🔄 Multi-Agent Workflows

Design and orchestrate complex workflows where multiple AI agents collaborate seamlessly

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🎯 Workflow Fundamentals

📋 Workflow Design

Multi-agent workflows coordinate multiple AI agents to complete complex tasks through sequential, parallel, or conditional execution patterns.

Example Workflow:

Research Agent → gathers information
Analysis Agent → processes data
Writer Agent → creates report
Review Agent → validates output

Design complex workflows with branching logic, error handling, and dynamic agent allocation based on task requirements.

# Workflow definition with LangGraph
from langgraph.graph import StateGraph, END
from typing import TypedDict, List

class WorkflowState(TypedDict):
    task: str
    results: List[str]
    current_step: str
    error: str

workflow = StateGraph(WorkflowState)

# Add agent nodes
workflow.add_node("research", research_agent)
workflow.add_node("analyze", analysis_agent)
workflow.add_node("write", writer_agent)
workflow.add_node("review", review_agent)

# Define edges with conditions
workflow.add_edge("research", "analyze")
workflow.add_conditional_edges(
    "analyze",
    lambda x: "write" if x["results"] else "research"
)
workflow.add_edge("write", "review")
workflow.add_conditional_edges(
    "review",
    lambda x: END if x["approved"] else "write"
)
                        

Implement adaptive workflows with machine learning-based routing, self-optimizing execution paths, and distributed orchestration.

                            Advanced Workflow Features
                            ML-based task routing
Dynamic workflow generation
Distributed execution
Workflow versioning
Performance optimization
Fault tolerance mechanisms

                        

🔀 Execution Patterns

Understand different workflow execution patterns including sequential, parallel, and hybrid approaches.

Sequential

Agents execute one after another in order

Parallel

Multiple agents work simultaneously

Conditional

Execution path depends on conditions

Implement complex execution patterns with map-reduce, scatter-gather, and pipeline architectures.

# Parallel execution with gather pattern
import asyncio
from typing import List, Dict

class ParallelWorkflow:
    def __init__(self):
        self.agents = {}
    
    async def scatter(self, tasks: List[str]):
        # Distribute tasks to agents
        coroutines = []
        for task in tasks:
            agent = self.select_agent(task)
            coroutines.append(agent.process(task))
        
        # Execute in parallel
        results = await asyncio.gather(*coroutines)
        return results
    
    async def gather(self, results: List[Dict]):
        # Aggregate results
        aggregator = self.agents["aggregator"]
        final_result = await aggregator.combine(results)
        return final_result
    
    async def execute_map_reduce(self, data):
        # Map phase
        mapped = await self.scatter(data)
        # Reduce phase
        reduced = await self.gather(mapped)
        return reduced
                        

Design event-driven workflows with complex orchestration patterns, saga implementations, and compensating transactions.

                            Enterprise Patterns
                            Saga pattern for distributed transactions
Event sourcing workflows
CQRS with workflow management
Choreography vs Orchestration
Workflow compensation logic

                        

📊 State Management

Manage workflow state including task progress, intermediate results, and agent communications.

State Components:

Current task and subtasks
Agent outputs and messages
Workflow metadata
Error and retry information

Implement persistent state management with checkpointing, recovery mechanisms, and distributed state synchronization.

# State management with persistence
import json
from datetime import datetime
from typing import Any, Dict

class WorkflowStateManager:
    def __init__(self, workflow_id: str):
        self.workflow_id = workflow_id
        self.state = {
            "id": workflow_id,
            "status": "initialized",
            "created_at": datetime.now().isoformat(),
            "steps": {},
            "context": {},
            "checkpoints": []
        }
    
    def update_step(self, step_name: str, data: Dict[str, Any]):
        self.state["steps"][step_name] = {
            "status": data.get("status"),
            "result": data.get("result"),
            "timestamp": datetime.now().isoformat(),
            "agent": data.get("agent")
        }
        self.checkpoint()
    
    def checkpoint(self):
        # Save state to persistent storage
        checkpoint = {
            "timestamp": datetime.now().isoformat(),
            "state": json.dumps(self.state)
        }
        self.state["checkpoints"].append(checkpoint)
        self.persist()
    
    def recover(self, checkpoint_id: int):
        # Restore from checkpoint
        checkpoint = self.state["checkpoints"][checkpoint_id]
        self.state = json.loads(checkpoint["state"])
                        

Build distributed state management with consensus protocols, event sourcing, and real-time state synchronization.

