Multi-Agent AI Systems 2025: When One AI Isn't Enough

In 2023, we asked "how do I write the perfect prompt?"

In 2024, we asked "how do I build a single agent with tools?"

In 2025, we're asking: "What if I had a TEAM of specialized AI agents working together?"

Welcome to multi-agent systems. Instead of one overloaded AI trying to be a coder, reviewer, AND documentation writer, you now build specialized agents - each an expert in their domain - and let them collaborate.

The results? Better than any single-agent system. Let me show you how.

Before building multi-agent systems, master single-agent development with LangChain - the foundation for understanding agent collaboration.

Why Multi-Agent Systems?

The Problem with Single Agents:

Asking one AI to "write code, test it, document it, and deploy it" is like asking one person to be a developer, QA engineer, DevOps specialist, and technical writer simultaneously. Technically possible, but the quality suffers.

The Multi-Agent Solution:

• Engineer Agent: Writes clean, efficient code
• Reviewer Agent: Critiques for bugs and style
• Tester Agent: Writes unit tests
• Documentation Agent: Creates clear docs

Each agent is specialized. They communicate. They iterate. The final output is better than what any single agent could produce.

The Framework: Microsoft AutoGen

There are several frameworks (CrewAI, LangGraph), but AutoGen remains the most mature for defining conversational patterns between agents.

pip install pyautogen python-dotenv

Building Your First Multi-Agent Team

Let's build a simple software development team: Product Manager (User), Engineer, and Code Reviewer.

Step 1: Configuration

import os
from dotenv import load_dotenv
import autogen

load_dotenv()

config_list = [
    {
        "model": "gpt-4o-mini",
        "api_key": os.getenv("OPENAI_API_KEY")
    }
]

llm_config = {
    "config_list": config_list,
    "temperature": 0,
    "timeout": 120
}

Step 2: Define Your Agents

Each agent has a specific role defined by its system message:

# 1. User Proxy (represents you, executes code)
user_proxy = autogen.UserProxyAgent(
    name="Admin",
    system_message="""A human admin. You interact with the team and execute code.
    After the Reviewer approves, execute the code to verify it works.""",

    code_execution_config={
        "work_dir": "coding",
        "use_docker": False,  # Set True for production
    },

    human_input_mode="TERMINATE",  # Only ask human when task is done
    max_consecutive_auto_reply=10
)

# 2. Engineer (writes code)
engineer = autogen.AssistantAgent(
    name="Engineer",
    llm_config=llm_config,
    system_message="""You are a Senior Python Engineer.

    SKILLS:
    - Write clean, well-documented Python code
    - Follow PEP 8 style guidelines
    - Include type hints
    - Handle errors gracefully

    WORKFLOW:
    - When given a task, write the complete solution
    - Output full code blocks with explanations
    - If code fails, debug and fix it based on error messages

    CONSTRAINTS:
    - Use only Python standard library unless told otherwise
    - Add docstrings to all functions
    - Keep code modular and testable
    """
)

# Agent system messages follow [advanced prompt engineering patterns like role-based prompting](/blog/gen-ai/prompt-engineering-patterns-2025) to define specialized agent personas.

# 3. Code Reviewer (reviews code quality)
reviewer = autogen.AssistantAgent(
    name="CodeReviewer",
    llm_config=llm_config,
    system_message="""You are a meticulous Code Reviewer.

    YOUR JOB:
    - Review code for bugs, security issues, and style violations
    - Check for edge cases not handled
    - Verify error handling is proper
    - Ensure code is readable and maintainable

    WORKFLOW:
    - If code is good, reply with "APPROVE"
    - If issues found, list them specifically
    - Suggest concrete improvements

    STANDARDS:
    - Code must have error handling
    - No hardcoded values (use constants/config)
    - Type hints required for function signatures
    - Docstrings required
    """
)

Step 3: Create a Group Chat

Instead of sequential A→B→C conversation, use a Group Chat where a manager decides who speaks next.

# Create the group chat
groupchat = autogen.GroupChat(
    agents=[user_proxy, engineer, reviewer],
    messages=[],
    max_round=12,  # Maximum conversation turns
    speaker_selection_method="auto"  # Manager decides who speaks
)

# Create the manager
manager = autogen.GroupChatManager(
    groupchat=groupchat,
    llm_config=llm_config
)

Step 4: Start the Collaboration

# Give the team a task
user_proxy.initiate_chat(
    manager,
    message="""Create a Python script that:
    1. Reads a CSV file named 'sales.csv'
    2. Calculates total sales per product
    3. Saves results to 'product_totals.json'

    Handle errors gracefully (file not found, invalid data, etc.)."""
)

What Happens Next (The Magic)

1. Manager receives the request, selects Engineer
2. Engineer writes the Python script with error handling
3. Manager sees code, selects Reviewer
4. Reviewer checks the code:

- "Good error handling, but you're not closing the file properly. Use context managers."

