📝 Session 9: Implementation Guide - Planning & Production Systems¶

@dataclass
class DataTaskDecomposition:
    """Represents a way to decompose a compound data processing task"""
    decomposition_id: str
    subtasks: List[DataTask]
    data_flow_constraints: List[Tuple[str, str]] = field(default_factory=list)
    processing_success_probability: float = 1.0

class DataHTNPlanner:
    """Hierarchical Task Network planner for data processing"""

    def __init__(self, agent, data_domain_knowledge: Dict[str, Any]):
        self.agent = agent
        self.data_domain = data_domain_knowledge
        self.current_pipeline_plan: Optional[List[DataTask]] = None
        self.data_planning_history: List[Dict[str, Any]] = []

    async def create_hierarchical_data_plan(
        self, data_goal: str, initial_data_state: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Create hierarchical data processing plan using HTN methodology"""

        # Phase 1: Data goal analysis and task creation
        root_task = await self._create_root_data_task(data_goal, initial_data_state)

        # Phase 2: Hierarchical data processing decomposition
        decomposition_result = await self._decompose_data_task_hierarchy(
            root_task, initial_data_state
        )

        # Phase 3: Data pipeline optimization
        optimized_plan = await self._optimize_data_plan(
            decomposition_result['plan'], initial_data_state
        )

        # Phase 4: Data quality and consistency risk assessment
        risk_analysis = await self._analyze_data_plan_risks(
            optimized_plan, initial_data_state
        )

        return {
            'data_plan': optimized_plan,
            'risk_analysis': risk_analysis,
            'confidence': decomposition_result['confidence'],
            'estimated_processing_duration': sum(
                t.estimated_duration or timedelta(0) for t in optimized_plan
            )
        }

async def _decompose_data_task_hierarchy(
    self, root_task: DataTask, data_state: Dict[str, Any]
) -> Dict[str, Any]:
    """Decompose data processing task using hierarchical method"""

    decomposition_queue = [root_task]
    final_plan = []
    confidence_scores = []

    while decomposition_queue:
        current_task = decomposition_queue.pop(0)

        if current_task.task_type == DataTaskType.PRIMITIVE:
            # Task can be executed directly
            final_plan.append(current_task)
            confidence_scores.append(0.9)

        elif current_task.task_type == DataTaskType.COMPOUND:
            # Task needs decomposition
            decomposition = await self._find_task_decomposition(current_task, data_state)

            if decomposition:
                # Add subtasks to processing queue
                decomposition_queue.extend(decomposition.subtasks)
                confidence_scores.append(decomposition.processing_success_probability)
            else:
                # Fallback: treat as primitive if no decomposition found
                final_plan.append(current_task)
                confidence_scores.append(0.5)

        elif current_task.task_type == DataTaskType.ABSTRACT:
            # High-level goal needs compound decomposition
            compound_tasks = await self._abstract_to_compound_decomposition(
                current_task, data_state
            )
            decomposition_queue.extend(compound_tasks)

    return {
        'plan': final_plan,
        'confidence': sum(confidence_scores) / len(confidence_scores) if confidence_scores else 0.5
    }

class DynamicDataReplanner:
    """Handles dynamic replanning during data pipeline execution"""

    def __init__(self, htn_planner: DataHTNPlanner):
        self.data_planner = htn_planner
        self.monitoring_active = False
        self.data_replanning_history: List[Dict[str, Any]] = []

    async def execute_with_data_replanning(
        self, data_plan: List[DataTask], initial_data_state: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Execute data plan with continuous monitoring and replanning"""

        current_data_state = initial_data_state.copy()
        remaining_tasks = data_plan.copy()
        completed_tasks = []
        execution_trace = []

        self.monitoring_active = True

        while remaining_tasks and self.monitoring_active:
            current_task = remaining_tasks[0]

            # Pre-execution data validation
            validation_result = await self._validate_data_task_execution(
                current_task, current_data_state
            )

            if not validation_result['can_execute']:
                # Trigger data processing replanning
                replanning_result = await self._trigger_data_replanning(
                    current_task, remaining_tasks, current_data_state,
                    validation_result['reason']
                )

                if replanning_result['success']:
                    remaining_tasks = replanning_result['new_data_plan']
                    execution_trace.append(('data_replan', replanning_result))
                    continue
                else:
                    execution_trace.append(('data_failure', replanning_result))
                    break

