class ReactiveAgent:
    def __init__(self, config):
        self.tools = ToolRegistry(config.available_tools)
        self.response_cache = ResponseCache()
        self.context_manager = SimpleContextManager()

    async def process_request(self, request):
        """Process request with immediate reactive response"""

        # Quick context preparation
        context = await self.context_manager.get_immediate_context(request)

        # Check cache for similar requests
        cached_response = await self.response_cache.get_similar(request)
        if cached_response and cached_response.confidence > 0.8:
            return cached_response.response

        # Pattern matching for response type
        response_type = self._classify_request_type(request)

        if response_type == 'information_query':
            return await self._handle_information_query(request, context)
        elif response_type == 'action_request':
            return await self._handle_action_request(request, context)
        elif response_type == 'tool_usage':
            return await self._handle_tool_usage(request, context)
        else:
            return await self._handle_general_request(request, context)

    def _classify_request_type(self, request):
        """Fast classification of request type"""
        content = request.get('content', '').lower()

        # Use pattern matching for speed
        if any(word in content for word in ['what', 'how', 'when', 'where', 'why']):
            return 'information_query'
        elif any(word in content for word in ['do', 'create', 'make', 'generate', 'build']):
            return 'action_request'
        elif any(word in content for word in ['search', 'calculate', 'analyze', 'process']):
            return 'tool_usage'
        else:
            return 'general_request'

    async def _handle_tool_usage(self, request, context):
        """Handle requests requiring tool usage"""
        # Simple tool selection based on keywords
        tool_name = self._select_tool_by_keywords(request)

        if tool_name:
            tool = self.tools.get_tool(tool_name)
            try:
                # Extract parameters using pattern matching
                parameters = await self._extract_tool_parameters(request, tool)
                result = await tool.execute(parameters)

                # Format response
                return {
                    'response': f"I used {tool_name} and found: {result}",
                    'tool_used': tool_name,
                    'result': result
                }
            except Exception as e:
                return {
                    'response': f"I encountered an error using {tool_name}: {str(e)}",
                    'error': True
                }
        else:
            return await self._handle_general_request(request, context)

    def _select_tool_by_keywords(self, request):
        """Select tool based on keyword patterns"""
        content = request.get('content', '').lower()

        tool_patterns = {
            'search': ['search', 'find', 'look up', 'google'],
            'calculator': ['calculate', 'compute', 'math', 'number'],
            'weather': ['weather', 'temperature', 'forecast', 'rain'],
            'calendar': ['schedule', 'appointment', 'meeting', 'calendar']
        }

        for tool_name, keywords in tool_patterns.items():
            if any(keyword in content for keyword in keywords):
                return tool_name

        return None

class EventDrivenAgent:
    def __init__(self, config):
        self.event_handlers = {}
        self.event_queue = asyncio.Queue()
        self.event_processor = EventProcessor()
        self.running = False

    async def start(self):
        """Start the event processing loop"""
        self.running = True
        await asyncio.gather(
            self._process_events(),
            self._monitor_triggers()
        )

    async def _process_events(self):
        """Main event processing loop"""
        while self.running:
            try:
                event = await self.event_queue.get()
                await self._handle_event(event)
            except Exception as e:
                logging.error(f"Error processing event: {e}")

    async def _handle_event(self, event):
        """Handle individual events"""
        event_type = event.get('type')
        handler = self.event_handlers.get(event_type)

        if handler:
            try:
                response = await handler(event)
                await self._emit_response(event, response)
            except Exception as e:
                await self._emit_error(event, e)
        else:
            logging.warning(f"No handler for event type: {event_type}")

    def register_event_handler(self, event_type, handler_func):
        """Register handler for specific event type"""
        self.event_handlers[event_type] = handler_func

    async def emit_event(self, event):
        """Add event to processing queue"""
        await self.event_queue.put(event)

# Example usage with specific event handlers
class CustomerSupportAgent(EventDrivenAgent):
    def __init__(self, config):
        super().__init__(config)
        self._register_support_handlers()

    def _register_support_handlers(self):
        """Register customer support specific handlers"""
        self.register_event_handler('customer_message', self._handle_customer_message)
        self.register_event_handler('ticket_created', self._handle_ticket_created)
        self.register_event_handler('escalation_requested', self._handle_escalation)
        self.register_event_handler('satisfaction_survey', self._handle_survey)

    async def _handle_customer_message(self, event):
        """Handle incoming customer message"""
        message = event['data']['message']
        customer_id = event['data']['customer_id']

        # Quick sentiment analysis
        sentiment = await self._analyze_sentiment(message)

        if sentiment == 'angry':
            priority = 'high'
            response_template = 'empathetic_response'
        elif sentiment == 'confused':
            priority = 'medium'  
            response_template = 'clarification_response'
        else:
            priority = 'normal'
            response_template = 'standard_response'

        # Generate contextual response
        response = await self._generate_response(message, response_template)

        return {
            'response': response,
            'priority': priority,
            'customer_id': customer_id,
            'sentiment': sentiment
        }

class ReactivePatternGuidelines:
    """Best practices for reactive agent patterns"""

    def __init__(self):
        self.response_time_target = 500  # milliseconds
        self.cache_hit_target = 0.8     # 80% cache hit rate
        self.pattern_confidence_threshold = 0.7

    async def optimize_for_speed(self):
        """Optimization strategies for reactive agents"""
        optimizations = [
            "Use pattern matching instead of LLM calls when possible",
            "Implement aggressive caching for common requests",
            "Pre-compute responses for frequent patterns",
            "Use lightweight tools and fast APIs",
            "Minimize context size for faster processing",
            "Implement circuit breakers for external dependencies"
        ]
        return optimizations

    async def handle_edge_cases(self):
        """Common edge case handling patterns"""
        edge_cases = {
            'ambiguous_request': 'Ask clarifying question with specific options',
            'out_of_scope': 'Politely redirect with alternative suggestions',
            'tool_failure': 'Provide fallback response with error explanation',
            'rate_limit_hit': 'Queue request or suggest alternative approach',
            'cache_miss': 'Generate response and update cache asynchronously'
        }
        return edge_cases

class HierarchicalPlanningAgent:
    def __init__(self, config):
        self.planner = HierarchicalPlanner()
        self.executor = PlanExecutor()
        self.monitor = ExecutionMonitor()
        self.memory = PlanningMemory()

    async def process_goal(self, goal):
        """Process complex goal using hierarchical planning"""

