Multi-agent systems distribute complex tasks across multiple specialized AI agents that communicate and collaborate to achieve shared goals. This architectural approach offers improved reliability, scalability, and maintainability compared to monolithic AI systems.
Core Architecture Patterns
1. Hierarchical Architecture
A coordinator agent receives tasks and delegates to specialized worker agents based on task type. The coordinator manages state, handles results aggregation, and implements retry logic.
Use cases: Document processing pipelines, customer service automation, complex workflow orchestration.
2. Peer-to-Peer Architecture
Agents communicate directly without centralized coordination. Each agent maintains awareness of other agents' capabilities and can request assistance as needed.
Use cases: Distributed problem-solving, collaborative research systems, decentralized decision-making.
3. Blackboard Architecture
Agents share information through a central knowledge repository (blackboard). Agents read from and write to the blackboard, with changes triggering relevant agent actions.
Use cases: Complex analysis tasks, multi-perspective evaluation, knowledge synthesis.
Communication Protocols
Message Passing
Agents exchange structured messages containing task requests, results, or state updates. Implement message queues (RabbitMQ, Redis) for reliable asynchronous communication.
Shared State
Agents access shared data stores (databases, key-value stores) with proper locking mechanisms to prevent race conditions. Suitable for scenarios requiring persistent state.
Event-Driven Communication
Agents subscribe to events and react when relevant events occur. Enables loose coupling and simplified scaling. Implement using event buses like Apache Kafka or AWS EventBridge.
Agent Specialization Strategies
Functional Specialization
- Data extraction agent: Processes documents and extracts structured information
- Analysis agent: Performs reasoning and generates insights
- Validation agent: Checks outputs for accuracy and consistency
- Communication agent: Formats results for end users
Domain Specialization
Agents specialize in specific domains (legal, medical, financial). Each agent uses domain-specific prompts, knowledge bases, and validation rules.
Coordination Mechanisms
Task Allocation
Strategies for distributing work:
- Round-robin: Simple distribution for homogeneous tasks
- Capability-based: Route tasks to agents with appropriate specialization
- Load-based: Consider current agent workload when assigning tasks
- Priority-based: Handle high-priority tasks first
Conflict Resolution
When agents produce conflicting results:
- Voting: Majority decision among agents
- Confidence-based: Trust the agent with highest confidence score
- Expert arbitration: Route conflicts to specialized arbitrator agent
- Human-in-the-loop: Escalate to human review for critical decisions
Code Example: LangGraph Multi-Agent System
python
from langgraph.graph import StateGraph, END
from langchain.chat_models import ChatAnthropic
from typing import TypedDict, Annotated, Sequence
import operator
# Define the agent state
class AgentState(TypedDict):
messages: Annotated[Sequence[str], operator.add]
current_task: str
results: dict
error_count: int
# Initialize models
coordinator_llm = ChatAnthropic(model="claude-sonnet-4.5", temperature=0)
worker_llm = ChatAnthropic(model="claude-sonnet-4.5", temperature=0.3)
def coordinator_node(state: AgentState):
"""Coordinator agent delegates tasks to workers"""
try:
messages = state["messages"]
task = state["current_task"]
# Analyze task and determine worker
prompt = f"""Analyze this task and determine which specialized agent should handle it:
Task: {task}
Available agents: data_extraction, analysis, validation
Return only the agent name."""
response = coordinator_llm.invoke(prompt)
assigned_agent = response.content.strip()
return {
"messages": messages + [f"Coordinator assigned task to {assigned_agent}"],
"current_task": task,
"results": {"assigned_to": assigned_agent}
}
except Exception as e:
return {
"messages": messages + [f"Coordinator error: {str(e)}"],
"error_count": state.get("error_count", 0) + 1
}
def worker_node(state: AgentState):
"""Worker agent executes assigned tasks"""
try:
task = state["current_task"]
assigned_to = state["results"].get("assigned_to")
# Execute task based on specialization
prompt = f"""You are a {assigned_to} agent. Execute this task:
{task}
Provide a detailed result."""