            # Execute data processing task
            execution_result = await self._execute_monitored_data_task(
                current_task, current_data_state
            )

            execution_trace.append(('data_execute', execution_result))

            if execution_result['success']:
                # Update data state and continue
                current_data_state = self._apply_data_task_effects(
                    current_task, current_data_state, execution_result
                )
                completed_tasks.append(current_task)
                remaining_tasks.pop(0)
            else:
                # Handle data processing failure
                failure_analysis = await self._analyze_data_execution_failure(
                    current_task, execution_result
                )

                if failure_analysis['should_replan']:
                    replanning_result = await self._trigger_data_replanning(
                        current_task, remaining_tasks, current_data_state,
                        execution_result['error']
                    )

                    if replanning_result['success']:
                        remaining_tasks = replanning_result['new_data_plan']
                        continue

                execution_trace.append(('data_abort', failure_analysis))
                break

        return {
            'completed_data_tasks': completed_tasks,
            'remaining_data_tasks': remaining_tasks,
            'final_data_state': current_data_state,
            'data_execution_trace': execution_trace,
            'success': len(remaining_tasks) == 0
        }

async def _analyze_data_execution_failure(
    self, failed_task: DataTask, execution_result: Dict[str, Any]
) -> Dict[str, Any]:
    """Analyze data processing failure to determine recovery strategy"""

    failure_type = self._classify_failure_type(execution_result['error'])

    recovery_strategies = {
        'transient_network': {'should_replan': False, 'retry_count': 3},
        'resource_exhaustion': {'should_replan': True, 'strategy': 'scale_down'},
        'data_quality_failure': {'should_replan': True, 'strategy': 'add_validation'},
        'schema_mismatch': {'should_replan': True, 'strategy': 'schema_adaptation'},
        'dependency_missing': {'should_replan': True, 'strategy': 'dependency_resolution'}
    }

    strategy = recovery_strategies.get(failure_type, {'should_replan': True, 'strategy': 'fallback'})

    return {
        'failure_type': failure_type,
        'should_replan': strategy['should_replan'],
        'recommended_strategy': strategy.get('strategy', 'retry'),
        'task_id': failed_task.task_id,
        'failure_analysis': execution_result
    }

async def _trigger_data_replanning(
    self, failed_task: DataTask, remaining_tasks: List[DataTask],
    current_state: Dict[str, Any], failure_reason: str
) -> Dict[str, Any]:
    """Trigger replanning process for data processing failure recovery"""

    # Create new planning context including failure information
    replanning_context = {
        'failed_task': failed_task,
        'failure_reason': failure_reason,
        'current_data_state': current_state,
        'remaining_objectives': [task.name for task in remaining_tasks],
        'execution_constraints': await self._analyze_execution_constraints(current_state)
    }

    # Generate alternative plan using HTN planner
    alternative_plan_result = await self.data_planner.create_hierarchical_data_plan(
        f"Recovery from {failure_reason}: {failed_task.name}",
        replanning_context
    )

    if alternative_plan_result['confidence'] > 0.6:
        # Record replanning event
        self.data_replanning_history.append({
            'original_task': failed_task.task_id,
            'failure_reason': failure_reason,
            'new_plan_confidence': alternative_plan_result['confidence'],
            'timestamp': datetime.now()
        })

        return {
            'success': True,
            'new_data_plan': alternative_plan_result['data_plan'],
            'replanning_confidence': alternative_plan_result['confidence'],
            'recovery_strategy': 'hierarchical_replanning'
        }
    else:
        return {
            'success': False,
            'reason': 'Unable to generate viable alternative plan',
            'attempted_confidence': alternative_plan_result['confidence']
        }