        # Phase 1: Goal analysis and decomposition
        goal_analysis = await self._analyze_goal(goal)

        # Phase 2: Create hierarchical plan
        plan = await self.planner.create_plan(goal_analysis)

        # Phase 3: Execute plan with monitoring
        execution_result = await self._execute_plan_with_monitoring(plan)

        # Phase 4: Learn from execution
        await self._learn_from_execution(goal, plan, execution_result)

        return execution_result

    async def _analyze_goal(self, goal):
        """Analyze goal to determine planning approach"""
        return {
            'goal_type': await self._classify_goal_type(goal),
            'complexity_level': await self._assess_complexity(goal),
            'required_capabilities': await self._identify_capabilities(goal),
            'constraints': await self._extract_constraints(goal),
            'success_criteria': await self._define_success_criteria(goal)
        }

    async def _execute_plan_with_monitoring(self, plan):
        """Execute plan with continuous monitoring and adaptation"""
        execution_context = ExecutionContext(plan)

        for step in plan.steps:
            try:
                # Monitor preconditions
                if not await self.monitor.check_preconditions(step, execution_context):
                    # Replan if preconditions not met
                    updated_plan = await self.planner.replan(plan, execution_context)
                    plan = updated_plan
                    continue

                # Execute step
                step_result = await self.executor.execute_step(step, execution_context)

                # Update context
                execution_context.update(step, step_result)

                # Monitor for adaptation needs
                if await self.monitor.needs_adaptation(execution_context):
                    adaptation = await self.planner.adapt_plan(plan, execution_context)
                    plan = adaptation.updated_plan

            except Exception as e:
                # Handle execution errors
                recovery_plan = await self._handle_execution_error(e, step, execution_context)
                if recovery_plan:
                    plan = recovery_plan
                    continue
                else:
                    return ExecutionResult(status='failed', error=e, context=execution_context)

        return ExecutionResult(status='completed', context=execution_context)

class HierarchicalPlanner:
    def __init__(self):
        self.planning_strategies = {
            'decomposition': self._decomposition_planning,
            'forward_search': self._forward_search_planning,
            'backward_chaining': self._backward_chaining_planning,
            'hybrid': self._hybrid_planning
        }

    async def create_plan(self, goal_analysis):
        """Create hierarchical plan based on goal analysis"""

        # Select planning strategy based on goal characteristics
        strategy = self._select_planning_strategy(goal_analysis)
        planning_method = self.planning_strategies[strategy]

        # Create initial plan
        initial_plan = await planning_method(goal_analysis)

        # Validate and optimize plan
        optimized_plan = await self._optimize_plan(initial_plan)

        return optimized_plan

    async def _decomposition_planning(self, goal_analysis):
        """Break down complex goals into subgoals"""
        goal = goal_analysis['goal']

        # Recursive decomposition
        subgoals = await self._decompose_goal(goal, depth=0, max_depth=5)

        # Create plan hierarchy
        plan = Plan(goal=goal, type='hierarchical')

        for subgoal in subgoals:
            if subgoal['is_primitive']:
                # Create executable action
                action = Action(
                    name=subgoal['action'],
                    parameters=subgoal['parameters'],
                    preconditions=subgoal.get('preconditions', []),
                    effects=subgoal.get('effects', [])
                )
                plan.add_action(action)
            else:
                # Recursively plan for subgoal
                subplan = await self._decomposition_planning({
                    'goal': subgoal['goal'],
                    **goal_analysis
                })
                plan.add_subplan(subplan)

        return plan

    async def _decompose_goal(self, goal, depth, max_depth):
        """Decompose goal into actionable subgoals"""
        if depth >= max_depth:
            return [{'goal': goal, 'is_primitive': True, 'action': 'generic_action'}]

        # Use LLM for goal decomposition
        decomposition_prompt = f"""
        Break down this goal into 3-5 specific, actionable subgoals:
        Goal: {goal}

        For each subgoal, specify:
        1. The specific subgoal
        2. Whether it can be directly executed (primitive) or needs further decomposition
        3. Any preconditions required
        4. Expected outcomes

        Format as JSON array.
        """

        decomposition_result = await self._llm_call(decomposition_prompt)

        try:
            subgoals = json.loads(decomposition_result)
            return subgoals
        except json.JSONDecodeError:
            # Fallback to simple decomposition
            return [{'goal': goal, 'is_primitive': True, 'action': 'manual_execution'}]

class GoalStackAgent:
    def __init__(self, config):
        self.goal_stack = GoalStack()
        self.goal_processor = GoalProcessor()
        self.conflict_resolver = ConflictResolver()
        self.priority_manager = PriorityManager()

    async def add_goal(self, goal, priority=5, dependencies=None):
        """Add new goal to the goal stack"""
        goal_obj = Goal(
            content=goal,
            priority=priority,
            dependencies=dependencies or [],
            timestamp=datetime.now(),
            status='pending'
        )

        # Check for conflicts with existing goals
        conflicts = await self.conflict_resolver.check_conflicts(goal_obj, self.goal_stack)

        if conflicts:
            resolution = await self.conflict_resolver.resolve_conflicts(conflicts)
            await self._apply_conflict_resolution(resolution)

        # Add to stack with proper ordering
        await self.goal_stack.push(goal_obj)

        # Trigger goal processing
        await self._process_goal_stack()

    async def _process_goal_stack(self):
        """Process goals from the stack based on priority and dependencies"""

        while not self.goal_stack.is_empty():
            # Get next executable goal
            current_goal = await self._get_next_executable_goal()

            if current_goal is None:
                # No executable goals, check for deadlocks
                deadlock_resolution = await self._resolve_deadlocks()
                if deadlock_resolution:
                    continue
                else:
                    break

            # Process the goal
            try:
                result = await self.goal_processor.process(current_goal)

                if result.status == 'completed':
                    await self._handle_goal_completion(current_goal, result)
                elif result.status == 'blocked':
                    await self._handle_goal_blocking(current_goal, result)
                elif result.status == 'failed':
                    await self._handle_goal_failure(current_goal, result)

            except Exception as e:
                await self._handle_goal_exception(current_goal, e)

    async def _get_next_executable_goal(self):
        """Get the next goal that can be executed based on priority and dependencies"""

