitions with proper validation
    """
    
    VALID_TRANSITIONS = {
        WorkflowState.INITIALIZING: [WorkflowState.RUNNING, WorkflowState.FAILED],
        WorkflowState.RUNNING: [WorkflowState.PAUSED, WorkflowState.COMPLETED, WorkflowState.FAILED],
        WorkflowState.PAUSED: [WorkflowState.RUNNING, WorkflowState.FAILED],
        WorkflowState.COMPLETED: [],
        WorkflowState.FAILED: [WorkflowState.RUNNING],  # Allow retry
    }
    
    def __init__(self, workflow_id: str):
        self.workflow_id = workflow_id
        self.state = WorkflowState.INITIALIZING
        self.transition_history: List[tuple] = []
        
    def transition(self, new_state: WorkflowState) -> bool:
        """Attempt state transition with validation"""
        if new_state in self.VALID_TRANSITIONS.get(self.state, []):
            old_state = self.state
            self.state = new_state
            self.transition_history.append((old_state, new_state, datetime.now()))
            logging.info(f"Workflow {self.workflow_id}: {old_state} -> {new_state}")
            return True
        return False

class ParallelTaskScheduler:
    """
    Core innovation: Schedules hundreds of sub-agents in parallel
    Supports cross-hundred-thousand-line codebase migrations
    """
    
    def __init__(
        self,
        max_concurrent_tasks: int = 100,
        retry_attempts: int = 3,
        timeout_seconds: int = 300
    ):
        self.max_concurrent_tasks = max_concurrent_tasks
        self.retry_attempts = retry_attempts
        self.timeout_seconds = timeout_seconds
        self.task_semaphore = asyncio.Semaphore(max_concurrent_tasks)
        
        # Task tracking
        self.pending_tasks: Dict[str, SubTask] = {}
        self.running_tasks: Dict[str, asyncio.Task] = {}
        self.completed_tasks: Dict[str, Any] = {}
        self.failed_tasks: Dict[str, Exception] = {}
        
        # Agent registry
        self.agent_registry: Dict[str, 'BaseAgent'] = {}
        
        # Event callbacks
        self.on_task_complete: Optional[Callable] = None
        self.on_task_fail: Optional[Callable] = None
        self.on_workflow_complete: Optional[Callable] = None
        
    def register_agent(self, agent_type: str, agent: 'BaseAgent') -> None:
        """Register a specialized agent for task execution"""
        self.agent_registry[agent_type] = agent
        logging.info(f"Registered agent: {agent_type}")
        
    async def execute_workflow(
        self,
        tasks: List[SubTask],
        context: SessionContext
    ) -> WorkflowExecution:
        """Execute a complete workflow with parallel task scheduling"""
        
        workflow_id = f"wf_{context.session_id}"
        execution = WorkflowExecution(
            workflow_id=workflow_id,
            state=WorkflowState.INITIALIZING,
            active_tasks={},
            completed_results={},
            failed_tasks={},
            started_at=datetime.now()
        )
        
        # Build dependency graph
        dependency_graph = self._build_dependency_graph(tasks)
        
        # Transition to running state
        state_machine = WorkflowStateMachine(workflow_id)
        state_machine.transition(WorkflowState.RUNNING)
        
        try:
            # Execute tasks with dependency awareness
            await self._execute_with_dependencies(
                tasks, 
                dependency_graph,
                context,
                execution
            )
            
            state_machine.transition(WorkflowState.COMPLETED)
            execution.completed_at = datetime.now()
            
        except Exception as e:
            logging.error(f"Workflow {workflow_id} failed: {e}")
            state_machine.transition(WorkflowState.FAILED)
            raise
            
        return execution
    
    async def _execute_with_dependencies(
        self,
        tasks: List[SubTask],
        dependency_graph: Dict[str, Set[str]],
        context: SessionContext,
        execution: WorkflowExecution
    ) -> None:
        """Execute tasks respecting dependency constraints"""
        
        # Track which tasks are ready to execute
        ready_tasks = set()
        completed = set()
        failed = set()
        
        # Initialize: tasks with no dependencies are ready
        for task in tasks:
            if not task.dependencies:
                ready_tasks.add(task.task_id)
        
        # Execute until all tasks are complete
        while ready_tasks or execution.active_tasks:
            # Launch ready tasks
            while ready_tasks and len(execution.active_tasks) < self.max_concurrent_tasks:
                task_id = ready_tasks.pop()
                task = next(t for t in tasks if t.task_id == task_id)
                
                # Check dependencies are met
                deps_met = all(
                    dep in completed 
                    for dep in task.dependencies
                )
                
                if not deps_met:
                    # Re-add to waiting list
                    continue
                
                # Launch task
                async_task = asyncio.create_task(
                    self._execute_single_task(task, context)
                )
                execution.active_tasks[task_id] = async_task
            
            # Wait for at least one task to complete
            if execution.active_tasks:
                done, pending = await asyncio.wait(
                    execution.active_tasks.values(),
                    return_when=asyncio.FIRST_COMPLETED
                )
                
                # Process completed tasks
                for completed_future in done:
                    task_id = self._get_task_id_from_future(
                        completed_future,
                        execution.active_tasks
                    )
                    
                    del execution.active_tasks[task_id]
                    
                    try:
                        result = completed_future.result()
                        execution.completed_results[task_id] = result
                        completed.add(task_id)
                        
                        # Update context
                        await context.checkpoint(task_id, result)
                        
                        # Find newly ready tasks
                        for potential_task in tasks:
                            if potential_task.task_id not in completed:
                                deps = dependency_graph.get(potential_task.task_id, set())
                                if deps.issubset(completed):
                                    ready_tasks.add(potential_task.task_id)
                        
                        # Trigger callback
                        if self.on_task_complete:
                            await self.on_task_complete(task_id, result)
                            
                    except Exception as e:
                        execution.failed_tasks[task_id] = e
                        failed.add(task_id)
                        
                        if self.on_task_fail:
                            await self.on_task_fail(task_id, e)
            
            # Check for deadlock (no progress possible)
            if not ready_tasks and execution.active_tasks and not done:
                raise RuntimeError(
                    f"Deadlock detected: {len(execution.active_tasks)} tasks blocked"
                )
    
    async def _execute_single_task(
        self,
        task: SubTask,
        context: SessionContext
    ) -> Any:
        """Execute a single task with retry logic"""
        
        async with self.task_semaphore:
            for attempt in range(self.retry_attempts):
                try:
                    # Get appropriate agent
                    agent = self._get_agent_for_task(task)
                    
                    # Prepare task context (include completed results)
                    task_context = self._prepare_task_context(task, context)
                    
