Multi-Agent Workflows - AI Training Roadmap

Design and implement sophisticated multi-agent systems where specialized AI agents collaborate to solve complex problems. Learn how to orchestrate multiple AI agents, implement communication protocols, and build robust multi-agent workflows for enterprise applications.

🏗️ Multi-Agent Architecture

Agent Types

Understanding different agent roles is crucial for building effective multi-agent systems.

Orchestrator Agent: Coordinates overall workflow
Specialist Agents: Domain-specific expertise
Validator Agent: Quality assurance
Memory Agent: Maintains context and state

Multi-Agent Framework

# Multi-Agent Framework
class AgentOrchestrator:
    def __init__(self):
        self.agents = {
            'researcher': ResearchAgent(),
            'writer': WriterAgent(),
            'critic': CriticAgent(),
            'editor': EditorAgent()
        }
    
    async def execute_workflow(self, task):
        # Research phase
        research = await self.agents['researcher'].gather_info(task)
        
        # Writing phase
        draft = await self.agents['writer'].create_content(research)
        
        # Review phase
        feedback = await self.agents['critic'].review(draft)
        
        # Refinement phase
        final = await self.agents['editor'].refine(draft, feedback)
        
        return final

💬 Agent Communication Patterns

Message Passing

Implement robust inter-agent communication protocols for reliable information exchange.

Direct Messaging: Point-to-point communication between agents
Broadcast: One-to-many message distribution
Request-Response: Synchronous communication patterns
Pub-Sub: Asynchronous event-based messaging

Agent Communication Protocol

# Message passing between agents
from dataclasses import dataclass
from typing import Any, Dict

@dataclass
class Message:
    sender: str
    recipient: str
    content: Any
    metadata: Dict

class CommunicationBus:
    def __init__(self):
        self.agents = {}
        self.message_queue = []
    
    async def send_message(self, message: Message):
        # Route message to recipient
        if message.recipient in self.agents:
            await self.agents[message.recipient].receive(message)
        else:
            self.message_queue.append(message)

Shared Memory Architecture

Design efficient shared state management for coordinated agent behavior.

Blackboard Pattern: Common knowledge base for all agents
State Synchronization: Distributed state management
Memory Pools: Shared context and history

Event-Driven Coordination

Build reactive agent systems with event streams for real-time coordination.

Event Sourcing: Maintain event history for replay
CQRS Pattern: Separate read and write operations
Streaming Processing: Real-time event handling

🛠️ Popular Frameworks

AutoGen (Microsoft)

Microsoft's framework for building conversational multi-agent systems with LLMs.

Conversational Agents: Natural dialogue between agents
Code Execution: Agents can write and execute code
Human-in-the-loop: Seamless human intervention
Group Chat: Multi-agent conversations

AutoGen Implementation

# AutoGen multi-agent setup
import autogen

# Configure LLM
config_list = [{
    "model": "gpt-4",
    "api_key": os.environ["OPENAI_API_KEY"]
}]

# Create agents
assistant = autogen.AssistantAgent(
    name="assistant",
    llm_config={"config_list": config_list}
)

user_proxy = autogen.UserProxyAgent(
    name="user_proxy",
    code_execution_config={"work_dir": "coding"}
)

# Start conversation
user_proxy.initiate_chat(
    assistant,
    message="Create a data analysis pipeline"
)

CrewAI

Role-based agent orchestration framework for complex task delegation.

Role Definition: Clear agent responsibilities
Task Management: Structured task delegation
Tool Integration: Easy tool assignment to agents
Process Flows: Sequential and hierarchical workflows

CrewAI Workflow

# CrewAI agent setup
from crewai import Agent, Task, Crew

# Define agents with roles
researcher = Agent(
    role='Research Analyst',
    goal='Find and analyze information',
    backstory='Expert in research and analysis',
    tools=[search_tool, scrape_tool]
)

writer = Agent(
    role='Content Writer',
    goal='Create compelling content',
    backstory='Skilled technical writer'
)

# Create tasks
research_task = Task(
    description='Research latest AI trends',
    agent=researcher
)

writing_task = Task(
    description='Write article based on research',
    agent=writer
)

# Assemble crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task]
)

result = crew.kickoff()

LangGraph

Build complex agent workflows as graphs with cycles and conditional logic.

Graph-based: Model workflows as state machines
Cycles Support: Handle iterative processes
Conditional Edges: Dynamic routing based on state
Persistence: Save and resume workflows

🤝 Multi-Agent Workflows