⚙️ Session 7 Advanced: Orchestration Patterns¶
⚙️ IMPLEMENTER PATH CONTENT Prerequisites: Complete 🎯 Observer and 📝 Participant paths in main session Time Investment: 3-4 hours Outcome: Deep mastery of centralized A2A orchestration systems
Advanced Learning Outcomes¶
After completing this module, you will master:
- Sophisticated workflow orchestration patterns for multi-agent systems
- Dependency resolution and parallel execution optimization
- Advanced error handling and retry strategies in distributed environments
- Enterprise-grade workflow monitoring and observability
The Art of Orchestration - Conducting Digital Symphonies¶
Orchestration in A2A systems is like conducting a symphony orchestra where each musician is an AI agent with unique capabilities. The conductor doesn't play any instruments but ensures that all parts come together to create something beautiful.
The Symphony Framework¶
The orchestration system requires sophisticated infrastructure for managing complex workflows:
# Advanced orchestration imports
import asyncio
from typing import Dict, List, Any, Optional
from dataclasses import dataclass
from datetime import datetime
import logging
These imports provide the foundation for complex workflow management with proper async handling.
# Orchestration foundation
from a2a.protocol import A2AMessage, MessageType, Priority
from a2a.router import MessageRouter
from a2a.registry import AgentRegistry
logger = logging.getLogger(__name__)
The orchestration system builds upon the core A2A components we've already established.
Defining Musical Movements: Workflow Steps¶
Each step in a workflow is like a movement in a symphony—it has specific requirements, depends on what came before, and contributes to the overall composition:
@dataclass
class WorkflowStep:
"""Represents a step in a workflow."""
step_id: str
action: str
agent_capability: str
input_mapping: Dict[str, str] # Map workflow data to step input
output_mapping: Dict[str, str] # Map step output to workflow data
dependencies: List[str] = None # Other steps that must complete first
timeout: int = 30
retry_count: int = 2
def __post_init__(self):
if self.dependencies is None:
self.dependencies = []
Workflow steps define clear dependencies and data mappings for reliable execution ordering.
@dataclass
class Workflow:
"""Defines a multi-agent workflow."""
workflow_id: str
name: str
description: str
steps: List[WorkflowStep]
initial_data: Dict[str, Any] = None
def __post_init__(self):
if self.initial_data is None:
self.initial_data = {}
Workflows encapsulate complete multi-step processes with initial context and metadata.
The Master Conductor: Workflow Orchestrator¶
The orchestrator manages the entire performance, ensuring each agent enters at the right time with the right information:
class WorkflowOrchestrator:
"""Orchestrates multi-agent workflows."""
def __init__(self, router: MessageRouter, registry: AgentRegistry):
self.router = router
self.registry = registry
self.active_workflows: Dict[str, Dict] = {}
async def execute_workflow(self, workflow: Workflow) -> Dict[str, Any]:
"""Execute a multi-agent workflow."""
workflow_state = {
"workflow_id": workflow.workflow_id,
"status": "running",
"started_at": datetime.now().isoformat(),
"data": workflow.initial_data.copy(),
"completed_steps": set(),
"failed_steps": set(),
"step_outputs": {}
}
self.active_workflows[workflow.workflow_id] = workflow_state
Workflow state management provides comprehensive tracking of execution progress and intermediate results.
The Performance: Dependency Resolution and Execution¶
The orchestrator carefully manages which steps can run in parallel and which must wait for others to complete:
try:
# Execute steps based on dependencies
remaining_steps = workflow.steps.copy()
while remaining_steps:
# Find steps that can be executed (all dependencies met)
ready_steps = [
step for step in remaining_steps
if all(dep in workflow_state["completed_steps"]
for dep in step.dependencies)
]
if not ready_steps:
# Check if we have failed steps blocking progress
if workflow_state["failed_steps"]:
workflow_state["status"] = "failed"
break
# Wait for more steps to complete
await asyncio.sleep(1)
continue
Dependency resolution ensures proper step ordering while maximizing parallelization opportunities.
# Execute ready steps in parallel
tasks = []
for step in ready_steps:
task = asyncio.create_task(
self._execute_step(workflow, step, workflow_state)
)
tasks.append(task)
# Wait for all ready steps to complete
results = await asyncio.gather(*tasks, return_exceptions=True)
Parallel execution of independent steps optimizes workflow performance and resource utilization.
Individual Performance: Step Execution¶
Each step is executed with careful attention to failure handling, retries, and agent selection:
async def _execute_step(self, workflow: Workflow, step: WorkflowStep,
workflow_state: Dict) -> Any:
"""Execute a single workflow step."""