        # Get all pending goals
        pending_goals = self.goal_stack.get_goals_by_status('pending')

        # Filter by dependency satisfaction
        executable_goals = []
        for goal in pending_goals:
            if await self._dependencies_satisfied(goal):
                executable_goals.append(goal)

        if not executable_goals:
            return None

        # Sort by priority (higher priority first)
        executable_goals.sort(key=lambda g: g.priority, reverse=True)

        # Consider goal age as tiebreaker for same priority
        same_priority_goals = [g for g in executable_goals if g.priority == executable_goals[0].priority]
        if len(same_priority_goals) > 1:
            same_priority_goals.sort(key=lambda g: g.timestamp)

        return same_priority_goals[0]

    async def _dependencies_satisfied(self, goal):
        """Check if all dependencies for a goal are satisfied"""
        for dependency in goal.dependencies:
            if isinstance(dependency, str):
                # Goal name dependency
                dependent_goal = self.goal_stack.find_goal_by_name(dependency)
                if not dependent_goal or dependent_goal.status != 'completed':
                    return False
            elif isinstance(dependency, Goal):
                # Direct goal dependency
                if dependency.status != 'completed':
                    return False
            elif callable(dependency):
                # Custom dependency check
                if not await dependency():
                    return False

        return True

class ConflictResolver:
    def __init__(self):
        self.conflict_strategies = {
            'resource_conflict': self._resolve_resource_conflict,
            'priority_conflict': self._resolve_priority_conflict,
            'temporal_conflict': self._resolve_temporal_conflict,
            'logical_conflict': self._resolve_logical_conflict
        }

    async def check_conflicts(self, new_goal, goal_stack):
        """Check for conflicts between new goal and existing goals"""
        conflicts = []
        existing_goals = goal_stack.get_all_goals()

        for existing_goal in existing_goals:
            conflict_types = await self._detect_conflict_types(new_goal, existing_goal)
            if conflict_types:
                conflicts.append({
                    'new_goal': new_goal,
                    'existing_goal': existing_goal,
                    'conflict_types': conflict_types
                })

        return conflicts

    async def _detect_conflict_types(self, goal1, goal2):
        """Detect types of conflicts between two goals"""
        conflicts = []

        # Resource conflicts
        if self._check_resource_conflict(goal1, goal2):
            conflicts.append('resource_conflict')

        # Priority conflicts (mutually exclusive high-priority goals)
        if self._check_priority_conflict(goal1, goal2):
            conflicts.append('priority_conflict')

        # Temporal conflicts (timing constraints)
        if self._check_temporal_conflict(goal1, goal2):
            conflicts.append('temporal_conflict')

        # Logical conflicts (contradictory objectives)
        if await self._check_logical_conflict(goal1, goal2):
            conflicts.append('logical_conflict')

        return conflicts

    async def resolve_conflicts(self, conflicts):
        """Resolve detected conflicts using appropriate strategies"""
        resolutions = []

        for conflict in conflicts:
            for conflict_type in conflict['conflict_types']:
                strategy = self.conflict_strategies.get(conflict_type)
                if strategy:
                    resolution = await strategy(conflict)
                    resolutions.append(resolution)

        return resolutions

class WorkflowAgent:
    def __init__(self, config):
        self.workflow_engine = WorkflowEngine()
        self.step_executor = StepExecutor()
        self.checkpoint_manager = CheckpointManager()
        self.error_handler = WorkflowErrorHandler()

    async def execute_workflow(self, workflow_definition):
        """Execute multi-step workflow with checkpointing and error recovery"""

        # Initialize workflow context
        context = WorkflowContext(workflow_definition)

        # Create checkpoint for recovery
        checkpoint_id = await self.checkpoint_manager.create_checkpoint(context)

        try:
            for step_index, step in enumerate(workflow_definition.steps):
                # Execute step with monitoring
                step_result = await self._execute_step_with_monitoring(
                    step, context, step_index
                )

                # Update context
                context.update_step_result(step_index, step_result)

                # Create incremental checkpoint
                await self.checkpoint_manager.update_checkpoint(checkpoint_id, context)

                # Check for workflow adaptation needs
                if await self._should_adapt_workflow(context, step_result):
                    adapted_workflow = await self._adapt_workflow(context)
                    workflow_definition = adapted_workflow

            # Workflow completed successfully
            await self.checkpoint_manager.mark_completed(checkpoint_id)
            return WorkflowResult(status='completed', context=context)

        except Exception as e:
            # Handle workflow failure
            recovery_result = await self._handle_workflow_failure(e, context, checkpoint_id)
            return recovery_result

    async def _execute_step_with_monitoring(self, step, context, step_index):
        """Execute individual workflow step with comprehensive monitoring"""

        # Pre-execution validation
        validation_result = await self._validate_step_preconditions(step, context)
        if not validation_result.valid:
            raise StepValidationError(f"Step {step_index} preconditions not met: {validation_result.reason}")

        # Execute step with timeout
        try:
            async with asyncio.timeout(step.timeout or 300):  # 5 minute default timeout
                step_result = await self.step_executor.execute(step, context)

        except asyncio.TimeoutError:
            raise StepTimeoutError(f"Step {step_index} timed out after {step.timeout} seconds")

        # Post-execution validation
        if not await self._validate_step_postconditions(step, step_result, context):
            raise StepPostconditionError(f"Step {step_index} postconditions not satisfied")

        return step_result

    async def _adapt_workflow(self, context):
        """Adapt workflow based on execution context and results"""

        adaptation_prompt = f"""
        Based on the current workflow execution context, suggest adaptations to improve success probability:

        Current Context:
        - Completed steps: {len(context.completed_steps)}
        - Last step result: {context.last_step_result}
        - Current state: {context.current_state}
        - Encountered errors: {context.error_history}

        Original workflow: {context.original_workflow}

        Suggest specific adaptations such as:
        1. Adding error recovery steps
        2. Modifying step parameters
        3. Inserting validation steps
        4. Changing step order
        5. Adding parallel execution branches

        Return adapted workflow definition.
        """

        adaptation_result = await self._llm_call(adaptation_prompt)

        try:
            adapted_workflow = WorkflowDefinition.from_json(adaptation_result)
            return adapted_workflow
        except Exception as e:
            # Fallback: continue with original workflow
            logging.warning(f"Failed to adapt workflow: {e}")
            return context.original_workflow

class StepExecutor:
    def __init__(self):
        self.step_handlers = {
            'llm_call': self._execute_llm_step,
            'tool_usage': self._execute_tool_step,
            'data_processing': self._execute_data_step,
            'external_api': self._execute_api_step,
            'conditional': self._execute_conditional_step,
            'loop': self._execute_loop_step,
            'parallel': self._execute_parallel_step
        }

    async def execute(self, step, context):
        """Execute workflow step based on step type"""
        step_type = step.get('type', 'llm_call')
        handler = self.step_handlers.get(step_type)

        if not handler:
            raise UnsupportedStepTypeError(f"Step type '{step_type}' not supported")

        return await handler(step, context)

    async def _execute_parallel_step(self, step, context):
        """Execute multiple steps in parallel"""
        parallel_steps = step.get('parallel_steps', [])

        # Execute all steps concurrently
        tasks = []
        for parallel_step in parallel_steps:
            task = asyncio.create_task(self.execute(parallel_step, context))
            tasks.append(task)