                    # Execute with timeout
                    result = await asyncio.wait_for(
                        agent.execute(task, task_context),
                        timeout=self.timeout_seconds
                    )
                    
                    return result
                    
                except asyncio.TimeoutError:
                    logging.warning(
                        f"Task {task.task_id} timed out on attempt {attempt + 1}"
                    )
                    if attempt == self.retry_attempts - 1:
                        raise
                        
                except Exception as e:
                    logging.error(
                        f"Task {task.task_id} failed on attempt {attempt + 1}: {e}"
                    )
                    if attempt == self.retry_attempts - 1:
                        raise
    
    def _build_dependency_graph(
        self,
        tasks: List[SubTask]
    ) -> Dict[str, Set[str]]:
        """Build a dependency graph from tasks"""
        graph = defaultdict(set)
        task_map = {t.task_id: t for t in tasks}
        
        for task in tasks:
            # Validate dependencies exist
            for dep in task.dependencies:
                if dep not in task_map:
                    raise ValueError(
                        f"Task {task.task_id} has invalid dependency: {dep}"
                    )
            graph[task.task_id] = task.dependencies
            
        return graph
    
    def _get_agent_for_task(self, task: SubTask) -> 'BaseAgent':
        """Route task to appropriate agent"""
        # Default routing based on task ID patterns
        if 'code_gen' in task.task_id:
            return self.agent_registry.get('code_generation')
        elif 'review' in task.task_id:
            return self.agent_registry.get('code_review')
        elif 'test' in task.task_id:
            return self.agent_registry.get('test_generation')
        elif 'search' in task.task_id:
            return self.agent_registry.get('code_search')
        elif 'docs' in task.task_id:
            return self.agent_registry.get('documentation')
        else:
            return self.agent_registry.get('default')
    
    def _prepare_task_context(
        self,
        task: SubTask,
        context: SessionContext
    ) -> Dict[str, Any]:
        """Prepare context for task execution including dependency results"""
        
        task_context = {
            'task': asdict(task),
            'session_id': context.session_id,
            'dependencies': {}
        }
        
        # Include results from dependencies
        for dep_id in task.dependencies:
            if dep_id in context.shared_memory:
                task_context['dependencies'][dep_id] = context.shared_memory[f'result_{dep_id}']
        
        return task_context
    
    def _get_task_id_from_future(
        self,
        future: asyncio.Future,
        task_map: Dict[str, asyncio.Task]
    ) -> Optional[str]:
        """Map future back to task ID"""
        for task_id, task in task_map.items():
            if task == future or task._future == future:
                return task_id
        return None

from abc import ABC, abstractmethod
from typing import Any, Dict
import asyncio

class BaseAgent(ABC):
    """Base class for all specialized agents"""
    
    def __init__(self, agent_id: str):
        self.agent_id = agent_id
        self.execution_count = 0
        
    @abstractmethod
    async def execute(
        self, 
        task: SubTask, 
        context: Dict[str, Any]
    ) -> Any:
        """Execute the agent's specialized task"""
        pass

class CodeGenerationAgent(BaseAgent):
    """
    Specialized agent for code generation, refactoring, and optimization
    """
    
    def __init__(self):
        super().__init__('code_generation')
        self.capabilities = [
            'code_generation',
            'refactoring',
            'optimization',
            'unit_test_generation'
        ]
    
    async def execute(
        self, 
        task: SubTask, 
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Execute code generation task"""
        self.execution_count += 1
        
        dependencies = context.get('dependencies', {})
        
        # Implementation would call Claude API for code generation
        code_prompt = f"""
        Task: {task.description}
        Dependencies: {dependencies}
        
        Generate code that:
        1. Fulfills the task description
        2. Uses outputs from dependency tasks
        3. Follows best practices
        4. Includes appropriate error handling
        """
        
        # In production, this would call Claude API
        generated_code = await self._generate_code(code_prompt)
        
        return {
            'agent': self.agent_id,
            'task_id': task.task_id,
            'generated_code': generated_code,
            'files_modified': self._track_modifications(generated_code),
            'execution_count': self.execution_count
        }
    
    async def _generate_code(self, prompt: str) -> str:
        """Generate code using Claude"""
        # Placeholder - in production, calls Claude API
        await asyncio.sleep(0.1)  # Simulate API call
        return "# Generated code placeholder"

class CodeReviewAgent(BaseAgent):
    """
    Specialized agent for code review, vulnerability detection,
    and performance analysis
    """
    
    def __init__(self):
        super().__init__('code_review')
        self.review_patterns = self._load_review_patterns()
    
    async def execute(
        self, 
        task: SubTask, 
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Execute code review task"""
        self.execution_count += 1
        
        dependencies = context.get('dependencies', {})
        code_to_review = dependencies.get('code_generation', {}).get(
            'generated_code', ''
        )
        
        review_results = {
            'vulnerabilities': await self._detect_vulnerabilities(code_to_review),
            'code_quality': await self._analyze_quality(code_to_review),
            'performance_issues': await self._analyze_performance(code_to_review),
            'best_practices': await self._check_best_practices(code_to_review),
        }
        
        return {
            'agent': self.agent_id,
            'task_id': task.task_id,
            'review_results': review_results,
            'overall_score': self._calculate_score(review_results),
            'execution_count': self.execution_count
        }
    
    async def _detect_vulnerabilities(self, code: str) -> List[Dict]:
        """Detect security vulnerabilities"""
        # Implementation
        return []

class TestGenerationAgent(BaseAgent):
    """
    Specialized agent for generating comprehensive test suites
    """
    
    def __init__(self):
        super().__init__('test_generation')
        self.test_frameworks = {
            'python': ['pytest', 'unittest'],
            'go': ['testing', 'ginkgo', 'testify'],
            'javascript': ['jest', 'mocha']
        }
    
    async def execute(
        self, 
        task: SubTask, 
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Execute test generation task"""
        self.execution_count += 1
        
        dependencies = context.get('dependencies', {})
        code_under_test = dependencies.get('code_generation', {}).get(
            'generated_code', ''
        )
        
        test_code = await self._generate_tests(
            code_under_test,
            context.get('language', 'python')
        )
        
        return {
            'agent': self.agent_id,
            'task_id': task.task_id,
            'test_code': test_code,
            'test_count': self._count_tests(test_code),
            'coverage_estimate': self._estimate_coverage(test_code),
            'execution_count': self.execution_count
        }

class CodeSearchAgent(BaseAgent):
    """
    Specialized agent for semantic code search and reference tracking
    """
    
    def __init__(self):
        super().__init__('code_search')
        self.index = CodeSearchIndex()
    
    async def execute(
        self, 
        task: SubTask, 
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Execute code search task"""
        self.execution_count += 1
        
        search_results = await self.index.search(
            task.description,
            context.get('search_options', {})
        )
        
        return {
            'agent': self.agent_id,
            'task_id': task.task_id,
            'results': search_results,
            'execution_count': self.execution_count
        }

class DocumentationAgent(BaseAgent):
    """
    Specialized agent for documentation generation
    """
    
    def __init__(self):
        super().__init__('documentation')
        self.doc_formats = ['markdown', 'html', 'pdf', 'api_reference']
    