# Prepare step input data
step_input = self._prepare_step_input(step, workflow_state["data"])
# Find suitable agent
agents = await self.registry.discover_agents(
required_capabilities=[step.agent_capability]
)
if not agents:
raise Exception(f"No agents found with capability: {step.agent_capability}")
# Try executing with retries
last_error = None
for attempt in range(step.retry_count + 1):
try:
# Create message for agent
message = A2AMessage(
sender_id="orchestrator",
recipient_id=agents[0].agent_id, # Use best available agent
action=step.action,
payload=step_input,
requires_response=True,
timeout=step.timeout,
priority=Priority.HIGH
)
Agent selection and message creation prepare each step for reliable execution.
# Send message and wait for response
response = await self.router.send_message(message, wait_for_response=True)
if response and response.payload:
logger.info(f"Step {step.step_id} completed successfully")
return response.payload
else:
raise Exception("No response received from agent")
except Exception as e:
last_error = e
logger.warning(f"Step {step.step_id} attempt {attempt + 1} failed: {e}")
if attempt < step.retry_count:
# Try with next available agent if available
if len(agents) > attempt + 1:
continue
await asyncio.sleep(2 ** attempt) # Exponential backoff
raise last_error
Robust retry logic with exponential backoff ensures maximum reliability in distributed environments.
Advanced Data Mapping and Transformation¶
The orchestrator provides sophisticated data transformation capabilities for complex workflows:
def _prepare_step_input(self, step: WorkflowStep, workflow_data: Dict[str, Any]) -> Dict[str, Any]:
"""Prepare input data for a workflow step."""
step_input = {}
for input_key, workflow_key in step.input_mapping.items():
if workflow_key in workflow_data:
# Support nested key access (e.g., "user.profile.name")
value = self._get_nested_value(workflow_data, workflow_key)
step_input[input_key] = value
else:
logger.warning(f"Workflow data missing key: {workflow_key}")
return step_input
Flexible input mapping supports complex data transformations between workflow steps.
def _apply_output_mapping(self, step: WorkflowStep, step_output: Any, workflow_data: Dict[str, Any]):
"""Apply step output mapping to workflow data."""
for workflow_key, output_key in step.output_mapping.items():
if isinstance(step_output, dict) and output_key in step_output:
# Support nested key assignment
self._set_nested_value(workflow_data, workflow_key, step_output[output_key])
else:
# Direct assignment for simple outputs
workflow_data[workflow_key] = step_output
Output mapping enables complex data flow patterns between interdependent workflow steps.
Enterprise Workflow Patterns¶
Advanced orchestration supports common enterprise workflow patterns:
async def execute_parallel_workflow(self, workflow: Workflow) -> Dict[str, Any]:
"""Execute workflow with maximum parallelization."""
# Group steps by dependency level
dependency_levels = self._analyze_dependencies(workflow.steps)
workflow_state = self._initialize_workflow_state(workflow)
for level in dependency_levels:
if not level: # Skip empty levels
continue
# Execute all steps at this level in parallel
tasks = [
self._execute_step(workflow, step, workflow_state)
for step in level
]
results = await asyncio.gather(*tasks, return_exceptions=True)
# Process results and update state
for step, result in zip(level, results):
if isinstance(result, Exception):
workflow_state["failed_steps"].add(step.step_id)
raise result
else:
workflow_state["completed_steps"].add(step.step_id)
workflow_state["step_outputs"][step.step_id] = result
self._apply_output_mapping(step, result, workflow_state["data"])
return workflow_state
Parallel workflow execution maximizes performance while maintaining proper dependency ordering.
Error Handling and Recovery Patterns¶
Enterprise orchestration requires sophisticated error handling:
async def execute_resilient_workflow(self, workflow: Workflow,
recovery_strategy: str = "rollback") -> Dict[str, Any]:
"""Execute workflow with advanced error recovery."""
workflow_state = self._initialize_workflow_state(workflow)
checkpoint_states = [] # For rollback capability
try:
for step in workflow.steps:
# Create checkpoint before each critical step
if self._is_critical_step(step):
checkpoint_states.append(workflow_state["data"].copy())
result = await self._execute_step_with_circuit_breaker(
workflow, step, workflow_state
)
# Update state on successful execution
workflow_state["completed_steps"].add(step.step_id)
self._apply_output_mapping(step, result, workflow_state["data"])
except Exception as e:
logger.error(f"Workflow {workflow.workflow_id} failed: {e}")
if recovery_strategy == "rollback":
await self._rollback_workflow(workflow_state, checkpoint_states)
elif recovery_strategy == "retry":
await self._retry_failed_steps(workflow, workflow_state)
workflow_state["status"] = "failed"
workflow_state["error"] = str(e)
return workflow_state
Advanced error recovery ensures workflows can handle failures gracefully in production environments.