        # Wait for all tasks to complete
        try:
            results = await asyncio.gather(*tasks, return_exceptions=True)

            # Check for exceptions
            exceptions = [r for r in results if isinstance(r, Exception)]
            if exceptions:
                # Handle partial failures based on step configuration
                if step.get('allow_partial_failure', False):
                    successful_results = [r for r in results if not isinstance(r, Exception)]
                    return {
                        'status': 'partial_success',
                        'successful_results': successful_results,
                        'failed_count': len(exceptions),
                        'errors': exceptions
                    }
                else:
                    raise ParallelExecutionError(f"Parallel execution failed: {exceptions}")

            return {
                'status': 'success',
                'results': results
            }

        except Exception as e:
            raise ParallelExecutionError(f"Parallel execution error: {e}")

class CoordinatorWorkerSystem:
    def __init__(self, config):
        self.coordinator = CoordinatorAgent(config.coordinator)
        self.workers = {}
        self.task_queue = TaskQueue()
        self.result_aggregator = ResultAggregator()
        self.worker_registry = WorkerRegistry()

    async def register_worker(self, worker_type, worker_agent, capabilities):
        """Register a specialized worker agent"""
        worker_id = str(uuid.uuid4())

        worker_info = {
            'id': worker_id,
            'type': worker_type,
            'agent': worker_agent,
            'capabilities': capabilities,
            'status': 'available',
            'current_tasks': [],
            'performance_metrics': PerformanceMetrics()
        }

        self.workers[worker_id] = worker_info
        await self.worker_registry.register(worker_info)

        return worker_id

    async def process_complex_request(self, request):
        """Process complex request using coordinator-worker pattern"""

        # Phase 1: Coordinator analyzes request
        task_decomposition = await self.coordinator.decompose_request(request)

        # Phase 2: Assign tasks to appropriate workers
        task_assignments = await self._assign_tasks_to_workers(task_decomposition.tasks)

        # Phase 3: Execute tasks in parallel
        execution_results = await self._execute_assigned_tasks(task_assignments)

        # Phase 4: Aggregate results
        final_result = await self.result_aggregator.combine_results(
            execution_results, task_decomposition.combination_strategy
        )

        # Phase 5: Coordinator post-processes result
        processed_result = await self.coordinator.post_process_result(
            final_result, request
        )

        return processed_result

    async def _assign_tasks_to_workers(self, tasks):
        """Assign tasks to optimal workers based on capabilities and load"""
        assignments = []

        for task in tasks:
            # Find capable workers
            capable_workers = await self._find_capable_workers(task)

            if not capable_workers:
                raise NoCapableWorkerError(f"No worker available for task: {task.type}")

            # Select optimal worker based on load and performance
            selected_worker = await self._select_optimal_worker(capable_workers, task)

            assignments.append({
                'task': task,
                'worker_id': selected_worker['id'],
                'estimated_duration': await self._estimate_task_duration(task, selected_worker)
            })

            # Update worker status
            selected_worker['current_tasks'].append(task.id)
            selected_worker['status'] = 'busy'

        return assignments

    async def _find_capable_workers(self, task):
        """Find workers capable of handling specific task"""
        capable_workers = []

        required_capabilities = task.required_capabilities

        for worker_id, worker_info in self.workers.items():
            if worker_info['status'] == 'available' or len(worker_info['current_tasks']) < worker_info.get('max_concurrent_tasks', 1):
                # Check capability match
                if self._capabilities_match(worker_info['capabilities'], required_capabilities):
                    capable_workers.append(worker_info)

        return capable_workers

    async def _select_optimal_worker(self, capable_workers, task):
        """Select the best worker for the task based on multiple factors"""
        scored_workers = []

        for worker in capable_workers:
            score = await self._calculate_worker_score(worker, task)
            scored_workers.append((worker, score))

        # Sort by score (higher is better)
        scored_workers.sort(key=lambda x: x[1], reverse=True)

        return scored_workers[0][0]  # Return best worker

    async def _calculate_worker_score(self, worker, task):
        """Calculate worker suitability score for task"""
        # Base capability score
        capability_score = self._calculate_capability_score(worker['capabilities'], task.required_capabilities)

        # Performance history score
        performance_score = worker['performance_metrics'].get_success_rate()

        # Load factor (prefer less loaded workers)
        load_factor = 1.0 - (len(worker['current_tasks']) / worker.get('max_concurrent_tasks', 1))

        # Specialization bonus (prefer specialized workers)
        specialization_score = self._calculate_specialization_score(worker, task)

        # Combine scores
        final_score = (
            capability_score * 0.3 +
            performance_score * 0.3 +
            load_factor * 0.2 +
            specialization_score * 0.2
        )

        return final_score

class CoordinatorAgent:
    def __init__(self, config):
        self.decomposition_strategy = TaskDecompositionStrategy()
        self.coordination_memory = CoordinationMemory()
        self.conflict_resolver = MultiAgentConflictResolver()

    async def decompose_request(self, request):
        """Decompose complex request into worker-assignable tasks"""

        # Analyze request complexity and requirements
        request_analysis = await self._analyze_request_complexity(request)

        # Determine decomposition strategy
        strategy = self._select_decomposition_strategy(request_analysis)

        # Apply decomposition strategy
        if strategy == 'sequential':
            tasks = await self._sequential_decomposition(request, request_analysis)
        elif strategy == 'parallel':
            tasks = await self._parallel_decomposition(request, request_analysis)
        elif strategy == 'hierarchical':
            tasks = await self._hierarchical_decomposition(request, request_analysis)
        else:
            tasks = await self._custom_decomposition(request, request_analysis, strategy)

        # Determine result combination strategy
        combination_strategy = self._determine_combination_strategy(tasks, request_analysis)

        return TaskDecomposition(
            tasks=tasks,
            strategy=strategy,
            combination_strategy=combination_strategy,
            dependencies=self._extract_task_dependencies(tasks)
        )

    async def _sequential_decomposition(self, request, analysis):
        """Break request into sequential tasks"""
        decomposition_prompt = f"""
        Break down this request into sequential tasks that build upon each other:
        Request: {request['content']}

        Requirements:
        - Each task should be completable by a single specialized agent
        - Tasks should have clear input/output specifications
        - Tasks should be ordered by dependency

        Analysis context: {analysis}

        Return as JSON array with task objects containing:
        - task_type: The type of specialist needed
        - description: What the task accomplishes
        - required_capabilities: List of needed capabilities
        - inputs: Expected inputs from previous tasks
        - outputs: What this task produces
        - dependencies: Which previous tasks must complete first
        """

        llm_response = await self._llm_call(decomposition_prompt)
        tasks = json.loads(llm_response)