                            Advanced State Management
                            Distributed state with Raft consensus
Event sourcing for audit trails
CRDT for conflict resolution
State machine replication
Time-travel debugging

                        

🏗️ Architecture Patterns

🎭 Orchestration vs Choreography

Orchestration uses a central coordinator to manage workflow execution, while choreography allows agents to collaborate directly.

Example: Customer Service Workflow

Orchestration: Central controller assigns tasks to support, billing, and technical agents.
Choreography: Agents respond to events and coordinate peer-to-peer.

Implement hybrid architectures combining orchestration for control with choreography for flexibility.

# Orchestrator pattern
class WorkflowOrchestrator:
    def __init__(self):
        self.agents = {}
        self.workflow_definition = {}
    
    def execute(self, input_data):
        context = {"input": input_data}
        
        for step in self.workflow_definition["steps"]:
            agent = self.agents[step["agent"]]
            
            # Orchestrator controls execution
            result = agent.execute(
                task=step["task"],
                context=context
            )
            
            context[step["name"]] = result
            
            # Check conditions
            if "condition" in step:
                if not self.evaluate(step["condition"], context):
                    break
        
        return context

# Choreography pattern
class ChoreographedAgent:
    def __init__(self, name, event_bus):
        self.name = name
        self.event_bus = event_bus
        self.subscribe_to_events()
    
    def handle_event(self, event):
        # Agent decides autonomously
        if self.can_handle(event):
            result = self.process(event)
            # Publish result for others
            self.event_bus.publish(result)
                        

Design adaptive systems that switch between orchestration and choreography based on workload and requirements.

                            Hybrid Architecture Benefits
                            Centralized control when needed
Distributed execution for scalability
Dynamic pattern switching
Fault isolation
Performance optimization

                        

🌊 Pipeline Architecture

Pipeline workflows process data through a series of transformation stages, with each agent handling a specific transformation.

Example: Data Processing Pipeline

Raw Data → Cleaning Agent → Enrichment Agent → Analysis Agent → Visualization Agent → Report

Build advanced pipelines with branching, merging, and parallel processing capabilities.

# Pipeline implementation
class Pipeline:
    def __init__(self):
        self.stages = []
    
    def add_stage(self, agent, name=None):
        self.stages.append({
            "agent": agent,
            "name": name or agent.__class__.__name__
        })
        return self
    
    def add_parallel(self, agents):
        # Parallel processing stage
        self.stages.append({
            "type": "parallel",
            "agents": agents
        })
        return self
    
    def add_conditional(self, condition, true_agent, false_agent):
        self.stages.append({
            "type": "conditional",
            "condition": condition,
            "true_branch": true_agent,
            "false_branch": false_agent
        })
        return self
    
    async def execute(self, input_data):
        data = input_data
        
        for stage in self.stages:
            if stage.get("type") == "parallel":
                # Execute agents in parallel
                tasks = [agent.process(data) for agent in stage["agents"]]
                results = await asyncio.gather(*tasks)
                data = self.merge_results(results)
            
            elif stage.get("type") == "conditional":
                if stage["condition"](data):
                    data = await stage["true_branch"].process(data)
                else:
                    data = await stage["false_branch"].process(data)
            
            else:
                # Sequential execution
                data = await stage["agent"].process(data)
        
        return data
                        

Implement streaming pipelines with backpressure, windowing, and real-time processing capabilities.