@dataclass
class AdvancedDataProductionConfig:
    """Advanced configuration for enterprise multi-agent data processing systems"""
    max_data_agents: int = 100
    consensus_timeout: timedelta = timedelta(seconds=45)
    data_health_check_interval: timedelta = timedelta(seconds=5)
    enable_data_monitoring: bool = True
    enable_performance_metrics: bool = True
    log_level: str = "INFO"
    data_processing_batch_size: int = 50000
    max_parallel_streams: int = 16
    agent_failure_threshold: int = 3
    automatic_recovery: bool = True
    monitoring_retention_days: int = 30

class AdvancedDataProductionSystem(BasicDataProductionSystem):
    """Advanced production multi-agent data processing system"""

    def __init__(self, config: AdvancedDataProductionConfig):
        super().__init__(config)
        self.config = config
        self.monitoring_active = False
        self.performance_metrics = AdvancedDataSystemMonitor(self)

    async def start_production_monitoring(self):
        """Start comprehensive monitoring for production deployment"""
        self.monitoring_active = True

        # Start health check monitoring
        asyncio.create_task(self._continuous_health_monitoring())

        # Start performance metrics collection
        if self.config.enable_performance_metrics:
            asyncio.create_task(self._performance_metrics_collection())

        # Start automatic recovery system
        if self.config.automatic_recovery:
            asyncio.create_task(self._automatic_recovery_monitoring())

async def _continuous_health_monitoring(self):
    """Continuously monitor agent health and system status"""
    while self.monitoring_active:
        try:
            # Check health of all registered agents
            health_results = []
            for agent_id, agent in self.data_agents.items():
                health = await self._comprehensive_data_health_check(agent)
                health_results.append((agent_id, health))

                # Track unhealthy agents for recovery
                if not health['healthy']:
                    await self._handle_unhealthy_agent(agent_id, agent, health)

            # Log system-wide health summary
            healthy_count = sum(1 for _, h in health_results if h['healthy'])
            total_count = len(health_results)

            logging.info(f"[DATA] System health check: {healthy_count}/{total_count} agents healthy")

            # Wait for next health check interval
            await asyncio.sleep(self.config.data_health_check_interval.total_seconds())

        except Exception as e:
            logging.error(f"[DATA] Health monitoring error: {e}")
            await asyncio.sleep(5)  # Brief pause before retry

async def _comprehensive_data_health_check(self, agent: 'BaseDataAgent') -> Dict[str, Any]:
    """Perform comprehensive health check on data processing agent"""
    health_result = {
        'healthy': True,
        'checks': {},
        'timestamp': datetime.now()
    }

    try:
        # Basic connectivity test
        start_time = time.time()
        basic_response = await asyncio.wait_for(
            agent.process_data_sample("health_check"),
            timeout=10.0
        )
        response_time = time.time() - start_time

        health_result['checks']['connectivity'] = {
            'passed': bool(basic_response),
            'response_time_ms': response_time * 1000
        }

        # Resource utilization check
        resource_check = await self._check_agent_resource_usage(agent)
        health_result['checks']['resources'] = resource_check

        # Message queue health
        queue_health = await self._check_agent_message_queue(agent)
        health_result['checks']['message_queue'] = queue_health

        # Overall health determination
        health_result['healthy'] = all(
            check.get('passed', True) for check in health_result['checks'].values()
        )

        return health_result

    except asyncio.TimeoutError:
        health_result['healthy'] = False
        health_result['checks']['timeout'] = {'passed': False, 'reason': 'Agent response timeout'}
        return health_result
    except Exception as e:
        health_result['healthy'] = False
        health_result['checks']['error'] = {'passed': False, 'error': str(e)}
        return health_result

class AdvancedDataSystemMonitor(BasicDataSystemMonitor):
    """Advanced monitoring for production multi-agent data processing systems"""

    def __init__(self, system: AdvancedDataProductionSystem):
        super().__init__(system)
        self.system = system
        self.detailed_metrics = {
            'throughput_history': [],
            'latency_percentiles': [],
            'error_rates': [],
            'coordination_efficiency': [],
            'resource_utilization': []
        }

    async def collect_advanced_metrics(self) -> Dict[str, Any]:
        """Collect comprehensive system performance metrics"""

        timestamp = datetime.now()

        # Collect throughput metrics across all agents
        throughput_data = await self._collect_throughput_metrics()

        # Measure coordination efficiency
        coordination_metrics = await self._measure_coordination_efficiency()

        # Assess resource utilization
        resource_metrics = await self._assess_system_resource_usage()

        # Calculate latency percentiles
        latency_metrics = await self._calculate_latency_percentiles()

        # Error rate analysis
        error_metrics = await self._analyze_error_rates()

        comprehensive_metrics = {
            'timestamp': timestamp,
            'throughput': throughput_data,
            'coordination_efficiency': coordination_metrics,
            'resource_utilization': resource_metrics,
            'latency_percentiles': latency_metrics,
            'error_rates': error_metrics,
            'system_health_score': self._calculate_overall_system_health()
        }