        # Add sequential dependencies
        for i, task in enumerate(tasks):
            if i > 0:
                task['dependencies'] = [tasks[i-1]['id']]

        return [Task.from_dict(task) for task in tasks]

    async def post_process_result(self, aggregated_result, original_request):
        """Post-process aggregated worker results"""

        # Quality validation
        quality_score = await self._validate_result_quality(aggregated_result, original_request)

        if quality_score < 0.7:
            # Request refinement from workers
            refinement_tasks = await self._generate_refinement_tasks(aggregated_result, original_request)
            refined_results = await self._execute_refinement_tasks(refinement_tasks)
            aggregated_result = await self._merge_refinements(aggregated_result, refined_results)

        # Format result for user
        formatted_result = await self._format_final_result(aggregated_result, original_request)

        # Update coordination memory
        await self.coordination_memory.store_coordination_example(
            original_request, aggregated_result, formatted_result
        )

        return formatted_result

class P2PAgentNetwork:
    def __init__(self, agent_id, config):
        self.agent_id = agent_id
        self.peer_discovery = PeerDiscoveryService(config)
        self.message_router = MessageRouter()
        self.consensus_manager = ConsensusManager()
        self.reputation_system = ReputationSystem()

    async def join_network(self):
        """Join the peer-to-peer agent network"""
        # Discover existing peers
        peers = await self.peer_discovery.discover_peers()

        # Announce presence to network
        announcement = {
            'type': 'peer_join',
            'agent_id': self.agent_id,
            'capabilities': self._get_capabilities(),
            'reputation': await self.reputation_system.get_my_reputation(),
            'timestamp': datetime.now().isoformat()
        }

        await self._broadcast_to_peers(announcement, peers)

        # Start message processing loop
        asyncio.create_task(self._process_incoming_messages())

    async def collaborate_on_task(self, task):
        """Collaborate with peers to complete complex task"""

        # Find relevant peers for collaboration
        relevant_peers = await self._find_relevant_peers(task)

        if not relevant_peers:
            # Handle task independently
            return await self._handle_task_independently(task)

        # Propose collaboration
        collaboration_proposal = {
            'type': 'collaboration_proposal',
            'task_id': task.id,
            'task_description': task.description,
            'required_capabilities': task.required_capabilities,
            'proposer_id': self.agent_id,
            'reward_sharing': task.reward_sharing,
            'deadline': task.deadline
        }

        responses = await self._send_to_peers_and_wait(collaboration_proposal, relevant_peers)

        # Form collaboration group
        collaborators = await self._form_collaboration_group(responses, task)

        if collaborators:
            # Execute collaborative task
            return await self._execute_collaborative_task(task, collaborators)
        else:
            # Fallback to independent execution
            return await self._handle_task_independently(task)

    async def _execute_collaborative_task(self, task, collaborators):
        """Execute task collaboratively with peer agents"""

        # Phase 1: Collaborative planning
        planning_session = CollaborativePlanningSession(task, collaborators)
        collaborative_plan = await planning_session.create_plan()

        # Phase 2: Role assignment and negotiation
        role_assignments = await self._negotiate_role_assignments(collaborative_plan, collaborators)

        # Phase 3: Coordinated execution
        execution_coordinator = P2PExecutionCoordinator(role_assignments)
        execution_results = await execution_coordinator.execute_plan(collaborative_plan)

        # Phase 4: Result integration and consensus
        integrated_result = await self._reach_consensus_on_result(execution_results, collaborators)

        # Phase 5: Reputation updates
        await self._update_collaboration_reputations(collaborators, integrated_result)

        return integrated_result

    async def _negotiate_role_assignments(self, plan, collaborators):
        """Negotiate role assignments among collaborators"""

        # Each agent proposes their preferred roles
        role_proposals = {}
        for collaborator in collaborators:
            proposal = await self._generate_role_proposal(plan, collaborator)
            role_proposals[collaborator.agent_id] = proposal

        # Resolve conflicts through auction-like mechanism
        conflict_resolver = RoleConflictResolver()
        resolved_assignments = await conflict_resolver.resolve_conflicts(
            role_proposals, plan.roles, collaborators
        )

        # Ensure all critical roles are assigned
        unassigned_roles = set(plan.roles.keys()) - set(resolved_assignments.values())

        if unassigned_roles:
            # Assign remaining roles based on capability scores
            capability_assignments = await self._assign_by_capability(
                unassigned_roles, collaborators, plan
            )
            resolved_assignments.update(capability_assignments)

        return resolved_assignments

    async def _reach_consensus_on_result(self, execution_results, collaborators):
        """Reach consensus on final result among collaborators"""

        # Collect individual results from all collaborators
        individual_results = {}
        for collaborator in collaborators:
            result = execution_results.get(collaborator.agent_id)
            if result:
                individual_results[collaborator.agent_id] = result

        # Apply consensus algorithm
        if len(individual_results) == 1:
            # Single result, no consensus needed
            return list(individual_results.values())[0]
        elif len(individual_results) == 0:
            # No results, task failed
            raise CollaborationFailedError("No collaborator produced results")
        else:
            # Multiple results, need consensus
            return await self.consensus_manager.reach_consensus(
                individual_results, collaborators
            )

class ConsensusManager:
    def __init__(self):
        self.consensus_algorithms = {
            'voting': self._voting_consensus,
            'reputation_weighted': self._reputation_weighted_consensus,
            'quality_based': self._quality_based_consensus,
            'hybrid': self._hybrid_consensus
        }

    async def reach_consensus(self, individual_results, collaborators):
        """Reach consensus on results using appropriate algorithm"""

        # Analyze result characteristics to choose algorithm
        result_analysis = await self._analyze_results(individual_results)

        algorithm = self._select_consensus_algorithm(result_analysis)
        consensus_func = self.consensus_algorithms[algorithm]

        consensus_result = await consensus_func(individual_results, collaborators)

        # Validate consensus quality
        consensus_quality = await self._validate_consensus_quality(
            consensus_result, individual_results
        )

        if consensus_quality < 0.6:
            # Low quality consensus, try hybrid approach
            consensus_result = await self._hybrid_consensus(individual_results, collaborators)

        return consensus_result

    async def _reputation_weighted_consensus(self, individual_results, collaborators):
        """Weight results by collaborator reputation"""
        weighted_results = {}
        total_weight = 0

        for collaborator in collaborators:
            if collaborator.agent_id in individual_results:
                reputation = collaborator.reputation_score
                result = individual_results[collaborator.agent_id]

                # Weight each component of the result
                if collaborator.agent_id not in weighted_results:
                    weighted_results[collaborator.agent_id] = {
                        'result': result,
                        'weight': reputation
                    }
                    total_weight += reputation

