Recently, I tried to get a single large language model to help me plan a business trip. It was great at suggesting flights, but when I asked it to also check my calendar, compare hotel reviews, and estimate ground transportation costs, the responses became messy and unreliable. It was trying to do too many different jobs at once. This experience is what pushed me toward a different approach: using a team of specialized AI agents, each with its own purpose and tools, working together. This is the foundation of a production-ready multi-agent system.

Think of it like building a small company. You wouldn’t have one person do sales, accounting, development, and customer support. You hire specialists. Multi-agent systems apply the same principle. Instead of one overloaded LLM, you create a coordinator that delegates tasks to a researcher (who finds information), an analyst (who processes it), and a writer (who formats the report). This separation makes the entire system more reliable, easier to fix, and simpler to improve over time.

So, how do you start building this team? The core of any agent is its purpose and its toolkit. In LangChain, you define an agent by giving it specific tools and clear instructions. Here’s a basic structure for a research agent designed to search the web.

from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain.tools import Tool
from langchain_openai import ChatOpenAI
import requests

def search_web(query: str) -> str:
    # Placeholder for a real search API call
    return f"Found information about: {query}"

web_search_tool = Tool(
    name="WebSearch",
    func=search_web,
    description="Useful for searching the internet for current information."
)

research_agent_prompt = "You are a research specialist. Your only job is to find accurate, recent information using your search tool. Return clear summaries."

But an agent working alone isn’t a system. The real magic—and the real challenge—happens in the handoff. How does the researcher send its findings to the analyst? This is where a shared communication channel, often called a message bus, becomes critical. You can use a simple in-memory queue or a robust system like Redis for production.

import asyncio
from collections import defaultdict

class AgentMessageBus:
    def __init__(self):
        self.queues = defaultdict(asyncio.Queue)
    
    async def send_message(self, to_agent: str, message: dict):
        await self.queues[to_agent].put(message)
    
    async def receive_message(self, agent_name: str) -> dict:
        return await self.queues[agent_name].get()

# Coordinator logic snippet
if task == "analyze_trends":
    await message_bus.send_message("research_agent", {"task": "find_latest_trends"})
    data = await message_bus.receive_message("coordinator")
    await message_bus.send_message("analysis_agent", {"data": data})

What keeps this team from collapsing when something goes wrong? A single agent getting stuck or producing nonsense shouldn’t bring down your entire application. You need a safety net. This means building resilience into the coordinator. For every task, you should define what success looks like, how long it should take, and what to do if it fails.

from tenacity import retry, stop_after_attempt, wait_exponential

class CoordinatorAgent:
    @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=2, max=10))
    async def delegate_with_retry(self, agent_name: str, task: dict):
        try:
            result = await self.call_agent(agent_name, task)
            if self.validate_result(result):
                return result
            else:
                raise ValueError("Result validation failed")
        except Exception as e:
            await self.log_failure(agent_name, task, str(e))
            return await self.activate_fallback_procedure(task)

Once your agents are cooperating and resilient, you face the final hurdle: deploying them into the real world where users depend on them. This shifts the focus to monitoring and cost. You can’t manage what you can’t measure. Simple logging is not enough. You need to track how many tasks each agent performs, how long they take, and the cost of each LLM call.

Imagine a dashboard that shows you the analyst agent is taking 20 seconds longer than the others. Is it thinking too hard? Is its tool broken? This visibility is what separates a prototype from a production system. Furthermore, setting clear limits on how many “steps” an agent can take and which expensive tools it can use is essential to prevent runaway costs.

Building this kind of system is a step-by-step process. You start with a single agent and a tool. You make it work perfectly. Then you add a second agent and teach them to communicate. You add error handling, then monitoring, and finally, deployment logic. It’s a complex puzzle, but the payoff is an AI application that is robust, scalable, and truly helpful.

Have you ever built a simple chain of prompts and felt it was becoming too complex to manage? What was the breaking point for you?

I hope this guide gives you a practical starting point. Building with multiple agents is one of the most effective ways to create powerful, real-world LLM applications. What kind of agent team would you build first? Share your thoughts or questions below—let’s discuss