        # Store for trend analysis
        self._store_metrics_for_trends(comprehensive_metrics)

        return comprehensive_metrics

async def generate_production_status_report(self) -> str:
    """Generate comprehensive production status report"""

    metrics = await self.collect_advanced_metrics()

    return f"""
Production Multi-Agent Data Processing System Report
==================================================
Report Generated: {metrics['timestamp']}

SYSTEM OVERVIEW

--------------
Total Agents: {len(self.system.data_agents)}
System Health Score: {metrics['system_health_score']:.2f}/10.0
Overall Status: {'HEALTHY' if metrics['system_health_score'] > 7.0 else 'DEGRADED' if metrics['system_health_score'] > 4.0 else 'CRITICAL'}

PERFORMANCE METRICS

------------------
Total Throughput: {metrics['throughput']['total_rps']:,} records/sec
Average Latency: {metrics['latency_percentiles']['p50']:.2f}ms
95th Percentile Latency: {metrics['latency_percentiles']['p95']:.2f}ms
99th Percentile Latency: {metrics['latency_percentiles']['p99']:.2f}ms

COORDINATION EFFICIENCY

----------------------
Message Success Rate: {metrics['coordination_efficiency']['message_success_rate']:.2%}
Consensus Success Rate: {metrics['coordination_efficiency']['consensus_success_rate']:.2%}
Average Coordination Overhead: {metrics['coordination_efficiency']['overhead_ms']:.2f}ms

RESOURCE UTILIZATION

-------------------
Average CPU Usage: {metrics['resource_utilization']['avg_cpu_percent']:.1f}%
Average Memory Usage: {metrics['resource_utilization']['avg_memory_percent']:.1f}%
Network Utilization: {metrics['resource_utilization']['network_mbps']:.2f} Mbps

ERROR ANALYSIS

--------------
Error Rate: {metrics['error_rates']['total_error_rate']:.4%}
Most Common Error: {metrics['error_rates']['most_common_error']}
Critical Errors (last hour): {metrics['error_rates']['critical_errors_count']}

RECOMMENDATIONS

--------------
{self._generate_performance_recommendations(metrics)}
"""

# Your task: Implement HTN planning for ETL workflows
class ETLHTNPlanner(DataHTNPlanner):
    async def plan_etl_workflow(
        self, source_configs: List[Dict], target_schema: Dict, transformations: List[str]
    ) -> Dict[str, Any]:
        """Plan ETL workflow using hierarchical task decomposition"""
        # TODO: Implement HTN planning for ETL
        # 1. Create abstract goal: "Process ETL workflow"
        # 2. Decompose into compound tasks: Extract, Transform, Load
        # 3. Break down compound tasks into primitive operations
        # 4. Optimize task ordering and dependencies
        # 5. Generate execution plan with resource allocation
        pass

# Your task: Build adaptive replanning for data quality issues
class DataQualityReplanner(DynamicDataReplanner):
    async def handle_data_quality_failure(
        self, failed_task: DataTask, quality_metrics: Dict[str, float]
    ) -> Dict[str, Any]:
        """Handle data quality failure with adaptive replanning"""
        # TODO: Implement quality-aware replanning
        # 1. Analyze quality failure patterns
        # 2. Generate alternative processing strategies
        # 3. Apply data cleaning and validation steps
        # 4. Adjust quality thresholds if needed
        # 5. Create recovery plan with enhanced validation
        pass

# Your task: Implement production monitoring with alerts
class ProductionMonitoringDashboard(AdvancedDataSystemMonitor):
    async def setup_monitoring_alerts(
        self, alert_thresholds: Dict[str, float]
    ) -> Dict[str, Any]:
        """Setup monitoring alerts for production system"""
        # TODO: Implement alerting system
        # 1. Define alert conditions (latency, error rate, health)
        # 2. Create notification mechanisms (email, slack, etc.)
        # 3. Implement escalation policies
        # 4. Generate alert history and trend analysis
        # 5. Create dashboard for real-time visibility
        pass