        # Combine weighted results
        if total_weight > 0:
            combined_result = await self._combine_weighted_results(
                weighted_results, total_weight
            )
            return combined_result
        else:
            # Fallback to simple voting
            return await self._voting_consensus(individual_results, collaborators)

class HierarchicalMultiAgentSystem:
    def __init__(self, config):
        self.hierarchy = AgentHierarchy()
        self.level_coordinators = {}
        self.communication_manager = HierarchicalCommunicationManager()
        self.resource_allocator = HierarchicalResourceAllocator()

    async def initialize_hierarchy(self, hierarchy_definition):
        """Initialize the hierarchical agent structure"""

        # Create hierarchy levels
        for level_config in hierarchy_definition.levels:
            level = HierarchyLevel(
                level_id=level_config.level_id,
                level_type=level_config.level_type,  # 'strategic', 'tactical', 'operational'
                coordination_scope=level_config.scope,
                decision_authority=level_config.authority
            )

            # Create coordinator for this level
            coordinator = await self._create_level_coordinator(level_config)
            self.level_coordinators[level.level_id] = coordinator

            # Add agents to this level
            for agent_config in level_config.agents:
                agent = await self._create_agent(agent_config, level)
                await self.hierarchy.add_agent(agent, level.level_id)

        # Establish inter-level communication channels
        await self._setup_communication_channels()

    async def process_hierarchical_task(self, task):
        """Process task through hierarchical delegation and coordination"""

        # Phase 1: Strategic planning at top level
        strategic_plan = await self._create_strategic_plan(task)

        # Phase 2: Tactical decomposition at middle levels
        tactical_plans = await self._create_tactical_plans(strategic_plan)

        # Phase 3: Operational execution at bottom level
        execution_results = await self._execute_operational_plans(tactical_plans)

        # Phase 4: Hierarchical result aggregation
        final_result = await self._aggregate_hierarchical_results(execution_results)

        return final_result

    async def _create_strategic_plan(self, task):
        """Create high-level strategic plan for task"""
        strategic_coordinator = self.level_coordinators['strategic']

        strategic_analysis = await strategic_coordinator.analyze_task_strategically(task)

        strategic_plan = StrategicPlan(
            objectives=strategic_analysis.objectives,
            resource_allocation=await self._plan_resource_allocation(task),
            timeline=strategic_analysis.timeline,
            success_criteria=strategic_analysis.success_criteria,
            risk_assessment=strategic_analysis.risks
        )

        # Communicate strategic plan to tactical level
        await self.communication_manager.broadcast_to_level(
            'tactical', {'type': 'strategic_plan', 'plan': strategic_plan}
        )

        return strategic_plan

    async def _create_tactical_plans(self, strategic_plan):
        """Create tactical plans from strategic objectives"""
        tactical_coordinators = self._get_coordinators_at_level('tactical')

        tactical_plans = []

        for objective in strategic_plan.objectives:
            # Assign objective to appropriate tactical coordinator
            assigned_coordinator = await self._assign_tactical_coordinator(objective)

            # Create tactical plan for objective
            tactical_plan = await assigned_coordinator.create_tactical_plan(
                objective, strategic_plan.constraints
            )

            tactical_plans.append(tactical_plan)

            # Communicate to operational level
            operational_agents = await self._get_subordinate_agents(assigned_coordinator)
            for agent in operational_agents:
                await self.communication_manager.send_to_agent(
                    agent.agent_id, {'type': 'tactical_plan', 'plan': tactical_plan}
                )

        return tactical_plans

    async def _execute_operational_plans(self, tactical_plans):
        """Execute operational tasks derived from tactical plans"""
        execution_tasks = []

        # Convert tactical plans to operational tasks
        for tactical_plan in tactical_plans:
            operational_tasks = await self._decompose_to_operational_tasks(tactical_plan)
            execution_tasks.extend(operational_tasks)

        # Assign tasks to operational agents
        task_assignments = await self._assign_operational_tasks(execution_tasks)

        # Execute tasks with coordination
        execution_coordinator = OperationalExecutionCoordinator(task_assignments)
        execution_results = await execution_coordinator.execute_tasks_coordinated()

        # Monitor execution and provide tactical feedback
        await self._monitor_and_feedback(execution_results)

        return execution_results

    async def _aggregate_hierarchical_results(self, execution_results):
        """Aggregate results through hierarchy levels"""

        # Phase 1: Operational to tactical aggregation
        tactical_summaries = await self._aggregate_to_tactical_level(execution_results)

        # Phase 2: Tactical to strategic aggregation
        strategic_summary = await self._aggregate_to_strategic_level(tactical_summaries)

        # Phase 3: Final strategic assessment
        final_assessment = await self._create_final_assessment(strategic_summary)

        return HierarchicalResult(
            operational_results=execution_results,
            tactical_summaries=tactical_summaries,
            strategic_summary=strategic_summary,
            final_assessment=final_assessment
        )

class HierarchicalCommunicationManager:
    def __init__(self):
        self.communication_channels = {}
        self.message_protocols = {
            'upward': self._handle_upward_communication,
            'downward': self._handle_downward_communication,
            'lateral': self._handle_lateral_communication,
            'cross_level': self._handle_cross_level_communication
        }

    async def setup_communication_channels(self, hierarchy):
        """Setup communication channels for hierarchical structure"""

        # Vertical channels (up/down hierarchy)
        for level in hierarchy.levels:
            parent_level = hierarchy.get_parent_level(level.level_id)
            child_levels = hierarchy.get_child_levels(level.level_id)

            if parent_level:
                channel_id = f"{level.level_id}_to_{parent_level.level_id}"
                self.communication_channels[channel_id] = VerticalChannel(
                    source_level=level.level_id,
                    target_level=parent_level.level_id,
                    direction='upward'
                )

            for child_level in child_levels:
                channel_id = f"{level.level_id}_to_{child_level.level_id}"
                self.communication_channels[channel_id] = VerticalChannel(
                    source_level=level.level_id,
                    target_level=child_level.level_id,
                    direction='downward'
                )

        # Lateral channels (same level)
        for level in hierarchy.levels:
            agents_in_level = hierarchy.get_agents_at_level(level.level_id)

            if len(agents_in_level) > 1:
                channel_id = f"lateral_{level.level_id}"
                self.communication_channels[channel_id] = LateralChannel(
                    level_id=level.level_id,
                    participants=agents_in_level
                )

    async def send_hierarchical_message(self, message, source_agent, target_agent):
        """Send message respecting hierarchical protocols"""

        source_level = await self._get_agent_level(source_agent)
        target_level = await self._get_agent_level(target_agent)