response = worker_llm.invoke(prompt)
result = response.content
return {
"messages": state["messages"] + [f"Worker completed: {result[:100]}..."],
"results": {**state["results"], "worker_output": result}
}
except Exception as e:
return {
"messages": state["messages"] + [f"Worker error: {str(e)}"],
"error_count": state.get("error_count", 0) + 1
}
def should_retry(state: AgentState) -> str:
"""Determine if task should be retried"""
if state.get("error_count", 0) > 3:
return "end"
if "worker_output" in state.get("results", {}):
return "end"
return "coordinator"
# Build the graph
workflow = StateGraph(AgentState)
# Add nodes
workflow.add_node("coordinator", coordinator_node)
workflow.add_node("worker", worker_node)
# Add edges
workflow.set_entry_point("coordinator")
workflow.add_edge("coordinator", "worker")
workflow.add_conditional_edges(
"worker",
should_retry,
{
"coordinator": "coordinator",
"end": END
}
)
# Compile the graph
app = workflow.compile()
# Execute
initial_state = {
"messages": [],
"current_task": "Extract customer sentiment from support tickets",
"results": {},
"error_count": 0
}
result = app.invoke(initial_state)
print(f"Final result: {result['results']}")Code Example: CrewAI Multi-Agent Workflow
python
from crewai import Agent, Task, Crew, Process
from langchain.chat_models import ChatAnthropic
from langchain.tools import Tool
# Initialize LLM
llm = ChatAnthropic(model="claude-sonnet-4.5", temperature=0.3)
# Define specialized agents
researcher = Agent(
role="Research Analyst",
goal="Gather and analyze information from documents",
backstory="""You are an expert at extracting insights from data.
You excel at finding patterns and relevant information.""",
verbose=True,
allow_delegation=False,
llm=llm
)
writer = Agent(
role="Content Writer",
goal="Create compelling, accurate content based on research",
backstory="""You are a skilled writer who transforms complex
information into clear, engaging content.""",
verbose=True,
allow_delegation=False,
llm=llm
)
reviewer = Agent(
role="Quality Reviewer",
goal="Ensure accuracy, consistency, and quality",
backstory="""You are a meticulous editor who catches errors
and ensures high-quality output.""",
verbose=True,
allow_delegation=True,
llm=llm
)
# Define tasks
research_task = Task(
description="""Research the topic: AI Multi-Agent Systems.
Gather key concepts, benefits, and implementation patterns.
Focus on production-ready approaches.""",
agent=researcher,
expected_output="Comprehensive research summary with key findings"
)
writing_task = Task(
description="""Using the research findings, write a technical blog post
about implementing multi-agent systems. Include:
- Architecture patterns
- Code examples
- Best practices
Target audience: Software engineers.""",
agent=writer,
expected_output="Complete blog post draft (800-1000 words)"
)
review_task = Task(
description="""Review the blog post for:
- Technical accuracy
- Clarity and readability
- Completeness
Provide specific improvement suggestions.""",
agent=reviewer,
expected_output="Reviewed content with feedback and corrections"
)
# Create crew with hierarchical process
crew = Crew(
agents=[researcher, writer, reviewer],
tasks=[research_task, writing_task, review_task],
process=Process.sequential, # Tasks executed in order
verbose=2
)
# Execute the workflow
try:
result = crew.kickoff()
print(f"\n\nFinal Output:\n{result}")
except Exception as e:
print(f"Error in crew execution: {str(e)}")
# Implement fallback logic hereCode Example: Event-Driven Communication with Redis
python
import redis
import json
from typing import Dict, Any
import asyncio
from datetime import datetime
class AgentCommunicationHub:
"""Event-driven communication system for multi-agent coordination"""
def __init__(self, redis_url: str = "redis://localhost:6379"):
self.redis_client = redis.from_url(redis_url)
self.pubsub = self.redis_client.pubsub()
self.agent_id = None
def register_agent(self, agent_id: str, capabilities: list):
"""Register agent with its capabilities"""
self.agent_id = agent_id
agent_info = {
"id": agent_id,
"capabilities": capabilities,
"status": "active",
"registered_at": datetime.utcnow().isoformat()
}
self.redis_client.hset(
"agents",
agent_id,
json.dumps(agent_info)
)
# Subscribe to agent-specific channel
self.pubsub.subscribe(f"agent:{agent_id}")
