Learn how to build multi-agent AI systems where multiple AI agents collaborate to solve complex tasks. Architecture patterns and implementation guide.

Stay Updated

Get the latest tutorials, guides, and insights on AI, DevOps, Cloud, and Infrastructure delivered directly to your inbox.

Open full subscribe page

Multi-Agent AI Systems: Building Collaborative AI Applications

Multi-agent systems enable AI applications to tackle complex tasks by coordinating multiple specialized agents. This guide covers architecture and implementation.

What Are Multi-Agent Systems?#

Multi-agent systems consist of multiple AI agents that:

Have specialized roles
Communicate and coordinate
Work together to solve problems
Can operate autonomously

Architecture Patterns #

Hierarchical Architecture #

python.python

from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder

# Coordinator Agent
coordinator_prompt = ChatPromptTemplate.from_messages([
    ("system", "You coordinate tasks between specialized agents."),
    ("human", "{input}")
])

# Specialist Agents
research_agent = create_openai_functions_agent(
    llm, tools=[research_tool], prompt=research_prompt
)

code_agent = create_openai_functions_agent(
    llm, tools=[code_tool], prompt=code_prompt
)

# Coordinator delegates to specialists
def coordinate_task(task):
    if task.type == "research":
        return research_agent.run(task)
    elif task.type == "code":
        return code_agent.run(task)

Swarm Architecture #

All agents work in parallel and share results:

python.python

import asyncio

async def swarm_solve(problem):
    agents = [research_agent, analysis_agent, code_agent]
    
    # Run all agents in parallel
    results = await asyncio.gather(*[
        agent.arun(problem) for agent in agents
    ])
    
    # Synthesize results
    return synthesize(results)

Communication Patterns #

Message Passing #

python.python

class Agent:
    def __init__(self, name, role):
        self.name = name
        self.role = role
        self.mailbox = []
    
    def send_message(self, recipient, message):
        recipient.receive_message(self.name, message)
    
    def receive_message(self, sender, message):
        self.mailbox.append((sender, message))

Shared Memory #

python.python

from langchain.memory import ConversationBufferMemory

shared_memory = ConversationBufferMemory()

# Agents read/write to shared memory
agent1.run("Research topic X", memory=shared_memory)
agent2.run("Analyze research", memory=shared_memory)

Use Cases #

1. Research and Writing #

Research Agent: Gathers information
Writing Agent: Creates content
Review Agent: Edits and improves

2. Software Development #

Planning Agent: Creates architecture
Coding Agent: Writes code
Testing Agent: Creates tests
Review Agent: Code review

3. Data Analysis #

Collection Agent: Gathers data
Analysis Agent: Analyzes data
Visualization Agent: Creates charts
Report Agent: Writes reports

Implementation with LangChain #

python.python

from langchain.agents import AgentExecutor
from langchain.tools import Tool

# Define tools for each agent
research_tool = Tool(
    name="research",
    func=search_knowledge_base,
    description="Search knowledge base for information"
)

# Create agent executor
research_executor = AgentExecutor.from_agent_and_tools(
    agent=research_agent,
    tools=[research_tool],
    verbose=True
)

# Coordinate agents
def multi_agent_pipeline(query):
    # Step 1: Research
    research_result = research_executor.run(query)
    
    # Step 2: Analyze
    analysis_result = analysis_executor.run(research_result)
    
    # Step 3: Generate
    final_result = generation_executor.run(analysis_result)
    
    return final_result

Best Practices #

Clear Roles: Each agent should have a specific purpose
Error Handling: Implement retry logic and fallbacks
Monitoring: Track agent performance and interactions
Cost Management: Monitor token usage across agents
Testing: Test each agent independently and together

Challenges #

Coordination Overhead: Managing multiple agents
Cost: Multiple LLM calls
Latency: Sequential agent execution
Debugging: Complex interaction patterns

Conclusion #

Multi-agent systems enable solving complex problems by leveraging specialized AI agents. Start with simple architectures and iterate based on your needs.

For Multi-Agent AI Systems: Building Collaborative AI Applications, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.

Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.

Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.

Production Notes 2 #

Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.

Production Notes 3 #

Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.

Multi-Agent AI Systems: Building Collaborative AI Applications

Stay Updated

Multi-Agent AI Systems: Building Collaborative AI Applications

What Are Multi-Agent Systems?#

Architecture Patterns #

Hierarchical Architecture #

Swarm Architecture #

Communication Patterns #

Message Passing #

Shared Memory #

Use Cases #

1. Research and Writing #

2. Software Development #

3. Data Analysis #

Implementation with LangChain #

Best Practices #

Challenges #

Conclusion #

Production Notes 1 #

Production Notes 2 #

Production Notes 3 #

Architecture Review: Ansible Role Design for Large Teams

Architecture Review: AWS Cost Control with Tagging and Budgets

More from AI

Operational Checklist: AI Inference Cost Optimization

Operational Checklist: Python Worker Queue Scaling Patterns

Operational Checklist: Model Serving Observability Stack

Operational Checklist: AI Inference Cost Optimization

Operational Checklist: Python Worker Queue Scaling Patterns

Operational Checklist: Model Serving Observability Stack

Operational Checklist: RAG Retrieval Quality Evaluation

Operational Checklist: SLO-Based Monitoring for APIs

Operational Checklist: Prompt Versioning and Regression Testing

About Kiril Urbonas