        # Determine communication direction
        if source_level < target_level:
            direction = 'upward'
        elif source_level > target_level:
            direction = 'downward'
        else:
            direction = 'lateral'

        # Apply appropriate protocol
        protocol_handler = self.message_protocols[direction]
        processed_message = await protocol_handler(message, source_agent, target_agent)

        # Route through appropriate channel
        channel = await self._select_channel(source_level, target_level, direction)
        await channel.deliver_message(processed_message)

    async def _handle_upward_communication(self, message, source_agent, target_agent):
        """Handle communication from subordinate to superior"""

        # Add hierarchical context
        hierarchical_message = {
            'original_message': message,
            'source_agent': source_agent,
            'target_agent': target_agent,
            'communication_type': 'status_report',
            'escalation_level': await self._determine_escalation_level(message),
            'summary': await self._create_executive_summary(message),
            'timestamp': datetime.now().isoformat()
        }

        return hierarchical_message

    async def _handle_downward_communication(self, message, source_agent, target_agent):
        """Handle communication from superior to subordinate"""

        # Add delegation context
        delegation_message = {
            'original_message': message,
            'source_agent': source_agent,
            'target_agent': target_agent,
            'communication_type': 'directive',
            'authority_level': await self._get_authority_level(source_agent),
            'execution_context': await self._create_execution_context(message),
            'timestamp': datetime.now().isoformat()
        }

        return delegation_message

class ArchitecturePatternSelector:
    def __init__(self):
        self.evaluation_criteria = {
            'task_complexity': self._evaluate_task_complexity,
            'response_time_requirements': self._evaluate_response_time,
            'scalability_needs': self._evaluate_scalability,
            'resource_constraints': self._evaluate_resources,
            'team_expertise': self._evaluate_team_expertise,
            'maintenance_requirements': self._evaluate_maintenance
        }

        self.pattern_scores = {}

    async def recommend_pattern(self, requirements):
        """Recommend optimal architecture pattern based on requirements"""

        # Evaluate requirements against each criterion
        criterion_scores = {}
        for criterion, evaluator in self.evaluation_criteria.items():
            score = await evaluator(requirements)
            criterion_scores[criterion] = score

        # Score each pattern against criteria
        patterns = ['reactive', 'planning', 'multi_agent', 'hybrid']
        pattern_scores = {}

        for pattern in patterns:
            pattern_score = await self._calculate_pattern_score(pattern, criterion_scores)
            pattern_scores[pattern] = pattern_score

        # Select top pattern and provide justification
        best_pattern = max(pattern_scores, key=pattern_scores.get)

        recommendation = {
            'recommended_pattern': best_pattern,
            'confidence': pattern_scores[best_pattern],
            'alternative_patterns': sorted(
                [(p, s) for p, s in pattern_scores.items() if p != best_pattern],
                key=lambda x: x[1], reverse=True
            )[:2],
            'justification': await self._generate_justification(
                best_pattern, criterion_scores, requirements
            ),
            'implementation_considerations': await self._get_implementation_considerations(
                best_pattern, requirements
            )
        }

        return recommendation

    async def _evaluate_task_complexity(self, requirements):
        """Evaluate task complexity to determine pattern suitability"""
        complexity_indicators = {
            'multi_step_reasoning': requirements.get('requires_multi_step', False),
            'state_management': requirements.get('requires_state', False),
            'long_running_tasks': requirements.get('long_running', False),
            'conditional_logic': requirements.get('conditional_logic', False),
            'external_integrations': len(requirements.get('external_apis', [])),
            'data_processing': requirements.get('data_intensive', False)
        }

        # Calculate complexity score (0-1)
        complexity_score = 0
        max_score = len(complexity_indicators)

        for indicator, value in complexity_indicators.items():
            if isinstance(value, bool):
                complexity_score += 1 if value else 0
            else:
                # Normalize numeric values
                complexity_score += min(value / 5, 1)  # Cap at 5 integrations = max score

        return complexity_score / max_score

    async def _calculate_pattern_score(self, pattern, criterion_scores):
        """Calculate suitability score for pattern against criteria"""

        # Pattern suitability weights for each criterion
        pattern_weights = {
            'reactive': {
                'task_complexity': 0.2,      # Better for simple tasks
                'response_time_requirements': 0.9,  # Excellent response time
                'scalability_needs': 0.8,    # Good scalability
                'resource_constraints': 0.9, # Low resource usage
                'team_expertise': 0.8,       # Easy to implement
                'maintenance_requirements': 0.9  # Low maintenance
            },
            'planning': {
                'task_complexity': 0.9,      # Excellent for complex tasks
                'response_time_requirements': 0.3,  # Slower response
                'scalability_needs': 0.6,    # Moderate scalability
                'resource_constraints': 0.4, # Higher resource usage
                'team_expertise': 0.5,       # Requires expertise
                'maintenance_requirements': 0.4  # Higher maintenance
            },
            'multi_agent': {
                'task_complexity': 0.8,      # Good for complex distributed tasks
                'response_time_requirements': 0.5,  # Variable response time
                'scalability_needs': 0.9,    # Excellent scalability
                'resource_constraints': 0.3, # High resource usage
                'team_expertise': 0.3,       # Requires high expertise
                'maintenance_requirements': 0.2  # High maintenance
            },
            'hybrid': {
                'task_complexity': 0.95,     # Best for very complex tasks
                'response_time_requirements': 0.6,  # Adaptable response time
                'scalability_needs': 0.8,    # Good scalability
                'resource_constraints': 0.2, # Very high resource usage
                'team_expertise': 0.2,       # Requires expert team
                'maintenance_requirements': 0.1  # Very high maintenance
            }
        }

        weights = pattern_weights[pattern]
        score = 0

        for criterion, criterion_score in criterion_scores.items():
            weight = weights.get(criterion, 0.5)  # Default neutral weight
            score += criterion_score * weight

        return score / len(criterion_scores)  # Normalize

class ImplementationPitfallGuide:
    def __init__(self):
        self.common_pitfalls = {
            'reactive_patterns': [
                {
                    'pitfall': 'Over-relying on pattern matching without LLM reasoning',
                    'consequence': 'Limited understanding of complex or ambiguous requests',
                    'solution': 'Combine pattern matching with LLM reasoning for edge cases',
                    'example': '''
                    # Bad: Only pattern matching
                    if 'weather' in request:
                        return get_weather()