    async def execute(
        self, 
        task: SubTask, 
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Execute documentation generation task"""
        self.execution_count += 1
        
        dependencies = context.get('dependencies', {})
        code_to_document = dependencies.get('code_generation', {}).get(
            'generated_code', ''
        )
        
        documentation = await self._generate_docs(
            code_to_document,
            task.description
        )
        
        return {
            'agent': self.agent_id,
            'task_id': task.task_id,
            'documentation': documentation,
            'format': task.constraints.get('format', 'markdown'),
            'execution_count': self.execution_count
        }

import subprocess
import tempfile
import os
from pathlib import Path
from typing import List, Dict, Any, Optional

class ToolExecutor:
    """
    Executes tools and manages execution environment
    """
    
    def __init__(self, workspace_root: str):
        self.workspace_root = Path(workspace_root)
        self.active_processes: Dict[str, subprocess.Popen] = {}
        
    async def execute_file_operation(
        self,
        operation: str,
        path: str,
        content: Optional[str] = None
    ) -> Dict[str, Any]:
        """Execute file system operations"""
        
        full_path = self.workspace_root / path
        
        if operation == 'read':
            return {'content': full_path.read_text()}
        
        elif operation == 'write':
            full_path.parent.mkdir(parents=True, exist_ok=True)
            full_path.write_text(content)
            return {'success': True, 'path': str(full_path)}
        
        elif operation == 'delete':
            full_path.unlink(missing_ok=True)
            return {'success': True}
        
        elif operation == 'list':
            return {'files': [str(p) for p in full_path.glob('**/*') if p.is_file()]}
        
        return {'error': f'Unknown operation: {operation}'}
    
    async def execute_git_operation(
        self,
        operation: str,
        **kwargs
    ) -> Dict[str, Any]:
        """Execute Git operations"""
        
        git_commands = {
            'status': ['git', 'status', '--porcelain'],
            'commit': ['git', 'commit', '-m', kwargs.get('message', '')],
            'branch': ['git', 'branch', '-a'],
            'log': ['git', 'log', f'--oneline', f'-n{kwargs.get("limit", 10)}'],
            'diff': ['git', 'diff', kwargs.get('ref', 'HEAD')],
        }
        
        if operation not in git_commands:
            return {'error': f'Unknown Git operation: {operation}'}
        
        result = subprocess.run(
            git_commands[operation],
            cwd=self.workspace_root,
            capture_output=True,
            text=True
        )
        
        return {
            'stdout': result.stdout,
            'stderr': result.stderr,
            'returncode': result.returncode
        }
    
    async def execute_shell_command(
        self,
        command: str,
        timeout: int = 60
    ) -> Dict[str, Any]:
        """Execute shell command"""
        
        result = subprocess.run(
            command,
            shell=True,
            cwd=self.workspace_root,
            capture_output=True,
            text=True,
            timeout=timeout
        )
        
        return {
            'stdout': result.stdout,
            'stderr': result.stderr,
            'returncode': result.returncode,
            'success': result.returncode == 0
        }
    
    async def execute_browser_operation(
        self,
        operation: str,
        url: str,
        **kwargs
    ) -> Dict[str, Any]:
        """Execute browser/web operations"""
        
        if operation == 'navigate':
            # Would use browser automation tool
            return {'url': url, 'status': 'navigated'}
        
        elif operation == 'screenshot':
            # Would capture screenshot
            return {'screenshot_path': f'/tmp/screenshot_{kwargs.get("name", "capture")}.png'}
        
        elif operation == 'extract':
            # Would extract page content
            return {'content': 'Extracted page content'}
        
        return {'error': f'Unknown browser operation: {operation}'}

#!/usr/bin/env python3
"""
Claude Opus 4.8 Dynamic Workflows - Parallel Task Scheduler
A production-ready implementation demonstrating parallel agent orchestration.
"""

import asyncio
import logging
from typing import Dict, List, Any, Optional, Set, Callable
from dataclasses import dataclass, field, asdict
from enum import Enum
from datetime import datetime
from collections import defaultdict
import hashlib
import json

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger('parallel_scheduler')

# ============================================================================
# Core Data Structures
# ============================================================================

class TaskStatus(Enum):
    """Task execution status"""
    PENDING = "pending"
    READY = "ready"
    RUNNING = "running"
    COMPLETED = "completed"
    FAILED = "failed"
    CANCELLED = "cancelled"

class TaskPriority(Enum):
    """Task priority levels"""
    CRITICAL = 1
    HIGH = 2
    NORMAL = 3
    LOW = 4

@dataclass
class Task:
    """
    Represents a unit of work in the Dynamic Workflows system.
    Supports dependencies, retries, timeouts, and result tracking.
    """
    task_id: str
    description: str
    task_type: str
    payload: Dict[str, Any] = field(default_factory=dict)
    dependencies: Set[str] = field(default_factory=set)
    priority: TaskPriority = TaskPriority.NORMAL
    status: TaskStatus = TaskStatus.PENDING
    max_retries: int = 3
    timeout_seconds: int = 300
    created_at: datetime = field(default_factory=datetime.now)
    started_at: Optional[datetime] = None
    completed_at: Optional[datetime] = None
    result: Optional[Any] = None
    error: Optional[str] = None
    retry_count: int = 0
    assigned_worker: Optional[str] = None
    
    def to_dict(self) -> Dict:
        """Convert task to dictionary for serialization"""
        data = asdict(self)
        data['priority'] = self.priority.value
        data['status'] = self.status.value
        return data
    
    @property
    def execution_time(self) -> Optional[float]:
        """Calculate execution time in seconds"""
        if self.started_at and self.completed_at:
            return (self.completed_at - self.started_at).total_seconds()
        return None

@dataclass
class WorkflowResult:
    """Complete result of workflow execution"""
    workflow_id: str
    total_tasks: int
    successful_tasks: int
    failed_tasks: int
    total_execution_time: float
    results: Dict[str, Any]
    errors: Dict[str, str]
    checkpoint_data: List[Dict]

# ============================================================================
# Task Queue Implementation
# ============================================================================

class TaskQueue:
    """
    Thread-safe priority queue for task scheduling.
    Uses a min-heap for O(log n) insertion and extraction.
    """
    
    def __init__(self):
        self._heap: List[Task] = []
        self._task_map: Dict[str, Task] = {}
        self._lock = asyncio.Lock()
    
    async def add(self, task: Task) -> None:
        """Add a task to the queue"""
        async with self._lock:
            self._heap.append(task)
            self._task_map[task.task_id] = task
            self._sift_up(len(self._heap) - 1)
    
    async def pop(self) -> Optional[Task]:
        """Remove and return the highest priority task"""
        async with self._lock:
            if not self._heap:
                return None
            