1. Manager selects Engineer again
2. Engineer fixes: Uses with open() context manager
3. Manager selects Reviewer
4. Reviewer: "APPROVE"
5. Manager selects User Proxy
6. User Proxy executes the code, confirms it works
7. Task complete!

The conversation transcript looks like:

Admin (to chat_manager):
Create a Python script that reads a CSV...

----------------
Engineer (to chat_manager):
I'll create a robust solution:

import csv
import json
from typing import Dict

def calculate_product_totals(input_file: str, output_file: str) -> None:
    """
    Reads sales data and calculates totals per product.
    ...

----------------
CodeReviewer (to chat_manager):
I see a potential issue. You're using csv.DictReader but not validating
that the expected columns exist. Add column validation.

----------------
Engineer (to chat_manager):
You're right. Here's the improved version:
[Fixed code]

----------------
CodeReviewer (to chat_manager):
APPROVE. Code is production-ready.

----------------
Admin (to chat_manager):
Executing code...
SUCCESS: Created product_totals.json

Communication Patterns

Pattern 1: Sequential Handoff (Simple Chain)

Agent A → Agent B → Agent C → Done

# Engineer writes code
user_proxy.initiate_chat(
    engineer,
    message="Write a function to validate email addresses"
)

# Manually pass to reviewer
engineer.initiate_chat(
    reviewer,
    message=engineer.last_message()
)

Pros: Simple, predictable Cons: Rigid, can't adapt to unexpected situations

Pattern 2: Group Chat with Manager (Dynamic)

What we just built. Manager decides who speaks based on context.

Pros: Flexible, handles complex workflows Cons: Manager can get confused with too many agents or too long conversations

Pattern 3: Hierarchical (Boss and Workers)

One "Boss" agent delegates sub-tasks to "Worker" agents.

boss = autogen.AssistantAgent(
    name="ProjectManager",
    system_message="""You are a project manager.
    Break down complex tasks into sub-tasks.
    Delegate to appropriate team members.
    Synthesize results into final deliverable."""
)

worker1 = autogen.AssistantAgent(name="DataAnalyst", ...)
worker2 = autogen.AssistantAgent(name="Visualizer", ...)

# Boss delegates
groupchat = autogen.GroupChat(
    agents=[boss, worker1, worker2],
    messages=[],
    max_round=20
)

Use case: Complex projects like "analyze sales data, create visualizations, and write a report."

Advanced: Giving Agents Tools

Agents can use tools just like single agents.

import requests

# Define a tool function
@user_proxy.register_for_execution()
@engineer.register_for_llm(description="Search the web for current information")
def search_web(query: str) -> str:
    """Search DuckDuckGo for information"""
    from duckduckgo_search import DDGS
    results = DDGS().text(query, max_results=3)
    return str(results)

# Now Engineer can call search_web during code generation

Integrate external tools using patterns from building with AI APIs.

Real example:

User: "Write a script to get the current Bitcoin price"

Engineer: Calls search_web("bitcoin price API") → Finds API → Writes script using that API

Real-World Use Case: Content Creation Team

# Researcher finds information
researcher = autogen.AssistantAgent(
    name="Researcher",
    system_message="""You research topics using available tools.
    Find accurate, recent information.
    Cite sources.""",
    llm_config=llm_config
)

# Writer creates content
writer = autogen.AssistantAgent(
    name="Writer",
    system_message="""You write engaging, accurate content.
    Use information from Researcher.
    Write in a clear, professional style.
    Include proper structure (intro, body, conclusion).""",
    llm_config=llm_config
)

# Editor polishes content
editor = autogen.AssistantAgent(
    name="Editor",
    system_message="""You edit content for clarity and correctness.
    Check grammar, flow, and factual accuracy.
    Suggest improvements.
    Reply with 'PUBLISH' when content is ready.""",
    llm_config=llm_config
)

# User manages the process
user_proxy = autogen.UserProxyAgent(
    name="ContentManager",
    human_input_mode="TERMINATE",
    code_execution_config=False  # No code execution needed
)

# Create workflow
groupchat = autogen.GroupChat(
    agents=[user_proxy, researcher, writer, editor],
    messages=[],
    max_round=15
)

manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)

# Start workflow
user_proxy.initiate_chat(
    manager,
    message="Write a 500-word blog post about 'The Future of Remote Work in 2025'. Research current trends first."
)

# Combine multi-agent systems with RAG for research agents

Combine multi-agent systems with RAG for research agents that retrieve and synthesize information.

# Continuing workflow...

Result:

1. Researcher searches for recent articles/stats
2. Writer creates draft based on research
3. Editor reviews, suggests changes
4. Writer revises
5. Editor approves with "PUBLISH"

Quality is significantly higher than a single agent writing alone.

