Here’s how to build AI systems that actually work together like a well-oiled machine. We’re talking about teams of specialized agents that collaborate, adapt on the fly, and handle complex tasks without falling apart. Think of it like assembling an Avengers team for your codebase, where each member has a unique superpower.
Why Multi-Agent Systems?
Most AI tools are one-trick ponies. Need weather data? Call an API. Need analysis? Write another script. But real-world problems are messy:
“What’s the weather in Singapore today compared to last week, and should I pack an umbrella?”
A single AI can’t do this well. But a team of agents can:
- Scout Agent – Fetches live weather
- Historian Agent – Pulls past data
- Analyst Agent – Spots trends
- Advisor Agent – Gives plain-English advice
How to Build This (Without Losing Your Mind)
1. Define Your Agents’ Roles
Each agent should have a razor-sharp focus:
| Agent Type | Real-World Analog | Example Task |
| Fetcher | Intern with a clipboard | Gets raw data (APIs, databases) |
| Analyzer | Data scientist | Finds patterns, runs calculations |
| Presenter | Storyteller | Turns numbers into insights |
Pro Tip: Start small. A 2-agent system (fetcher + analyzer) is easier to debug than a 10-agent monstrosity.
2. Make Them Talk to Each Other
Agents need to share information without stepping on each other’s toes. Two clean ways to do this:
Option A: Message Passing (Like Slack for Bots)
python
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class AnalystAgent:
def __init__(self):
self.inbox = [] # Messages go here
def receive(self, message: dict):
self.inbox.append(message)
def send(self, recipient, data):
recipient.receive({“from”: self, “payload”: data})
Option B: Shared Whiteboard (Global Memory)
python
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shared_memory = {}
def fetcher_agent(city):
shared_memory[“weather”] = get_weather(city)
def analyzer_agent():
trend = analyze(shared_memory[“weather”])
shared_memory[“trend”] = trend
Real Example: Weather Analysis Squad
Let’s build that weather team we talked about earlier.
Agent 1: The Scout
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import requests
class WeatherScout:
def run(self, city):
api_key = “your_key_here”
url = f”https://api.weatherapi.com/v1/current.json?key={api_key}&q={city}”
response = requests.get(url).json()
return {
“temp”: response[“current”][“temp_c”],
“condition”: response[“current”][“condition”][“text”]
}
Agent 2: The Historian
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class WeatherHistorian:
def run(self, city):
# Mocked data – in reality, use a weather API’s history endpoint
return [22, 23, 21, 24, 22, 23, 25] # Last 7 days’ temps
Agent 3: The Analyst
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class TrendDetector:
def run(self, current_temp, historical_temps):
avg = sum(historical_temps) / len(historical_temps)
return “warming” if current_temp > avg else “cooling”
Agent 4: The Advisor
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from langchain.llms import OpenAI
class WeatherAdvisor:
def __init__(self):
self.llm = OpenAI(temperature=0)
def run(self, city, current_weather, trend):
prompt = f”””
It’s currently {current_weather[‘temp’]}°C and {current_weather[‘condition’]} in {city}.
The trend shows temperatures are {trend} compared to last week.
Give a one-sentence packing suggestion.
“””
return self.llm(prompt)
Tying It All Together
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def weather_report(city):
scout = WeatherScout()
historian = WeatherHistorian()
analyst = TrendDetector()
advisor = WeatherAdvisor()
current = scout.run(city)
past = historian.run(city)
trend = analyst.run(current[“temp”], past)
advice = advisor.run(city, current, trend)
print(f”Weather Report for {city}:”)
print(f”- Current: {current[‘temp’]}°C, {current[‘condition’]}”)
print(f”- Trend: {trend}”)
print(f”- Advice: {advice}”)
weather_report(“Tokyo”)
Sample Output:
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Weather Report for Tokyo:
– Current: 25°C, Partly cloudy
– Trend: warming
– Advice: Pack light with a foldable umbrella for occasional showers.
When to Use Dynamic Workflows
Sometimes you don’t know what steps are needed until runtime. Example:
User asks:
“Is it safe to hike Mount Fuji tomorrow?”
Your system might need to:
- Check weather → 2. Get trail conditions → 3. Search for recent bear sightings
But if the user asks about beach safety, the steps change.
Solution: Let an LLM decide the workflow:
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def dynamic_workflow(user_question):
planner_prompt = f”””
Based on this question: “{user_question}”
List the tools needed in order:
– weather
– location_data
– safety_database
“””
tools_needed = llm(planner_prompt)
# Now execute only the required steps
Key Takeaways
- Specialize your agents – One job, done well
- Keep communication simple – Shared memory or direct messaging
- Plan for chaos – Dynamic workflows handle unpredictable queries
- Start small – 2-3 agents can solve most business cases
This isn’t just academic—we’ve used this approach for:
- Customer support (triage bot → tech specialist → billing agent)
- Market research (scraper → sentiment analyzer → report generator)
- Smart home systems (speech recognizer → device controller → confirmation speaker)
The future isn’t monolithic AIs—it’s teams of nimble, collaborative agents. Now go build yours.