            # Swap root with last element
            task = self._heap[0]
            last = self._heap.pop()
            
            if self._heap:
                self._heap[0] = last
                self._sift_down(0)
            
            del self._task_map[task.task_id]
            return task
    
    async def peek(self) -> Optional[Task]:
        """View the highest priority task without removing it"""
        async with self._lock:
            if not self._heap:
                return None
            return self._heap[0]
    
    async def remove(self, task_id: str) -> bool:
        """Remove a specific task by ID"""
        async with self._lock:
            if task_id not in self._task_map:
                return False
            
            # Mark as cancelled instead of removing from heap
            task = self._task_map[task_id]
            task.status = TaskStatus.CANCELLED
            return True
    
    async def get(self, task_id: str) -> Optional[Task]:
        """Get a task by ID"""
        async with self._lock:
            return self._task_map.get(task_id)
    
    async def size(self) -> int:
        """Get the number of tasks in the queue"""
        async with self._lock:
            return len(self._heap)
    
    def _sift_up(self, index: int) -> None:
        """Maintain heap property after insertion"""
        while index > 0:
            parent = (index - 1) // 2
            if self._heap[index].priority.value < self._heap[parent].priority.value:
                self._heap[index], self._heap[parent] = self._heap[parent], self._heap[index]
                index = parent
            else:
                break
    
    def _sift_down(self, index: int) -> None:
        """Maintain heap property after removal"""
        while True:
            smallest = index
            left = 2 * index + 1
            right = 2 * index + 2
            
            if (left < len(self._heap) and 
                self._heap[left].priority.value < self._heap[smallest].priority.value):
                smallest = left
            
            if (right < len(self._heap) and 
                self._heap[right].priority.value < self._heap[smallest].priority.value):
                smallest = right
            
            if smallest != index:
                self._heap[index], self._heap[smallest] = self._heap[smallest], self._heap[index]
                index = smallest
            else:
                break

# ============================================================================
# Worker Pool
# ============================================================================

class Worker:
    """Individual worker that executes tasks"""
    
    def __init__(self, worker_id: str):
        self.worker_id = worker_id
        self.current_task: Optional[Task] = None
        self.is_busy = False
        self.tasks_completed = 0
        self.tasks_failed = 0
    
    async def execute_task(
        self,
        task: Task,
        executor_func: Callable
    ) -> Any:
        """Execute a task with the given executor function"""
        self.is_busy = True
        self.current_task = task
        
        try:
            logger.info(f"Worker {self.worker_id} executing task {task.task_id}")
            
            # Execute with timeout
            result = await asyncio.wait_for(
                executor_func(task),
                timeout=task.timeout_seconds
            )
            
            self.tasks_completed += 1
            return result
            
        except asyncio.TimeoutError:
            logger.error(f"Worker {self.worker_id}: Task {task.task_id} timed out")
            self.tasks_failed += 1
            raise
            
        except Exception as e:
            logger.error(f"Worker {self.worker_id}: Task {task.task_id} failed: {e}")
            self.tasks_failed += 1
            raise
            
        finally:
            self.is_busy = False
            self.current_task = None

class WorkerPool:
    """Manages a pool of workers for parallel task execution"""
    
    def __init__(self, size: int):
        self.size = size
        self.workers = [Worker(f"worker_{i}") for i in range(size)]
        self.available_workers: asyncio.Queue = asyncio.Queue()
        
        # Initialize available worker queue
        for worker in self.workers:
            self.available_workers.put_nowait(worker)
    
    async def get_worker(self) -> Optional[Worker]:
        """Get an available worker (blocks if none available)"""
        try:
            return await asyncio.wait_for(
                self.available_workers.get(),
                timeout=1.0
            )
        except asyncio.TimeoutError:
            return None
    
    async def release_worker(self, worker: Worker) -> None:
        """Release a worker back to the pool"""
        if not worker.is_busy:
            await self.available_workers.put(worker)
    
    async def execute_task(
        self,
        task: Task,
        executor_func: Callable
    ) -> Any:
        """Execute a task using an available worker"""
        worker = await self.get_worker()
        
        if worker is None:
            # Wait for a worker to become available
            worker = await self.available_workers.get()
        
        try:
            return await worker.execute_task(task, executor_func)
        finally:
            await self.release_worker(worker)
    
    def get_stats(self) -> Dict[str, Any]:
        """Get worker pool statistics"""
        return {
            'pool_size': self.size,
            'total_completed': sum(w.tasks_completed for w in self.workers),
            'total_failed': sum(w.tasks_failed for w in self.workers),
            'busy_workers': sum(1 for w in self.workers if w.is_busy),
            'available_workers': self.size - sum(1 for w in self.workers if w.is_busy)
        }

# ============================================================================
# Dependency Graph Manager
# ============================================================================

class DependencyGraph:
    """
    Manages task dependencies and determines execution order.
    Uses topological sort for dependency resolution.
    """
    
    def __init__(self):
        self.graph: Dict[str, Set[str]] = defaultdict(set)
        self.reverse_graph: Dict[str, Set[str]] = defaultdict(set)
        self.tasks: Dict[str, Task] = {}
    
    def add_task(self, task: Task) -> None:
        """Add a task to the dependency graph"""
        self.tasks[task.task_id] = task
        
        for dep_id in task.dependencies:
            self.graph[dep_id].add(task.task_id)
            self.reverse_graph[task.task_id].add(dep_id)
    
    def get_ready_tasks(self, completed: Set[str]) -> List[Task]:
        """Get tasks that are ready to execute (all dependencies met)"""
        ready = []
        
        for task_id, task in self.tasks.items():
            if task.status != TaskStatus.PENDING:
                continue
            
            if task_id in completed:
                continue
            
            # Check if all dependencies are completed
            if task.dependencies.issubset(completed):
                ready.append(task)
        
        # Sort by priority
        return sorted(ready, key=lambda t: t.priority.value)
    
    def get_blocked_tasks(self, completed: Set[str]) -> Dict[str, Set[str]]:
        """Get tasks that are blocked with their blocking dependencies"""
        blocked = {}
        
        for task_id, task in self.tasks.items():
            if task_id in completed:
                continue
            
            pending_deps = task.dependencies - completed
            if pending_deps:
                blocked[task_id] = pending_deps
        
        return blocked
    
    def topological_sort(self) -> List[str]:
        """Return tasks in topologically sorted order"""
        in_degree = {task_id: len(task.dependencies) for task_id, task in self.tasks.items()}
        queue = [task_id for task_id, degree in in_degree.items() if degree == 0]
        result = []
        
        while queue:
            task_id = queue.pop(0)
            result.append(task_id)
            
            for dependent in self.graph[task_id]:
                in_degree[dependent] -= 1
                if in_degree[dependent] == 0:
                    queue.append(dependent)
        
        if len(result) != len(self.tasks):
            raise ValueError("Cycle detected in dependency graph")
        
        return result

# ============================================================================
# Main Parallel Task Scheduler
# ============================================================================

class ParallelTaskScheduler:
    """
    Main scheduler for parallel task execution.
    Implements Dynamic Workflows core innovation: parallel scheduling
    of hundreds of sub-agents with dependency management.
    """
    
    def __init__(
        self,
        max_workers: int = 100,
        max_concurrent_tasks: int = 100,
        enable_checkpoints: bool = True,
        checkpoint_interval: int = 10
    ):
        self.max_workers = max_workers
        self.max_concurrent_tasks = max_concurrent_tasks
        self.enable_checkpoints = enable_checkpoints
        self.checkpoint_interval = checkpoint_interval
        
        self.task_queue = TaskQueue()
        self.worker_pool = WorkerPool(max_workers)
        self.dependency_graph = DependencyGraph()
        
        self.active_tasks: Dict[str, asyncio.Task] = {}
        self.completed_results: Dict[str, Any] = {}
        self.failed_tasks: Dict[str, Exception] = {}
        self.checkpoints: List[Dict] = []
        
        self.workflow_id: Optional[str] = None
        self.start_time: Optional[datetime] = None
        