Workflow Monitoring and Observability¶
Production orchestration requires comprehensive monitoring:
async def monitor_workflow_execution(self, workflow_id: str) -> Dict[str, Any]:
"""Monitor real-time workflow execution."""
if workflow_id not in self.active_workflows:
return {"error": "Workflow not found"}
state = self.active_workflows[workflow_id]
# Calculate execution metrics
total_steps = len(state.get("workflow", {}).get("steps", []))
completed_steps = len(state.get("completed_steps", set()))
failed_steps = len(state.get("failed_steps", set()))
progress = (completed_steps / total_steps * 100) if total_steps > 0 else 0
# Estimate remaining time based on average step duration
avg_step_duration = self._calculate_average_step_duration(state)
remaining_steps = total_steps - completed_steps
estimated_completion = remaining_steps * avg_step_duration
return {
"workflow_id": workflow_id,
"status": state["status"],
"progress_percentage": progress,
"completed_steps": completed_steps,
"failed_steps": failed_steps,
"total_steps": total_steps,
"estimated_completion_seconds": estimated_completion,
"current_step": self._get_current_step_info(state),
"performance_metrics": self._gather_performance_metrics(state)
}
Comprehensive monitoring provides real-time visibility into workflow execution and performance.
Real-World Orchestration Example: Financial Trading Pipeline¶
Here's how orchestration works in a complex financial trading scenario:
Here's how we structure a complex financial trading workflow with multiple coordinated steps:
def create_trading_workflow() -> Workflow:
"""Create a sophisticated trading decision workflow."""
# Define the core workflow structure
return Workflow(
workflow_id="trading_pipeline_v1",
name="Multi-Agent Trading Decision Pipeline",
description="Orchestrated trading workflow with risk analysis",
steps=create_trading_steps(),
initial_data=get_trading_initial_data()
)
def create_trading_steps() -> List[WorkflowStep]:
"""Create the workflow steps for trading pipeline."""
return [
# Market data collection step
WorkflowStep(
step_id="market_data_collection",
action="collect_market_data",
agent_capability="market_data_provider",
input_mapping={"symbols": "trading_symbols", "timeframe": "analysis_period"},
output_mapping={"market_data": "raw_market_data"},
dependencies=[], timeout=10
),
The workflow starts with market data collection, which forms the foundation for all subsequent analysis steps.
# Parallel analysis steps
WorkflowStep(
step_id="technical_analysis",
action="analyze_technical_indicators",
agent_capability="technical_analysis",
input_mapping={"market_data": "raw_market_data"},
output_mapping={"signals": "technical_signals", "confidence": "technical_confidence"},
dependencies=["market_data_collection"], timeout=30
),
WorkflowStep(
step_id="sentiment_analysis",
action="analyze_market_sentiment",
agent_capability="sentiment_analysis",
input_mapping={"symbols": "trading_symbols"},
output_mapping={"sentiment": "market_sentiment", "news_impact": "news_analysis"},
dependencies=["market_data_collection"], timeout=20
),
Technical and sentiment analysis can run in parallel once market data is available, optimizing workflow execution time.
# Risk assessment combines multiple inputs
WorkflowStep(
step_id="risk_assessment",
action="assess_trading_risk",
agent_capability="risk_management",
input_mapping={
"signals": "technical_signals",
"sentiment": "market_sentiment",
"portfolio": "current_portfolio"
},
output_mapping={"risk_score": "calculated_risk", "max_position": "position_limit"},
dependencies=["technical_analysis", "sentiment_analysis"], timeout=15
),
# Final trading decision and compliance
WorkflowStep(
step_id="trading_decision",
action="make_trading_decision",
agent_capability="trading_strategy",
input_mapping={"signals": "technical_signals", "risk": "calculated_risk"},
output_mapping={"trades": "recommended_trades"},
dependencies=["risk_assessment"], timeout=10
)
]
Risk assessment waits for both analysis steps, then trading decisions are made and verified for compliance.
This workflow demonstrates sophisticated dependency management, parallel execution, and data flow patterns typical of enterprise systems.
Production Considerations¶
Scalability Patterns¶
Enterprise orchestration must handle varying loads:
- Horizontal scaling: Multiple orchestrator instances with shared state
- Step partitioning: Breaking large workflows into manageable chunks
- Resource pooling: Efficient agent utilization across workflows
- Queue management: Handling workflow backlogs during peak periods
Reliability Patterns¶
Production systems require bulletproof reliability:
- Circuit breakers: Preventing cascade failures in agent networks
- Bulkhead isolation: Isolating failures to prevent system-wide impact
- Saga patterns: Managing distributed transactions across agents
- Compensation actions: Rolling back partially completed workflows
🧭 Navigation¶
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Next: Session 8 - Production Ready →