Cost Optimization

Multi-agent systems use MORE API calls. Optimize carefully:

1. Use Cheaper Models for Simple Agents

# Engineer needs gpt-4 for complex reasoning
engineer_config = {
    "config_list": [{"model": "gpt-4o", "api_key": api_key}],
    "temperature": 0
}

# Reviewer can use cheaper model
reviewer_config = {
    "config_list": [{"model": "gpt-4o-mini", "api_key": api_key}],
    "temperature": 0
}

2. Set Strict Termination Conditions

# Terminate when specific phrase appears
def is_termination_msg(msg):
    return "TASK COMPLETE" in msg.get("content", "").upper()

user_proxy = autogen.UserProxyAgent(
    name="Admin",
    is_termination_msg=is_termination_msg,
    max_consecutive_auto_reply=5  # Hard limit
)

3. Limit Conversation Rounds

groupchat = autogen.GroupChat(
    agents=[user_proxy, engineer, reviewer],
    messages=[],
    max_round=10  # Stop after 10 turns even if not done
)

Error Handling and Recovery

Agents will make mistakes. Build resilience:

# Add fallback logic
user_proxy = autogen.UserProxyAgent(
    name="Admin",
    system_message="""If an agent gets stuck or repeats itself 3 times,
    intervene with: 'Let's try a different approach.'""",

    human_input_mode="TERMINATE",

    # Fallback function
    function_map={
        "get_human_input": lambda: input("Human intervention needed: ")
    }
)

Testing Multi-Agent Systems

Unit test individual agents:

def test_engineer_generates_code():
    response = engineer.generate_reply(
        messages=[{"role": "user", "content": "Write a hello world function"}]
    )
    assert "def" in response
    assert "hello" in response.lower()

Integration test the full workflow:

def test_full_workflow():
    # Capture the conversation
    messages = []

    user_proxy.initiate_chat(
        manager,
        message="Write a function to add two numbers"
    )

    # Verify sequence
    assert any("def add" in msg for msg in groupchat.messages)
    assert any("APPROVE" in msg for msg in groupchat.messages)

Best Practices for 2025

1. Keep Agent Roles Crystal Clear

Bad: "You are a helpful assistant who can code and review code."

Good: "You are a Code Reviewer. You ONLY review code. You do NOT write code. You do NOT execute code."

2. Limit Agents Per Chat (3-5 Max)

Too many agents = confused conversations. If you need more, use hierarchical structure.

3. Give Agents Escape Hatches

system_message="""...
If you're stuck or unsure, say 'I NEED HUMAN HELP' and explain what you need.
"""

4. Log Everything

# Save conversation for debugging
with open("conversation_log.txt", "w") as f:
    for msg in groupchat.messages:
        f.write(f"{msg['name']}: {msg['content']}\n\n")

5. Human in the Loop for Critical Decisions

user_proxy = autogen.UserProxyAgent(
    name="Admin",
    human_input_mode="ALWAYS",  # Ask human before every action
)

Use ALWAYS for production systems handling sensitive operations.

Common Pitfalls

1. Agents Argue Forever No termination condition, they debate endlessly. Fix: Set max_round and explicit termination messages ("APPROVE", "TASK COMPLETE").

2. Manager Picks Wrong Agent Manager's selection logic fails with similar agents. Fix: Make agent roles/names very distinct. "CodeWriter" vs "CodeReviewer", not "Assistant1" vs "Assistant2".

3. Runaway Costs Multi-agent chat uses 5-10x API calls of single agent. Fix: Use cheaper models, strict max_round limits, cache when possible.

4. Context Window Explosion Long conversations hit token limits. Fix: Summarize periodically, or use a "memory manager" agent that summarizes history.

5. No Clear Output Conversation ends but no final deliverable extracted. Fix: Have user_proxy explicitly extract and save results:

# After conversation ends
final_code = extract_code_from_messages(groupchat.messages)
with open("output.py", "w") as f:
    f.write(final_code)

The Bottom Line

Multi-agent systems represent the shift from "using AI" to "managing AI teams."

When to use:

• Complex tasks requiring different expertise
• Quality matters more than speed
• You need built-in review/validation
• Single agent keeps failing on complex workflows

When NOT to use:

• Simple tasks (overkill)
• Tight latency requirements (multi-agent is slower)
• Limited budget (uses more API calls)

Start small:

1. Build 2 agents (Creator + Reviewer)
2. Get them working in sequence
3. Add a manager for dynamic routing
4. Add more specialized agents
5. Add tools

Within weeks, you can have AI teams that code, test, document, and deploy better than any single AI ever could.

The future isn't one super-intelligent AI. It's specialized AI agents collaborating like a well-oiled team.

AI could be the workforce you are missing. What's your excuse?