        # Callbacks
        self.on_task_start: Optional[Callable] = None
        self.on_task_complete: Optional[Callable] = None
        self.on_task_fail: Optional[Callable] = None
        self.on_checkpoint: Optional[Callable] = None
    
    def set_workflow_id(self, workflow_id: str) -> None:
        """Set the workflow ID for tracking"""
        self.workflow_id = workflow_id
    
    async def submit_task(self, task: Task) -> str:
        """Submit a new task for execution"""
        task.status = TaskStatus.PENDING
        self.dependency_graph.add_task(task)
        await self.task_queue.add(task)
        logger.info(f"Task {task.task_id} submitted")
        return task.task_id
    
    async def submit_tasks(self, tasks: List[Task]) -> List[str]:
        """Submit multiple tasks at once"""
        return [await self.submit_task(task) for task in tasks]
    
    async def execute(
        self,
        tasks: List[Task],
        executor_func: Callable,
        workflow_id: Optional[str] = None
    ) -> WorkflowResult:
        """
        Execute a workflow of tasks with parallel scheduling.
        This is the core method that implements Dynamic Workflows.
        """
        
        # Initialize workflow
        self.workflow_id = workflow_id or self._generate_workflow_id()
        self.start_time = datetime.now()
        
        # Build dependency graph
        for task in tasks:
            self.dependency_graph.add_task(task)
        
        # Create initial ready tasks
        ready_tasks = self.dependency_graph.get_ready_tasks(set())
        for task in ready_tasks:
            task.status = TaskStatus.READY
        
        logger.info(f"Starting workflow {self.workflow_id} with {len(tasks)} tasks")
        
        try:
            # Main execution loop
            completed = set()
            pending_futures: Set[asyncio.Future] = set()
            
            while ready_tasks or pending_futures:
                # Launch ready tasks up to concurrency limit
                while ready_tasks and len(self.active_tasks) < self.max_concurrent_tasks:
                    task = ready_tasks.pop(0)
                    
                    # Launch task execution
                    future = asyncio.create_task(
                        self._execute_task(task, executor_func)
                    )
                    self.active_tasks[task.task_id] = future
                    pending_futures.add(future)
                    
                    # Trigger callback
                    if self.on_task_start:
                        await self.on_task_start(task)
                
                # Wait for at least one task to complete
                if pending_futures:
                    done, pending_futures = await asyncio.wait(
                        pending_futures,
                        return_when=asyncio.FIRST_COMPLETED
                    )
                    
                    # Process completed tasks
                    for future in done:
                        task_id = self._get_task_id_from_future(future, self.active_tasks)
                        del self.active_tasks[task_id]
                        
                        try:
                            result = future.result()
                            self.completed_results[task_id] = result
                            completed.add(task_id)
                            
                            task = self.dependency_graph.tasks[task_id]
                            task.status = TaskStatus.COMPLETED
                            task.result = result
                            task.completed_at = datetime.now()
                            
                            # Trigger callback
                            if self.on_task_complete:
                                await self.on_task_complete(task, result)
                            
                            # Find newly ready tasks
                            newly_ready = self.dependency_graph.get_ready_tasks(completed)
                            ready_tasks.extend(newly_ready)
                            
                            # Create checkpoint if needed
                            if (self.enable_checkpoints and 
                                len(completed) % self.checkpoint_interval == 0):
                                await self._create_checkpoint(completed)
                            
                        except Exception as e:
                            task = self.dependency_graph.tasks[task_id]
                            self.failed_tasks[task_id] = e
                            task.status = TaskStatus.FAILED
                            task.error = str(e)
                            
                            # Trigger callback
                            if self.on_task_fail:
                                await self.on_task_fail(task, e)
                            
                            # Check retry logic
                            if task.retry_count < task.max_retries:
                                task.retry_count += 1
                                task.status = TaskStatus.READY
                                ready_tasks.append(task)
                            else:
                                completed.add(task_id)
                
                # Check for deadlock
                if not ready_tasks and pending_futures and not done:
                    logger.warning("Potential deadlock detected, waiting...")
                    await asyncio.sleep(0.1)
            
            # Final checkpoint
            await self._create_checkpoint(completed, final=True)
            
        except Exception as e:
            logger.error(f"Workflow {self.workflow_id} failed: {e}")
            raise
        
        # Calculate results
        total_time = (datetime.now() - self.start_time).total_seconds()
        
        return WorkflowResult(
            workflow_id=self.workflow_id,
            total_tasks=len(tasks),
            successful_tasks=len(self.completed_results),
            failed_tasks=len(self.failed_tasks),
            total_execution_time=total_time,
            results=self.completed_results,
            errors={k: str(v) for k, v in self.failed_tasks.items()},
            checkpoint_data=self.checkpoints
        )
    
    async def _execute_task(
        self,
        task: Task,
        executor_func: Callable
    ) -> Any:
        """Execute a single task with worker pool"""
        
        task.status = TaskStatus.RUNNING
        task.started_at = datetime.now()
        
        # Get worker and execute
        result = await self.worker_pool.execute_task(task, executor_func)
        
        return result
    
    async def _create_checkpoint(
        self,
        completed: Set[str],
        final: bool = False
    ) -> None:
        """Create a workflow checkpoint for recovery"""
        
        checkpoint = {
            'timestamp': datetime.now().isoformat(),
            'workflow_id': self.workflow_id,
            'completed_tasks': list(completed),
            'total_completed': len(completed),
            'failed_tasks': list(self.failed_tasks.keys()),
            'total_failed': len(self.failed_tasks),
            'is_final': final
        }
        
        self.checkpoints.append(checkpoint)
        
        logger.info(
            f"Checkpoint created: {len(completed)} completed, "
            f"{len(self.failed_tasks)} failed"
        )
        
        if self.on_checkpoint:
            await self.on_checkpoint(checkpoint)
    
    def _get_task_id_from_future(
        self,
        future: asyncio.Future,
        task_map: Dict[str, asyncio.Future]
    ) -> Optional[str]:
        """Map a future back to its task ID"""
        for task_id, f in task_map.items():
            if f == future:
                return task_id
        return None
    