                            Streaming Pipeline Features
                            Backpressure handling
Windowing and aggregation
Exactly-once processing
Dynamic scaling
Fault recovery

                        

🎯 Event-Driven Workflows

Event-driven workflows react to events and triggers, allowing for flexible, decoupled agent interactions.

Example: Order Processing

Order Placed Event → Inventory Check → Payment Processing → Shipping → Notification Events

Design complex event-driven systems with event sourcing, CQRS, and saga patterns.

# Event-driven workflow
from dataclasses import dataclass
from typing import List, Callable
import asyncio

@dataclass
class Event:
    type: str
    data: dict
    timestamp: float
    source: str

class EventDrivenWorkflow:
    def __init__(self):
        self.event_handlers = {}
        self.event_queue = asyncio.Queue()
        self.running = False
    
    def register_handler(self, event_type: str, handler: Callable):
        if event_type not in self.event_handlers:
            self.event_handlers[event_type] = []
        self.event_handlers[event_type].append(handler)
    
    async def emit_event(self, event: Event):
        await self.event_queue.put(event)
    
    async def process_events(self):
        self.running = True
        while self.running:
            event = await self.event_queue.get()
            
            # Find and execute handlers
            handlers = self.event_handlers.get(event.type, [])
            
            # Execute handlers in parallel
            tasks = [handler(event) for handler in handlers]
            results = await asyncio.gather(*tasks, return_exceptions=True)
            
            # Process results and emit new events
            for result in results:
                if isinstance(result, Event):
                    await self.emit_event(result)
                        

Build enterprise event-driven architectures with event stores, projections, and complex event processing.

                            Enterprise Event Architecture
                            Event store implementation
Event sourcing patterns
Complex event processing (CEP)
Event-driven sagas
Temporal workflows

                        

🛠️ Implementation Tools

🔧

LangGraph

LangGraph provides a framework for building stateful, multi-agent workflows with built-in persistence and streaming.

# Basic LangGraph workflow
from langgraph.graph import Graph

# Define the graph
workflow = Graph()

# Add nodes (agents)
workflow.add_node("researcher", research_function)
workflow.add_node("writer", write_function)
workflow.add_node("editor", edit_function)

# Add edges (flow)
workflow.add_edge("researcher", "writer")
workflow.add_edge("writer", "editor")

# Set entry point
workflow.set_entry_point("researcher")

# Compile and run
app = workflow.compile()
result = app.invoke({"topic": "AI workflows"})
                        

Advanced LangGraph features including conditional routing, subgraphs, and human-in-the-loop workflows.

# Advanced LangGraph with conditions
from langgraph.graph import StateGraph, END
from langgraph.checkpoint import MemorySaver

# Define state
class State(TypedDict):
    messages: List[str]
    next_action: str
    human_feedback: bool

# Create graph with checkpointing
workflow = StateGraph(State)
memory = MemorySaver()

# Add conditional routing
def route_decision(state):
    if state["human_feedback"]:
        return "human_review"
    elif len(state["messages"]) > 5:
        return "summarize"
    else:
        return "continue"

workflow.add_conditional_edges(
    "process",
    route_decision,
    {
        "human_review": "human",
        "summarize": "summary",
        "continue": "process"
    }
)

# Compile with checkpointing
app = workflow.compile(checkpointer=memory)
                        

Production LangGraph deployments with distributed execution, monitoring, and enterprise integration.

                            LangGraph Production Features
                            Distributed graph execution
Persistence backends (Redis, PostgreSQL)
Streaming and async support
Graph versioning and migration
Monitoring and observability

                        

🚀

Apache Airflow

Use Airflow to orchestrate complex multi-agent workflows with scheduling, monitoring, and retry capabilities.