                    # Good: Pattern matching + LLM fallback
                    if 'weather' in request:
                        return get_weather()
                    else:
                        intent = await classify_intent_llm(request)
                        return handle_by_intent(intent)
                    '''
                },
                {
                    'pitfall': 'Not implementing proper caching strategies',
                    'consequence': 'Repeated expensive operations for similar requests',
                    'solution': 'Implement semantic caching for similar requests',
                    'example': '''
                    # Cache responses based on semantic similarity
                    cache_key = await generate_semantic_key(request)
                    cached = await cache.get_similar(cache_key, threshold=0.9)
                    if cached:
                        return cached.response
                    '''
                }
            ],
            'planning_patterns': [
                {
                    'pitfall': 'Not implementing plan adaptation mechanisms',
                    'consequence': 'Plans fail when conditions change during execution',
                    'solution': 'Build adaptive planning with execution monitoring',
                    'example': '''
                    async def execute_plan_adaptively(plan):
                        for step in plan.steps:
                            if not await validate_preconditions(step):
                                plan = await replan(plan, current_context)
                            await execute_step(step)
                    '''
                },
                {
                    'pitfall': 'Insufficient error recovery in multi-step plans',
                    'consequence': 'Single step failures cause complete plan abandonment',
                    'solution': 'Implement hierarchical error recovery strategies',
                    'example': '''
                    try:
                        result = await execute_step(step)
                    except StepError as e:
                        recovery_plan = await generate_recovery_plan(step, e)
                        if recovery_plan:
                            return await execute_plan(recovery_plan)
                        else:
                            raise PlanFailedError(f"No recovery for {step}")
                    '''
                }
            ],
            'multi_agent_patterns': [
                {
                    'pitfall': 'Inadequate coordination protocols',
                    'consequence': 'Race conditions, conflicts, and inconsistent state',
                    'solution': 'Implement proper synchronization and conflict resolution',
                    'example': '''
                    async def coordinate_resource_access(resource_id, agents):
                        async with resource_lock(resource_id):
                            coordinator = select_coordinator(agents)
                            return await coordinator.manage_access(resource_id)
                    '''
                },
                {
                    'pitfall': 'Not handling agent failures gracefully',
                    'consequence': 'System-wide failures when individual agents crash',
                    'solution': 'Implement fault tolerance with redundancy and recovery',
                    'example': '''
                    async def execute_with_redundancy(task, agent_pool):
                        primary_agent = select_primary(agent_pool)
                        backup_agents = select_backups(agent_pool, n=2)

                        try:
                            return await primary_agent.execute(task)
                        except AgentFailure:
                            return await execute_with_backup(task, backup_agents)
                    '''
                }
            ]
        }

    def get_pitfall_prevention_checklist(self, pattern_type):
        """Get prevention checklist for specific pattern type"""
        return [
            pitfall['pitfall'] + ': ' + pitfall['solution']
            for pitfall in self.common_pitfalls.get(pattern_type, [])
        ]

class PatternPerformanceOptimizer:
    def __init__(self):
        self.optimization_strategies = {
            'reactive': self._optimize_reactive_performance,
            'planning': self._optimize_planning_performance,
            'multi_agent': self._optimize_multiagent_performance,
            'hybrid': self._optimize_hybrid_performance
        }

    async def optimize_for_pattern(self, pattern_type, agent_system):
        """Apply pattern-specific performance optimizations"""
        optimizer = self.optimization_strategies.get(pattern_type)
        if optimizer:
            return await optimizer(agent_system)
        else:
            return await self._apply_general_optimizations(agent_system)

    async def _optimize_reactive_performance(self, agent_system):
        """Optimize reactive pattern performance"""
        optimizations_applied = []

        # 1. Implement request preprocessing pipeline
        preprocessor = RequestPreprocessor()
        await preprocessor.setup_fast_classification()
        optimizations_applied.append("Fast request classification")

        # 2. Optimize tool selection
        tool_selector = FastToolSelector()
        await tool_selector.precompute_tool_mappings()
        optimizations_applied.append("Precomputed tool mappings")

        # 3. Implement response caching
        cache = SemanticResponseCache(max_size=10000)
        await cache.setup_similarity_indexing()
        optimizations_applied.append("Semantic response caching")

        # 4. Optimize context preparation
        context_manager = StreamlinedContextManager()
        await context_manager.setup_template_based_context()
        optimizations_applied.append("Template-based context preparation")

        return {
            'optimizations_applied': optimizations_applied,
            'expected_improvement': '40-60% reduction in response time',
            'monitoring_metrics': ['response_time', 'cache_hit_rate', 'tool_selection_time']
        }

    async def _optimize_planning_performance(self, agent_system):
        """Optimize planning pattern performance"""
        optimizations_applied = []

        # 1. Implement plan caching
        plan_cache = PlanCache()
        await plan_cache.setup_goal_similarity_indexing()
        optimizations_applied.append("Goal-based plan caching")

        # 2. Optimize plan execution with parallelization
        executor = ParallelPlanExecutor()
        await executor.analyze_plan_dependencies()
        optimizations_applied.append("Parallel step execution")

        # 3. Implement incremental planning
        incremental_planner = IncrementalPlanner()
        await incremental_planner.setup_partial_plan_reuse()
        optimizations_applied.append("Incremental planning with reuse")

        # 4. Optimize monitoring overhead
        lightweight_monitor = LightweightExecutionMonitor()
        await lightweight_monitor.setup_sampling_based_monitoring()
        optimizations_applied.append("Sampling-based execution monitoring")

        return {
            'optimizations_applied': optimizations_applied,
            'expected_improvement': '25-40% reduction in planning time',
            'monitoring_metrics': ['plan_generation_time', 'execution_time', 'plan_cache_hit_rate']
        }

    async def _optimize_multiagent_performance(self, agent_system):
        """Optimize multi-agent pattern performance"""
        optimizations_applied = []

        # 1. Optimize communication protocols
        comm_optimizer = CommunicationOptimizer()
        await comm_optimizer.implement_message_batching()
        await comm_optimizer.setup_priority_queues()
        optimizations_applied.append("Optimized communication protocols")

        # 2. Implement intelligent load balancing
        load_balancer = AgentLoadBalancer()
        await load_balancer.setup_capability_aware_routing()
        optimizations_applied.append("Capability-aware load balancing")

        # 3. Optimize coordination overhead
        coordinator = EfficientCoordinator()
        await coordinator.implement_lazy_coordination()
        optimizations_applied.append("Lazy coordination protocols")

        # 4. Setup agent pooling
        agent_pool = AdaptiveAgentPool()
        await agent_pool.implement_dynamic_scaling()
        optimizations_applied.append("Dynamic agent pool scaling")

        return {
            'optimizations_applied': optimizations_applied,
            'expected_improvement': '30-50% reduction in coordination overhead',
            'monitoring_metrics': ['coordination_time', 'message_latency', 'agent_utilization']
        }