    def _generate_workflow_id(self) -> str:
        """Generate a unique workflow ID"""
        timestamp = datetime.now().isoformat()
        hash_input = f"{timestamp}_{len(self.dependency_graph.tasks)}"
        return f"wf_{hashlib.md5(hash_input.encode()).hexdigest()[:12]}"
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get workflow execution statistics"""
        if not self.start_time:
            return {'status': 'not_started'}
        
        elapsed = (datetime.now() - self.start_time).total_seconds()
        total = len(self.dependency_graph.tasks)
        completed = len(self.completed_results)
        failed = len(self.failed_tasks)
        active = len(self.active_tasks)
        
        return {
            'workflow_id': self.workflow_id,
            'status': 'running' if active > 0 else 'completed',
            'elapsed_time': elapsed,
            'total_tasks': total,
            'completed_tasks': completed,
            'failed_tasks': failed,
            'active_tasks': active,
            'completion_rate': completed / total if total > 0 else 0,
            'worker_pool_stats': self.worker_pool.get_stats(),
            'checkpoints_created': len(self.checkpoints)
        }

# ============================================================================
# Demo: Usage Example
# ============================================================================

async def demo_executor(task: Task) -> Dict[str, Any]:
    """Demo executor function that simulates task execution"""
    await asyncio.sleep(0.1)  # Simulate work
    
    # Simulate some tasks failing randomly for demonstration
    import random
    if random.random() < 0.1:  # 10% failure rate
        raise ValueError(f"Simulated failure for task {task.task_id}")
    
    return {
        'task_id': task.task_id,
        'status': 'completed',
        'result': f"Result of {task.description}",
        'timestamp': datetime.now().isoformat()
    }

async def main():
    """Demonstrate the Parallel Task Scheduler"""
    
    # Create scheduler with 10 workers
    scheduler = ParallelTaskScheduler(
        max_workers=10,
        max_concurrent_tasks=10,
        enable_checkpoints=True
    )
    
    # Create tasks with dependencies
    tasks = [
        Task(
            task_id="task_1",
            description="Initialize project structure",
            task_type="setup",
            priority=TaskPriority.HIGH
        ),
        Task(
            task_id="task_2",
            description="Generate core modules",
            task_type="code_generation",
            dependencies={"task_1"},
            priority=TaskPriority.HIGH
        ),
        Task(
            task_id="task_3",
            description="Create database schema",
            task_type="database",
            dependencies={"task_1"},
            priority=TaskPriority.NORMAL
        ),
        Task(
            task_id="task_4",
            description="Write unit tests",
            task_type="testing",
            dependencies={"task_2"},
            priority=TaskPriority.NORMAL
        ),
        Task(
            task_id="task_5",
            description="Code review",
            task_type="review",
            dependencies={"task_2"},
            priority=TaskPriority.NORMAL
        ),
        Task(
            task_id="task_6",
            description="Integration testing",
            task_type="testing",
            dependencies={"task_2", "task_3"},
            priority=TaskPriority.HIGH
        ),
        Task(
            task_id="task_7",
            description="Generate documentation",
            task_type="documentation",
            dependencies={"task_2"},
            priority=TaskPriority.LOW
        ),
        Task(
            task_id="task_8",
            description="Final validation",
            task_type="validation",
            dependencies={"task_4", "task_5", "task_6"},
            priority=TaskPriority.CRITICAL
        ),
    ]
    
    # Set up callbacks
    async def on_task_complete(task: Task, result: Any):
        logger.info(f"Task {task.task_id} completed")
    
    scheduler.on_task_complete = on_task_complete
    
    # Execute workflow
    result = await scheduler.execute(tasks, demo_executor, "demo_workflow")
    
    # Print results
    print("\n" + "=" * 60)
    print("WORKFLOW EXECUTION RESULTS")
    print("=" * 60)
    print(f"Workflow ID: {result.workflow_id}")
    print(f"Total Tasks: {result.total_tasks}")
    print(f"Successful: {result.successful_tasks}")
    print(f"Failed: {result.failed_tasks}")
    print(f"Total Time: {result.total_execution_time:.2f}s")
    print("\nStatistics:")
    print(json.dumps(scheduler.get_statistics(), indent=2))
    print("\nCheckpoints:")
    for checkpoint in result.checkpoint_data:
        print(f"  - {checkpoint['timestamp']}: {checkpoint['total_completed']} completed")

if __name__ == "__main__":
    asyncio.run(main())

package main

import (
	"context"
	"encoding/json"
	"fmt"
	"log"
	"sync"
	"time"
)

// ============================================================================
// Core Agent Types and Interfaces
// ============================================================================

// AgentCapability represents a specific capability an agent can perform
type AgentCapability string

const (
	CapabilityCodeGeneration  AgentCapability = "code_generation"
	CapabilityCodeReview      AgentCapability = "code_review"
	CapabilityTestGeneration  AgentCapability = "test_generation"
	CapabilityCodeSearch      AgentCapability = "code_search"
	CapabilityDocumentation   AgentCapability = "documentation"
	CapabilityDebugging      AgentCapability = "debugging"
)

// TaskPriority represents task priority levels
type TaskPriority int

const (
	PriorityCritical TaskPriority = 1
	PriorityHigh      TaskPriority = 2
	PriorityNormal    TaskPriority = 3
	PriorityLow       TaskPriority = 4
)

// TaskStatus represents the current status of a task
type TaskStatus string

const (
	StatusPending   TaskStatus = "pending"
	StatusRunning   TaskStatus = "running"
	StatusCompleted TaskStatus = "completed"
	StatusFailed     TaskStatus = "failed"
	StatusCancelled  TaskStatus = "cancelled"
)

// Task represents a unit of work for an agent
type Task struct {
	TaskID       string            `json:"task_id"`
	Description  string            `json:"description"`
	TaskType     AgentCapability   `json:"task_type"`
	Payload      map[string]interface{} `json:"payload"`
	Dependencies []string          `json:"dependencies"`
	Priority     TaskPriority      `json:"priority"`
	Status       TaskStatus        `json:"status"`
	Result       interface{}       `json:"result,omitempty"`
	Error        string            `json:"error,omitempty"`
	CreatedAt    time.Time         `json:"created_at"`
	StartedAt    *time.Time        `json:"started_at,omitempty"`
	CompletedAt  *time.Time        `json:"completed_at,omitempty"`
}

// Agent represents an AI agent capable of executing specific tasks
type Agent interface {
	GetID() string
	GetCapabilities() []AgentCapability
	Execute(ctx context.Context, task *Task, dependencies map[string]*Task) (interface{}, error)
}

// BaseAgent provides common functionality for all agents
type BaseAgent struct {
	agentID       string
	capabilities  []AgentCapability
	executionCount int
	mu            sync.RWMutex
}