def publish_event(self, event_type: str, data: Dict[Any, Any]):
"""Publish event to the communication bus"""
event = {
"type": event_type,
"source": self.agent_id,
"data": data,
"timestamp": datetime.utcnow().isoformat()
}
self.redis_client.publish(
f"events:{event_type}",
json.dumps(event)
)
def request_task(self, task_type: str, task_data: Dict[Any, Any]):
"""Request another agent to perform a task"""
task = {
"id": f"task_{datetime.utcnow().timestamp()}",
"type": task_type,
"requester": self.agent_id,
"data": task_data,
"status": "pending"
}
# Store task in Redis
self.redis_client.hset(
"tasks",
task["id"],
json.dumps(task)
)
# Publish task request
self.publish_event("task_request", task)
return task["id"]
async def listen_for_events(self, callback):
"""Listen for events and invoke callback"""
for message in self.pubsub.listen():
if message["type"] == "message":
try:
event = json.loads(message["data"])
await callback(event)
except Exception as e:
print(f"Error processing event: {e}")
def get_agent_status(self, agent_id: str) -> Dict[Any, Any]:
"""Get status of another agent"""
agent_data = self.redis_client.hget("agents", agent_id)
if agent_data:
return json.loads(agent_data)
return None
# Example usage
async def main():
# Initialize communication hub for multiple agents
coordinator_hub = AgentCommunicationHub()
coordinator_hub.register_agent(
"coordinator_1",
["task_delegation", "result_aggregation"]
)
worker_hub = AgentCommunicationHub()
worker_hub.register_agent(
"worker_1",
["data_processing", "analysis"]
)
# Coordinator requests task
task_id = coordinator_hub.request_task(
"data_analysis",
{"dataset": "customer_feedback.csv", "metric": "sentiment"}
)
# Worker processes events
async def handle_event(event):
if event["type"] == "task_request":
print(f"Worker received task: {event['data']['id']}")
# Process task and publish result
worker_hub.publish_event(
"task_complete",
{"task_id": event['data']['id'], "result": "Analysis complete"}
)
await worker_hub.listen_for_events(handle_event)
# asyncio.run(main())Implementation Considerations
Error Handling
- Implement retry logic with exponential backoff
- Design fallback strategies when agents fail
- Monitor agent health and automatically restart failed agents
- Log failures for debugging and improvement
State Management
- Maintain conversation history for context-aware responses
- Implement checkpointing for long-running tasks
- Use distributed caching (Redis) for shared state
- Design for idempotency to handle message redelivery
Observability
- Track metrics: latency, success rate, cost per task
- Implement distributed tracing across agents
- Log agent decisions and reasoning paths
- Dashboard for real-time system monitoring
Model Selection
Different agents can use different models based on requirements:
- GPT-5: General reasoning and coordination
- Claude Sonnet 4.5: Code generation and technical tasks
- Gemini 2.5 Pro: Mathematical and analytical tasks
- Llama 4: Cost-effective for high-volume processing
Scaling Strategies
Horizontal Scaling
Deploy multiple instances of each agent type behind load balancers. Use containerization (Docker, Kubernetes) for simplified scaling and management.
Asynchronous Processing
Decouple request handling from processing using message queues. Enables handling traffic spikes without system overload.
Security Considerations
- Implement authentication between agents
- Encrypt inter-agent communication
- Apply least-privilege principles for agent permissions
- Audit trails for all agent actions
- Rate limiting to prevent abuse
Testing Strategies
- Unit tests for individual agent logic
- Integration tests for agent interactions
- End-to-end tests for complete workflows
- Chaos testing for failure scenarios
- Performance testing under load
Production Deployment
Deploy agents incrementally:
- Start with single agent handling simple tasks
- Add specialized agents as needs emerge
- Monitor performance and costs continuously
- Iterate based on real-world usage patterns
Multi-agent systems require careful design but offer significant advantages in maintainability, specialization, and fault tolerance. Success depends on clear architecture, robust communication protocols, and comprehensive monitoring.