# Airflow DAG for agent workflow
from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta

default_args = {
    'owner': 'ai-team',
    'retries': 3,
    'retry_delay': timedelta(minutes=5)
}

dag = DAG(
    'multi_agent_workflow',
    default_args=default_args,
    schedule_interval='@daily',
    start_date=datetime(2024, 1, 1)
)

# Define agent tasks
research = PythonOperator(
    task_id='research_agent',
    python_callable=run_research_agent,
    dag=dag
)

analyze = PythonOperator(
    task_id='analysis_agent',
    python_callable=run_analysis_agent,
    dag=dag
)

report = PythonOperator(
    task_id='report_agent',
    python_callable=run_report_agent,
    dag=dag
)

# Define dependencies
research >> analyze >> report
                        

Advanced Airflow patterns with dynamic task generation, branching, and external system integration.

# Dynamic task generation
from airflow.decorators import task, dag
from airflow.operators.python import BranchPythonOperator

@dag(
    schedule_interval='@hourly',
    start_date=datetime(2024, 1, 1),
    catchup=False
)
def dynamic_agent_workflow():
    
    @task
    def get_tasks():
        # Dynamically determine tasks
        return ['task1', 'task2', 'task3']
    
    @task
    def process_task(task_name):
        # Select and run appropriate agent
        agent = select_agent(task_name)
        return agent.execute(task_name)
    
    @task.branch
    def check_quality(results):
        if all(r['quality'] > 0.8 for r in results):
            return 'publish'
        else:
            return 'review'
    
    # Dynamic task mapping
    tasks = get_tasks()
    results = process_task.expand(task_name=tasks)
    quality_check = check_quality(results)

workflow = dynamic_agent_workflow()
                        

Enterprise Airflow deployments with Kubernetes executors, custom operators, and multi-tenant configurations.

                            Enterprise Airflow Features
                            Kubernetes executor for scaling
Custom operators for agents
Multi-tenant isolation
External trigger integration
Advanced monitoring with Prometheus

                        

⚡

Temporal

Temporal provides durable workflow execution with automatic retries, error handling, and long-running workflow support.

# Temporal workflow
from temporalio import workflow, activity

@activity.defn
async def research_activity(topic: str) -> str:
    # Agent performs research
    return f"Research on {topic}"

@activity.defn
async def write_activity(research: str) -> str:
    # Agent writes content
    return f"Article based on {research}"

@workflow.defn
class ContentWorkflow:
    @workflow.run
    async def run(self, topic: str) -> str:
        # Durable workflow execution
        research = await workflow.execute_activity(
            research_activity,
            topic,
            start_to_close_timeout=timedelta(minutes=10)
        )
        
        article = await workflow.execute_activity(
            write_activity,
            research,
            start_to_close_timeout=timedelta(minutes=15)
        )
        
        return article
                        

Advanced Temporal patterns including child workflows, signals, and long-running workflows with human interaction.

# Complex Temporal workflow
@workflow.defn
class ReviewWorkflow:
    def __init__(self):
        self.approved = False
        self.feedback = []
    
    @workflow.signal
    async def submit_feedback(self, feedback: str):
        self.feedback.append(feedback)
    
    @workflow.signal
    async def approve(self):
        self.approved = True
    
    @workflow.run
    async def run(self, document: str) -> str:
        # Start review process
        await workflow.execute_activity(
            notify_reviewers_activity,
            document
        )
        
        # Wait for approval (with timeout)
        await workflow.wait_condition(
            lambda: self.approved,
            timeout=timedelta(days=3)
        )
        
        if self.approved:
            # Process feedback and finalize
            return await workflow.execute_activity(
                finalize_document_activity,
                document,
                self.feedback
            )
        else:
            # Handle timeout
            return "Review timed out"
                        

Enterprise Temporal deployments with multi-region support, versioning strategies, and complex saga implementations.

                            Temporal Enterprise Features
                            Multi-region deployment
Workflow versioning
Saga pattern implementation
Advanced retry policies
Workflow migration strategies

                        