// ============================================================================
// Code Generation Agent
// ============================================================================

type CodeGenerationAgent struct {
	BaseAgent
	modelEndpoint string
}

func NewCodeGenerationAgent(endpoint string) *CodeGenerationAgent {
	return &CodeGenerationAgent{
		BaseAgent: BaseAgent{
			agentID:      "code_generation_agent",
			capabilities: []AgentCapability{CapabilityCodeGeneration},
		},
		modelEndpoint: endpoint,
	}
}

func (a *CodeGenerationAgent) GetID() string {
	return a.agentID
}

func (a *CodeGenerationAgent) GetCapabilities() []AgentCapability {
	return a.capabilities
}

func (a *CodeGenerationAgent) Execute(
	ctx context.Context,
	task *Task,
	dependencies map[string]*Task,
) (interface{}, error) {
	a.mu.Lock()
	a.executionCount++
	a.mu.Unlock()

	// Extract relevant context from dependencies
	var codeContext string
	if dep, ok := dependencies["analysis"]; ok && dep.Result != nil {
		codeContext = fmt.Sprintf("%v", dep.Result)
	}

	// Generate code based on task description and dependencies
	generatedCode := fmt.Sprintf(
		"// Generated by %s\n// Task: %s\n// Context: %s\n\npackage main\n\nimport \"fmt\"\n\nfunc main() {\n    fmt.Println(\"Generated implementation for: %s\")\n}",
		task.TaskID,
		task.Description,
		codeContext,
	)

	return map[string]interface{}{
		"agent":         a.GetID(),
		"task_id":       task.TaskID,
		"generated_code": generatedCode,
		"language":      "go",
		"execution_num": a.executionCount,
	}, nil
}

// ============================================================================
// Code Review Agent
// ============================================================================

type CodeReviewAgent struct {
	BaseAgent
	reviewRules []ReviewRule
}

type ReviewRule struct {
	Name        string
	Description string
	Severity    string // "critical", "warning", "info"
}

func NewCodeReviewAgent() *CodeReviewAgent {
	return &CodeReviewAgent{
		BaseAgent: BaseAgent{
			agentID:      "code_review_agent",
			capabilities: []AgentCapability{CapabilityCodeReview},
		},
		reviewRules: []ReviewRule{
			{Name: "G001", Description: "Variables should be named descriptively", Severity: "warning"},
			{Name: "G002", Description: "Error handling should be explicit", Severity: "critical"},
			{Name: "G003", Description: "Avoid global variables", Severity: "warning"},
		},
	}
}

func (a *CodeReviewAgent) GetID() string {
	return a.agentID
}

func (a *CodeReviewAgent) GetCapabilities() []AgentCapability {
	return a.capabilities
}

func (a *CodeReviewAgent) Execute(
	ctx context.Context,
	task *Task,
	dependencies map[string]*Task,
) (interface{}, error) {
	a.mu.Lock()
	a.executionCount++
	a.mu.Unlock()

	// Get code to review from dependencies
	var codeToReview string
	if dep, ok := dependencies["code_generation"]; ok && dep.Result != nil {
		if result, ok := dep.Result.(map[string]interface{}); ok {
			if code, ok := result["generated_code"].(string); ok {
				codeToReview = code
			}
		}
	}

	// Perform code review
	issues := a.analyzeCode(codeToReview)
	
	return map[string]interface{}{
		"agent":           a.GetID(),
		"task_id":        task.TaskID,
		"issues_found":   len(issues),
		"issues":         issues,
		"overall_score":  a.calculateScore(issues),
		"execution_num":  a.executionCount,
	}, nil
}

func (a *CodeReviewAgent) analyzeCode(code string) []map[string]string {
	issues := []map[string]string{}
	
	// Simulate code analysis
	if len(code) < 50 {
		issues = append(issues, map[string]string{
			"rule":    "G001",
			"severity": "warning",
			"message": "Code seems too short, may need more implementation",
		})
	}
	
	return issues
}

func (a *CodeReviewAgent) calculateScore(issues []map[string]string) float64 {
	if len(issues) == 0 {
		return 10.0
	}
	
	criticalCount := 0
	for _, issue := range issues {
		if issue["severity"] == "critical" {
			criticalCount++
		}
	}
	
	return 10.0 - float64(criticalCount)*2.0 - float64(len(issues)-criticalCount)*0.5
}

// ============================================================================
// Test Generation Agent
// ============================================================================

type TestGenerationAgent struct {
	BaseAgent
	testFrameworks map[string][]string
}

func NewTestGenerationAgent() *TestGenerationAgent {
	return &TestGenerationAgent{
		BaseAgent: BaseAgent{
			agentID:      "test_generation_agent",
			capabilities: []AgentCapability{CapabilityTestGeneration},
		},
		testFrameworks: map[string][]string{
			"go":     {"testing", "testify", "ginkgo"},
			"python": {"pytest", "unittest", "mock"},
			"java":   {"junit", "testng", "mockito"},
		},
	}
}

func (a *TestGenerationAgent) GetID() string {
	return a.agentID
}

func (a *TestGenerationAgent) GetCapabilities() []AgentCapability {
	return a.capabilities
}

func (a *TestGenerationAgent) Execute(
	ctx context.Context,
	task *Task,
	dependencies map[string]*Task,
) (interface{}, error) {
	a.mu.Lock()
	a.executionCount++
	a.mu.Unlock()

	// Get code to test from dependencies
	var codeToTest string
	var language string
	
	if dep, ok := dependencies["code_generation"]; ok && dep.Result != nil {
		if result, ok := dep.Result.(map[string]interface{}); ok {
			if code, ok := result["generated_code"].(string); ok {
				codeToTest = code
			}
			if lang, ok := result["language"].(string); ok {
				language = lang
			}
		}
	}

	// Generate tests
	tests := a.generateTests(codeToTest, language)
	
	return map[string]interface{}{
		"agent":            a.GetID(),
		"task_id":         task.TaskID,
		"test_code":       tests,
		"test_count":      3, // Simulated
		"coverage_estimate": 85.5,
		"framework":       a.testFrameworks[language],
		"execution_num":   a.executionCount,
	}, nil
}

func (a *TestGenerationAgent) generateTests(code, language string) string {
	framework := "testing"
	if lang, ok := a.testFrameworks[language]; ok {
		if len(lang) > 0 {
			framework = lang[0]
		}
	}

	return fmt.Sprintf(`// Generated tests using %s
package main

import "testing"

func TestMainLogic(t *testing.T) {
    // Test case 1
    result := main()
    if result == nil {
        t.Error("Expected non-nil result")
    }
}

func TestEdgeCases(t *testing.T) {
    // Test edge cases
    tests := []struct {
        name    string
        input   interface{}
        expect  interface{}
    }{
        {"empty input", "", nil},
        {"nil input", nil, nil},
    }
    
    for _, tt := range tests {
        t.Run(tt.name, func(t *testing.T) {
            // Test implementation
        })
    }
}

func TestConcurrency(t *testing.T) {
    // Test concurrent execution
    done := make(chan bool)
    go func() {
        // Concurrent test
        done <- true
    }()
    <-done
}`, framework)
}

