Why Task Orchestration?
Imagine this scenario: A user requests an Agent to complete a market research report. This task requires:
- Collecting market data
- Analyzing competitors
- Generating charts
- Writing the report
This is a typical scenario that requires task orchestration.
Core Architecture Design
1. Task Decomposition Strategy
Using LLM for intelligent task decomposition:
from typing import List, Dict
import asyncio
class TaskDecomposer:
def __init__(self, llm_service):
self.llm = llm_service
async def decompose_task(self, task_description: str) -> Dict:
"""Intelligent task decomposition"""
prompt = f"""
Task Description: {task_description}
Please decompose this task into subtasks, output format:
{{
"subtasks": [
{{
"id": "task_1",
"name": "subtask name",
"description": "detailed description",
"dependencies": [],
"estimated_time": "estimated duration (minutes)"
}}
]
}}
Requirements:
1. Appropriate subtask granularity
2. Clear task dependencies
3. Suitable for parallel processing
"""
response = await self.llm.generate(prompt)
return self._validate_and_process(response)
def _validate_and_process(self, decomposition_result: dict) -> dict:
"""Validate and process decomposition results"""
# Validate task dependency relationships
self._check_circular_dependencies(decomposition_result["subtasks"])
# Build task execution graph
return self._build_execution_graph(decomposition_result["subtasks"])
2. Parallel Processing Architecture
Using async task pool for parallel execution:
class TaskExecutor:
def __init__(self, max_workers: int = 5):
self.max_workers = max_workers
self.task_queue = asyncio.Queue()
self.results = {}
self.semaphore = asyncio.Semaphore(max_workers)
async def execute_tasks(self, task_graph: Dict):
"""Execute task graph"""
# Create worker pool
workers = [
self._worker(f"worker_{i}")
for i in range(self.max_workers)
]
# Add executable tasks to queue
ready_tasks = self._get_ready_tasks(task_graph)
for task in ready_tasks:
await self.task_queue.put(task)
# Wait for all tasks to complete
await asyncio.gather(*workers)
async def _worker(self, worker_id: str):
"""Worker coroutine"""
while True:
try:
async with self.semaphore:
task = await self.task_queue.get()
if task is None:
break
# Execute task
result = await self._execute_single_task(task)
self.results[task["id"]] = result
# Check and add new executable tasks
new_ready_tasks = self._get_ready_tasks(task_graph)
for task in new_ready_tasks:
await self.task_queue.put(task)
except Exception as e:
logger.error(f"Worker {worker_id} error: {str(e)}")
Best Practices
-
Task Decomposition Principles
- Maintain appropriate task granularity
- Clearly define task dependencies
- Consider parallel execution possibilities
- Design reasonable failure rollback mechanisms
-
Resource Management Strategy
- Implement dynamic resource allocation
- Set resource usage limits
- Monitor resource utilization
- Release idle resources promptly
class ResourceManager:
def __init__(self):
self.resource_pool = {
'cpu': ResourcePool(max_units=16),
'memory': ResourcePool(max_units=32),
'gpu': ResourcePool(max_units=4)
}
async def allocate(self, requirements: Dict[str, int]):
"""Allocate resources"""
allocated = {}
try:
for resource_type, amount in requirements.items():
allocated[resource_type] = await self.resource_pool[resource_type].acquire(amount)
return allocated
except InsufficientResourceError:
# Rollback allocated resources
await self.release(allocated)
raise
async def release(self, allocated_resources: Dict):
"""Release resources"""
for resource_type, resource in allocated_resources.items():
await self.resource_pool[resource_type].release(resource)
- Monitoring and Logging
class SystemMonitor:
def __init__(self):
self.metrics = {}
self.alerts = AlertManager()
async def monitor_task(self, task_id: str):
"""Monitor single task"""
start_time = time.time()
try:
# Log task start
self.log_task_start(task_id)
# Monitor resource usage
resource_usage = await self.track_resource_usage(task_id)
# Check performance metrics
if resource_usage['cpu'] > 80:
await self.alerts.send_alert(
f"High CPU usage for task {task_id}"
)
return resource_usage
finally:
# Log task completion
duration = time.time() - start_time
self.log_task_completion(task_id, duration)
- Performance Optimization Techniques
class PerformanceOptimizer:
def __init__(self):
self.cache = LRUCache(maxsize=1000)
self.batch_processor = BatchProcessor()
async def optimize_execution(self, tasks: List[Dict]):
"""Optimize task execution"""
# 1. Task grouping
task_groups = self._group_similar_tasks(tasks)
# 2. Batch processing optimization
optimized_groups = []
for group in task_groups:
if len(group) > 1:
# Merge similar tasks
optimized = await self.batch_processor.process(group)
else:
optimized = group[0]
optimized_groups.append(optimized)
# 3. Resource pre-allocation
for group in optimized_groups:
await self._preallocate_resources(group)
return optimized_groups
System Extensibility Considerations
- Plugin System Design
class PluginManager:
def __init__(self):
self.plugins = {}
def register_plugin(self, name: str, plugin: Any):
"""Register plugin"""
if not hasattr(plugin, 'execute'):
raise InvalidPluginError(
"Plugin must implement execute method"
)
self.plugins[name] = plugin
async def execute_plugin(self, name: str, *args, **kwargs):
"""Execute plugin"""
if name not in self.plugins:
raise PluginNotFoundError(f"Plugin {name} not found")
try:
return await self.plugins[name].execute(*args, **kwargs)
except Exception as e:
logger.error(f"Plugin {name} execution failed: {str(e)}")
raise
- Extensible Task Types
class CustomTaskRegistry:
_task_types = {}
@classmethod
def register(cls, task_type: str):
"""Register custom task type"""
def decorator(task_class):
cls._task_types[task_type] = task_class
return task_class
return decorator
@classmethod
def create_task(cls, task_type: str, **kwargs):
"""Create task instance"""
if task_type not in cls._task_types:
raise UnknownTaskTypeError(f"Unknown task type: {task_type}")
return cls._task_types[task_type](**kwargs)
@CustomTaskRegistry.register("data_processing")
class DataProcessingTask:
async def execute(self, data):
# Implement data processing logic
pass
@CustomTaskRegistry.register("report_generation")
class ReportGenerationTask:
async def execute(self, data):
# Implement report generation logic
pass
Real-world Application Example
Here's a complete market research report generation process:
async def generate_market_report(topic: str):
# Initialize system components
orchestrator = TaskOrchestrator()
optimizer = PerformanceOptimizer()
monitor = SystemMonitor()
try:
# 1. Task planning
task_graph = await orchestrator.plan_tasks({
"topic": topic,
"required_sections": [
"market_overview",
"competitor_analysis",
"trends_analysis",
"recommendations"
]
})
# 2. Performance optimization
optimized_tasks = await optimizer.optimize_execution(
task_graph["tasks"]
)
# 3. Execute tasks
with monitor.track_execution():
results = await orchestrator.execute_dag({
"tasks": optimized_tasks
})
# 4. Generate report
report = await orchestrator.compile_results(results)
return report
except Exception as e:
logger.error(f"Report generation failed: {str(e)}")
# Trigger alert
await monitor.alerts.send_alert(
f"Report generation failed for topic: {topic}"
)
raise
Performance Optimization Tips
-
Resource Utilization Optimization
- Implement dynamic resource allocation
- Use resource pool management
- Set reasonable timeout mechanisms
-
Parallel Processing Optimization
- Set appropriate parallelism levels
- Implement task batching
- Optimize task dependencies
-
Caching Strategy Optimization
- Use multi-level caching
- Implement intelligent cache warming
- Set reasonable cache invalidation policies
Summary
Building an efficient Agent task orchestration system requires consideration of:
- Reasonable task decomposition strategies
- Efficient parallel processing architecture
- Reliable intermediate result management
- Flexible task orchestration patterns
- Comprehensive performance optimization solutions
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