// ============================================================================
// Multi-Agent Coordinator
// ============================================================================

type MultiAgentCoordinator struct {
	agents      map[string]Agent
	taskQueue   chan *Task
	resultStore sync.Map
	mu          sync.RWMutex
	wg          sync.WaitGroup
}

func NewMultiAgentCoordinator(workerCount int) *MultiAgentCoordinator {
	coord := &MultiAgentCoordinator{
		agents:    make(map[string]Agent),
		taskQueue: make(chan *Task, 1000),
	}
	
	// Start worker goroutines
	for i := 0; i < workerCount; i++ {
		coord.wg.Add(1)
		go coord.worker(i)
	}
	
	return coord
}

func (c *MultiAgentCoordinator) RegisterAgent(agent Agent) {
	c.mu.Lock()
	defer c.mu.Unlock()
	c.agents[agent.GetID()] = agent
	log.Printf("Registered agent: %s with capabilities: %v", 
		agent.GetID(), agent.GetCapabilities())
}

func (c *MultiAgentCoordinator) SubmitTask(task *Task) {
	c.taskQueue <- task
	log.Printf("Submitted task: %s (type: %s)", task.TaskID, task.TaskType)
}

func (c *MultiAgentCoordinator) ExecuteWorkflow(ctx context.Context, tasks []*Task) map[string]interface{} {
	// Build dependency graph
	taskMap := make(map[string]*Task)
	for _, task := range tasks {
		taskMap[task.TaskID] = task
	}
	
	// Find ready tasks (no dependencies)
	var readyTasks []*Task
	for _, task := range tasks {
		if len(task.Dependencies) == 0 {
			readyTasks = append(readyTasks, task)
		}
	}
	
	// Submit ready tasks
	for _, task := range readyTasks {
		c.SubmitTask(task)
	}
	
	// Process tasks with dependency resolution
	completed := make(map[string]bool)
	var mu sync.Mutex
	
	for len(completed) < len(tasks) {
		// Wait for task completion
		select {
		case result := <-c.resultStoreCh():
			completed[result.TaskID] = true
			log.Printf("Task %s completed", result.TaskID)
			
			// Find newly ready tasks
			for _, task := range tasks {
				if completed[task.TaskID] {
					continue
				}
				
				// Check if all dependencies are met
				allDepsMet := true
				for _, dep := range task.Dependencies {
					if !completed[dep] {
						allDepsMet = false
						break
					}
				}
				
				if allDepsMet {
					c.SubmitTask(task)
				}
			}
			
		case <-ctx.Done():
			log.Printf("Context cancelled, %d tasks remaining", len(tasks)-len(completed))
			return c.gatherResults()
		}
	}
	
	return c.gatherResults()
}

func (c *MultiAgentCoordinator) worker(id int) {
	defer c.wg.Done()
	
	for task := range c.taskQueue {
		agent := c.findAgentForTask(task)
		if agent == nil {
			log.Printf("Worker %d: No agent found for task %s", id, task.TaskID)
			continue
		}
		
		log.Printf("Worker %d executing task %s with agent %s", 
			id, task.TaskID, agent.GetID())
		
		// Gather dependencies
		dependencies := make(map[string]*Task)
		// ... dependency gathering logic
		
		// Execute task
		ctx, cancel := context.WithTimeout(context.Background(), 5*time.Minute)
		result, err := agent.Execute(ctx, task, dependencies)
		cancel()
		
		// Store result
		if err != nil {
			task.Status = StatusFailed
			task.Error = err.Error()
		} else {
			task.Status = StatusCompleted
			task.Result = result
		}
		
		c.resultStore.Store(task.TaskID, task)
	}
}

func (c *MultiAgentCoordinator) findAgentForTask(task *Task) Agent {
	c.mu.RLock()
	defer c.mu.RUnlock()
	
	for _, agent := range c.agents {
		for _, cap := range agent.GetCapabilities() {
			if cap == task.TaskType {
				return agent
			}
		}
	}
	return nil
}

func (c *MultiAgentCoordinator) resultStoreCh() chan *Task {
	// Implementation for result channel
	ch := make(chan *Task, 100)
	go func() {
		c.resultStore.Range(func(key, value interface{}) bool {
			if task, ok := value.(*Task); ok {
				ch <- task
			}
			return true
		})
	}()
	return ch
}

func (c *MultiAgentCoordinator) gatherResults() map[string]interface{} {
	results := make(map[string]interface{})
	
	c.resultStore.Range(func(key, value interface{}) bool {
		if task, ok := value.(*Task); ok {
			results[task.TaskID] = task.Result
		}
		return true
	})
	
	return results
}

func (c *MultiAgentCoordinator) Shutdown() {
	close(c.taskQueue)
	c.wg.Wait()
}

// ============================================================================
// Demo
// ============================================================================

func main() {
	log.Println("Starting Multi-Agent System Demo")
	
	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
	defer cancel()
	
	// Create coordinator with 5 workers
	coordinator := NewMultiAgentCoordinator(5)
	
	// Register specialized agents
	coordinator.RegisterAgent(NewCodeGenerationAgent("claude-api"))
	coordinator.RegisterAgent(NewCodeReviewAgent())
	coordinator.RegisterAgent(NewTestGenerationAgent())
	
	// Define workflow tasks
	tasks := []*Task{
		{
			TaskID:       "task_1",
			Description:  "Analyze requirements and create specification",
			TaskType:     CapabilityCodeSearch,
			Priority:     PriorityHigh,
			Dependencies: []string{},
		},
		{
			TaskID:       "task_2",
			Description:  "Generate core implementation",
			TaskType:     CapabilityCodeGeneration,
			Priority:     PriorityHigh,
			Dependencies: []string{"task_1"},
		},
		{
			TaskID:       "task_3",
			Description:  "Review generated code",
			TaskType:     CapabilityCodeReview,
			Priority:     PriorityNormal,
			Dependencies: []string{"task_2"},
		},
		{
			TaskID:       "task_4",
			Description:  "Generate test suite",
			TaskType:     CapabilityTestGeneration,
			Priority:     PriorityNormal,
			Dependencies: []string{"task_2"},
		},
		{
			TaskID:       "task_5",
			Description:  "Create documentation",
			TaskType:     CapabilityDocumentation,
			Priority:     PriorityLow,
			Dependencies: []string{"task_2", "task_3"},
		},
	}
	
	// Execute workflow
	results := coordinator.ExecuteWorkflow(ctx, tasks)
	
	// Print results
	fmt.Println("\n" + strings.Repeat("=", 60))
	fmt.Println("WORKFLOW EXECUTION RESULTS")
	fmt.Println(strings.Repeat("=", 60))
	
	for taskID, result := range results {
		resultJSON, _ := json.MarshalIndent(result, "", "  ")
		fmt.Printf("\n%s:\n%s\n", taskID, string(resultJSON))
	}
	
	coordinator.Shutdown()
}